The full process behind our MLB game predictions, published without omission.
MLB predictions run on a pure quantitative model. Unlike KBO, which combines sabermetric factors with an AI judge-agent debate layer, MLB skips the LLM debate layer entirely — at30 teams / 435possible matchups the scale doesn't fit a daily per-game debate — and derives win probability purely from a weighted sum of 14 quantitative factors (single scoring_rule='mlb_v0.1' version, no agent re-judging confidence day to day).
Every factor comes from public data sources only. Full weights and rationale are published on the 14-factor model page.
The 14-factor model is the 10 KBO-equivalent factors (FIP, xFIP, wOBA, bullpen FIP, recent form, WAR, head-to-head, park factor, Elo, defensive SFR) plus 4 Statcast-only factors. Team Elo ratings aren't sourced externally like KBO's — we run our own K-factor update loop (K=4, K=6in the postseason, values cited from FiveThirtyEight's published MLB Elo model) after every game. Home teams get the same empirically measured home-field bonus as KBO (+1.5pp).
Note: defensive SFR, starter xwOBA-against, and wOBA standard deviation (12% combined) are shown for reference on team/matchup pages, but since no MLB-specific data source exists for them yet, a neutral team-agnostic value is fixed in the win-probability calculation above — they don't actually move the number. Recent form, head-to-head, and Elo are all real measured values feeding into the calculation.
Full weight table, definitions, and sources are on /en/mlb/factors.
Every prediction is auto-scored after the game ends and published live on /en/mlb/accuracy. We publish Brier score (a calibration measure) and a calibration chart alongside raw accuracy, so overconfidence shows up transparently rather than getting hidden behind a single win-rate number.