#4Cam Cook

RB·TCU·2025 2024

Cam Cook is a Committee Back for TCU.

What projects, and what doesn't · RBs · held out 2019-2025

How well one season predicts the next, measured on seasons the model never trained on. 1.00 would be perfectly predictable; 0.00 means last year told us nothing.

Usage share (volume)0.52
Beats guessing the RB average by 18%. n=4,219 RB seasons
EPA per play (efficiency)0.10
Not projectable — we do not forecast this. n=3,575 RB seasons
Total EPA (value)0.41
Beats guessing the RB average by 9%. n=3,575 RB seasons

Across all positions, usage carries year to year at 0.47 and efficiency at 0.07. So Cam Cook's projection is a projection of opportunity — how much of the offense he runs through. How well he converts it is something this model does not claim to know a year in advance, and the number above is why.

2024 Production

Rushing

460 Rush yards9 Rush TD119 Carries3.9 Yards/carry

Receiving

18 Receptions75 Rec yards0 Rec TD4.2 Yards/rec

Performance Analysis · 2024 · vs RB peers

Efficiency0
Volume50
Explosiveness18
Consistency0
Receiving53

Player type

Committee Back

Part of a rotation — contributes in a complementary role and keeps the featured back fresh.

Rotational roleSituational use

Peer percentiles

Opponent-adjusted EPA (WEPA/play)0th %ile · below avg

Game-to-game consistency0th %ile · below avg

Key findings

▸Below-average efficiency vs RB peers — value comes through volume, not per-play impact.
▸High game-to-game variance — boom-or-bust profile.
▸Particularly dangerous on passing downs — efficiency spikes in obvious pass situations.
▸Strong second-half surge — EPA/play improved 0.51 from the first to second half of the season.
▸Peak game: 2.57 EPA/play in Wk 9 vs Texas Tech (SP+ 4).

NIL Market Tier· 2024

On3 valuation ↗

Contributor

Rotational contributor. Smaller collective or local deals.

Tier is a model estimate based on position, school brand, performance rank, and usage — not a reported deal. NIL deals are private. For a real market valuation, see On3's NIL profile, which factors in social following and actual deal tracking.

Game Log · box score + EPA, week by week

EPA per play · x-axis: weekabove 0 = added points · below = lost

Wk	Opponent	Result	Opp SP+	Car	Rush Yds	Avg	Rush TD	Rec	Rec Yds	Rec TD	EPA/play
1	@Stanford	W34-27	-8.2	20	81	4.1	1	1	3	0	-0.09
2	vsLong Island University	W45-0	—	13	58	4.5	3	1	2	0	0.24
3	vsUCF	L34-35	2.8	11	35	3.2	0	4	8	0	-0.33
4	@SMU	L42-66	17.5	14	24	1.7	1	4	20	0	-0.29
5	vsKansas	W38-27	4.9	15	61	4.1	1	3	11	0	-0.00
6	vsHouston	L19-30	-8.2	14	77	5.5	0	—	—	—	0.21
8	@Utah	W13-7	7.7	9	13	1.4	0	4	22	0	-0.42
9	vsTexas Tech	W35-34	4.3	1	19	19.0	0	—	—	—	2.57
10	@Baylor	L34-37	8.4	3	-1	-0.3	0	—	—	—	-0.52
11	vsOklahoma State	W38-13	-2.6	7	47	6.7	2	1	9	0	0.66
13	vsArizona	W49-28	-2.8	6	20	3.3	1	—	—	—	0.35
14	@Cincinnati	W20-13	-0.1	6	26	4.3	0	—	—	—	0.17

Usage & Situational · Pro

Snap-share proxy

Overall

17.5%

Passing plays

5.1%

Rushing plays

33.7%

Standard downs

19.0%

Passing downs

14.1%

EPA by down type

Standard downs: -0.11
Passing downs: 0.29
Pass / Rush EPA: -0.29 / 0.04

Usage = share of team plays (CFBD has no true snap counts).

Career · rolling EPA, game by game

Per-game EPA5-game avg

EPA per play · 2025 · 13 games

Chart shows per-game EPA (bars) and rolling 5-game average (line). Season breaks marked with dashed lines. Line value = est. points over replacement per game.