PRESENTATION TO MIS480/580

GABE HAZLEWOODJOSH HOTTENSTEIN

SCOTTIE WANGJAMES CHEN

MAY 5, 2008

Betting in Super Bowl match ups

Who did what 2

Literature Review

Subject Matter Expert

Data Extraction

Analysis

Statistical Modeling

Gabe Hazlewood X X X

Josh Hottenstein X X X

James Chen X X

Scottie Wang 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”

Introduction4

Purpose Provide bettors with an “angle” that can be used to

exploit certain inefficiencies in NFL betting marketObjective

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 Reviews5

1. 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

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NFL games Examines whether, at any point, a moving line becomes more significant as to the outcome of a bet Old but NOT outdated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

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 exploitableUse 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 inefficienciesCertain inefficiencies can be exploitedExploit, most profitably, by finding exogenous

variables that provide an “angle”Aim for 60% probability, above 52.4%

acceptableConfidence in data set

Apply to Super Bowl!

Data collection10

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 Stats11

Simple Statistics Averages of the favorites regular season:

Averages of the underdogs regular season:

Super Bowl averages: Final Score First Downs Total Yards Rush Attempts

Time of Possession

Underdog Average 20.18182 16.27273 314 24.45455 1.15 Median 20 17 339 22 1.12Favorite Average 22.90909 19.09091 352.5455 30.27273 1.39 Median 23 20 331 33 1.38

Total score

First Downs

Total Yards

Rush Attempts

Time of Possession

Average 23.71134 18.67526 330.7113 30.25258 1.311419Median 23 19 333.5 208 6.8

Total score First Downs Total Yards

Rush Attempts

Time of Possession

Average 28.23529 21.390374 371.973262 29.5828877 1.317375966

Median 28.00 21.00 377.00 30.00 1.32

Betting Line Averages 12

BettingLine Over Actual Over

Average 7.636364 46.63636 43.09091 Median 7 48 46

Correlation Analysis 13

Line to Regular Season Score

Over to Regular Season ScoreUnderdog

Favorite

Underdog

Favorite

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

Conclusion18

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 study19

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 Learned20

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.