This is an automated archive made by the Lemmit Bot.
The original was posted on /r/cfb by /u/Prudent-Cheetah1656 on 2025-12-23 19:46:56+00:00.
With the regular season over, the Michigan firestorm calming down, and a weekend of playoff games in the books, I can share the results after 722 qualified regular season games. Final reminder that service academies are not included due to talent composite’s lack of proper evaluation.
Methodology reminder: for those of you who haven’t been following along, I used 24/7’s talent composite to evaluate (almost) every FBS football game this season.
I tracked the talent gap and home v away to see how big those factors were in explaining scoring margins. I am fully aware that coaching, play styles, matchups, statistically random plays, and magic all play a role. I chose to isolate these variables alone because on-paper talent is often cited as a leading criteria for which teams should get credit for which wins, which teams deserve playoff consideration, who would win in a hypothetical game on a neutral site, and all of that garbage.
With that out of the way, here are the results for this past season:
Season Overview
| Week | Overall Record of More Talented Team | In P4 Matchups | In G6 Matchups |
|---|---|---|---|
| 1 | 36-12 | 6-6 | 8-4 |
| 2 | 34-14 | 5-6 | 6-5 |
| 3 | 32-13 | 9-5 | 7-5 |
| 4 | 28-20 | 7-10 | 8-2 |
| 5 | 27-21 | 14-11 | 10-9 |
| 6 | 29-19 | 13-11 | 13-8 |
| 7 | 32-21 | 17-11 | 14-10 |
| 8 | 29-28 | 15-15 | 13-12 |
| 9 | 24-28 | 15-13 | 9-15 |
| 10 | 30-20 | 15-13 | 15-7 |
| 11 | 25-23 | 14-9 | 11-13 |
| 12 | 34-22 | 19-8 | 14-14 |
| 13 | 34-24 | 14-12 | 16-12 |
| 14 | 32-31 | 18-16 | 14-15 |
| Total | 426-296 (59.00%) | 181-146 (55.35%) | 158-131 (54.67%) |
These percentages are almost identical to last season’s. The more talented team certainly wins more, but it only increases their odds of beating level competition by about 5 percentage points. It’s almost as if on-paper talent isn’t as big a factor as the talking heads on TV would have us to believe.
Broken Down by Talent Gap Size
Surely the larger the talent gap, the less likely the upset. How does the favorite fare depending on talent gap category?
| Talent Gap | Record | Average Score Margin |
|---|---|---|
| 250+ | 52-12 (81.25%) | 22.22 |
| 200-250 | 44-14 (75.86%) | 14.88 |
| 150-200 | 38-18 (67.86%) | 8.96 |
| 100-150 | 68-46 (59.65%) | 4.60 |
| 50-100 | 94-80 (54.02%) | 3.35 |
| 0-50 | 128-128 (50.00%) | 0.57 |
No surprise, the larger the gap, the larger the average score margin, and the better the record. But both margins and winning percentages shrunk across the board. Another supporting piece of evidence that talent’s impact on game outcomes declines over time.
Tranches can provide a good, quick and dirty visualization, but it lacks nuance, so I made a chart. It’s a pity that I can’t put my chart in here, but if you put a scatter plot together that has talent composite gap on the x-axis and margin of victory on the y-axis for every game, you get an equation of
-1.82 + 0.0697x, with an R-squared of 0.113
In other words, from our limited sample, talent disparity predicts just over 10% of a game’s scoring margin, and a talent gap of 100 points is worth a little less than a touchdown, roughly.
How Home v Away Impacts Things
How does home field advantage interact with talent?
Home teams with a talent advantage went 252-125 (66.84%), while home teams with a disadvantage went 172-173 (49.86%).
In P4 games, home teams with a talent advantage went 95-64 (59.75%), while home teams with a disadvantage went 81-87 (48.21%).
I can’t in good conscience, break this up into tranches. Too few games per tranche would produce inconclusive results not worth the effort. I also feel strongly that, at a time when roster volatility is at it’s peak, using prior seasons’ data would create more noise than benefit. In 3-4 years, hopefully the CFB calendar will make more sense and we can start stacking seasons.
I will, however, offer this commentary: the more talented home team winning ~60% of their games against fair competition seems low.
Regression Time!!!
Who doesn’t love a good regression? I took scoring margin as my Y variable, with talent gap and home/away as my x variables.
For all of you non stats/econometrics folks, a regression takes a multi-dimensional scatter plot (in this case, it’s 3-dimensional), and draws a trend line that minimizes the distance between that line and the datapoints. It then calculates the slope of the line for you, as well as spit out a few other key calculations.
Here is a reddit table reading of the key outputs:
| Statistics |
|---|
| Multiple R |
| R Square |
| Adjusted R Square |
| Standard Error |
| Observations |
| Coef | SE | t Stat | P-val | Lower 95% | Upper 95% |
|---|---|---|---|---|---|
| Intercept | -4.74844 | 1.20834 | -3.92971 | 9.33E-05 | -7.12074 |
| Talent Gap | 0.063293 | 0.007274 | 8.700406 | 2.24E-17 | 0.049011 |
| Home Game | 6.943702 | 1.443393 | 4.810681 | 1.83E-06 | 4.109934 |
Again, for non-stats folks, this basically means that talent gap and home/away only accounts for approximately 14% of the scoring variation observed (which is very low), but the two variables are extremely significant from a statistical perspective. It also says that, in our sample, being the home team was worth roughly 7 points, while a talent gap of 100 is worth roughly 6.3 points.
Again, I will state for everyone in the comments saying a home/away split was critical, the difference it created both in the r-squared value in the regression and the percentage point difference it created in the win percentage of the more talented team were tiny.
Upsets
It’s always fun to see the little guy put a pelt on the wall. Here is a look at the biggest upsets of the year so far:
Top 5 upsets according to talent composite gap:
| Game | Talent Gap | Score Margin |
|---|---|---|
| New Mexico over UCLA | 387.82 | -25 |
| Delaware over UConn | 325.27 | -3 |
| New Mexico over UNLV | 321.74 | -5 |
| Delaware over FIU | 319.76 | -22 |
| Delaware over UTEP | 309.03 | -30 |
Maybe Delaware and New Mexico have the most incredible combination of coaching quality and luck of all time, or maybe their talent evaluations aren’t great.
And here are the top P4 upsets:
| Game | Talent Gap | Score Margin |
|---|---|---|
| Indiana over Oregon | 287.89 | -10 |
| Indiana over Penn State | 265.08 | -3 |
| Duke over Clemson | 248.97 | -1 |
| Vanderbilt over LSU | 234.63 | -7 |
| Northwestern over Penn State | 232.45 | -1 |
What a season for underdogs.
Conclusion
On-paper talent is a significant predictor of game outcomes, but it takes a huge gap before it becomes all that noticeable.
Since I have the data assembled, I may look at team-specific data to see which teams play above/below their talent the most, and by how much.
It’s been real, everyone. Happy holidays!