1. The Best Investment in this Economy (Safer than the S&P) Ana Burcroff Kathleen Fregeau Brett Koons Alistair Meadows March 3 rd 2009 – Team 7

2. Determine the best predictors of the over-under and the spread in college basketball games and use that knowledge to make millions. Over-Under The Spread The Goal

3. Over/Under Analysis“Against the Spread” Analysis Games Analyzed A regression model was developed to predict the total points scored by the home and away teams. Two Regression models were developed to predict the point spread between the home and away teams. Source: Complete if required Rules of the Game We looked solely at NCAA Men’s Basketball: Only intra-divisional games within the following 6 conferences were considered

4. What is the spread?What is the over/under? The bookmaker specifies that one team will beat another by a certain amount of points  This amount is known as the spread and is usually quoted in half points to avoid ties, known as a push  Gamblers then wage on whether the actual margin of victory will be higher or lower than the spread  Example:  On Feb. 21, UNC was favored over Maryland by 12.5  If you bet on UNC, they would have to win by 13 points or more in order for you to win the bet  If UNC wins by anything less than 12.5 points, gamblers who bet on UNC lose their money although the team won. In addition to the spread, you can also bet the over/under  The bookmaker predicts that the combined score of the two teams will be a certain number  Bettors then wager on whether the actual score total will be higher or lower than that number We wanted to see if we could build a model that gave us “lock” picks – games where the model predicts that the spread is too high or low or the over/under is too high or low, so that we could bet accordingly. The basics for everyone who is not a degenerate gambler. The Spread and Over/Under

5. We considered away and home team offensive and defensive statistics along with how frequently the away team covers the spread and the over. Home Team StatisticsAway Team Statistics Offensive Statistics  Field Goal Attempts  Field Goal Percentage  3 Point Attempts  3 Point Percentage  Free Throw Attempts  Free Throw Percentage  Defensive Statistics  Average Points Allowed Offensive Statistics  Field Goal Attempts  Field Goal Percentage  3 Point Attempts  3 Point Percentage  Free Throw Attempts  Free Throw Percentage  Defensive Statistics  Average Points Allowed The Independent Variables Away Home

6. Bets are based on 3 models: one predicts over/under, two predict against the spread The models are based on historical data from different time periods Model 2 Against the Spread Model 3 Against the Spread Model 1 Over/Under The Models For all models: P value for F Test = 0 P value for all coefficients < 5% Adjusted R 2 Model 1: 41.9% Model 2: 55.4% Model 3: 49.9%

7. Model 2 - ATS Model 1 – Over/Under  Vegas O/U  Away Average Def.  Away Over %  Away ATS%  Away Win%  Away Field Goal Attempts  Away Free Throw %  Home 3PT Attempts  Home Free Throw %  Vegas Over/Under  Home Line (ATS)  Away ATS %  Away Win %  Away Field Goal %  Away 3PT %  Home Average Offense  Home Field Goal Attempts  Home Field Goal %  Home 3PT %  Home Free Throw Attempts  Home Avg. Opp. Power Rating The Independent Variables Model 3 - ATS  Home Line (ATS)  Away Win %  Away Field Goal %  Away 3PT %  Away Free Throw Attempts  Home Average Offense  Home Average Def.  Home ATS %  Home Field Goal Attempts  Home Field Goal %  Home 3PT %  Home Free Throw Attempts  Home Avg. Opp. Power Rating

8. How to Bet O/U Model - If predicted y hat indicates the game as a super lock or extra super lock – then bet. Against the Spread (Version 1) – If predicted y hat indicates the game as a lock, super lock, or extra super lock – then bet. Against the Spread (Version 2) - If predicted y hat indicates the game as a lock, super lock, or extra super lock – then bet.

9. Model 2 (ATS) Good BetBetter BetLockSuper Lock Extra Super Lock O/U +/- 0pts+/- 5pts+/- 10pts+/- 15pts+/- 20pts Win 52 31 25 4 1 Losses 47 22 7 0 0 Total 99 53 32 4 1 Bet Amounts $200 Juice 10% Amount Invested $21,780 $11,660 $7,040 $880 $220 Account $60 $1,360 $3,460 $800 $200 Return 0.3% 11.7% 49.1% 90.9%

10. Models’ Fit with Data Model’s Fit with Data Last recorded actual data was 2/28

11. Model’s Fit with Data

12. Vegas O/U vs. Model’s Predicted Total Scoring Vegas O/U Model’s Predicted Total Scoring The Model’s predicted total scoring is positively correlated with the Vegas O/U. This correlation is stronger among all games than among solely the Super Locks and Extra Super Locks. All GamesSuper Locks and Extra Super Locks

13. Model’s Predicted Total Scoring Actual Total Scoring Vegas’ Predicted Total Scoring The Model predicts 21.8% of actual total scoring, while Vegas’ O/U explains 31.96% of total scoring when looking at all games analyzed. Model vs. Vegas’ Prediction Accuracy

14. Residual Analysis (Model Prediction vs. Actual Result) All Residuals vs. Model’s Prediction Residuals of Super Locks and Extra Super Locks vs. Model’s Prediction Residual vs. Prediction Scatterplot Residual Values Histogram Residuals have a relatively normal distribution with a mean of 5.6. The residuals of Super Locks and Extra Super Locks are not normally distributed and have a mean of -10.

15. Heteroscedasticity patterns imply that the model’s error generally increases as the expected total scoring increases. However, the model is better at making betting predictions when it underestimates the O/U for high scoring games and overestimates O/U for low scoring games. Model’s Predicted Scoring Difference between Predicted and Actual Super Locks and Extra Super Locks Positive Heteroscedasticity Negative Heteroscedasticity All GamesSuper Locks and Extra Super Locks Heteroscedasticity and Prediction Accuracy

16. Difference between Vegas’ O/U and Actual Difference between Predicted and Actual All GamesSuper Locks and Extra Super Locks The correlation (or lack of correlation) between the difference in the Model’s prediction and the actual outcome vs. the difference between Vegas’ O/U and the actual outcome implies that the model makes more accurate predictions when it is positively correlated (but not directly correlated) with Vegas’ O/U prediction. No significant correlation Significant positive correlation Correlation between Model’s Residual and Vegas’ Residual

17. Correlation Analysis A correlation matrix with all 26 variables generated only 16 coefficients greater than 0.5. Of those, the average correlation was 0.5842, with a median of 0.5804, and a max of 0.722 (the relationship between the Home teams average offensive points, Home team field goal attempts).

18. Durban-Watson Calculation O/U Model’s D-stat Numerator26,808.5 Denominator16,908.2 D-stat1.6 Are the observations independent of one another – does the date of the game affect the volatility of the model (i.e. more uncertainty as March Madness approaches) Null Hypothesis: Correlation = 0 Alternative Hypothesis: Correlation >0 N = 95 X variables = 9 dL = 1.484 dU = 1.85 Our d-stat is between the lower and upper critical values, so we do not reject the Null (but we don’t accept it either) and conclude that there is either no serial autocorrelation or weak serial autocorrelation in our data. Durbin-Watson Analysis

19. It’s the safest investment around!! We beat the S&P! We beat the professional handicapper! We’re making $$$!! Model Returns = 26.2%!! S & P 500 Returns = -22.41% year-to-date – that is scary! Dow closed below 7,000 for the first time in 11 years Model accuracy = 75.7%!! Handicappers pick bets Even good handicappers are right just over 50% of the time 52.38% is the magical number to be profitable So far the “company” has invested $11,100 37 bets Profit of $2,910 Invested Capital of only $3,000 ROIC = 97%!! Models’ Success-to-date

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21. Questions