Betting in super bowl match ups
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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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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”


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


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    • Old but NOT outdated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


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


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


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


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!


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


Simple Stats

  • Simple Statistics

    • Averages of the favorites regular season:

    • Averages of the underdogs regular season:

    • Super Bowl averages:


Betting Line Averages


Correlation Analysis

  • Line to Regular Season Score

  • Over to Regular Season Score

Favorite

Underdog

Underdog

Favorite


Complex Statistic Model

  • Multiple Linear Regression


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)


Regression Process and Result

  • P-Value for the Favorite Team Analysis


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


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.


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


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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