2010 Alabama Mr. Football
Coty Blanchard
THEORY OF WINNING
Coaching, recruiting and spending in college football
Table of Contents







Introduction
How to predict a win
Data sources
Initial Model
Out of sample prediction
Practical applications
Next steps
Jason Campbell,
Auburn University
Authors Introduction

McDonald “Mac” Mirabile
 Manager
of Strategic & Financial Analysis at WWF
 Undergraduate and graduate thesis on the predictors
of a successful transition from college to NFL
 Prior academic publications on topics such as biases in
college football polls, the NFL Rookie Cap, the
Wonderlic Test, and the Peer Effect in the NFL draft

Mark Witte
 Assistant
Professor at College of Charleston
 Generally awesome guy
Topic Introduction

The importance of winning in college
 Shapes
alumni support, attendance
 Influences quality of recruiting
 Self-enforcing cycle
How to predict a win



Vegas point spread, totals, and money line
theoretically capture all available information
under the efficient market hypothesis (EMH)
Existing literature consistently enforces EMH, though
there are some published examples of deviations
and profitable strategies within wagering markets
Within the framework of this paper, we will assume
EMH holds within college football wagering markets
and will measure the success of our developed
models relative to the baseline Vegas model
Predicting Wins with the Vegas Line

Bubble chart
illustrates the
home team’s
winning percent
by the Vegas
Line, with the
size of the
bubble based
on the number
of observations
Predicting Wins with the Vegas Line

Bar chart of home team’s winning percentage by the Vegas line
The Vegas Line model





Home Win (0,1) = b1*Line + error
This model within our data explains 29% of the
variation in wins (Pseudo R2).
The line coefficient is 0.1091, with a standard error
of 0.00437, and an Odds Ratio of 1.115
Interpretation: for each additional point a team is
favored, their odds of winning increase by 11.5%
Non-linear model shows similar results
Improving the Vegas Line model


Can it be done, or does the Vegas line incorporate all
publically available information?
To test this, we added several variables:
 Home,
Away win and losing streaks
 Home, Away AP Rankings, Top 25 matchups
 Dummy variables for conference games, neutral field
matchups, and night games
 Distance between schools, stadium size, rivalry information
 Conference dummy variables
Improving the Vegas Line model
Effect
Line
ETP
HWS
HLS
AWS
ALS
Hrank
Arank
HNR
ANR
TrueT25
ConfGame
Neutral
Nightgame
Stadium
Distance
Rivalry
Conf
DF
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
12
Wald
285.4691
1.1213
0.522
0.8024
1.8483
0.1004
0.7195
0.1588
1.591
1.5452
0.2535
0.003
0.001
0.3414
1.078
0.0145
2.0766
11.5154
Pr > ChiSq
<.0001
0.2896
0.47
0.3704
0.174
0.7513
0.3963
0.6903
0.2072
0.2138
0.6146
0.9566
0.9743
0.559
0.2992
0.9042
0.3541
0.4853
• Table on left shows these
additional variables and a their
corresponding Wald Chi2 statistics
• The Vegas line successfully
incorporates all available
information.
• Adding more explanatory
variables does not improve the
model’s fit.
• None of the added variables are
statistically significant as their
importance is already captured in
the Line variable.
Data Sources

To develop a model of winning without utilizing the
Vegas line, the authors gathered data on the
following topics:
 Game-specific
factors
 Institutional factors/history
 Team player composition/recruiting
 Team coach factors/history

We will discuss the collection and organization of
this data next
Game-specific Factors




Matchup data comes from Covers.com
Data includes game location, time, day, conference
information
Each matchup (home vs away) is one observation in
the dataset
There are about 500 games per season
Institutional Factors & History



Historical team performance comes from
CFBDatawarehouse.com
University football team expenditure and student
body size data come from the Equity in Athletics
website
Each of these variables is reported for a particular
year (e.g., Michigan’s historical team performance
through 2007 and their team expenditure data for
the 2008 season would all be used as predictors
for the 2008 season matchups)
Team player composition and recruiting



Class recruiting data comes from Rivals.com,
Scouts.com, and Prepstar.com
Recruiting classes in 2005 (RS-Senior), 2006 (Senior
/ RS-Junior), 2007 (Junior, RS-Sophomore), 2008
(Sophomore, RS-Freshman), an 2009 (Freshman) are
used as predictors for the 2009 season matchups.
Due to the NFL draft, transfers, and general
attrition, these variables are imperfect measures of
the talent comprising a team in a particular season
Team coach factors and history



Historical coach performance comes from
CFBDatawarehouse.com
Coach biographical information comes from various
university athletics department websites
Each of these variables is reported for a particular
year (e.g., Michigan’s coach’s historical performance
through 2007 would be used as a predictor for the
2008 season matchups)
Summary Statistics of Model variables
Variable
Mean Std Dev Minimum Maximum N
home_win
0.6
Stadium
55.458
Student_Body_H
17.883
cum_winpct_adf
0.008
total_expense_all_football_ldf 0.109
class_rank_scouts_l4_adf
-5.843
first_year_HC_H
0.072
first_year_HC_A
0.083
coach_age_adf
0.239
coach_experience_adf
0.447
seasons_coach_adf
0.323
cum_winpct_coach_adf
0.017
nfl_years_adf
-0.028
Home_Coach_Minority
0.047
Away_Coach_Minority
0.048
0.49
22.246
8.017
0.11
0.563
31.682
0.258
0.275
11.764
12.456
11.091
0.246
3.369
0.213
0.213
0
16
0.002
-0.392
-1.611
-106.25
0
0
-46
-46
-41
-0.826
-16
0
0
1
107.501
43.026
0.408
2.386
100.75
1
1
48
54
42
0.84
16
1
1
3418
3418
3304
3418
3204
3340
3418
3418
3418
3417
3418
3418
3417
3418
3418
N Miss
0
0
114
0
214
78
0
0
0
1
0
0
1
0
0
Initial Model
Odds Ratio Estimates
Effect
Estimate
Stadium
Student_Body_H
cum_winpct_adf
total_expense_all_fo
class_rank_scouts_l4
first_year_HC_H
first_year_HC_A
coach_age_adf
coach_experience_adf
seasons_coach_adf
cum_winpct_coach_adf
nfl_years_adf
Home_Coach_Minority
Away_Coach_Minority
N: 2,948
R-Square: .215
1.004
1.005
1.714
2.529
0.992
0.754
1.291
0.984
1.002
1.007
6.806
0.961
0.581
1.866
95% Wald
Confidence
1 1.008
0.99 1.017
0.63 4.647
2.03 3.153
0.99 0.996
0.54 1.047
0.93 1.784
0.97 0.996
0.99 1.013
1 1.019
4.43 10.45
0.93
0.99
0.38 0.892
1.16 2.997
Matchup-specific variables:
• Stadium Size
• Home team student size
School-specific variables:
• Cumulative Team Win Pct Diff
• Log Diff of Total Team expenditures
Team-specific variables (Difference home – away):
• Scouts.com weighted average class ranking
Coach-specific variables (Difference home –
away) :
• First year head coach Home team dummy
• First year head coach Away team dummy
• Coach age
• Coach experience (assistant + HC)
• Head coach seasons
• Lifetime Coach Win Pct Diff
• Years as NFL player
• Home team’s head coach minority dummy
• Away team’s head coach minority dummy
Initial Model - Interpretations
Matchup-specific variables:
• Stadium Size – for every additional 10,000 seats, the home team is 4% more likely to win
• (also considered game time, location, rivalry variables)
School-specific variables:
• Log Diff of Total Team expenditures – the odds ratio of the % difference (home/away) in team spending of 2.5 suggests
that a team spending 100% more (twice as much) is 150% more likely to win, (Alternative, equivalent interpretation: odds
of winning increase 15% for each 10% increase in excess of your opponent’s expenditures)
Team-specific variables (all Difference home – away) :
• Scouts.com average class ranking – for each unit increase in average class ranking between the home and away, the
home team is 1% more likely to win
Coach-specific variables (all Difference home – away) :
• First year head coach dummy variables – marginally significant and coefficients in the direction one would expect
• Diff in HC’s ages – for each additional year in age difference b/w the Home and Away team’s coach, the home team is
1% less likely to win
• Diff in HC’s cumulative Win % – for each 1% difference in lifetime win percentage between the home team’s HC and
the away team’s HC, the home team is about 6% more likely to win
• Years as NFL player – for each additional year of NFL playing experience between the home team’s HC and the away
team’s HC, the home team is about 4% less likely to win
• Home team Head Coach Minority – minority coaches are 42% less likely to win than non-minority coaches at home
• Away team Head Coach Minority – home teams are 87% more likely to win when playing against a minority coach
Out of Sample prediction
Analysis Variable : Vegas_Model_Correct
sample
Correct
Incorrect
% Correct
In
1,450
541
72.8%
Out
612
233
72.4%
Analysis Variable : Our_Model_Correct
sample
Correct
Incorrect
% Correct
In
1,420
648
68.7%
Out
603
278
68.4%
Both models have
comparable in and out of
sample performance
Out of Sample by Line
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
11) favorite 28+
10) favorite 21 - 27
09) favorite 14 - 20
08) favorite 7 - 13
07) favorite 3 - 6
12) favorite 0 - 2
06) underdog .5 - 2
05) underdog 3 - 6
04) underdog 7 - 13
03) underdog 14 - 20
02) underdog 21 - 27
01) underdog 28+
Vegas line does a better job predicting everything except
games where the line is between -2 and +2

Home Win
Our Model
Vegas Model
2009 Season (SEC results)


Data from 2004-2008 used to develop the model
Data from 2009 used in an out-of-sample validation
School
Alabama
Arkansas
Auburn
Florida
Georgia
Kentucky
LSU
Mississippi
Mississippi St.
South Carolina
Tennessee
Vanderbilt
Model
Actual
Evaluation
Wins Losses Pct Wins Losses Pct Correct Incorrect PCT
11
1 92%
12
0 100%
11
1 92%
8
4 67%
7
5 58%
11
1 92%
7
5 58%
7
5 58%
10
2 83%
12
0 100%
11
1 92%
11
1 92%
11
1 92%
7
5 58%
8
4 67%
4
8 33%
6
6 50%
8
4 67%
10
3 77%
9
4 69%
10
3 77%
5
6 45%
7
4 64%
7
4 64%
2
9 18%
4
7 36%
7
4 64%
6
5 55%
5
6 45%
10
1 91%
8
4 67%
6
6 50%
8
4 67%
3
7 30%
1
9 10%
8
2 80%
Note: Non Div1A opponents not scored/modeled
Practical Applications

Predict 2010 season results – conference standings,
national champion, before a single game has been
played
Next steps

What can be added to the model?
 New sources of data (attendance,
compensation/bonus – impute missing values based
on relative rank of team within conference?)
 Additional data cleanup (game time, more years
2001-2003)
 Different estimation methodologies
BACKUP/OLD SLIDES BEGIN HERE
Who is hiring minority coaches?
Analysis of Maximum Likelihood Estimates
Parameter
Estimate Standard
Wald
Pr > ChiSq
Error
Chi-Square
Intercept
8.4961
2.1216
16.0358
<.0001
Stadium
0.0311
0.0116
7.1846
0.0074
School_Seasons_Home
-0.014 0.00771
3.2851
0.0699
Cum_WinPCT_School_Home
-6.9228
3.0907
5.0172
0.0251
MA5_Win_PCT_School_Home
-2.1436
1.3669
2.4592
0.1168
Coach_Age_H
-0.1657
0.0537
9.526
0.002
Coach_Experience_H
0.0479
0.0497
0.9289
0.3352

The coach is more likely to be young (see coach_age), belong
to a historically crappy program (Cum_WinPCT_School_H) as
well as belong to a recently crappy program
(MA5_Win_PCT_School_H) of relatively newer schools
(School_Seasons_H) and larger schools (Stadium).
Predicting recruiting classes
Parameter
Estimate Std Error Wald 95%
Chi2 Pr > Chi2
Intercept
43.38
4.49 34.58 52.17 93.4
<.0001
Prev_WinPCT_School
-9.69
3.34 -16.23 -3.15 8.43
0.0037
MA5_Win_PCT_School
-15.52
4.81 -24.96 -6.08 10.4
0.0013
Class_Rank_Scouts_Lag1
0.27
0.04 0.20 0.34 55.8
<.0001
Class_Rank_Scouts_Lag2
0.18
0.04 0.10 0.26 20.6
<.0001
Class_Rank_Scouts_Lag3
0.10
0.04 0.02 0.18 6.23
0.0126
Class_Rank_Scouts_Lag4
0.13
0.04 0.05 0.20 10.1
0.0015
ACC
-17.79
2.96 -23.58 -11.99 36.2
<.0001
Big 12
-16.18
2.84 -21.75 -10.61 32.4
<.0001
Big East
-15.02
2.84 -20.59 -9.45 27.9
<.0001
Big Ten
-16.26
2.85 -21.84 -10.67 32.5
<.0001
Conf USA
-7.57
2.38 -12.23 -2.90 10.1
0.0015
I-A Ind
-12.09
3.73 -19.41 -4.77 10.5
0.0012
Indep
-11.87
8.44 -28.41 4.68 1.98
0.1597
MAC
0.17
2.30 -4.34 4.68 0.01
0.9412
MWC
-3.67
2.54 -8.66 1.32 2.08
0.149
Pac-10
-18.79
3.05 -24.78 -12.81 37.9
<.0001
SEC
-21.78
3.05 -27.75 -15.80 51.1
<.0001
Sun Belt
-3.33
2.68 -8.59 1.93 1.54
0.2149
WAC
0.00
0.00 0.00 0.00 .
.




GLM estimation of
dependent variable:
Scouts class ranking
Previous year and
5-year MA Win %
impact recruiting
Previous classes are
also good predictors
of current year’s
class ranking
Conference impacts
recruiting
Alabama (2010) = 43.4 – (9.7*1) – (15.5*.77) + (.27*2) + (.18*1) + (.1*22) + (.13*18) – 21.8 = 3 (Actual rank 4)
Auburn (2010) = 43.4 – (9.7*.615) – (15.5*.66) + (.27*16) + (.18*18) + (.1*6) + (.13*9) – 21.8 = 15 (Actual rank 5)
Vanderbilt (2010) = 43.4 – (9.7*.167) – (15.5*.38) + (.27*72) + (.18*74) + (.1*87) + (.13*61) – 21.8 = 63 (Actual rank 61)
2009 out of sample (A-F)
School
Akron
Alabama
Arizona
Arizona St.
Arkansas
Arkansas State
Auburn
BYU
Ball State
Baylor
Boise St.
Boston College
Bowling Green
California
Central Michigan
Cincinnati
Clemson
Colorado
Colorado State
Connecticut
Duke
East Carolina
Eastern Michigan
Florida
Florida State
Fresno State
Model
Losses
Wins
4
11
4
8
8
3
7
10
4
2
10
10
5
9
7
10
10
9
6
5
2
6
1
12
9
7
Pct Wins
6 40%
2
1 92%
12
8 33%
7
3 73%
3
4 67%
7
5 38%
1
5 58%
7
2 83%
10
5 44%
2
9 18%
3
3 77%
13
2 83%
7
6 45%
5
3 75%
7
3 70%
8
2 83%
11
3 77%
8
2 82%
3
4 60%
2
7 42%
7
7 22%
3
6 50%
7
9 10%
0
0 100%
11
3 75%
6
5 58%
7
Actual
Losses
Pct
8 20%
0 100%
5 58%
8 27%
5 58%
7 13%
5 58%
2 83%
7 22%
8 27%
0 100%
5 58%
6 45%
5 58%
2 80%
1 92%
5 62%
8 27%
8 20%
5 58%
6 33%
5 58%
10 0%
1 92%
6 50%
5 58%
Evaluation
Correct Incorrect PCT
8
2 80%
11
1 92%
7
5 58%
6
5 55%
11
1 92%
6
2 75%
10
2 83%
10
2 83%
5
4 56%
8
3 73%
10
3 77%
9
3 75%
7
4 64%
6
6 50%
9
1 90%
11
1 92%
11
2 85%
5
6 45%
4
6 40%
10
2 83%
6
3 67%
11
1 92%
9
1 90%
11
1 92%
9
3 75%
8
4 67%
2009 out of sample (G-M)
School
Georgia
Georgia Tech
Hawaii
Houston
Idaho
Illinois
Indiana
Iowa
Iowa St.
Kansas
Kansas State
Kent State
Kentucky
LSU
Louisiana Tech
Louisville
Marshall
Maryland
Memphis
Miami-Florida
Miami-Ohio
Michigan
Michigan State
Middle Tennessee
Minnesota
Mississippi
Model
Losses
Wins
11
11
7
2
1
3
3
8
1
4
4
0
4
10
4
4
3
3
5
4
1
8
5
4
1
5
Pct Wins
1 92%
7
2 85%
10
4 64%
4
9 18%
9
12 8%
8
8 27%
2
8 27%
3
4 67%
11
10 9%
4
8 33%
4
6 40%
4
10 0%
4
8 33%
6
3 77%
9
6 40%
3
7 36%
3
9 25%
6
8 27%
1
6 45%
1
8 33%
8
9 10%
0
3 73%
4
7 42%
5
6 40%
7
10 9%
5
6 45%
7
Actual
Losses
Pct
5 58%
3 77%
7 36%
2 82%
5 62%
9 18%
8 27%
1 92%
7 36%
8 33%
6 40%
6 40%
6 50%
4 69%
7 30%
8 27%
6 50%
10 9%
10 9%
4 67%
10 0%
7 36%
7 42%
3 70%
6 45%
4 64%
Evaluation
Correct Incorrect PCT
8
4 67%
10
3 77%
6
5 55%
4
7 36%
6
7 46%
8
3 73%
11
0 100%
9
3 75%
8
3 73%
12
0 100%
8
2 80%
6
4 60%
8
4 67%
10
3 77%
7
3 70%
10
1 91%
9
3 75%
7
4 64%
7
4 64%
8
4 67%
9
1 90%
7
4 64%
8
4 67%
7
3 70%
7
4 64%
7
4 64%
2009 out of sample (M-S)
School
Mississippi St.
Missouri
NC State
Nebraska
Nevada
New Mexico
New Mexico State
North Carolina
North Texas
Northern Illinois
Northwestern
Notre Dame
Ohio
Ohio State
Oklahoma
Oklahoma State
Oregon
Oregon St.
Penn State
Pittsburgh
Purdue
Rice
Rutgers
SMU
San Diego State
San Jose St.
Model
Losses
Wins
2
7
3
12
7
1
0
6
1
7
5
8
5
11
10
6
8
5
9
4
6
5
9
8
5
3
Pct Wins
9 18%
4
4 64%
7
7 30%
3
2 86%
10
6 54%
8
10 9%
1
12 0%
2
5 55%
6
7 13%
1
4 64%
6
7 42%
6
3 73%
6
6 45%
7
0 100%
9
2 83%
7
6 50%
8
5 62%
10
7 42%
7
3 75%
10
6 40%
7
5 55%
4
6 45%
2
0 100%
5
3 73%
7
5 50%
3
8 27%
1
Actual
Losses
Pct
7 36%
4 64%
7 30%
4 71%
5 62%
10 9%
10 17%
5 55%
7 13%
5 55%
6 50%
5 55%
4 64%
2 82%
5 58%
4 67%
3 77%
5 58%
2 83%
3 70%
7 36%
9 18%
4 56%
4 64%
7 30%
10 9%
Evaluation
Correct Incorrect PCT
7
4 64%
9
2 82%
8
2 80%
12
2 86%
12
1 92%
9
2 82%
10
2 83%
7
4 64%
6
2 75%
8
3 73%
7
5 58%
7
4 64%
5
6 45%
9
2 82%
9
3 75%
8
4 67%
7
6 54%
8
4 67%
9
3 75%
7
3 70%
5
6 45%
8
3 73%
5
4 56%
8
3 73%
6
4 60%
9
2 82%
2009 out of sample (S-U)
School
South Carolina
South Florida
Southern Cal
Southern Miss
Stanford
Syracuse
TCU
Temple
Tennessee
Texas
Texas A&M
Texas Tech
Troy State
Tulane
Tulsa
UAB
UCF
UCLA
UL-Lafayette
UL-Monroe
UNLV
UTEP
Utah
Utah State
Model
Losses
Wins
6
5
12
6
2
2
9
4
8
13
6
8
7
0
10
0
5
11
2
0
2
5
8
2
Actual
Pct Wins
Losses
5 55%
5
5 50%
5
1 92%
9
6 50%
6
11 15%
8
9 18%
3
2 82%
10
4 50%
5
4 67%
6
1 93%
13
7 46%
6
4 67%
8
3 70%
6
10 0%
1
1 91%
4
11 0%
4
6 45%
6
2 85%
7
6 25%
4
8 0%
2
8 20%
4
6 45%
4
4 67%
9
9 18%
3
Evaluation
Pct Correct Incorrect PCT
6 45%
10
1 91%
5 50%
8
2 80%
4 69%
8
5 62%
6 50%
8
4 67%
5 62%
7
6 54%
8 27%
8
3 73%
1 91%
8
3 73%
3 63%
7
1 88%
6 50%
8
4 67%
1 93%
14
0 100%
7 46%
11
2 85%
4 67%
8
4 67%
4 60%
9
1 90%
9 10%
9
1 90%
7 36%
5
6 45%
7 36%
7
4 64%
5 55%
10
1 91%
6 54%
7
6 54%
4 50%
6
2 75%
6 25%
6
2 75%
6 40%
6
4 60%
7 36%
8
3 73%
3 75%
11
1 92%
8 27%
8
3 73%
2009 out of sample (V-W)
School
Vanderbilt
Virginia
Virginia Tech
Wake Forest
Washington
Washington St.
West Virginia
Western Michigan
Wisconsin
Wyoming
Model
Losses
Wins
3
3
10
3
2
3
6
4
11
1
Pct Wins
7 30%
1
8 27%
3
3 77%
10
7 30%
4
10 17%
5
9 25%
1
6 50%
8
5 44%
2
1 92%
9
9 10%
5
Actual
Losses
Pct
9 10%
8 27%
3 77%
6 40%
7 42%
11 8%
4 67%
7 22%
3 75%
5 50%
Evaluation
Correct Incorrect PCT
8
2 80%
7
4 64%
11
2 85%
7
3 70%
9
3 75%
10
2 83%
10
2 83%
7
2 78%
10
2 83%
6
4 60%
Other considerations (backup slide)





Off the field model .18
On the field model .26
Are the coefficients robust?
Future problems: things that recruits like – new
stadiums, new weight rooms, facilities
Could we do a recruiting paper modeled on NCAA
football recruiting info – coach history, academic
prestige, location, tv time, etc
Out of Sample prediction (intercept)
Analysis Variable : Vegas_Model_Correct
sample
Correct
Incorrect
% Correct
In
837
315
72.7%
Out
384
132
74.4%
Analysis Variable : Our_Model_Correct
sample
Correct
Incorrect
% Correct
In
787
365
68.3%
Out
352
164
68.2%
Both models have
comparable in and out of
sample performance

Friday
 Meet
with profs about research
 Present to a class
 Lunch
 Seminar presentation
 Dinner
Models



To begin, we will look at each of these data sources
and its relationship to our outcome variable
individually.
Because each of these data sources is described with
dozens of potential variables, this initial modeling will
inform our final set of models where data from all
possible sources are considered in development.
All models are developed using a Logit function as our
outcome variable, Home Win, is binary. We will
discuss the resulting coefficients as Odds Ratios to aid
interpretation.
Model 1: Game specific factors
Odds Ratio Estimates
Effect
Point Estimate
Neutral
Nightgame
Stadium
Conference ACC vs WAC
Conference Big East vs WAC
Conference Big Ten vs WAC
Conference Big Twelve vs WAC
Conference CUSA vs WAC
Conference INDP vs WAC
Conference MAC vs WAC
Conference Mountain West vs WAC
Conference NC vs WAC
Conference Pac Ten vs WAC
Conference SEC vs WAC
Conference Sun Belt vs WAC
day_of_week Fri vs Wed
day_of_week Mon vs Wed
day_of_week Sat vs Wed
day_of_week Sun vs Wed
day_of_week Thu vs Wed
day_of_week Tue vs Wed
Distance
0.804
0.868
1.018
0.573
0.741
0.444
0.665
0.576
0.554
0.789
0.766
0.992
0.483
0.347
0.955
1.184
0.662
1.407
1.103
1.686
1.727
1
90% Wald
Confidence
Limits
0.62 1.05
0.76 0.99
1.01 1.02
0.4 0.83
0.49 1.12
0.3 0.66
0.46 0.96
0.4 0.84
0.36 0.86
0.55 1.13
0.52 1.12
0.74 1.34
0.33
0.7
0.24 0.51
0.64 1.43
0.65 2.16
0.31 1.43
0.81 2.44
0.55 2.21
0.92 3.09
0.85 3.52
1
1
Model 1: Game specific factors

Other considered variables
 Distance
b/w schools
 Rivalry game (major/minor/none)

Other variables to consider in the future:
 Game-time
(need to clean some data)
Model 2: Institutional factors & history
Effect
Odds Ratio Estimates
Point Estimate
Cum_Losses_School_H
MA5_Wins_School_H
TOTAL_EXPENSE_ALL_Fo
EFMaleCount_H
EFFemaleCount_H
Cum_Losses_School_A
MA5_Win_PCT_School_A
TOTAL_EXPENSE_ALL_Fo
school_seasons_ldf
cum_winpct_adf
total_expense_all_fo
school_seasons_31t75
school_seasons_31t75
school_seasons_m101_
1.005
1.039
0.972
1
1
0.996
0.082
1.028
0.53
185.1
3.366
1.912
0.711
0.715
95% Wald
Confidence
Limits
1.003
1.007
1.03
1.047
0.932
1.014
1
1
1
1
0.994
0.998
0.048
0.14
0.986
1.072
0.363
0.775
20.63 >999.999
2.149
5.273
1.269
2.882
0.491
1.031
0.586
0.873
Model 2: Institutional factors & history

Other considered variables

Other variables to consider in the future:
Model 3: Recruiting
Effect
Odds Ratio Estimates
Point Estimate
Cum_Losses_School_H
MA5_Wins_School_H
TOTAL_EXPENSE_ALL_Fo
EFMaleCount_H
EFFemaleCount_H
Cum_Losses_School_A
MA5_Win_PCT_School_A
TOTAL_EXPENSE_ALL_Fo
school_seasons_ldf
cum_winpct_adf
total_expense_all_fo
school_seasons_31t75
school_seasons_31t75
school_seasons_m101_
1.005
1.039
0.972
1
1
0.996
0.082
1.028
0.53
185.1
3.366
1.912
0.711
0.715
95% Wald
Confidence
Limits
1.003
1.007
1.03
1.047
0.932
1.014
1
1
1
1
0.994
0.998
0.048
0.14
0.986
1.072
0.363
0.775
20.63 >999.999
2.149
5.273
1.269
2.882
0.491
1.031
0.586
0.873