How our picks actually performed.

Published-pick hit rate (the 57.6%). Every Tuesday morning of the 2025 NFL season, we ran the Top-10 Ranker, locked the weekly top-10 by position, and timestamped the picks to published_picks before any game kicked off. After the slate finished, each pick was scored against its position-floor (the bar we set per position before the season started). A "hit" means the player suited up and beat that floor. 144 of our 170 published picks had a player who actually played and could be scored; 83 of those 144 hit. That's the 57.6%.

Top-10 Ranker training. The "who's actually starting this week" model. Trained on 2023+2024 with XGBoost rank:ndcg, a +1 monotone constraint on projection (higher projection can never drop a player's score), and a -1 monotone constraint on injury severity. Cross-validated on recall@10: of the players who actually finished in the weekly top 10 (within position), what share did our top 10 catch? The per-position breakdown above is the strict cross-val view; the 57.6% above is the real-world published-pick view.

Why two scoreboards. Cross-validation recall@10 asks "did we catch the actual top 10," which is the right question for tuning the model. The published-pick hit rate asks "of the calls we actually shipped to users, how many beat the position-floor," which is the right question for trusting the product. Both are honest; they measure different things. The headline above is the second one because it's the one a user actually experiences.

Train / validation split. Trained on 2023 + 2024. 2025 is held out entirely - no peeking. Inside training we use TimeSeriesSplit so each validation fold is strictly later than its training fold (no leakage from future weeks into past predictions). Per-position rankers throughout - pooling across positions would have the model compare a QB30 to a WR30, which is not the query a user actually issues.

Features. Sixteen columns per row, all observable before kickoff: ten composite factors (volume trend, underrated vs ADP, matchup, game environment, health, momentum, ownership velocity, garbage-time risk, weather penalty, script boost), the raw projection itself, opponent DvP rank normalized to 0-1, three-week target-share trend, week-over-week snap delta, an ordinal 0-6 injury-severity scale, and a binary injury-active flag. Iter 3 added the bottom five; iter 3.5 dropped the projection base_margin anchor so the trees can learn arbitrary deltas around projection instead of being bounded to consensus.

What's next. We're backfilling historical pre-game weekly projections into the projections table. Once that lands we'll add a stricter 1.5x-floor scoreboard alongside the 57.6%, for direct apples-to- apples comparison to FantasyPros and friends. The 2026 season will run on the same publish-then-score loop, so the leaderboard updates live every Tuesday.

We do not claim to beat the field on season-long ROI - there is no field-wide ROI number that is honest to publish for a tool whose outputs reach a few thousand users in different leagues, stakes, and rule sets. We are not selling locks. The 57.6% / 55.2% are the share of published Top-10 / Sleeper Finder picks that hit their bar; what you do with either read in your specific lineup is on you.

We do not claim Sleeper Finder is equally strong at every position. QB precision@10 sits at 18.9%, while RB / WR / TE run 65 to 70%. The model leans hard on volume / target-share signals, which travel well for skill positions and poorly for QBs. Iteration 5 will introduce QB-specific features (rushing-share, deep-attempt rate, supporting-cast injury overlay) to close the gap. Until then, treat any QB Sleeper Finder pick as a lower-confidence call.

We do not claim our pre-game projections are better than ESPN's or Sleeper's. We consume both as inputs. Our edge - if there is one - is in the contextual reranking around those projections (Top-10) and the anti-consensus stack (Sleeper Finder), which is exactly what the published-pick hit rates above are measuring.

We do not claim the K-position numbers are predictive. Kickers are noise even in fantasy circles. We train a model for K because the slate has the position and we'd rather emit a score than a NULL, but K's recall@10 is roughly chance and we do not publish K picks in either model.

The numbers on this page move when the published-picks leaderboard moves. If the next retrain or a bad week of picks drags either hit rate down, the page shows it - there is no manual gate between the leaderboard and the scoreboard.