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Trap Games in College Football

Trap Games in College Football. Ryan Gimarc EC499 – Spring 2013. Objective. To test for a potential inefficiency in the college football betting market. Betting Market efficiency.

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Trap Games in College Football

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  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 should be minimal, P value will be very high as it doesn’t have much effect on points margin Coefficient is usually equal to or very close to 1

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

  9. What can we add into this equation that significantly affects the point spread? 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 Weaker opponent “Trapped” team

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

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

  16. My working definition • 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 “Trapped” team #7 Texas Favored by at least 13.5 points

  17. My working definition • 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 “Trapped” team #7 Texas UCLA Favored by at least 13.5 points

  18. My working definition • 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 “Trapped” team #7 Texas UCLA vs. 9/25/2010 Favored by at least 13.5 points 16 point favorite

  19. My working definition • 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 “Trapped” team #7 Texas UCLA vs. 9/25/2010 Texas #8 Oklahoma vs. #21 10/2/2010 Favored by at least 13.5 points 16 point favorite

  20. My working definition • 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 “Trapped” team #7 Texas UCLA vs. 9/25/2010 Texas #8 Oklahoma vs. #21 10/2/2010 --trap game (vs. UCLA) falls before a game against a higher ranked opponent Favored by at least 13.5 points 16 point favorite

  21. My working definition • 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 “Trapped” team #7 Texas UCLA vs. 9/25/2010 Texas #8 Oklahoma vs. #21 10/2/2010 --trap game (vs. UCLA) falls before a game against a higher ranked opponent --trap game (vs. UCLA) falls before a top 25 matchup Favored by at least 13.5 points 16 point favorite

  22. My working definition • 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 “Trapped” team #7 Texas UCLA vs. 9/25/2010 Texas #8 Oklahoma vs. #21 10/2/2010 --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 Favored by at least 13.5 points 16 point favorite

  23. My working definition • 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 “Trapped” team ADDED AS A LINE OF DATA AS A “TRAP GAME” #7 Texas UCLA vs. 9/25/2010 --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 Favored by at least 13.5 points 16 point favorite

  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 • Coded as: • 0 • 0 • 0 • 1 • 0 • 1 • 0 • 1 • 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

  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. 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

  32. 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.”

  33. 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)…

  34. 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!

  35. GO MAKE MILLIONS

  36. GO MAKE MILLIONS …not so fast…

  37. Shortcomings: • Constant terms • Imply that the betting line is off already

  38. 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”

  39. 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)

  40. 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

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

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