The statistical record

What holds up, and what doesn't.

Every claim this project makes, with the interval around it. Two of these results are the reason to take the model seriously. One is the reason not to oversell it.

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The headline claim does not survive a significance test

And the game-level claim does, decisively

Effect sizes with 95% confidence intervals

An interval crossing the dashed line is indistinguishable from no effect

Show the numbers

The season projection is the impressive-looking artifact and the weaker evidence. Its edge over a regressed run-differential baseline is +0.142 wins per team-season, and the interval runs from −0.47 to +0.76. It wins in 76 of 150 team-seasons, which is a coin flip.

Reaching 80% power at that effect size would take roughly 5,800 team-seasons, or 193 years of baseball. This is not a problem more work fixes. The honest phrasing is "indistinguishable from the baseline over five seasons", and the game model is the claim to lead with.

Runs are not Poisson, and it matters

Which is why the run model uses a negative binomial

Distribution of runs scored, one team one game

18,180 team-games, 2023 to 2026

actual negative binomial Poisson
Show the numbers

The home-field constant is barely identified

Which is how three different values coexisted in the codebase

Brier score against the home-field adjustment

7,289 games, 2023 to 2025. Lower is better.

Are the probabilities honest?

A 55% call should win 55% of the time

Calibration of the forward predictions

Points on the diagonal are perfectly calibrated. Dot size is the number of games.

The forward record, with its uncertainty

Predictions timestamped before first pitch, and only those

Accuracy by model, 95% interval

The shaded band is where home-field advantage alone would land