Transcript Slides
Betting in Super Bowl match ups PRESENTATION TO MIS480/580 GABE HAZLEWOOD JOSH HOTTENSTEIN SCOTTIE WANG JAMES CHEN MAY 5, 2008 Who did what 2 Literature Review Subject Matter Expert Gabe Hazlewood X X Josh Hottenstein X James Chen Scottie Wang Data Extraction Analysis Statistical Modeling X X X X X X X X Research Question 3 “Can patterns in historical game performance allow the bettor to gain a better understanding of what makes a good bet” Introduction 4 Purpose Provide bettors with an “angle” that can be used to exploit certain inefficiencies in NFL betting market Objective Analyze whether there are any exogenous variables that could aid in better determining the outcome of a Super Bowl bet relative to its line Usefulness Seasoned bettors can add any findings to repertoire for future use, as it pertains only to a game played once a year Literary Reviews 5 Walker, Sam. "The Man Who Shook Up Vegas." The Wall Street Journal 5 Jan. 2007. 11 March 2008 <http://online.wsj.com/public/article/SB116796079037267731wjPu4ACcg5J5Qvjh05IYEI_Ooeo_20070112.html>. 1. Examines success rates of experts in sports betting Introduces the viewing of betting as an investment rather than a gamble Gray, Philip K., and Stephen F. Gray. "Testing Market Efficiency: Evidence From The NFL Sports Betting Market." The Journal of Finance, Vol. 52, No. 4, (Sep., 1997), pp. 1725-1737. 2. Examines the efficiency of the NFL betting market Introduces more sophisticated betting strategies (i.e. bets are placed only when there is a relatively high probability of success) Gandar, John, Richard Zuber, Thomas O'Brien, and Ben Russo. "Testing Rationality in the Point Spread Betting Market." The Journal of Finance, Vol. 43, No. 4, (Sep., 1988), pp. 9951008. 3. Presents empirical tests of market rationality using data from the point spread betting market on NFL games Examines whether, at any point, a moving line becomes more significant as to the outcome of a bet Old but NOT outdated Avery, Christopher, and Judith Chevalier. "Investor Sentiment From Price Paths: The Case of Football Betting." The Journal of Business, Vol. 72, No. 4, (Oct., 1999), pp. 493-521. 4. Further examination on previous citation’s findings Validates that movement of a spread is predictable, and attempting to exploit it yields a very low profit at best Literary Reviews (cont.) 6 “The Man Who Shook Up Vegas” Significant Findings When betting against a point spread, bettors must win 52.4% of their wagers to make a profit Experts realize close to 60% winning percentage Most highly regarded expert is Bob Stoll Looks for “angles” that predict future results (i.e. team favored by 7 or more in minor bowl game after losing their last game, fail to cover spread 77% of the time) Use in project Only accept findings yielding greater than 52.4% probability; aim for closer to 60% Find “angles” similar to Bob Stoll example; proven effective Literary Reviews (cont.) 7 “Testing Market Efficiency: Evidence From The NFL Sports Betting Market” Significant Findings Model indicates that the market overreacts to a team's recent performance and discounts the overall performance of the team over the season Exogenous variables such as rushing/passing yards could be added to increase the predictive power of the model Inefficiencies exist, but not all are exploitable Use in project We will use season long stats, taking overall performance into account Attempt to find which exogenous variables, if any, will increase predictive power (angles; consistent with expert methodology) Look for inefficiency in Super Bowl betting market and if it can be exploited Literary Reviews (cont.) 8 “Testing Rationality in the Point Spread Betting Market” Significant Findings In the NFL, the closing line does not provide a more accurate forecast than does the opening line; and vice-versa Use in project Using closing lines, available in our data set, will not compromise validity of our findings Literary Reviews (cont.) 9 NFL spreads are biased predictors of actual results Creates inefficiencies Certain inefficiencies can be exploited Exploit, most profitably, by finding exogenous variables that provide an “angle” Aim for 60% probability, above 52.4% acceptable Confidence in data set Apply to Super Bowl! Data collection 10 Data source Spider data from Databasefootball.com Collected all game play stats for the 17 regular session games and the Super Bowl for the last 10 years Collected betting line and over data for the last 10 Super Bowls Collection Technique Spider data for the site Load the data into excel workbook Load work books into respective tools Analysis techniques Tools used SPSS and MathLab Simple stats, correlation analysis and multi factor statistical modeling Simple Stats 11 Simple Statistics Averages of the favorites regular season: Total score Average Median 28.23529 21.390374 371.973262 28.00 21.00 Time of Possession Rush Attempts 29.5828877 1.317375966 30.00 1.32 377.00 Averages of the underdogs regular season: Average Median First Downs Total Yards First Total Rush Time of Total score Downs Yards Attempts Possession 23.71134 18.67526 330.7113 30.25258 1.311419 23 19 333.5 208 6.8 Super Bowl averages: Final Score Underdog Average Median Favorite Average Median First Downs Total Yards Rush Attempts Time of Possession 20.18182 20 16.27273 17 314 339 24.45455 22 1.15 1.12 22.90909 23 19.09091 20 352.5455 331 30.27273 33 1.39 1.38 Betting Line Averages 12 Betting Line Average Median Over 7.636364 7 Actual Over 46.63636 43.09091 48 46 Correlation Analysis 13 Line to Regular Season Score Favorite Underdog Over to Regular Season Score Favorite Underdog Complex Statistic Model 14 Multiple Linear Regression Factors selected 15 Average Difference of Each season Total Yards (X1)-General ability to offense Time of Possession (X2)-Ability to control the game Second Half Score (X3)-Ability to adapt and change Rush Attempts (X4)-How aggressive the team is Super Bowl Score (Y) Regression Process and Result 16 P-Value for the Favorite Team Analysis 0.0026 0.00558 0.00276 0.0124 Regression Process and Result 17 Result for Favorite Team Y=0.129*X1+11.02*X2+1.028*X3+0.792*X4 R Square:0.6969 Conclusion 18 We developed a procedure to help gamblers to make a better bet: Use the Multiple Linear Regression method to calculate the final estimate result for both the favorite team and underdog team. Calculate the final estimate line and over data. Bet when you found the difference is large enough, the larger difference it is, the larger possibility you will win on this bet. Future work and study 19 Organize some mathematics experts and football experts to build a model using reasonable and complex method of Statistical hypothesis testing. Using standard deviation to help prediction Uncertain factor which would influence the match a lot such as weather, big event in super bowl team should be considered in the prediction Lessons Learned 20 With the statistical model, we are capable of winning the profit and the model could be more effective than some of the expert estimation. the gamblers could use our method to exploit certain inefficiencies in NFL betting market and make profit of them.