Trap Games in College Football

transcript

Trap Games in College FootballRyan GimarcEC499 – Spring 2013

Objective

•To test for a potential inefficiency in the college football betting market

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

Betting Market efficiency

Normal betting market regression:

Points margin = β1 * betting line

Coefficient is usually equal to or very close to 1

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

REASON: Injury reports are generally compensated for in the betting line, as is home/away and many other variables

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 wecan find trap games to put into the regression.

“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

“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

My working definitionTwo teams

“Trapped” team Weaker opponent

Texas UCLA#7

Texas UCLA

Favored by at least 13.5 points

(In this example from their 2010 matchup, Texas was favored by 16)

My working definition

“Trapped” team

• 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

• 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

“Trapped” team

Weaker opponent

16 point favorite

vs.9/25/2010 #7

“Trapped” team

opponent

16 point favorite

vs.9/25/2010

10/2/2010 Texas vs. Oklahoma#7#21 #8

“Trapped” team

opponent

16 point favorite

vs.9/25/2010

--trap game (vs. UCLA) falls before a game against a higher ranked opponent

“Trapped” team

opponent

16 point favorite

vs.9/25/2010

--trap game (vs. UCLA) falls before a top 25 matchup

“Trapped” team

opponent

16 point favorite

vs.9/25/2010

--trap game (vs. UCLA) falls before a top 25 matchup--trap game (vs. UCLA) falls before a rivalry game

“Trapped” team

opponent

16 point favorite

vs.9/25/2010 #7

--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”

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 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▫0▫0▫1▫0▫1▫0▫1

Regressions

•Adding this to the efficiency model I talked about earlier:

Regressions

Points margin = β1 * betting line + β2 * trap variable…

Regressions

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)

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)

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

Results 4n=632

vs. Division 1 teams

I II III IV V VI VII VIII IX X XI XII XIII

percdiff-12.974 -12.824 -12.274 -12.528 -12.021 -12.153(.004) (.004) (.006) (.005) (.007) (.006)

pointsdiff0.072 0.071 .072 .072 .072 .073(.000) (.000) (.000) (.000) (.000) (.000)

bettingline 0.851 0.847 .883 .870 .874 .900 .889

(.000) (.000) (.000) (.000) (.000) (.000) (.000)

trapBFbye-1.024 0.046(.575) (.981)

trapAFbye1.601 -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)** (.001)** (.000)**

trapBFbig0.965 3.527 1.126 3.529 .472 2.376

(.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)

_cons11.366 5.017 11.540 4.945 10.631 3.846 11.086 4.276 10.311 3.257 10.552 3.406 3.348

(.000) (.010) (.000) (.009) (.000) (.043) (.000) (.022) (.000) (.081) (.000) (.068) (.073)

R-squared 0.2821 0.1887 0.2791 0.1883 .2637 .1638 .2759 .1782 .2629 .1638 .2670 .1643 .1609

Adj R-sq 0.2706 0.1770 0.2722 0.1818 .2601 .1612 .2724 .1756 .2594 .1612 .2635 .1617 .1596

Results 6n=825

vs. Both (Div. 1 and FCS)

percdiff-21.228 -21.144 -20.747 -21.200 -20.842 -20.922

(.000) (.000) (.000) (.000) (.000) (.000)

pointsdiff0.07 0.069 .068 .068 .068 .069

(.000) (.000) (.000) (.000) (.000) (.000)

bettingline 0.906 0.902 .914 .905 .908 .914 .913 (.000) (.000) (.000) (.000) (.000) (.000) (.000)

trapBFbye-0.282 1.194(.880) (.500)

trapAFbye3.760 -0.519

(.005)** (.683)

trapBFhigh0.677 -2.845 0.336 -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)** (.001)**

trapBFbig0.050 3.087 0.035 2.867 -.123 1.809

(.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)

_cons17.373 3.444 18.483 3.438 17.768 2.818 18.486 3.192 17.941 2.424 19.193 2.820 2.587(.000) (.021) (.000) (.017) (.000) (.049) (.000) (.025) (.000) (.089) (.000) (.048) (.068)

R-squared 0.2006 0.2839 0.1901 0.2822 .1756 .2678 .1871 .2769 .1754 .2680 .1830 .2694 .2666

Adj R-sq 0.1907 0.2760 0.1842 0.2778 .1726 .2660 .1841 .2752 .1724 .2662 .1800 .2676 .2657

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

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.”

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)…

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!

…not so fast…

Shortcomings:

•Constant terms▫Imply that the betting line is off already

Results 6n=825

vs. Both (Div. 1 and FCS)

percdiff-21.228 -21.144 -20.747 -21.200 -20.842 -20.922

(.000) (.000) (.000) (.000) (.000) (.000)

pointsdiff0.07 0.069 .068 .068 .068 .069

(.000) (.000) (.000) (.000) (.000) (.000)

bettingline 0.906 0.902 .914 .905 .908 .914 .913 (.000) (.000) (.000) (.000) (.000) (.000) (.000)

trapBFbye-0.282 1.194(.880) (.500)

trapAFbye3.760 -0.519

(.005)** (.683)

trapBFhigh0.677 -2.845 0.336 -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)** (.001)**

trapBFbig0.050 3.087 0.035 2.867 -.123 1.809

(.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)

_cons17.373 3.444 18.483 3.438 17.768 2.818 18.486 3.192 17.941 2.424 19.193 2.820 2.587(.000) (.021) (.000) (.017) (.000) (.049) (.000) (.025) (.000) (.089) (.000) (.048) (.068)

R-squared 0.2006 0.2839 0.1901 0.2822 .1756 .2678 .1871 .2769 .1754 .2680 .1830 .2694 .2666

Adj R-sq 0.1907 0.2760 0.1842 0.2778 .1726 .2660 .1841 .2752 .1724 .2662 .1800 .2676 .2657

Shortcomings:

Probably because of not 100% accurate betting lines

Could also be a result of the “Long-shot bias”

Shortcomings:

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)

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

Conclusion

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

Trap Games in College Football

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