A Better Picture of Form
Going into 2026, I’ve made the most significant changes to the prediction model since the site launched. The short version: the model now has a better sense of how teams have been playing, not just whether they’ve been winning.
Previously, the main form signals came from things like recent win rates, scoring margins, and player quality (measured through fantasy scoring). These are solid indicators, but they miss something. A team can win a few scrappy games and look fine on paper while actually playing pretty poor football — and vice versa. A team can lose a close game while dominating possession and territory.
The new version of the model picks up on some of those subtler signals.
What’s New
How far teams move the ball. One of the new inputs tracks how much territory a team’s players gain with the ball in hand — not just whether they’re taking possessions, but whether those possessions are actually advancing the team forward. A team that consistently pushes the play into attacking positions tends to create more scoring opportunities, and the model now accounts for that.
How often teams are involved in scores. This measures how many players in a team’s lineup are regularly contributing to scoring chains — the sequences of kicks and handballs that lead directly to a goal or behind. It’s a sign of a team playing connected, structured football rather than relying on individuals to do everything.
Quality of ball use. Two sides of the same coin: how often players use the ball effectively, and how often they give it straight back to the opposition through poor decisions. Raw disposal counts have always been in the model, but this new layer separates the clean, purposeful ball use from the messy stuff.
Why It Matters
All four of these measures are calculated as rolling averages across each team’s last six games, then compared against the opposing team. A large gap in any of these areas tends to be a meaningful predictor of the result — more so than the raw score from last week.
The model still uses all the same signals it always has (recent wins and margins, player experience, travel disadvantage, scoring shot rates). The new features sit alongside them, giving a fuller picture.
A Note on the Numbers
Test accuracy on historical data came in at 69.4% with the new model — a solid improvement over previous versions. That figure comes from games the model had never seen during training, so it’s a genuine out-of-sample measure rather than a self-congratulatory one.
For the top 30% of predictions where the model is most confident, historical accuracy is considerably higher. Those are the picks worth paying most attention to.
Round 1 predictions are coming. The new model has Opening Round data to work with, so it’s already picking up on how teams looked in the first week back.
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