Resolve Research
Resolve Projections ncaa · wcbb
d1 women's basketball

NCAA women's basketball — Bayesian projection rebuild

D1 women's hoops projections built on the same hierarchical NB2 architecture as the men's side — independent fits per stat, per-(position × experience) quadratic, fitted to a separate Phase 1 women's substrate. Same experience axis (Fr/So/Jr/Sr) since DOB isn't available in any NCAA source. Same architectural lineage as the WNBA pipeline.

Substrate covers 327,913 player-games across 7,626 players and 17,343 games. Position split G/F/C ≈ 214k / 98k / 16k; experience split Fr/So/Jr/Sr ≈ 66k / 69k / 80k / 113k.

model hierarchical_exp_quadratic_v1 · likelihood NB2 vectorized · levels league → position → player · chains 4 × 1000/1000

Pipeline status

phase 2 grid running
Phase 2 fit grid in progress. Production posteriors land sport-by-stat as the grid completes. Per-player projection function (Phase 3), team rollup (Phase 5), game model (Phase 6), walk-forward backtest (Phase 7), and the live tables on this page (Phase 8) all queue behind the fit grid. Player projections, conference standings, and the NCAA Tournament bracket all wire in once the Bayesian skill posteriors land.

Methodology

how it works

Why this isn't 538-style team Elo: a Bayesian per-player pipeline lets us combine prior strength, per-position aging, and live in-season updates the way the WNBA stack does. The pipeline below is the spec being built; subscribe to the changelog in the parent NCAA hub for ship updates.

What's in the model

  • 3-level hierarchy: league → position (G/F/C) → player, with non-centered parameterization on the position and player offsets for clean mixing.
  • Per-position quadratic: log_rate = mu_player + beta_exp_pos[k]·(exp - 1.5) - gamma_pos[k]·(exp - peak_exp_pos[k])². Each position carries its own peak experience and curvature.
  • Sport-specific priors: W mu_league per stat (PTS N(-1.15, 1.0), STL N(-3.35, 1.0), BLK N(-3.7, 1.5), TOV N(-2.55, 1.0)). Same widened sigmas as men's (Exp(1) on position + player) for the wide CBB skill range.
  • Context features (Phase 4, ready): per-(athlete, season) z-scored pace, team quality, mates' usage, and gravity. v2 folds these into Stan; v1 ships first.

What's not in v1

  • No per-player experience tilt (sigma_exp_player not identifiable with NCAA's ~1.7-season average career window). Same call as the men's side and the WNBA pipeline.
  • No per-row recency weighting yet (deferred to v2 along with the live in-season blend).