Tested CFB run game stats (line yards, second level, open field, stuff rate) against the spread, 2018-2025. They predict winning, the market catches up during the week

[u/Quaizimo]

I ran this on the NFL side first with broken tackles and yards after contact and got nothing against the spread, so I wanted to see if college was different since the talent gaps are bigger.

CFBD doesn't chart broken tackles so I used the closst thing it has from the advanced game stats: line yards per rush, second level yards (5 to 10 yards downfield), open field yards (10+ yards downfield, the closest thing to breaking tackles and getting loose), and stuff rate (runs stopped at or behind the line). For each team I used what the offense did and what the defense allowed, only from games already played that season, so it's info you'd have before kickoff.

FBS vs FBS, regular season, both teams with 3+ games played. 3,786 games from 2018-2024 and I held out 2025 (601 games) until the first results were in. Line is the CFBD consensus line, or the average of books when there's no consensus. Pass rule written down before running: 95% interval (resampling whole weeks) above or below zero in 2018-24 and same direction in 2025.

Did these stats actually predict winning? Yes, a lot more than the NFL stats did. Correlation with final margin: line yards 0.38, second level 0.34, stuff rate 0.28, open field 0.28. The spread on the same games was 0.65.

Do they beat the spread? Open field yards, the tackle breaking one, was dead flat. 0.003 in 2018-24 and wrong direction in 2025. Second level didn't pass either.

Line yards and stuff rate both technically passed, barely. Low end of the interval was 0.008 for line yards and 0.002 for stuff rate. Betting the bigger line yards edge in the top and bottom fifths went 787-709 (52.6%) in 2018-24 and 107-96 (52.7%) in 2025. Break even at -110 is 52.4%.

Here's why I don't think it's real:

Line yards and stuff rate correlate at 0.81 with each other, so that's one signal counted twice, not two.

2020 carried it. Line yards went 70-43 in 2020 alone. Take that season out and 2018-24 is 717-666, 51.8%, below break even. 2018 was 50.0% and 2022 was 47.7%.

The market moves toward it. When a team has the line yards edge, its line moves their way between the open and the late number (correlation -0.13 in 2018-24, -0.16 in 2025). The edge is a bit bigger against the opener than the late line, so by kickoff most of it is already priced in. CFBD only has an opener for about 41% of these games so take that part as rough.

So same answer as the NFL. These stats explain why a team wins but the line already has it, with one difference in college: the run blocking edge doesn't look fully priced at the open and gets there during the week.

Has anyone tested line yards against openers with a full set of opening lines? That's the only place I'd look for something left over.

[u/Administrative-Flan9]

I'm not clear on what you're measuring and how so my comment may not apply, but I've always thought of rushing yards as often being driven by overall team performance relative to the opponent. If you are up, you are likely to run more and kill clock, but if you're losing, you're more likely to throw the ball. In other words, it may be correlated with winning, but it's a consequence of winning and not a predictor of winning.

[u/Quaizimo → u/Administrative-Flan9]

Fair point. What I was testing wasn’t whether run game stats predict winning, they do. It was whether they tell you anything the spread or total doesn’t already have. For each game I used both teams’ run numbers from the games they’d already played that season, then checked if those numbers picked covers or overs and unders. Mostly they didn’t, the line already had it.

Your game script point is a good one though. Teams that are ahead run more and see softer fronts late, and I didn’t take garbage time out, so the better teams’ numbers are probably a little inflated. I’m going to rerun it with garbage time removed and see if the line yards result holds up.

[u/flimflambam]

Great stuff

[u/QuesoHusker]

Try actually writing about your model rather than letting Claude do it. My first thought when I read something like that is that you don’t know what you’re doing.

Here’s news: there are a thousand different models that pick winners straight up at 70-75%. The challenge is against the spread and when the p(win) spread is small.

[u/Quaizimo → u/QuesoHusker]

Agree on that. Picking winners straight up is the easy part and that’s kind of the point of the post, see a ton of predictions showing high win rates for SU but the ROI is basically flat or in some cases negative because of the premium you pay to take the pick. All I care about in my prediction models is can we beat the spread or predict the totals and trying to find that edge that beats across multiple seasons and especially beats a null randomized test (these are few and far between and most modelers miss this last step). These stats do a decent job of telling you who wins, but the spread already does it better, and once you test them against the line there’s basically nothing left. The close games are where I am looking next.

[u/Darling_Pinky]

I’ve been trying to develop CFB rushing model after I made a pretty unscientific one last year. I hit 14 out of 15 legs over 3 parlays in 3 straight weekends. I cashed over $6k, but trying to replicate that success with a more sophisticated model. Would love to chat about your model more to see where I can improve.

In general, I’m targeting double digit spreads with teams who are built run first and are playing bad rush defenses.