Transcript Steamer Projections The Basics of Projection Systems Forecasting the upcoming season is essentially the same as determining current ability. Most projection systems are.

Steamer Projections
The Basics of Projection Systems
Forecasting the upcoming season is essentially the same as
determining current ability.
Most projection systems are modifications on the same simple
system (Marcel “the monkey):


Weighs stats from more recent seasons more heavily
Regress to the mean
Why regress to the mean?
Results = Ability + Luck
Two Examples of Marcel in Action
23.0%
18.3%
Steamer
Along with most “fancier” systems:
 Uses adjusted minor league statistics in addition to MLB stats.
 Adjusts for home ballparks, league, starting v. relieving
What makes Steamer distinct:
 We use a different system for each component (K%, BB%,
HR%…)
 We regress to a different “prior” for each player
Projecting Joaquin Benoit’s K% in 2011:
4 possible forecasts
26.1%
28.0%
Actual K%: 26.1%
23.7%
24.9%
K/PA for All Pitchers: 1993-2011
HR/PA for All Pitchers: 1993-2011
Regression is Bayes
K% v. FBV for Starters
K% v. FBV for Relievers
Matt Thornton 2012
24.0%
27.2%
Marcel error v. Fastball Velocity
More regression = Stronger Relationship
It might be working…
Where to go from here?
For Pitchers:
 Develop a better measure of stuff than fastball velcoity
 Jeremy Greenhouse: StuffRV based on velocity and movement
 Josh Kalk/Brooksbaseball: Similarity Scores based on pitchf/x
For Hitters:
 Can something similar be done with hitf/x? Trackman?
 Speed off the bat
 Trajectory