FBS vs FCS Score Differentials Equated to FBS vs FBS Score Differentials

Every matchup is unique. Either team could win. Although related to the outcomes of the other matchups of either team, the outcome of any one matchup is somewhat independent of the others. This notion underlies the nature of competition, the allure of sports betting, and the precedence for retold stories of unlikely winners. For football, because of its small sample size relative to other games, this notion underlies the complexity of numerating many activities on the gridiron and is, to some extent, the topic of this post. 

A recent undertaking at work portends a new analytic technique: observed-score linking and equating. I will undoubtedly seek guidance from our expert colleagues, but, of course, I prefer to be informed before that day is upon us. Linking and equating have distinct definitions, applications, and procedures but I will refer to these casually as equating. Equating allows us to generate uniform score-ranges between sections or items belonging to different versions of a single assessment, two unique assessments, or an old and a new version. 

More practically, consider the ACT, for example. Let us imagine that ACT Inc (the ACT developer) develops 20 versions of the ACT Reading Section. ACT Inc needs the scores for each version to be equitable so that a 36 is always a 36. Of the imaginary 20 versions, let us focus on Versions 6 and 12, or V6 and V12, for short. So, to test these versions, ACT Inc has 200 freshmen in college complete both versions. Say, 100 freshmen completed V12 in the first test session and V6 in the second whereas the other 100 freshmen completed V6 in the first and V12 in the second session. Afterwards, ACT Inc realizes that the average score for V6 is 18.5 and the average for V12 is 20.5, whoops. However, the average for all tests completed in the first session is 20.4 and all tests completed in the second session is 20.3, so ACT Inc knows that the disparity in Version-scores is not due to sequence of test administration. Likewise, because the same freshmen completed both versions, the 2-point disparity in Version-scores is not due to differences in the test-takers. ACT Inc must conclude that the disparity is due to differences in V6 and V12. Then, ACT Inc could use equating procedures to develop uniform scores to ensure little Johnny sets realistic standards for his future based on an ACT Reading Version 12 score of 30 instead of the inflated 36 it would have been without equating.

Here, I use equating to generate equivalency score-differentials for interdivisional college football games. That is, a 35-point win (or, +35 score differential) by a FBS team over a FCS team, for instance, is equivalent to what differential in an FBS versus FBS matchup. Let us relate this to the above example. This analysis would get restrictively complex if we sought to equate scores between all FBS and FCS teams—ACT Reading V6 and V12 would be tantamount to FBS Teams 1, 2, 3, …, 128! However, for FBS and FCS programs alike, most matchups each season are versus FBS and FCS foes, respectively. Like many FBS teams face a smattering of inferior opponents with FCS status, many FCS teams face a few inferior opponents with DII or NAIA memberships. So, we can consider two types (or versions) of games: [i] intradivisional games and [ii] interdivisional games. Intradivisional games are FBS vs FBS or FCS vs FCS whereas interdivisional games are FBS vs FCS or FCS vs non-DI. Thus, if the distributions for score differentials of FBS-FBS and FCS-FCS games are similar, and the same is true for FBS-FCS and FCS-non-DI, we can generate FBS-FCS scores that equate to FBS-FBS scores.

Chart 1: Distributions of Score Differentials

First, I obtained all Division I NCAA football game scores for 2012-2016 from this vast resource hosted by Kenneth Massey, that includes 8,349 games in which either an FBS or FCS team played. Second, I specified whether the home team won each game because home advantages are well-documented (here, here, here, but cf. here). Third, I specified one of four classifications for each game, the first two of which are intradivisional and the second two, interdivisional:

•    FBS vs FBS,
•    FCS vs FCS,
•    FBS vs FCS, or
•    FCS vs non-DI teams.

Fourth, I removed all games in which both teams did not play in an interdivisional game in that season, leaving 6,397 games for the analysis. For example, in 2012, neither UCLA nor USC played an FCS team so, the UCLA vs USC game was excluded from the analysis. However, the 2012 USC versus Washington game was included because Washington played a FCS team (Portland St.). The data was prepared in this manner because I only want to analyze score differentials of teams that played both types of games. That is, although it is only one FCS game, we know about FBS-FBS and FBS-FCS games that involve ’12 Washington whereas we only know about FBS-FBS games that involve ’12 USC.

Chart 2: D1 Teams Ranked by Win% and Mean Score Diff.

Fifth, I prepared Chart 1. It shows the distributions of score differentials for the four categories of games. Chart 1 demonstrates that FBS vs FBS scores (green) differentials are distributed almost identically to FCS vs FCS (brown) score differentials. Likewise, the score differentials are similarly distributed for the interdivisional games, but with some distinct dissimilarities. I attribute the dissimilarity in interdivisional distributions to the similar talent levels of lesser-FBS/better-FCS teams and lesser-FCS/non-DI teams while, concurrently, more better-FBS teams play FCS opponents (green) than better-FCS teams play non-DI opponents (orange). Hence, there are more 35-point blowouts in FBS-FCS games. This is evident in the ad hoc chart below, which was the sixth thing I did. 

Anyhow, because the distributions for FBS vs FBS and FCS vs FCS are nonetheless similar, we will consider in the analysis only home field advantage and whether a game was intra- or inter-divisional (i.e., we will ignore whether a team was FCS or FBS). I do this for simplicity—mostly for me, but maybe also for you. 

Seventh, the equating procedure was performed using a nonequivalent-groups design with one anchor, a home team win. Here, the anchor informs the equating procedure that differences in these games might be due to home-field advantage. The influence of including home team victory is evident in Chart 3. The black line represents the intradivisional score differential and the other lines are the corresponding interdivisional scores with or without home advantage. Some descriptive statistics appear in the table below. A table with unadjusted and adjusted score differentials and SEs appears at the close of the post.

Table 1. Descriptive Statistics for NCAA 1 D1 Intra- & Inter-Division Games, 2012-16

	mean	sd	skew	kurt	min	max	n
Intradivisional	17.49	13.5	0.96	3.53	1	78	5493
Interdivisional	30.52	19.69	0.36	2.29	1	86	904
Intra- Home Wins	0.54	0.5	-0.15	1.02	0	1	5493
Inter- Home Wins	0.87	0.34	-2.16	5.67	0	1	904

Chart 3: Equated Interdivisional Score Differentials

Controlling for home-advantage—the green line—produces equated scores which are more sound, in my estimation. Notice how the green line equals the black line in the bottom left corner. The green line diverges at the 7-point differential. So, with this equating procedure, if an FBS team wins by 7 or fewer points over an FCS, it is the same differential as an FBS-FBS victory. To this author, this validly reflects in the score differential the competitiveness of an FBS-FCS game decided by one touchdown or less. Without adjusting for home winning, there are inflated point differentials in this range. Also, compared to the orange and the black lines, there is less of a difference between the green and black lines as the score differential increases (if such a feat were meaningful, Baylor). Likewise, Iowa St. is not additionally penalized for succumbing to a last-second field-goal whereas the orange line equates a 7-point FBS-FCS victory to 16 FBS-FBS points and a field-goal lead at 00:00 in the 4th quarter to 5 points. 

Now, there are of course shortcomings to this study, primarily one. Recall in the verbose example I provided earlier that the same 200 college freshmen completed both V6 and V12 of the ACT Reading sections. By doing so, we could be relatively certain that any disparity in V6 and V12 averages was not due to the test takers.  In the analysis, however, I included only games involving at least one team that played in intra- and inter-division games in the season. Thus, this analysis rests on the potentially fallible assumption that all intra- or inter-divisional opponents to these teams are identical—which is patently untrue. Hence, the reason we considered the distribution of different classifications of games in Chart 1.

Summarily, an equating procedure was used to generate score-differential equivalencies for FBS-FCS games to FBS-FBS games. This author concluded that adjusting for well-documented home field advantages provided more valid equivalencies. Secondarily, an ad hoc analysis demonstrated that upper echelon FBS teams more frequently play FCS opponents than upper echelon FCS teams play non-DI teams.

	Adjusted			Unadjusted
FBS Scr Diff	Est.	SE		Est.	SE
1	0.974	0.2		1.358	0.175
2	1.743	0.285		2.672	0.21
3	2.81	0.208		5.106	0.779
4	3.762	0.756		7.546	0.79
5	5.054	0.896		9.956	1.156
6	6.127	0.8		12.3	1.166
7	7.359	0.891		15.918	1.072
8	10.068	1.461		19.907	1.127
9	10.968	1.578		20.872	0.771
10	13.186	1.415		22.593	1.161
11	14.47	1.015		24.394	0.867
12	15.391	1.017		25.328	0.954
13	16.529	1.173		26.464	1.014
14	18.299	1.438		28.122	1.024
15	20.713	1.223		30.582	0.942
16	21.174	1.141		31.068	0.82
17	23.077	1.324		32.161	0.962
18	24.61	1.284		34.025	0.9
19	26.413	1.334		35.059	1.109
20	27.782	1.262		36.855	1.117
21	30.308	1.197		38.215	0.688
22	31.551	0.981		39.434	0.943
23	32.615	1.131		40.542	1.061
24	34.24	1.375		41.97	1.075
25	37.279	1.458		44.231	1.228
26	38.21	1.093		45.077	1.165
27	39.011	1.182		45.894	1.214
28	41.563	1.188		47.865	1.147
29	42.478	1.111		49.033	1.238
30	44.187	1.182		49.589	1.415
31	45.563	1.132		51.843	1.423
32	47.876	1.165		53.61	1.266
33	48.85	1.181		54.631	1.102
34	50.254	1.454		55.331	0.775
35	52.735	1.534		56.108	0.747
36	54.72	1.304		56.876	1.063
37	55.346	1.134		58.072	1.234
38	56.116	1.085		59.217	1.359
39	57.228	1.251		61.627	1.452
40	58.761	1.543		62.474	1.195
41	59.231	1.681		62.889	1.011
42	61.718	1.732		63.532	1.122
43	62.719	1.574		64.919	1.179
44	62.99	1.426		65.649	1.247
45	63.557	1.384		66.142	1.177
46	65.6	1.338		66.812	1.522
47	65.788	1.319		67.265	1.673
48	65.991	1.261		68.845	1.777
49	66.467	1.093		69.941	1.934
50	67.188	1.348		71.796	2.045
51	68.502	1.659		72.619	2.149
52	69.592	1.949		73.615	1.891
53	70.118	2.188		74.109	1.831
54	70.359	2.166		74.335	1.861
55	72.129	2.096		75.157	1.77
56	73.833	2.102		76.604	1.884
57	74.595	1.891		77.468	1.617
58	75.772	1.74		77.78	1.724
59	77.016	1.535		78.302	2.118
60	77.7	1.329		78.892	2.151
61	77.876	1.252		79.221	2.14
62	78.095	1.368		79.633	2.24
63	78.405	1.623		80.209	2.572
64	78.877	1.726		81.62	2.747
65	79.142	1.818		81.785	2.716
66	79.672	1.996		82.114	2.65
67	80.336	2.151		83.525	2.608
68	81.733	2.146		83.772	2.547
69	82.131	2.244		84.019	2.447
70	83.794	2.309		84.43	2.217
71	84.192	2.288		85.677	1.879
72	84.325	2.255		85.759	1.872
73	85.59	2.218		85.924	1.778
74	85.855	2.275		86.089	1.748
75	85.988	2.341		86.171	1.739
76	86.116	2.343		86.253	1.714
77	86.244	2.251		86.335	1.633
78	86.372	2.17		86.418	1.613

FBS Rivalry Games Decline Parallel to an Increase in Bowls and FCS Opponents

Figure 1. Margin of Victory, 1990-2016

I discussed college football program rivalries in a previous post. Preparing a forthcoming post elucidated a decline in rivalry games occurring parallel to changes in the college football environment. Some operational definitions appear at the close of the post. All data were culled from Sports Reference. 

There was the implementation of the BCS in 1998 to resolve that annual, irrevocable controversy about the national champion. The BCS spawned myriad controversies and intensified that which it was implemented to resolve. There were also concerns that teams were maximizing margins of victory to exploit innerworkings of the BCS algorithm until it was remodeled. However, any broad impact in FBS vs FBS games is not readily deducible from Figure 1. The gradual increase in margin of victory over FCS teams however, is likely attributable to an overall increase of FCS opponents and, thus also, an increase in higher-quality FBS teams playing FCS teams, as seen in Figure 2 (in purple).

We also see in Figure 2 that, although it may be spurious epochal fluctuations, there was a few years from ~2005-10 when the proportion of FBS vs FBS between two top-25 ranked opponents dipped considerably (orange). Indeed, when schedules are set years in advance, come game day, the ranking of either team is unknowable. But, we might also note that teams were playing gradually fewer games versus top-25 ranked opponents (brown). However, there were more FBS teams as time progressed and a lower proportion of those teams could be ranked at a given time as there was still only 25 ranked teams in a given season.

Figure 2. Attributes of FBS Games, 1990-2016

In 2006 and after, FBS programs were permitted to schedule one additional regular season game in any season. Prior to this ruling, programs could schedule one additional game in select seasons. It was around 2006 when the quantity of bowl games began to increase as well. Enter the Playoff in 2014. The concept of a playoff was, itself, accompanied by some controversy. Critics of the CFP cite similar issues as the BCS. Alas, this author is too indolent to cite anything in this paragraph. 

Figure 3 indicates that programs and their teams were pressured to respond to these and other environmental changes. Accompanying the 2006 rule-change permitting one additional game was another statute allowing one win over an FCS team to count toward bowl eligibility in each season. Previously, since at least 1997, one FCS win “every four years” could count toward bowl eligibility.  Thus, programs scheduling an FCS foe essentially guaranteed their teams 12.5% of the six wins necessary to secure an official invitation from one of an ever-increasing quantity of certified licensed bowl games.  

Figure 3. FBS Bowl Team Attributes, 1990-2016

The green line in Fig 3 corresponds to the proportion of bowl teams with 6 or fewer scheduled wins. Annual vicissitudes are evident but only in 2013 or -14 does a trend beginning in about 1998 appear to ossify. The orange and brown lines add some context; until about the inauguration of the BCS the two generally overlap. Although the proportion of bowl teams scheduling multiple FCS games appears relatively constant 1990-2016, we see that even before the 2006 ruling, increasing proportions of bowl teams were distending their win totals with wins over FCS teams, though this may or may not have increased the likelihood they received bowl invites. Lest there is no mention of monetary implications, what was the cost to rivalries?

Summarily, as seen in Figure 4, the programs are scheduling more games versus FCS opponents, but the increase has been gradual, while simultaneously, more teams played in bowls…because there were more bowls. However, we see that, on average, teams are playing fewer games versus rivals, ~15% to ~10% from 1990 to 2016. Realistically, this equates to from ~1.8 to 1.3 rival games per team over that span (out of ~12 to ~13 scheduled games).

Some Operational Definitions 
To examine how the foregoing adaptations are associated with college football rivalries, at the FBS level, I examined the variables described below. The rivalry data are described elsewhere and expounded upon below.  I define scheduled games as games that are not bowl games but include conference championship games as these, when not de facto, are scheduled per se. 

Figure 4.

Minimalists

• Set the minimum wins of team playing in bowls at 6 scheduled games for each season (although that minimum was 5 in several seasons)
• Computed for each season the proportion of teams in bowls with at or less than 6 scheduled game wins
• Subtracted the quantity of wins versus FCS opponents from each team scheduled game win total in each season and computed the proportion of teams playing bowls with < 6 scheduled game wins versus FBS opponents
• Computed the proportion of bowl teams that played ≥1 FCS opponent
• Computed the proportion of bowl teams with ≥2 wins over FCS opponents
• Indeed, this value is meaningless in regards to bowl eligibility. 

Team Games vs Rivals

• Compute proportion of scheduled games versus rival(s) for each FBS team in each season and then compute average for FBS teams for each season.
• I would have included an adjusted version of this value adjusted by subtracting FCS games from the denominator, i.e., rivalry games / (scheduled games – FCS games), but it is essentially overlaps with the main variable
• I included bowl games in these totals because imagine the sensation of a Michigan-Ohio St. playoff game.Non-FBS teams were excluded from the computation of this variable because of the following bullet.
• I used a data set for rivalries generated for a previous posting. In that post, I set the rivalry inclusion criterion at ≥50 rivalry games as major college football programs. This data pertained to games from 1891 to 2015. To apply a similar stringency to the present data, a threshold of rivalry games was set for each season proportional to 50 / (2015-1891), which equals ~.403. For instance, the 1999 rivalry inclusion criterion was set at 43.45 games = (1999 – 1891) × .403 = 108 × .403. So any rivalry with less than 43.45 games by 1999 was excluded in the computation of that season.

FBS Games with Two Ranked Teams

• The quantity of scheduled games pitting two FBS-ranked teams divided by the quantity of scheduled games with at least one FBS-ranked team.
• Indeed, this could have been computed as games with two ranked teams divided by total games. But, there were 107 FBS teams in 1990 and 128 in 2016. However, there were 25 ranked teams in each week of those seasons or 23.3% and 19.5% of total teams, respectively. Likewise, teams played, on average, about 12 games from 1990-2005 but 13.5 games from 2006-2016. Thus, as the eras progressed, there were more games to be scheduled and thus a higher likelihood of facing an unranked team. It seemed to me, most objective to derive the value in this manner as it standardizes across seasons, to some extent.

Games with One Ranked- and One non-FBS Team

• The quantity of scheduled games with one ranked FBS team pitted against a non-FBS team divided by the quantity of scheduled games with at least one FBS-ranked team.
• Indeed, this could have been computed with total games in the denominator but the results are similar.

Analysis of Collegiate Football Rivalries at the Aggregate Level

Rivalry games are an occasion for hate for college football fans and their silent, meticulous superstitions. Many hold a degree granted by the name on the front of the jersey, conferred upon them after four or five years of personal growth and diligent pupilage. It was during these years that many fans learned to hate whereas others, who may never have enrolled, will, throughout the life course, continually refine a hate acquired in early childhood. Not all rivalries are vicarious campaigns of hatred staged in the last week before post-season play. Not all rivalries are staged annually. Sometimes, members of the opposing student body are abducted and ransomed; occasionally, a festivity such as homecoming is borne some  existed prior to statehood;  yet others are simply old.

Perhaps meta-rivalries emerge when considering the most preeminent rivalries. Indeed, a Google search will reveal countless lists. A recent study concluded that the Auburn-Alabama football rivalry is the greatest in sports, based on a thorough analyses of troves of fanbases’ Twitter activities.  The conclusion seems reasonable and surely other empirical analyses exist but I am too lazy to perform a more thorough lit review.

Let us consider the characteristics of a rivalry based on how the matchups have played out because fans are wonderfully delusional. Data were extracted from the CFB SR Rivalry Finder and include only rivalries in which ≥50 matchups were available with both parties classified as major college teams. Included in the analyses were 116 rivalries from 1891 through 2015, a total of 9499 games.

The characteristics below are essential in my estimation. I intend to revisit this subject to analyze rivalries on a game-by-game basis but for this analysis, we will examine rivalries using summary statistics of the entire history of each rivalry.

• Frequency: because absence makes the heart grow fonder
• How often rivals play, we'll call this regularity, as measured by dividing the age of the rivalry (in years) by the quantity of matchups
• Lesser values indicate greater frequency
• The length of the longest span (of years) between matchups
• Lesser values indicate greater frequency
• Competitiveness: because there are no moral victories
• How often a winning/losing streak was ended divided by the quantity of matchups, we'll call this streaks ending
• Higher values indicate higher competitiveness
• Team A winning one year and team B winning the next would produce two ended-streaks
• The length of the longest streak
• Smaller values indicate higher competitiveness
• How close is the series overall, we'll refer to this as closeness and it is computed by (win% team A to-date)² * (win% team B to-date)² * 16;
• Value will be ≤1 and ≥0.
• Greater values indicate higher competitiveness
• if each team won 50% of all games (i.e., no ties), this value will equal 1
• Arithmetically, this value will be inflated for rivalries with fewer or no ties because imagine if the Big-10 declared a tie at the end of regulation in the OSU-UM game last week
• Margin of Victory: because, for the fanatics there is always a thirst for additional goblets of enemy blood but for the enthusiasts and executives there is a preference for games decided in the fourth quarter. Indeed, there is likely collinearity between margin of victory and competitiveness but margin of victory provides additional nuance. For instance, BYU-Utah St. rivalry is 47-35-3 but 54.1% of the matchups have been blowouts—or 54.1% of matchups have been boring for unemotional audiences. Closeness ranks BYU-USU higher than the Colorado-Utah rivalry which is 29-28-3 although only 28.3% of the games have been blowouts.
• How often matchups are close-scoring, as measured by the quantity of matchups in which the margin of victory is ≤ 7 points divided by total quantity of matchups
• Greater value indicates higher competitiveness
• How often matchups are blowouts, as measured by the quantity of matchups in which the margin of victory is ≥ 17 points divided by total quantity of matchups
• Lesser values indicate higher competitiveness
• How often matchups end in ties, as measured by the quantity of ties divided by the quantity of matchups
• Greater values indicate more tie-games
• Ties suggest a competitive game but have not occurred since 1995, thereby penalizing or rewarding rivalries with more games prior to 1996. Inherently, rivalries with more ties will be rewarded by several (likely collinear) variables described above. Moreover, winning is the only thing. Thusly, ties will be penalized slightly.
• Home invasion: because magic moments are made winning in front of the fans but few spoils of battle are more savory than the arrangement of the muscles on the faces of enemies defeated in their own territory
• How often matchups are won by the road team, as measured by the quantity of matchups in which the road team was victorious divided by the quantity of matchups not played at a neutral site
• Greater values indicate more road wins

I debated including age (in years) and the quantity of matchups but decided against it. The inclusion/exclusion criteria of ≥50 matchups as major college teams excluded Kent St.-Akron or Harvard-Yale and would unfairly penalized included rivalries such as Cinci-Miami(OH) who had matched-up 120 times as of 2015 but only had 54 of those included in the analysis.

Once the foregoing values were obtained for each rivalry, the values of each variable were standardized using the entire sample of rivalries (mean=0, SD=1). A number of standardized scores were inverted to ensure those scores entered a purely additive equation positively; for instance, higher regularity values would yield a positive standardized score instead of the negative score yielded directly from the raw values. The percentage of tie games was inverted but the inversion was weight as to reduce the direct penalty to ties (i.e., standard score multiplied by -0.5). Finally, all standard scores were summed yielding a rivalry rating with a mean of 0 and a SD of 1 across the sample.

WVU-Penn St. came out as the lowest rated rivalry and, thankfully, it has been on hiatus since 1992. Notre Dame-Navy was rated only slightly less uninteresting. Despite an historical dearth of competitiveness in both, Auburn-Miss. St. and LSU-Miss. State are the most average rivalries. Florida St.-Miami was the highest rated rivalry. The rivalry was 29-29-0 until earlier in the 2016 season when FSU took control. Having played 58 games in 60 seasons, there has been no ties, no neutral sites, and 44.8% of matchups were decided by ≤7 points. Although 32.8 of games were decided by ≥17 points, visiting teams won 58.6% of matchups. The rivalry approached or surpassed 1 SD above average in all variables except for ending streaks, in which it was average; the rivalry has been streaky. Summarily, at the close of the post there is a massive table with the summary statistics (raw values) for the 116 rivalries included in the analysis. It is not sortable but it is searchable—indeed, we here at POTH bring our readership the foremost developments in information technology.

We should also consider how this rivalry rating is associated with the raw values used in its imputation and some others. Doing so provides some insight into rivalry ratings  This can be seen in the table below. I also included the proportions of games with total scores within each rivalry that were ≤25th or ≥75th percentiles of total game scores for 5 years before and after a game. All p-values < .05.

Variable		r
History
	Matches	0.319
	Age	-0.103
Frequency
	Longest Hiatus	-0.378
	Age / Matchups	-0.478
Competitiveness
	Closeness	0.634
	Longest Streak	-0.662
	Streaks End %	0.706
Margin of Victory
	Margin of Victory ≤ 7pts	0.719
	Margin of Victory ≥ 17	-0.678
	Ties	0.181
Home Invasion
	Visiting Team Wins	0.369
Inferred Style of Play
	Total Pts ≤ 25th%ile	0.244
	Total Pts ≥ 75th%ile	-0.178

 I made no a priori suggestions about the valence each variable should contribute statistically to the rivalry rating. Other than the adjustment to ties (and while excluding age and matchup quantity), the rating was computed by a simple summation of standardized scores. So, in the future, when I elect to revisit this topic and perform a game-level analysis, I have some prior notions on how the  model model be constructed and tested for validity.

	History					Frequency		Competitiveness			Margin of Victory
	Rivalry	Matchups	First-Latest	W-L-T	Neutral Site%	Max Hiatus	Regularity	Closeness	Max Streak	Streaks End%	≤ 7 %	≥ 17 %	Road Team Win%	Rating
1	Florida State-Miami (FL)	58	1955-2015	29-29-0	0.000	3	1.034	1.000	7	0.397	0.448	0.328	0.586	8.977
2	Auburn-Georgia	111	1902-2015	50-55-6	0.351	3	1.018	0.797	9	0.495	0.432	0.297	0.583	7.85
3	Texas-Oklahoma	100	1903-2015	53-42-5	0.950	6	1.120	0.793	8	0.440	0.46	0.32	0.6	7.015
4	Oregon State-Oregon	99	1916-2015	43-50-6	0.061	3	1.000	0.770	8	0.465	0.414	0.293	0.559	6.751
5	Florida-Miami (FL)	55	1938-2013	26-29-0	0.109	14	1.364	0.994	7	0.400	0.418	0.255	0.531	6.428
6	Cincinnati-Miami (OH)	54	1962-2015	26-27-1	0.019	1	0.981	0.927	11	0.444	0.444	0.333	0.453	6.243
7	Clemson-South Carolina	107	1909-2015	63-40-4	0.000	1	0.991	0.775	7	0.486	0.374	0.393	0.561	5.902
8	Michigan-Ohio State	99	1904-2015	47-48-4	0.000	14	1.121	0.848	7	0.525	0.384	0.273	0.485	5.716
9	UCLA-Southern California	85	1929-2015	30-48-7	0.000	6	1.012	0.636	8	0.494	0.459	0.329	0.541	5.425
10	Oregon-Washington	96	1916-2015	40-52-4	0.000	3	1.031	0.815	12	0.438	0.469	0.323	0.479	5.4
11	Kansas-Missouri	110	1901-2011	48-54-8	0.145	2	1.000	0.734	5	0.573	0.427	0.345	0.404	5.325
12	Washington-Washington State	96	1917-2015	62-30-4	0.000	3	1.021	0.652	8	0.510	0.406	0.333	0.49	5.236
13	Southern California-Stanford	91	1922-2015	57-31-3	0.011	4	1.022	0.728	12	0.352	0.396	0.308	0.622	5.22
14	Baylor-Texas Christian	103	1903-2015	46-52-5	0.097	11	1.087	0.813	8	0.524	0.398	0.369	0.538	5.201
15	Texas A&M-Texas Tech	69	1932-2011	36-32-1	0.188	10	1.145	0.937	6	0.449	0.449	0.377	0.446	5.114
16	Indiana-Purdue	112	1899-2015	40-66-6	0.018	3	1.036	0.709	10	0.420	0.464	0.348	0.536	5.078
17	Toledo-Bowling Green State	54	1962-2015	27-26-1	0.000	1	0.981	0.927	7	0.463	0.352	0.315	0.389	4.981
18	Kentucky-Vanderbilt	82	1916-2015	42-36-4	0.000	9	1.207	0.809	7	0.463	0.439	0.293	0.463	4.909
19	Georgia-Georgia Tech	105	1902-2015	61-39-5	0.000	9	1.076	0.745	8	0.381	0.438	0.295	0.514	4.491
20	North Carolina-North Carolina State	95	1902-2015	57-34-4	0.021	15	1.189	0.738	9	0.411	0.453	0.305	0.548	4.409
21	Duke-North Carolina	95	1922-2015	33-58-4	0.000	1	0.979	0.720	13	0.379	0.453	0.347	0.526	4.403
22	Illinois-Northwestern	109	1892-2015	55-49-5	0.009	7	1.128	0.823	7	0.486	0.413	0.413	0.509	4.358
23	Louisiana State-Mississippi	99	1902-2015	57-38-4	0.010	5	1.141	0.781	8	0.485	0.424	0.364	0.459	4.347
24	California-Stanford	95	1918-2015	39-50-6	0.000	4	1.021	0.747	7	0.453	0.421	0.379	0.505	4.344
25	Iowa State-Kansas	94	1916-2015	39-49-6	0.000	3	1.053	0.748	7	0.521	0.351	0.34	0.479	4.326
26	Iowa State-Missouri	103	1908-2011	33-61-9	0.000	2	1.000	0.576	10	0.466	0.398	0.282	0.524	4.203
27	Army-Navy	112	1891-2015	49-56-7	0.804	7	1.107	0.766	14	0.491	0.438	0.277	0.455	4.169
28	Ohio-Miami (OH)	54	1962-2015	24-29-1	0.000	1	0.981	0.912	6	0.389	0.389	0.407	0.444	4.119
29	Arkansas-Mississippi	62	1908-2015	33-28-1	0.242	11	1.726	0.924	6	0.500	0.435	0.323	0.426	3.833
30	Georgia-Florida	94	1904-2015	49-43-2	0.872	11	1.181	0.910	7	0.415	0.394	0.404	0.5	3.496
31	Illinois-Purdue	90	1892-2015	43-41-6	0.000	12	1.367	0.758	6	0.511	0.4	0.389	0.544	3.395
32	Florida-Florida State	60	1958-2015	34-24-2	0.033	1	0.950	0.822	9	0.383	0.367	0.35	0.448	3.379
33	Wisconsin-Minnesota	123	1892-2015	58-57-8	0.000	2	1.000	0.764	12	0.455	0.431	0.358	0.439	3.278
34	New Mexico State-Texas-El Paso	76	1935-2015	26-49-1	0.000	4	1.053	0.778	8	0.382	0.461	0.355	0.368	3.198
35	Rice-Southern Methodist	90	1916-2012	41-48-1	0.000	5	1.067	0.944	10	0.422	0.333	0.344	0.411	3.069
36	Memphis-Southern Mississippi	50	1963-2012	17-32-1	0.000	2	0.980	0.758	7	0.380	0.42	0.38	0.4	3.03
37	Iowa-Wisconsin	88	1906-2015	42-44-2	0.000	7	1.239	0.911	10	0.443	0.352	0.352	0.443	2.787
38	Alabama-Louisiana State	78	1902-2015	49-24-5	0.013	14	1.449	0.598	11	0.474	0.397	0.346	0.623	2.565
39	Mississippi-Mississippi State	108	1902-2015	61-42-5	0.324	4	1.046	0.772	12	0.472	0.361	0.444	0.507	2.282
40	Notre Dame-Southern California	87	1926-2015	46-36-5	0.000	4	1.023	0.766	11	0.414	0.402	0.368	0.437	2.133
41	Arizona-Arizona State	79	1931-2015	39-39-1	0.000	4	1.063	0.950	11	0.392	0.316	0.405	0.456	2.131
42	Syracuse-West Virginia	60	1945-2012	33-27-0	0.033	9	1.117	0.980	8	0.433	0.317	0.4	0.397	2.088
43	Baylor-Texas A&M	102	1903-2011	29-64-9	0.010	5	1.059	0.509	13	0.441	0.451	0.333	0.495	2.023
44	Virginia Tech-Virginia	92	1902-2015	55-32-5	0.250	18	1.228	0.692	12	0.478	0.435	0.359	0.493	1.865
45	Auburn-Florida	84	1904-2011	44-38-2	0.024	10	1.274	0.898	7	0.488	0.393	0.357	0.305	1.747
46	Texas A&M-Texas	109	1903-2011	36-69-4	0.046	4	0.991	0.699	10	0.486	0.349	0.376	0.385	1.71
47	Arkansas-Texas A&M	71	1910-2015	40-28-3	0.099	18	1.479	0.790	9	0.479	0.423	0.366	0.469	1.642
48	Notre Dame-Michigan State	63	1918-2013	34-28-1	0.000	30	1.508	0.921	8	0.429	0.413	0.349	0.492	1.567
49	Southern Methodist-Texas Christian	93	1916-2015	38-48-7	0.011	4	1.065	0.712	15	0.376	0.376	0.344	0.533	1.555
50	Colorado State-Wyoming	101	1905-2015	52-45-4	0.000	5	1.089	0.842	10	0.426	0.297	0.416	0.495	1.505
51	New Mexico-Utah	52	1939-2010	17-33-2	0.019	15	1.365	0.689	5	0.462	0.442	0.404	0.412	1.158
52	North Carolina-Virginia	110	1902-2015	61-45-4	0.018	3	1.027	0.823	9	0.382	0.391	0.436	0.398	1.097
53	Auburn-Tennessee	51	1929-2013	27-21-3	0.039	17	1.647	0.760	6	0.471	0.471	0.333	0.388	0.978
54	Air Force-Colorado State	54	1957-2015	32-21-1	0.000	3	1.074	0.850	7	0.407	0.296	0.5	0.463	0.759
55	Maryland-Virginia	78	1919-2013	44-32-2	0.026	12	1.205	0.857	16	0.385	0.333	0.346	0.487	0.668
56	Arkansas-Texas	75	1906-2014	22-53-0	0.040	9	1.440	0.687	12	0.480	0.36	0.453	0.486	0.588
57	North Carolina-South Carolina	55	1903-2015	32-19-4	0.018	16	2.036	0.646	5	0.491	0.418	0.291	0.481	0.525
58	Alabama-Tennessee	97	1903-2015	53-37-7	0.000	14	1.155	0.695	11	0.381	0.402	0.371	0.505	0.487
59	Kansas-Kansas State	104	1912-2015	57-42-5	0.000	1	0.990	0.784	11	0.394	0.337	0.481	0.471	0.411
60	Southern California-California	96	1922-2015	67-25-4	0.000	2	0.969	0.529	13	0.333	0.323	0.375	0.531	0.264
61	Michigan-Michigan State	90	1918-2015	53-33-4	0.000	7	1.078	0.746	10	0.389	0.289	0.356	0.444	0.158
62	Mississippi State-Auburn	88	1910-2015	27-58-3	0.000	8	1.193	0.654	11	0.307	0.42	0.364	0.443	0.009
63	Mississippi State-Louisiana State	106	1902-2015	34-69-3	0.000	4	1.066	0.698	14	0.358	0.377	0.396	0.425	-0.027
64	California-UCLA	86	1933-2015	32-53-1	0.000	1	0.953	0.841	18	0.395	0.302	0.43	0.419	-0.109
65	Oklahoma-Nebraska	86	1912-2010	45-38-3	0.035	7	1.140	0.855	16	0.360	0.372	0.419	0.446	-0.267
66	Pittsburgh-West Virginia	93	1909-2011	55-35-3	0.000	4	1.097	0.793	15	0.419	0.323	0.462	0.462	-0.29
67	Iowa-Minnesota	108	1901-2015	45-61-2	0.000	4	1.056	0.886	11	0.389	0.315	0.472	0.38	-0.635
68	Brigham Young-Utah	90	1922-2015	30-56-4	0.011	4	1.033	0.688	9	0.322	0.4	0.467	0.427	-0.641
69	Arkansas-Louisiana State	60	1906-2015	21-37-2	0.433	26	1.817	0.745	7	0.450	0.417	0.317	0.412	-0.732
70	Baylor-Texas	104	1903-2015	25-74-5	0.010	4	1.077	0.468	16	0.462	0.356	0.413	0.437	-1.125
71	Baylor-Texas Tech	72	1932-2015	34-37-1	0.111	8	1.153	0.942	15	0.347	0.347	0.444	0.391	-1.128
72	Arizona-New Mexico	50	1931-2015	31-17-2	0.020	10	1.680	0.711	10	0.340	0.34	0.34	0.49	-1.267
73	North Carolina-Wake Forest	101	1908-2015	67-32-2	0.050	4	1.059	0.707	16	0.366	0.356	0.455	0.417	-1.343
74	Brigham Young-Utah State	85	1922-2015	47-35-3	0.035	4	1.094	0.829	10	0.388	0.306	0.541	0.451	-1.346
75	Penn State-Pittsburgh	89	1905-2000	43-42-4	0.000	16	1.067	0.832	14	0.416	0.348	0.438	0.416	-1.364
76	Alabama-Auburn	74	1902-2015	43-30-1	0.676	41	1.527	0.888	9	0.446	0.392	0.365	0.417	-1.48
77	Wisconsin-Ohio State	80	1913-2014	17-58-5	0.013	8	1.263	0.380	21	0.413	0.413	0.325	0.481	-1.611
78	Missouri-Oklahoma	93	1912-2011	22-66-5	0.022	3	1.065	0.451	14	0.430	0.301	0.409	0.462	-1.689
79	Pittsburgh-Syracuse	71	1916-2015	37-31-3	0.028	25	1.394	0.828	11	0.352	0.394	0.38	0.435	-1.865
80	New Mexico-New Mexico State	82	1931-2015	59-21-2	0.000	4	1.024	0.543	18	0.305	0.39	0.427	0.451	-1.908
81	Notre Dame-Purdue	83	1899-2014	56-25-2	0.024	13	1.386	0.661	11	0.434	0.301	0.458	0.469	-2.07
82	Georgia-South Carolina	63	1903-2015	44-17-2	0.000	17	1.778	0.568	10	0.397	0.397	0.365	0.46	-2.12
83	Oklahoma State-Tulsa	58	1917-2011	34-22-2	0.000	16	1.621	0.791	5	0.466	0.293	0.328	0.293	-2.223
84	Colorado-Utah	60	1905-2015	29-28-3	0.000	49	1.833	0.814	9	0.383	0.467	0.283	0.467	-2.285
85	Utah-Utah State	98	1912-2015	68-26-4	0.000	3	1.051	0.542	12	0.388	0.296	0.439	0.418	-2.303
86	Penn State-Syracuse	71	1922-2013	43-23-5	0.042	18	1.282	0.616	16	0.352	0.408	0.366	0.471	-2.415
87	Texas-Texas Tech	64	1934-2015	48-16-0	0.000	8	1.266	0.563	8	0.375	0.281	0.453	0.406	-2.666
88	Virginia Tech-West Virginia	50	1915-2005	21-28-1	0.060	35	1.800	0.885	7	0.440	0.3	0.32	0.404	-2.683
89	Kent State-Bowling Green State	53	1962-2015	9-44-0	0.019	2	1.000	0.318	14	0.264	0.264	0.396	0.5	-2.939
90	Maryland-West Virginia	52	1919-2015	22-28-2	0.038	24	1.846	0.830	7	0.404	0.327	0.462	0.5	-3.1
91	Tennessee-Vanderbilt	105	1902-2015	75-26-4	0.000	3	1.076	0.501	22	0.286	0.371	0.429	0.486	-3.257
92	Mississippi-Vanderbilt	86	1902-2015	49-35-2	0.105	9	1.314	0.860	15	0.314	0.302	0.419	0.39	-3.26
93	Missouri-Nebraska	95	1901-2010	33-59-3	0.011	9	1.147	0.745	24	0.316	0.347	0.389	0.426	-3.304
94	Tennessee-Kentucky	99	1915-2015	73-18-8	0.000	3	1.010	0.288	26	0.333	0.394	0.374	0.515	-3.564
95	Mississippi-Tulane	62	1902-2012	39-23-0	0.000	10	1.774	0.871	11	0.290	0.29	0.419	0.403	-4.01
96	Air Force-Army	50	1959-2015	35-14-1	0.040	4	1.120	0.615	8	0.480	0.2	0.54	0.333	-4.216
97	Georgia Tech-Clemson	79	1902-2015	50-27-2	0.013	10	1.430	0.749	15	0.380	0.38	0.418	0.256	-4.426
98	Texas A&M-Texas Christian	89	1903-2001	54-28-7	0.056	6	1.101	0.583	24	0.315	0.326	0.427	0.5	-4.536
99	Michigan-Minnesota	102	1892-2015	74-25-3	0.000	9	1.206	0.506	16	0.294	0.314	0.48	0.49	-4.59
100	Texas Christian-Texas	82	1904-2015	23-58-1	0.000	12	1.354	0.630	24	0.378	0.268	0.476	0.512	-4.85
101	Iowa-Iowa State	60	1899-2015	41-19-0	0.000	43	1.933	0.749	15	0.367	0.367	0.367	0.483	-5.059
102	Utah State-Wyoming	65	1912-2015	36-25-4	0.000	23	1.585	0.726	10	0.415	0.277	0.431	0.415	-5.217
103	Clemson-Georgia	59	1902-2014	16-39-4	0.119	10	1.898	0.514	10	0.441	0.356	0.407	0.346	-5.426
104	Oklahoma State-Oklahoma	102	1914-2015	18-77-7	0.069	1	0.990	0.284	19	0.343	0.284	0.529	0.537	-5.429
105	Indiana-Michigan State	61	1927-2015	13-46-2	0.000	12	1.443	0.413	8	0.393	0.262	0.541	0.459	-5.815
106	Louisiana State-Tulane	90	1904-2009	63-20-7	0.000	7	1.167	0.387	18	0.378	0.244	0.478	0.489	-6.276
107	Nebraska-Minnesota	56	1900-2015	23-31-2	0.000	21	2.054	0.827	16	0.321	0.357	0.464	0.446	-6.563
108	Georgia-Vanderbilt	72	1903-2015	53-17-2	0.000	20	1.556	0.483	11	0.333	0.264	0.486	0.486	-6.598
109	Alabama-Mississippi State	96	1902-2015	77-16-3	0.000	5	1.177	0.286	22	0.229	0.333	0.427	0.438	-6.986
110	Wisconsin-Michigan	64	1892-2010	13-50-1	0.031	16	1.844	0.403	14	0.313	0.281	0.422	0.468	-7.21
111	Nebraska-Colorado	68	1902-2010	48-18-2	0.000	41	1.588	0.559	18	0.368	0.25	0.412	0.471	-8.6
112	Kentucky-Florida	66	1917-2015	16-50-0	0.000	11	1.485	0.540	30	0.258	0.288	0.5	0.409	-9.818
113	Georgia Tech-Tulane	50	1916-2015	37-13-0	0.000	32	1.980	0.592	14	0.240	0.26	0.44	0.4	-10.637
114	Washington State-Idaho	63	1917-2013	53-8-2	0.063	24	1.524	0.183	21	0.190	0.254	0.444	0.508	-11.521
115	Navy-Notre Dame	89	1927-2015	12-76-1	0.360	1	0.989	0.212	43	0.202	0.281	0.562	0.404	-13.236
116	West Virginia-Penn State	55	1909-1992	9-44-2	0.018	14	1.509	0.274	25	0.273	0.236	0.545	0.389	-13.283