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August 23, 2026

Is Being At Home Really an Advantage?

Most believe home field and home court advantage are a given, a universal truth. But are they really?

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Home field advantage is one of those things nobody bothers to argue about. Sportsbooks bake a fixed number of points into the spread for it before either team snaps the ball or tips it off. Broadcasters mention it as background noise, the same way they mention the weather. It's treated as a fixed law of sports, not something anyone still needs to check. But how do we know it even exists? For all we know it's just an excuse for an away team to not perform as well. How about switching between sports? Does being at home work the same way? We can at least compare between college football and college basketball which we'll do in this piece.

Test 1: A Naive Comparison

Method: Raw Score Differential

Before any adjustment, the simplest possible question: on average, how many more points does the home team win by than the away team, across every game where both teams had a rating going in?

  • CFB 2025: 714 games, home teams outscored their opponents by an average of 4.858 points.
  • CBB 2025-26: 5,438 games, home teams outscored their opponents by an average of 5.167 points.

Those two numbers are almost identical, and on their own they'd suggest home field and home court advantage both give a modest bump that may definitely swing some games. But this number doesn't control for something obvious: home teams might simply be better teams on average. A good program schedules more home games against overmatched opponents and fewer true road tests against ranked teams. If that's happening, some of this "home advantage" isn't about the building at all - it's just better teams being better. To find out how much of it survives once team strength is accounted for, the next test adjusts for exactly that.

Test 2: Accounting for Team Strength

Method: Opponent/Rating-Adjusted Regression

This test regresses each game's point differential on two things: the gap between the two teams' ratings entering that game, and whether the team in question was playing at home. The rating-diff term absorbs the "better teams win by more" effect; whatever's left over on the home/away term is the real question - the edge a team gets just from being at home, once both teams' actual quality is already priced in.

  • CFB: the adjusted home-field edge drops to +0.11 points (rating-diff coefficient 0.827, R² = 0.273).
  • CBB: the adjusted home-court edge holds at +3.927 points (rating-diff coefficient 0.500, R² = 0.433).

We can already see the two sports starting to split. CFB's home-field edge doesn't just shrink, it essentially disappears - a tenth of a point, from a raw gap of nearly five. Almost all of that 4.858-point margin was better teams playing more home games, not the building itself. Notice how hard the rating term is working in football: a coefficient of 0.827 means the gap between two teams' ratings accounts for most of the final margin by itself, leaving almost nothing for home field to explain. CBB's edge holds up far better, still sitting near four points after the same adjustment. That's a real divergence, but a single regression coefficient isn't proof of anything on its own - it could easily be noise, especially for CFB's much shorter season. Before trusting either number, it's worth checking whether the model itself is behaving sensibly in a situation where it should show no home advantage at all.

Test 3: Comparing Neutral-Site Games

Method: Opponent/Rating-Adjust Regression, Neutral-Site Games Only

If the adjusted regression from Test 2 is doing its job, then restricting it to games with no home team at all should produce an intercept close to zero - no home edge, because there isn't one to find. A result that isn't close to zero here is a signal to be skeptical of Test 2's numbers, not confident in them.

  • CFB: 17 neutral-site games, intercept = +3.267 (95% CI: -5.452 to 14.878).
  • CBB: 589 neutral-site games, intercept = -0.173 (95% CI: -1.069 to 0.690).

CBB's check passes cleanly - the interval sits tight around zero, exactly what a well-behaved model should show with nothing to explain. That's reassuring for Test 2's CBB number. CFB's is murkier: the point estimate isn't near zero, but the confidence interval is enormous and comfortably includes zero anyway, because 17 games is a tiny sample to draw any real conclusion from. The honest read here isn't "CFB's model is broken" - it's "this check simply can't confirm or rule out anything for CFB with so little neutral-site data." Either way, the real test of whether either sport's Test 2 number is statistically meaningful is still ahead.

Test 4: Statistical Likelihood of The Advantage

Method: Cluster Bootstrap + Permutation Test

This test is really getting to the meat of the question. A single regression coefficient can look real and still be noise, especially once you remember that the same teams show up in many games each season - those games aren't independent observations. To account for that, this test resamples teams (not individual games) with replacement 2,000 times, refits the regression each time, and builds a 95% confidence interval on the home-edge coefficient from that spread. A separate permutation test shuffles the home/away label thousands of times and checks how often a spread this large happens purely by chance.

  • CFB: 95% CI on the home-field edge = [-10.569, 7.941] (comfortably includes zero) - permutation p = 0.983.
  • CBB: 95% CI on the home-court edge = [2.963, 4.854] (clearly excludes zero) - permutation p ≈ 0.0.

Now we've really landed somewhere interesting. CBB's home-court advantage is real by any reasonable statistical standard - the interval doesn't come close to touching zero, and the permutation test essentially rules out chance entirely. CFB's adjusted home-field edge is not statistically distinguishable from zero - the interval is wide enough to include a meaningful home disadvantage on one end and a solid advantage on the other, and the permutation test says a gap this size would show up by pure chance roughly 98% of the time. That's not the same as proving CFB's home-field advantage doesn't exist; it's a genuine "we can't tell" from one season of ~13-game schedules. Before fully trusting that CBB/CFB split, though, it's worth ruling out one more thing: whether Test 2's regression was distorted by home/away status being secretly tangled up with team strength.

Test 5: Testing Colinearity

Method: Correlation + Variance Inflation Factor (VIF)

If home teams in this dataset happened to systematically be the stronger team (or the weaker one), the regression in Test 2 could end up attributing one variable's effect to the other, muddying everything downstream. This test checks the raw correlation between the home/away indicator and the rating-difference term, plus the standard VIF diagnostic - a value near 1.0 means no meaningful inflation from collinearity at all.

  • CFB: corr(is_home, rating_diff) = 0.061, VIF = 1.004.
  • CBB: corr(is_home, rating_diff) = -0.024, VIF = 1.001.

Both sports land right at the no-collinearity floor. That closes the door on the most obvious objection to Test 4's split result: CFB's null finding isn't an artifact of home teams happening to be mismatched in strength, and CBB's real effect isn't inflated by the same problem either. Whatever each sport's number is, it's a genuine feature of the data, not a modeling side effect.

Conclusion

Pulling all five tests together: college basketball has a real, statistically solid home-court advantage. Once you adjust for team strength, it settles at roughly 3.9 points, its 95% confidence interval clears zero by a wide margin, and the permutation test puts the odds of that happening by chance at essentially none. College football's home-field advantage looks similar on the surface - the raw, unadjusted numbers were nearly identical for both sports - but almost all of it turns out to be explained by which teams simply tend to play more home games. What's left after adjusting for team strength is barely a tenth of a point - not just small, but close enough to nothing that the model can't separate it from zero at all, given one season of ~13-game schedules. Test 5 rules out the easiest explanation for why these two results diverge - it isn't a colinearity artifact in either sport.

None of this proves college football's home-field advantage doesn't exist; a modest true effect can easily hide inside a sample this small. It does mean the confident, sport-agnostic version of the claim - "home field/court advantage is a given" - may not actually hold up. As we've seen today it very much could vary sport to sport. CFB's smaller neutral-site sample (17 games) and shorter season leave real room for that answer to sharpen with more seasons of data; CBB's, with over twice the number games per team and a clean neutral-site check to lean on, is about as settled as one season of data can make it.