Methodology & backtest

It's tempting to think a hard counter-pick (Renekton into Shen, Ashe into Teemo) should let a draft-only model predict much better than ~55%. We tested that directly, with a leak-free walk-forward harness (each model trained only on games strictly before the games it's scored on), and none of these beat the shipped model on both accuracy and calibration:

• Solo-queue counter-picks. A dense champion-vs-champion lane-matchup prior from ~9,900 solo-queue pairs (emerald+, far more than pro data ever sees). Result: no change (54.7% → 54.7%). Pro lane outcomes are dominated by team coordination, not the solo matchup.
• Off-meta / off-role picks. Flagging picks played out of their usual role (the “Ashe top” signal). Nudged accuracy up ~0.3pp but hurt calibration — directionally real, too noisy to trust as a probability.
• More history.Training on 3–4 seasons instead of 2. No gain: recency decay already fades old seasons to near-zero weight.
• League effects & pick order. Per-league blue-side bias, region-local metas, and pro pick-order (who counter-picked whom). All within noise or accuracy-up/calibration-down.

The honest conclusion: at the pro level, which champions get drafted is a genuinely weak predictor once you can't see who's piloting them. That's exactly why the “teams + players” model jumps ~10 points to ~66% — the predictable signal lives in team and player identity, not the champion select screen. Disaster drafts are real but rare (a handful per season), so even predicting every one perfectly moves aggregate accuracy under a point.

Should it train on the current season only? We measured that too. Training on this season alone (walk-forward within it) scores 63.7% with teams, ~2 points BELOW the shipped model that also uses last season as history — the early-season games starve without it. More recent-weighted data doesn't beat the current recency decay; less data just loses signal.

International events (MSI / Worlds / EWC) are the hardest. Held-out accuracy on cross-region events runs well below regional play — ~57% aggregate (EWC 66%, Worlds 58%, MSI ~52% on a tiny 80-game sample) vs 65% inside a single league. The cause is structural: team Elo only bridges regions through the handful of inter-region games, so when the best of two regions meet the rating gap is genuinely uncertain. We help it where we can — inter-region games (an international league, or any game where the two teams' home leagues differ) move Elo 3× as much, since they're the only games that calibrate strength across regions. That lifted held-out international accuracy 55.7% → 56.5% and improved its calibration (log-loss 0.715 → 0.714) with no cost to regional games — a real but modest gain; cross-region prediction stays fundamentally data-starved. The draft builder flags these matchups so you can weight them accordingly.

Every prediction shows fair decimal odds (1 ÷ probability) for each side. If a bookmaker offers longer odds than the fair number on a side, the model sees positive expected value on that side — before costs.

Be honest about the bar: a bookmaker's implied probabilities contain a built-in margin (typically ~4–7% across both sides), and closing lines on major leagues are sharp. To profit you need the model's calibration edge to exceed that margin consistently — check the log-loss and calibration table above, not just accuracy, and treat small-sample leagues (see the breakdown below) with extra skepticism. One structural caveat: team Elo is anchored across leagues only by the few inter-league games (MSI, Worlds, EWC), so ratings are most trustworthy for matchups WITHIN a league or at international events — an isolated league's ratings can drift high or low as a block. Nothing on this page is betting advice; it's a measured, walk-forward-validated probability estimate with known error bars.