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Betting in Super Bowl match ups

Betting in Super Bowl match ups. PRESENTATION TO MIS480/580 GABE HAZLEWOOD JOSH HOTTENSTEIN SCOTTIE WANG JAMES CHEN MAY 5, 2008 . Who did what . Research Question . “Can patterns in historical game performance allow the bettor to gain a better understanding of what makes a good bet” .

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Betting in Super Bowl match ups

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

  2. Who did what

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

  4. Introduction • 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 • Walker, Sam. "The Man Who Shook Up Vegas." The Wall Street Journal 5 Jan. 2007. 11 March 2008 <http://online.wsj.com/public/article/SB116796079037267731-wjPu4ACcg5J5Qvjh05IYEI_Ooeo_20070112.html>. • 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. • 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. 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games • Examines whether, at any point, a moving line becomes more significant as to the outcome of a bet • Old but NOT outdated • Avery, Christopher, and Judith Chevalier.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.) “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.) “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.) “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.) • 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 • 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 • Simple Statistics • Averages of the favorites regular season: • Averages of the underdogs regular season: • Super Bowl averages:

  12. Betting Line Averages

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

  14. Complex Statistic Model • Multiple Linear Regression

  15. Factors selected • 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 • P-Value for the Favorite Team Analysis

  17. Regression Process and Result • Result for Favorite Team Y=0.129*X1+11.02*X2+1.028*X3+0.792*X4 R Square:0.6969

  18. Conclusion 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 • 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 • 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.

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