USL Championship AI statistical preview

Match preview

USL Championship: Pittsburgh Riverhounds (#6) host Birmingham Legion (#11). Recent form — Pittsburgh Riverhounds: 2W 1D 2L; Birmingham Legion: 2W 1D 2L. Pittsburgh Riverhounds is the model’s favourite.

PickLab model: Pittsburgh Riverhounds win 46%, draw 34%, Birmingham Legion win 20%. Expected goals: Pittsburgh Riverhounds 1.35 – 0.90 Birmingham Legion (total 2.3). Most likely score: 1–1 (15%). Both teams to score: 45%. Over 2.5 goals: 38%.

Generated automatically from the PickLab statistical model and recent results. Not betting advice.

Match outcome

1X2 · Poisson + Dixon–Coles
46%Pittsburgh Riverhounds winHighest estimate
=34%Draw
20%Birmingham Legion win

Estimated probabilities, not betting odds. Rounded to total 100%.

Goals & scorelines

Expected goals and market probabilities
Pittsburgh Riverhounds1.35xG0.90Birmingham Legion
Most likely scores1–115.1%0–013.8%1–013.2%
66%Over 1.5
38%Over 2.5
20%Over 3.5
45%Both score

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Tick Over 2.5, Both teams to score, a correct score or any combination and see the probability from the v2 model.

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Form & trends

Last completed matches

How this prediction is built

  1. League baseline. Expected goals start from this competition’s real scoring averages at home (1.49) and away (1.22) per match.
  2. Attack and defence ratings. Each team is rated against that baseline from the season table and its last 20 matches, with older matches fading (half-life ~90 days).
  3. Opponent adjustment. Goals against weak defences count less, goals against strong ones count more.
  4. Regression to the mean. Small samples are pulled toward the league average so one big result does not distort the estimate.
  5. Poisson + Dixon–Coles. Expected goals generate a probability for every scoreline; a low-score correction fixes the known underestimation of draws and 1-0 / 0-1 / 1-1.
  6. Markets. 1X2, totals and both-teams-to-score are read from that scoreline grid.
0.75 / 0.65Pittsburgh Riverhounds attack / defence
1.04 / 1.17Birmingham Legion attack / defence

Ratings are relative to the league average (1.00). Attack above 1 scores more than average; defence above 1 concedes more.

A statistical model, not a trained AI. It does not know about injuries, lineups, motivation or odds. Use it as one more data point.

Match center, lineups & H2H