1. Trap Games in College Football Ryan Gimarc EC499 – Spring 2013

2. Objective To test for a potential inefficiency in the college football betting market

3. Betting Market efficiency Generally, the betting line of a game (predicted home score – predicted away score) is a near perfect indicator of the actual point margin ▫Each game will have some variation, but it comes out to be nearly perfect

4. Betting Market efficiency Normal betting market regression: Points margin = β1 * betting line

5. Betting Market efficiency Normal betting market regression: Points margin = β1 * betting line Coefficient is usually equal to or very close to 1

6. Betting Market efficiency Normal betting market regression: Points margin = β1 * betting line + β2 * QB health Coefficient is usually equal to or very close to 1

7. Betting Market efficiency Normal betting market regression: Points margin = β1 * betting line + β2 * QB health Coefficient is usually equal to or very close to 1 Coefficient should be minimal, P value will be very high as it doesn’t have much effect on points margin

8. Betting Market efficiency Normal betting market regression: Points margin = β1 * betting line + β2 * QB health Coefficient is usually equal to or very close to 1 Coefficient should be minimal, P value will be very high as it doesn’t have much effect on points margin REASON: Injury reports are generally compensated for in the betting line, as is home/away and many other variables

9. Could the fact that a game is a “trap game” have an effect on the outcome? First, we have to define a trap game so that we can find trap games to put into the regression.

10. “Trap Game” Defined “Simply put, a trap game is a game on a team's schedule that tends to get lost among all of the other games a team is playing.” AUTHOR: Joe Penkala, Bleacher Report columnist “Trap games have been argued to be a media- manufactured myth, but in NCAA football there tends to be an ample supply of strange circumstances to cause a team near the top of the rankings to falter in shocking fashion.” AUTHOR: Matt Fitzgerald, Bleacher Report columnist

11. “Trap Game” Defined (cont’d) “They are trap games if you forget how to go to work and do those things.” AUTHOR: Brian Kelly, ND Head Coach “This is a textbook trap game for Denver. The Broncos (9-3) just won the AFC West title. They are traveling on a short week. They are playing a team that will be emotionally charged after the death of Grady Allen, the father of Oakland coach Dennis Allen. The Broncos visit Baltimore in Week 15 in a game that could go a long way in determining the No. 2 seed and a bye in the first round of the AFC playoffs. This is the type of game a team can come into flat.” AUTHOR: Bill Williamson, ESPN

12. My working definition Two teams

13. My working definition Two teams “Trapped” team Weaker opponent

14. My working definition Two teams “Trapped” team Weaker opponent TexasUCLA#7

15. My working definition Two teams “Trapped” team Weaker opponent TexasUCLA Favored by at least 13.5 points (In this example from their 2010 matchup, Texas was favored by 16) #7

16. My working definition “Trapped” team Texas Favored by at least 13.5 points A trap game has at least one of the following “trap” conditions: ▫Trap game is after a bye week ▫Trap game is before a bye week ▫Trap game is after a win over a higher-ranked opponent ▫Trap game is before a matchup against a higher-ranked opponent ▫Trap game is after a win in a “big game” (defined as a top 25 matchup) ▫Trap game is before a “big game” ▫Trap game is after a win in a rivalry game ▫Trap game is before a rivalry game #7

17. My working definition “Trapped” team Texas Favored by at least 13.5 points Conditions ▫After bye week ▫Before bye week ▫After win over higher-ranked team ▫Before matchup w/higher-ranked team ▫After win in top 25 matchup ▫Before a top 25 matchup ▫After win over rival ▫Before game vs. rival Weaker opponent UCLA#7

18. My working definition “Trapped” team Texas Favored by at least 13.5 points Conditions ▫After bye week ▫Before bye week ▫After win over higher-ranked team ▫Before matchup w/higher-ranked team ▫After win in top 25 matchup ▫Before a top 25 matchup ▫After win over rival ▫Before game vs. rival Weaker opponent UCLA 16 point favorite vs. 9/25/2010 #7

19. My working definition “Trapped” team Texas Favored by at least 13.5 points Conditions ▫After bye week ▫Before bye week ▫After win over higher-ranked team ▫Before matchup w/higher-ranked team ▫After win in top 25 matchup ▫Before a top 25 matchup ▫After win over rival ▫Before game vs. rival opponent UCLA 16 point favorite vs. 9/25/2010 10/2/2010 Texas vs. Oklahoma #7 #21 #8

20. My working definition “Trapped” team Texas Favored by at least 13.5 points Conditions ▫After bye week ▫Before bye week ▫After win over higher-ranked team ▫Before matchup w/higher-ranked team ▫After win in top 25 matchup ▫Before a top 25 matchup ▫After win over rival ▫Before game vs. rival opponent UCLA 16 point favorite vs. 9/25/2010 10/2/2010 Texas vs. Oklahoma #7 #21 #8 --trap game (vs. UCLA) falls before a game against a higher ranked opponent

21. My working definition “Trapped” team Texas Favored by at least 13.5 points Conditions ▫After bye week ▫Before bye week ▫After win over higher-ranked team ▫Before matchup w/higher-ranked team ▫After win in top 25 matchup ▫Before a top 25 matchup ▫After win over rival ▫Before game vs. rival opponent UCLA 16 point favorite vs. 9/25/2010 10/2/2010 Texas vs. Oklahoma #7 #21 #8 --trap game (vs. UCLA) falls before a game against a higher ranked opponent --trap game (vs. UCLA) falls before a top 25 matchup

22. My working definition “Trapped” team Texas Favored by at least 13.5 points Conditions ▫After bye week ▫Before bye week ▫After win over higher-ranked team ▫Before matchup w/higher-ranked team ▫After win in top 25 matchup ▫Before a top 25 matchup ▫After win over rival ▫Before game vs. rival opponent UCLA 16 point favorite vs. 9/25/2010 10/2/2010 Texas vs. Oklahoma #7 #21 #8 --trap game (vs. UCLA) falls before a game against a higher ranked opponent --trap game (vs. UCLA) falls before a top 25 matchup --trap game (vs. UCLA) falls before a rivalry game

23. My working definition “Trapped” team Texas Favored by at least 13.5 points Conditions ▫After bye week ▫Before bye week ▫After win over higher-ranked team ▫Before matchup w/higher-ranked team ▫After win in top 25 matchup ▫Before a top 25 matchup ▫After win over rival ▫Before game vs. rival opponent UCLA 16 point favorite vs. 9/25/2010 #7 --trap game (vs. UCLA) falls before a game against a higher ranked opponent --trap game (vs. UCLA) falls before a top 25 matchup --trap game (vs. UCLA) falls before a rivalry game ADDED AS A LINE OF DATA AS A “TRAP GAME”

24. Coded as dummy variables Conditions ▫After bye week ▫Before bye week ▫After win over higher-ranked team ▫Before matchup w/higher-ranked team ▫After win in top 25 matchup ▫Before a top 25 matchup ▫After win over rival ▫Before game vs. rival

25. Coded as dummy variables Conditions ▫After bye week ▫Before bye week ▫After win over higher-ranked team ▫Before matchup w/higher-ranked team ▫After win in top 25 matchup ▫Before a top 25 matchup ▫After win over rival ▫Before game vs. rival Coded as: ▫0 ▫1 ▫0 ▫1 ▫0 ▫1

26. Regressions Adding this to the efficiency model I talked about earlier: Points margin = β1 * betting line

27. Regressions Adding this to the efficiency model I talked about earlier: Points margin = β1 * betting line + β2 * trap variable…

28. Regressions Adding this to the efficiency model I talked about earlier: Points margin = β1 * betting line + β2 * trap variable… Any combination of trap variables Looking to see what effect they have on the points margin (coefficients) and if they’re significant (P-value)

29. Quick note on the data I used: Samples were from 2009-2012 college football seasons The “trapped team” (in example game, Texas) limited to Big 6 conferences (Big East, Big Ten, Big 12, SEC, ACC, Pac-10/12) Two data sets ▫One was the 65-68 teams from Big 6 conferences vs. Division 1 opponents (n=632) (called “Results 4,” the first chart) ▫One was the 65-68 teams from Big 6 conferences vs. Division 1 and FCS opponents (n=825) (called “Results 6,” the second chart)

30. Regression Charts Each row captures an independent variable (i.e. betting line, trap variables, constant terms) Each column captures a regression run (i.e. I, II, III, etc.) Numbers on top are the coefficients of the independent variables, numbers below in parentheses are P-value ▫Cells are red (and have “**” after P-value) if the variable was significant with a P-value of less than.05 ▫Cells are yellow (and have “*”) if the variable was significant with a P-value of greater than.05 but less than.1

31. Results 4 n=632 vs. Division 1 teams IIIIIIIVVVIVIIVIIIIXXXIXIIXIII percdiff -12.974-12.824-12.274-12.528-12.021-12.153 (.004) (.006)(.005)(.007)(.006) pointsdiff 0.0720.071.072.073 (.000) bettingline 0.8510.847.883.870.874.900.889 (.000) trapBFbye -1.0240.046 (.575)(.981) trapAFbye 1.601-0.469 (.268)(.762) trapBFhigh -2.299-4.349-2.325-4.302-1.412-2.543 (.187)(.018)**(.179)(.018)**(.384)(.139) trapAFhigh -7.430-8.817-7.589-8.746-8.314-9.523 (.005)**(.002)**(.004)**(.001)** (.000)** trapBFbig 0.9653.5271.1263.529.4722.376 (.561)(.043)**(.490)(.039)**(.757)(.140) trapAFbig -1.875-2.222-2.336-2.192-3.806-3.415 (.378)(.327)(.267)(.326)(.057)*(.109) trapBFriv -0.081-0.014 (.972)(.996) trapAFriv -3.086-2.027 (.423)(.620) _cons 11.3665.01711.5404.94510.6313.84611.0864.27610.3113.25710.5523.4063.348 (.000)(.010)(.000)(.009)(.000)(.043)(.000)(.022)(.000)(.081)(.000)(.068)(.073) R-squared 0.28210.18870.27910.1883.2637.1638.2759.1782.2629.1638.2670.1643.1609 Adj R-sq 0.27060.17700.27220.1818.2601.1612.2724.1756.2594.1612.2635.1617.1596

32. Results 6 n=825 vs. Both (Div. 1 and FCS) IIIIIIIVVVIVIIVIIIIXXXIXIIXIII percdiff -21.228-21.144-20.747-21.200-20.842-20.922 (.000) pointsdiff 0.070.069.068.069 (.000) bettingline 0.9060.902.914.905.908.914.913 (.000) trapBFbye -0.2821.194 (.880)(.500) trapAFbye 3.760-0.519 (.005)**(.683) trapBFhigh 0.677-2.8450.336-2.903.771-1.656 (.677)(.059)*(.835)(.052)*(.611)(.243) trapAFhigh -6.147-7.226-6.983-7.289-8.533-8.027 (.020)**(.004)**(.008)**(.003)**(.001)** trapBFbig 0.0503.0870.0352.867-.1231.809 (.976)(.047)**(.983)(.061)*(.937)(.208) trapAFbig -2.882-2.323-3.844-2.335-5.832-3.527 (.204)(.278)(.089)*(.270)(.006)**(.075)* trapBFriv -0.964-1.171 (.669)(.583) trapAFriv -4.594-3.779 (.239)(.306) _cons 17.3733.44418.4833.43817.7682.81818.4863.19217.9412.42419.1932.8202.587 (.000)(.021)(.000)(.017)(.000)(.049)(.000)(.025)(.000)(.089)(.000)(.048)(.068) R-squared 0.20060.28390.19010.2822.1756.2678.1871.2769.1754.2680.1830.2694.2666 Adj R-sq 0.19070.27600.18420.2778.1726.2660.1841.2752.1724.2662.1800.2676.2657

33. Findings so far… Very significant (max. P=.02): ▫If the “trapped team” is just coming off a win over a higher ranked opponent, they tend to underperform the expected margin by 6 to 9 points. ▫Examples of this:  #24 Illinois vs. Western Michigan (9/24/2011) Final Score 23-20 (margin of 3, betting line was 14, underperformed by 11)  Satisfies this condition because on 9/17, then unranked Illinois upset #22 Arizona State

34. Somewhat significant… If the trap game comes the week after the “trapped team” plays in a big game (big game is where the trapped team and their opponent are both ranked), the trapped team tends to underperform by 2 to 6 points. Example of this: ▫#7 Oklahoma vs. Air Force (9/18/2010) Final Score was 27-24 (margin was 3, betting line was 16.5, underperformed by 13.5) ▫On 9/11/2010, then #10 Oklahoma played and beat #17 Florida State in a “big game.”

35. Significance? If those conditions are unaccounted for by Vegas odds-makers, there is a potential opportunity to take advantage of the market. Example: ▫11/23/2013-MSU vs. Northwestern  If MSU is favored by over 13.5 points and the week before we beat #4 Nebraska (just a guess)…

36. Significance? If those conditions are unaccounted for by Vegas odds-makers, there is a potential opportunity to take advantage of the market. Example: ▫11/23/2013-MSU vs. Northwestern  If MSU is favored by over 13.5 points and the week before we beat #4 Nebraska (just a guess)…  BET THAT MSU WON’T COVER THE SPREAD!

38. …not so fast…

39. Shortcomings: Constant terms ▫Imply that the betting line is off already

40. Results 6 n=825 vs. Both (Div. 1 and FCS) IIIIIIIVVVIVIIVIIIIXXXIXIIXIII percdiff -21.228-21.144-20.747-21.200-20.842-20.922 (.000) pointsdiff 0.070.069.068.069 (.000) bettingline 0.9060.902.914.905.908.914.913 (.000) trapBFbye -0.2821.194 (.880)(.500) trapAFbye 3.760-0.519 (.005)**(.683) trapBFhigh 0.677-2.8450.336-2.903.771-1.656 (.677)(.059)*(.835)(.052)*(.611)(.243) trapAFhigh -6.147-7.226-6.983-7.289-8.533-8.027 (.020)**(.004)**(.008)**(.003)**(.001)** trapBFbig 0.0503.0870.0352.867-.1231.809 (.976)(.047)**(.983)(.061)*(.937)(.208) trapAFbig -2.882-2.323-3.844-2.335-5.832-3.527 (.204)(.278)(.089)*(.270)(.006)**(.075)* trapBFriv -0.964-1.171 (.669)(.583) trapAFriv -4.594-3.779 (.239)(.306) _cons 17.3733.44418.4833.43817.7682.81818.4863.19217.9412.42419.1932.820 2.587 (.000)(.021)(.000)(.017)(.000)(.049)(.000)(.025)(.000)(.089)(.000)(.048)(.068) R-squared 0.20060.28390.19010.2822.1756.2678.1871.2769.1754.2680.1830.2694.2666 Adj R-sq 0.19070.27600.18420.2778.1726.2660.1841.2752.1724.2662.1800.2676.2657

41. Shortcomings: Constant terms ▫Imply that the betting line is off already  Probably because of not 100% accurate betting lines  Could also be a result of the “Long-shot bias”

42. Shortcomings: Constant terms ▫Imply that the betting line is off already  Probably because of not 100% accurate betting lines  Could also be a result of the “Long-shot bias” ▫Minimum betting line of 13.5 set arbitrarily  Based on a survey of my friends  This large line could possibly influence the dummy variables (trap variables)

43. Shortcomings: Constant terms ▫Imply that the betting line is off already  Probably because of not 100% accurate betting lines  Could also be a result of the “Long-shot bias” ▫Minimum betting line of 13.5 set arbitrarily  Based on a survey of my friends  This large line could possibly influence the dummy variables (trap variables) ▫Recent trend?  Results only based on 4 years of data, despite a large N-value

44. Conclusion Despite these shortcomings, the two variables found are significant, one of them especially, and do suggest an inefficiency in the betting market.