Handicap Workshop The Annual Review February / March / April 2015 AHR Process To ensure all players in the Club have a handicap.
Download ReportTranscript Handicap Workshop The Annual Review February / March / April 2015 AHR Process To ensure all players in the Club have a handicap.
Slide 1
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 2
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 3
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 4
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 5
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 6
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 7
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 8
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 9
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 10
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 11
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 12
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 13
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 14
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 15
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 16
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 17
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 18
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 2
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 3
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 4
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 5
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 6
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 7
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 8
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 9
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 10
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 11
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 12
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 13
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 14
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 15
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 16
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 17
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
FAQs and Audio
description will follow
Slide 18
Handicap Workshop
The Annual Review
February / March / April 2015
AHR Process
To ensure all players in the Club have a handicap that
reasonably reflects playing ability
– Not simply to ensure decreases are applied
– In these days of ageing memberships the focus is far more on
ensuring the handicaps of declining players are adjusted upwards
Many committees, faced with looking at the handicaps of
300+ members, simply didn’t bother
AHR Process
How to look productively at the performance over the
previous year of all players in the club?
How to standardise the approach to assessing each player’s
performance?
It was concluded that the CONGU system had developed to the
point where administration by computers was almost universal.
Thus devising a computer program to carry out the AHR
process would solve both issues
How to establish from their performance data that a
player’s handicap reasonably reflects their playing
ability?
Computer Model
Obtaining sufficient data from correctly handicapped
players, to enable valid statistical conclusions, was a
problem
However in 2003 Peter Wilson (English Golf Union) had
developed a mathematical model that effectively
produced the scores of the statistically “perfect golfer”
The model is based on an observation that irrespective of
handicap, each round resulting in a given gross score
contains a predictable number of gross eagles, birdies,
pars, bogies, double-bogeys, triple-bogeys etc.
Computer Model
Using this information the program simulates hole-by-hole
scores for 10,000 rounds so that, averaged over the 10,000
rounds, the hole-by-hole profile matches that for any given
gross score
Nett Double Bogey adjustment is then applied to the scores
and the resulting CONGU handicap is calculated
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player
has a MGD of 31 (ie. 2 and 7 over handicap respectively)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they
could indicate a handicap anywhere between
10.5 and 21.0!
AHR Report
The linear relationship is used to test whether each player
has a handicap that represents their current ability
The variability of player scoring patterns, and the number of
scores returned, affects the precision that can be expected from
the computed result
This is recognised by building in a “tolerance factor” around
the exact calculation. For 7 or more scores it was shown that
this is + / - 3
Example: player (handicap 7.5) returns 11 scores
+12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8
Example
The player’s Median Gross Differential is determined by first
arranging the scores in ascending order
+3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR
The year-end handicap is their “current ability” and this is
subtracted from their MGD to get the Nett of their Median Gross
Differential. So here NMGD = 10 – 7.5 = 2.5
The MND of the “ideally handicapped” 7.5 player is calculated:
(0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35
Then comparing the Actual with the Ideal for this player:
Actual - Ideal = 2.5 – 3.35 = - 0.85
So the player is well within the tolerance of + / -3 and can
be considered correctly handicapped
AHR Report
•For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
•For handicap increase 7 scores or more was desirable to give
an adequate level of precision, amended in 2012 to recommend
increases for players who returned 3 or more Q scores
Size of the “tolerance” reflects the
lack of precision inherent in
having fewer scores available
HOW CAN A COMPUTERISED SIMULATION
BEAR ANY RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): Gross Differential v score frequency for 2,000
returns (Men)
For 24: Nett Differential v score frequency for 2,000 returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap
returns from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24
Handicap returns from the CDH (Men)
Actual MND is 7.0
Plot of ND v score frequency from sample of 2,000 24 Handicap
returns from the CDH (Men)
Predicted MND is (0.237*24) + 1.5745 = 7.26
Summary
The AHR report is based on a computer model that has been
confirmed (using actual scores obtained from 2011/2012
CDH data) to reflect reality at all handicap levels
The AHR report will assess the handicap performance of
players in more detail, more objectively and more efficiently
than can be achieved manually by handicap committees
Proper application of the AHR process will assist in ensuring
that all players in the Club have a handicap that reasonably
reflects playing ability
The report can only make recommendations based on the
scores returned. The committee must consider the
recommendations and take any other factors into account
before applying changes
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