1. PRESENTATION TO MIS480/580 GABE HAZLEWOOD JOSH HOTTENSTEIN SCOTTIE WANG JAMES CHEN MAY 5, 2008 Betting in Super Bowl match ups

2. Who did what 2 Literature Review Subject Matter Expert Data Extraction AnalysisStatistical Modeling Gabe Hazlewood XXX Josh Hottenstein XXX James Chen XX Scottie Wang XXX

3. Research Question 3 “Can patterns in historical game performance allow the bettor to gain a better understanding of what makes a good bet”

4. 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

5. Literary Reviews 5 1. Walker, Sam. "The Man Who Shook Up Vegas." The Wall Street Journal 5 Jan. 2007. 11 March 2008.  Examines success rates of experts in sports betting  Introduces the viewing of betting as an investment rather than a gamble 2. 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.  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) 3. 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. 995- 1008.  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games  Examines whether, at any point, a moving line becomes more significant as to the outcome of a bet  Old but NOT outdated 4. 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.  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

6. 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

7. 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

8. 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

9. 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!

10. 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

11. Simple Stats 11 Simple Statistics  Averages of the favorites regular season:  Averages of the underdogs regular season:  Super Bowl averages: Final ScoreFirst DownsTotal YardsRush Attempts Time of Possession Underdog Average20.1818216.2727331424.454551.15 Median2017339221.12 Favorite Average22.9090919.09091352.545530.272731.39 Median2320331331.38 Total score First Downs Total Yards Rush Attempts Time of Possession Average23.7113418.67526330.711330.252581.311419 Median2319333.52086.8 Total scoreFirst DownsTotal Yards Rush Attempts Time of Possession Average28.2352921.390374371.97326229.58288771.317375966 Median28.0021.00377.0030.001.32

12. Betting Line Averages 12 Betting LineOverActual Over Average7.63636446.6363643.09091 Median74846

13. Correlation Analysis 13 Line to Regular Season Score Over to Regular Season Score Underdog Favorite Underdog Favorite

14. Complex Statistic Model 14 Multiple Linear Regression

15. 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)

16. Regression Process and Result 16 P-Value for the Favorite Team Analysis 0.00260.005580.002760.0124

17. 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

18. 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.

19. 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

20. 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.