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Soccer

Every match across the top leagues, one place — win/draw/win, totals, BTTS and player props, the WizLine beside the book's line, graded in the open. MLS is priced on xG; the European leagues price off actual goals.

On today

La LigaFull timeThe match is doneView board & grades →BundesligaFull timeThe match is doneView board & grades →Ligue 1Full timeThe match is doneView board & grades →Serie AFull timeThe match is doneView board & grades →

Off today

MLSOff todayView board & grades →Premier LeagueOff todayView board & grades →Champions LeagueOff todayView board & grades →

Every league here carries the goal model and the market — win/draw/win probabilities, totals, both-teams-to-score, and player prop projections, the WizLine next to the book's, each graded against the result. What differs is the signal underneath: MLS is priced on expected goals, the European leagues on actual goals. That's worth explaining rather than hiding.

What the European model is priced on

A goal model is sharpest with per-shot data. For MLS, that data is public and good: American Soccer Analysis publishes expected-goals figures for every match, which denoise a low-scoring sport — a side can play well and lose 1–0, and xG reads the chances under the scoreline. For the European leagues that detail sits behind commercial licensing, and the free public source most analysts relied on stopped publishing advanced stats in early 2026.

So the European boards are built on what we do have: actual goals, shots, and minutes from the match feed. The same Dixon-Coles model runs on them, priced off goals instead of xG — noisier over a short run, and we'd rather say that than dress it up. We show the book's line beside every one of them, but until a league has a graded record, read its edges as a question, not an answer.

What the MLS model produces

Every match gets a projected goal total for each side, built from each team's shot volume and shot quality rather than from results. Scorelines are noisy in a sport this low-scoring — a team can play well and lose 1–0 — so the model reads the underlying chances and lets the outcomes settle over a season.

From that projection come the match markets: win, draw, and win probabilities, a goals total, and a both-teams-to-score read. Each sits beside the book's number so you can see where the model disagrees. Player props come off the same engine — shots, shots on target, assists, anytime scorer, and score-or-assist — priced from per-game xG and projected minutes.

What expected goals actually measures

Everything on the MLS board traces back to xG, so it's worth being precise about what it is. Every shot gets a probability of becoming a goal based on where it was taken from, what part of the body took it, and the situation around it — a tap-in from six yards might be 0.7, a hopeful strike from 25 yards 0.03. Add up a team's shots and you get the goals an average finisher would have scored from those chances.

The reason it beats using goals directly is sample size. A team takes fifteen or so shots a match and scores once or twice, so goals are a tiny sample of a much larger one. xG uses the whole sample. A side losing 1–0 having generated 2.4 xG to 0.4 was the better team and will usually be the better team next week, whatever the table says today.

It isn't a complete picture. xG doesn't know that a defender was closing down, it treats all finishers as average when some genuinely are better, and it says nothing about game state — a team 2–0 up sits deep and concedes chances by choice. We use it because it's the best available signal at this sample size, not because it settles anything.

Where a soccer model is weakest

The draw. Low-scoring sports have fat tails. A model can read a match correctly and still watch a deflection settle it. Draw probabilities are the hardest number on the board and the one we'd trust least in isolation.

Rotation. MLS squads rotate hard around midweek fixtures, cup ties, and travel that spans four time zones. A projection built on a first-choice XI overstates a player who's about to be rested, and lineups land late.

Sample size. A 34-game season is a fraction of a baseball season. Season numbers here carry far more noise than the equivalent figure on an MLB board, and we'd rather say so than dress a small sample up as a strong read.

How it's graded

The same way everything else on this site is. Each board freezes at kickoff, and every projection is scored against the actual match — wins, losses, and the ugly ones. The record is published on the board itself rather than in a marketing claim, and the archive rolls back so you can audit any past matchday. The season ledger puts every league's record in one place — win/draw/win, Over 2.5 and both-teams-to-score, with the sample behind each one. How MatchWiz works covers the method in full; the mechanics are identical across sports.

Not betting advice — statistical projections for information only, no outcome guaranteed. 21+, play responsibly (1-800-GAMBLER).

Soccer model — FAQ

Which soccer leagues does MatchWiz model?

MLS plus the big-5 European leagues — the Premier League, La Liga, Bundesliga, Ligue 1 and Serie A. Every one of them carries the goal model: win/draw/win probabilities, a goals total, both-teams-to-score, and player prop projections, each graded against the result.

Why is the European model priced off goals, not xG?

Every league now shows the WizLine beside the book's line. The difference is under the hood: MLS is priced on per-shot expected goals, public via American Soccer Analysis, while the European leagues price off actual goals, shots, and minutes — the advanced xG data there sits behind commercial licensing. Goals are a noisier signal than xG over a short run, so treat the European edges as unproven until they grade out.

What are expected goals (xG)?

Every shot gets a probability of becoming a goal based on where it was taken from, what part of the body took it, and the situation around it — a tap-in from six yards might be 0.7, a strike from 25 yards 0.03. Summed across a match it gives the goals an average finisher would have scored from those chances. It beats using goals directly because a team takes fifteen or so shots and scores once or twice, so goals are a tiny sample of a much larger one.

How accurate is the soccer model?

Each board freezes at kickoff and every projection is scored against the actual match, wins and losses both. The season ledger publishes every league's record in one place, and the archive rolls back so you can audit any past matchday. Soccer is low-scoring and high-variance, so a season is the horizon to judge it on — not a weekend. On the three-way result the model does not beat the closing market, and we say so on the ledger rather than leaving you to work it out.

Where is the soccer model weakest?

The draw, first — low-scoring sports have fat tails, and a model can read a match correctly and still watch a deflection settle it. Then rotation, since squads rotate hard around midweek fixtures and lineups land late. And sample size: a 34-game season carries far more noise than the equivalent figure on an MLB board.

Are these soccer betting picks?

No. Every number is a research signal from a statistical model, published with its graded record so you can judge it yourself. Not betting advice, and no outcome is guaranteed. 21+, please play responsibly (1-800-GAMBLER).