Soccer Dixon-Coles Match Predictor

Per-match soccer probabilities computed from a real statistical model — not a scrape of someone else's picks.

This Actor fits a Dixon-Coles bivariate Poisson model (Dixon & Coles, 1997) to each team's recent results, then uses it to price every upcoming fixture in the league: 1X2 (home/draw/away), Over/Under 2.5 goals, Both Teams To Score, and the 5 most likely exact scorelines — all derived from the same attack/defense ratings and expected goals (λ), so the numbers are internally consistent (they always sum to 1) instead of independently-guessed percentages.

Why Dixon-Coles instead of a raw Poisson model?

A plain Poisson model assumes a team's home and away goals are independent, which overstates how often 0-0, 1-0, 0-1 and 1-1 actually happen. Dixon-Coles adds a low-score correlation correction (rho) fitted from the league's own history, plus exponential time-decay so recent form matters more than a result from three years ago. The result is a model built for exactly the markets that sportsbooks and prediction-market platforms (Kalshi, Robinhood Prediction Markets) price on a per-match basis.

What you get

For every scheduled fixture in the chosen league and time window, one dataset row with:

• 1X2: prob1 (home win), probX (draw), prob2 (away win)
• Over/Under 2.5 goals: probOver25, probUnder25
• Both Teams To Score: probBttsYes, probBttsNo
• Top 5 scorelines with their individual probabilities
• The fitted expected goals (lambdaHome, lambdaAway) and league rho
• Metadata: how much history was used, when the prediction was computed, and a dataQuality flag for fixtures involving a team with no historical matches (e.g. newly promoted)

Supported leagues

Verified end-to-end against live ESPN data: MLS (usa.1), Liga MX (mex.1), Liga de Expansión MX (mex.2), Brasileirão Série B (bra.2), USL Championship (usa.usl.1), Primera División Uruguay (uru.1), Categoría Primera A Colombia (col.1), and Eliteserien Norway (nor.1) — the leagues that also have per-match event contracts on Kalshi and Robinhood Prediction Markets. You can also type in any other ESPN soccer league slug; the model will work as long as ESPN has enough historical results for that league.

Input

Field	Description	Default
leagueSlug	ESPN league slug (pick from the list or type your own)	usa.1
seasonsBack	Years of history to fit team ratings on	3
xi	Daily time-decay rate (higher = recent form matters more)	0.0018 (~1 season half-life)
upcomingDays	How many days ahead to predict	14
maxGoals	Highest per-team scoreline modeled	10

How it works

1. Pulls finished matches for the selected league from ESPN's public scoreboard API, going back seasonsBack years.
2. Fits each team's attack and defense strength plus a league-wide home-advantage and baseline via maximum likelihood (weighted so older matches count less).
3. Fits the Dixon-Coles rho correlation parameter for low-scoring games.
4. Pulls scheduled (not yet played) fixtures for the next upcomingDays days.
5. Builds a full score-grid per fixture (0-0 through maxGoals-maxGoals) with the Dixon-Coles correction applied, normalizes it to 1, and derives every market from that single grid.

A note on newly promoted or newly added teams

If a team has no historical matches in the lookback window (typically a side newly promoted from a lower division), it's treated as league-average strength until it plays enough games to build a real rating. Those fixtures are flagged dataQuality: "partial-new-team" so you can decide how much weight to give them.

Use cases

• Comparing model-implied probabilities against live prices on Kalshi/Robinhood per-match soccer contracts
• Building your own value-betting or trading workflow on top of consistent, per-match probabilities
• Research and analysis of goal-scoring patterns across leagues