MLS AI picks across moneyline, draw, and goal totals with expected-goals modeling, travel adjustments, and a public tracked record.
MLS is uniquely difficult because of the travel: a team flying coast-to-coast on three days' rest plays measurably worse than its underlying numbers suggest, and the books still under-price this. The model is built on expected goals (xG) and expected goals against (xGA) over a rolling 10-match window, adjusted for designated-player availability and the home-field bump unique to each stadium. We grade three-way markets (home/draw/away) directly off Poisson simulations rather than implied probability from the moneyline, which surfaces draw-no-bet and double-chance edges that pure win-probability models miss.