The dashboard's projections aren't guesses — they come from statistical models fit on real play-by-play and results, then tested on games they never saw. This page explains how they work, shows how well they actually perform, and gives you a playbook for using them to size up any team.
Pick any two teams. The model takes the gap in their SP+ ratings (a neutral-field points margin) and runs it through the project's margin model — final margins are roughly Normal with a 13.5-point standard deviation — to turn that gap into a win probability. Drag the pick around and watch where it lands on the curve.
A logistic-regression model that predicts the home team's win probability from six pregame signals — efficiency edges, recent form, win rate and schedule strength. It's the most rigorously tested model here: fit on earlier games, then scored on held-out games it never saw during training.
Each dot is a bucket of games grouped by predicted win probability. The closer the dots sit to the diagonal, the more honest the probabilities — when the model says 70%, those teams really do win about 70% of the time.
Every input's pull on the outcome, as an odds ratio — how much the home team's winning odds multiply for a one-unit edge in that stat (above 1 helps, below 1 hurts). The same model is fit independently in R and Python; the build fails unless the two agree, so both estimates are shown side by side.
The 2026 Projected Wins on the dashboard come from a separate model that has to predict games before any current-season form exists — using only prior-year strength, returning production and recruiting. Naturally harder than the in-game model, but it still clears its naive baseline by a clear margin.
Once the season starts, a third model updates the preseason forecast from results. Every team begins at its preseason strength and moves toward its season-to-date, opponent-adjusted scoring margins — a Bayesian ridge where the prior is worth a fixed number of games of evidence. It needs no play-by-play, so it re-forecasts the rest of the schedule after every game day; each re-forecast is sealed as a snapshot and graded only on games that had not yet kicked off. Tested on the held-out season before going live, it beats the preseason-only model on the same games. Follow it on the Forecast Scoreboard.
The efficiency and talent numbers you see are themselves outputs of a modeling library — each built twice (R and Python) and parity-checked. The headline figure on each card is a real result from the data.