Team-seasons
1,231
Athletics Research · Roster Retention
Athletics Research · Ten seasons of college football
The second recruiting battle
Across 1,231 team-seasons at 129 programs, two things predict a college football season: the season before it, and how much of the team came back. Coach pay, athletic budget and recruiting spend show no effect.
Download the report (PDF)Programs
129
Seasons
10
Coaching changes
302
What moves your win total
Bar length is how much each factor shifts a season, compared like for like. Only winning the season before and keeping your players move it.
Ten seasons of college football, each program measured against its own past.
Recruited talent shows no effect once last season’s record is in the model, because a program with talent is usually one that won last year.
Last season’s record is fixed before a season starts. How much of the team comes back is decided every year by players who can transfer.
How the roster is measured
Three definitions
Retention
The share of last season’s production still on the roster this season.
a typical season
A point
One percentage point of that roster. Six points is six percent of last season’s production, kept or lost.
Level and change
Both are in the model, so each is read while the other is held still.
A team that goes 6-6 then 9-3 has a level of .750 and a change of +.250. A team that goes 9-3 then 9-3 has the same level and no change.
Retention counts production rather than bodies, so losing a starter costs more than losing a walk-on.
The question
What keeps players
1,231 team-seasons, 129 programs, each measured against its own past.
What predicts the share of a roster that stays · program and year fixed effects · within R² 0.30
| Factor | Effect on retention | p |
|---|---|---|
| The record improved | +0.228 | <0.001 |
| The head coach changed | −0.050 | 0.001 |
| Retention the year before | −0.242 | <0.001 |
| How good the record was, at a given change | −0.131 | 0.005 |
| Four- and five-star share of the roster | −0.264 | 0.052 |
| The money lines | ||
| Recruiting budget (doubling) | −0.026 | 0.07 |
| Athletic aid (doubling) | −0.019 | 0.11 |
| Head coach pay (doubling) | −0.036 | 0.15 |
| Athletic budget (doubling) | +0.015 | 0.75 |
| NIL and booster money raised | +0.011 | 0.04 |
The estimates for recruiting budget, athletic budget, coach pay and athletic aid all include zero. NIL and booster money is significant only in one specification, on a 186-season subset (a seventh of the sample). That is too little to call an effect.
Raw average retention for three of these factors:
.585vs.523
The coach stayed, or was replaced.
A change costs about six points of the roster.
.605vs.530
The record improved, or declined.
Teams heading the right way keep more, whatever the record.
.563vs.574
Most four- and five-star players, or fewest.
The better the roster, the more of it leaves.
Retention is higher when the head coach stays and when the record improves. At a given change in record, a higher level of winning goes with lower retention (−0.131), while a positive change in record goes with higher retention (+0.228). The more four- and five-star talent on the roster, the more of it leaves, because those are the players other programs recruit.
What that implies
Retention follows the coaching staff
A coaching change costs about six points of a roster, while doubling the recruiting budget shows no effect. This suggests that players stay because of the coaches who recruited them.
A staff has to recruit its own roster again every year, against opponents who can see which of its players are worth a call.
Survey evidence
Asking the players
Survey research from Tudor Collegiate Strategies with Niche, 7,129 to 31,000 respondents per wave.
School accounting records spending. Whether a recruit felt wanted, or whether a message sounded written for him, has to come from surveys.
63%
Say personal, relevant contact influences whether they visit or apply
16%
Say the outreach they received was very personalized
The survey covers prospects during a college search. It does not cover athletes at a transfer window. A coaching change removes the coach who made that personal contact.
Where this goes next
The next step
The spending measures show no effect on retention, and a coaching change does. One hypothesis is that personal recruiting contact, the kind the surveys measure, keeps players on a roster. This study does not test it.
- Ask athletes who stayed and who left, in the same season. Use the existing survey with current players at the transfer window instead of prospects during a college search.
- Track movement player by player. Record who left, from which position group, after how much playing time, and to where. That separates a walk-on’s departure from a starter’s.
- Get what each athlete was paid. The accounts used here are school-level and cover facilities and scholarships too. Without per-athlete figures, the money explanation is ruled out only in aggregate.
Limitations
What this study cannot tell you
- These are associations
- Coaching changes are not random. They follow bad seasons, and a program losing players may be why a coach was replaced rather than the result of it. The model holds the change in record constant, which helps, but it is not an experiment.
- Partly by construction
- Retention the year before runs negative partly because of how rosters work. Keep everyone one season and there are more seniors to lose the next.
- Most of it is unexplained
- The model explains 30% of retention, and most of that is not the measured factors. Coaching change, momentum, talent and the money lines account for about 11 points of it. The rest comes from the year (the portal era hit every program at once) and from which program it is. Roughly 70% is unexplained either way.
- The NIL proxy is rough
- The NIL and booster figures come from accounts that cover a school’s whole athletics nonprofit, not payments to players, and per-athlete figures are not public. Our earlier work on who funds college athletics sets out what those filings can and cannot show.
- Football, top two divisions
- Mostly the top two divisions, which is where retention and talent data exist.
- The survey is about prospects
- The survey figures are Tudor Collegiate Strategies’ published results with Niche and cover prospects instead of current athletes. The newest wave is from November 2022.
- No revenue-sharing year
- Direct payments to athletes began in 2025-26. The Equity in Athletics survey for that year publishes around spring 2027, and its results may change these findings.
Appendix
Data and definitions
Sources
| Source | What it provides | Coverage | N |
|---|---|---|---|
| Equity in Athletics Disclosure Act surveyUS Department of Education | Recruiting spend, athletic aid, coaching counts and salaries, participation, enrollment, revenue and expense | 2015-16 to 2024-25, 37 sports, all divisions | 20,353 school-years 2,037 schools/yr |
| 247Sports Composite ratingsvia sportsdataverse | Per-recruit star rating and composite grade, and roster talent | Signing classes 2015-2026, football | 48,362 recruits |
| Published game schedulesvia sportsdataverse | Win-loss records, folded up from completed games | Seasons 2015-2026, football | 5,360 team-seasons |
| Niche / Tudor Collegiate Strategies surveysincl. the 7,129-junior wave | Student college-search behavior and how outreach was received | Classes of 2021-2023 | 31,000+; 20,045; ~12,000; 9,461; 7,129 |
Variables
| Variable | Definition | Role |
|---|---|---|
| Recruiting budget | Reported recruiting expense: travel, lodging and meals for prospects and staff on visits, phone, postage and related costs. Men’s figure in the football model, total elsewhere. | Predictor, logged |
| Class rating | Mean composite grade of a program’s signees in one class. Restricted to classes with 15 or more rated recruits, since an average over a handful is unstable. | Outcome, football |
| Athletes | Participants across all sports, men and women, as reported. A count of people, not a measure of quality. | Outcome, small colleges, logged |
| Win percentage | Wins plus half of ties, over completed games. | Predictor and outcome |
| Roster talent | 247Sports team talent composite: the summed recruiting grades of players on the roster, a four-year rolling measure rather than one class. | Predictor |
| Returning production | Share of the previous season’s production back on the roster. | Control |
| Rest of athletic budget | Total athletic expense minus the recruiting line, so the control does not contain the predictor. | Control, logged |
| Head / assistant coach pay | Average annual institutional salary per head or assistant coach, as the survey asks it. Not a payroll. | Control, logged |
| Athletic aid | Athletically related student aid awarded. | Control, logged |
| Program and year fixed effects | Absorbs what is constant about a school and what is common to a year, so estimates come from a school changing against its own past. | Design |
| Within R² | Share of the remaining variation the model explains once those fixed effects have removed the differences between schools and between years. Not comparable to an ordinary R², which is much higher here because knowing which school a row belongs to predicts most of its recruiting. | Fit |
Social Listening & Clips for athletics
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Civly Athletics Research · report dated August 30, 2026, published September 13, 2026. Download the report as a PDF.
Spending, participation, aid and staffing from the US Department of Education Equity in Athletics Disclosure Act survey. Football ratings from the 247Sports Composite. Win-loss records folded up from published schedules. Student survey figures as published by Tudor Collegiate Strategies with Niche.
Findings describe association in observed data and are not investment, contractual or compliance advice.