[OC] How I built an NBA + WNBA rating system using deep reinforcement learning (based on my MIT Sloan 2026 Best Overall Paper)
I built [hoopq.dev](https://hoopq.dev) as a live implementation of my MIT Sloan 2026 Best Overall Paper on deep-RL player valuation. Free, open, covers the NBA back to 2000-01 and the WNBA back to 2002. Here's a plain-English walkthrough of the methodology.
RAPM (the baseline)
Regularized adjusted plus/minus with box score priors. You take every possession, mark who was on the floor for each team, and solve a big regression to figure out each player's independent impact on point margin. Widely used, decades old, still the analytics standard.
Q-Rating (deep-RL action value)
A neural network learns Q(state, action, player): the expected remaining points in a possession given the game context, the action taken, and who took it. It decomposes into "action" (per-bucket contributions per event) and "presence" (the marginal value of just being on the floor), with the same points-per-100 units as RAPM.
V-Rating (temporal-difference state value)
A causal transformer scores V(state) at every prefix within a possession. Each event's ΔV = V(after) − V(before) is credited to the actor. Aggregated over a season, this is the player's contribution to the team's expected point production per possession. Q-Rating asks, "what did you do?"V-Rating asks, "how did the possession's outlook change while you were involved?"
Interpretable RAPM (Shapley role attribution)
Ridge regression on every event, with actor + assister + blocker + stealer + on-floor lineup as columns. Post-fit, the coefficients roll up into 8 roles: Scoring, Playmaking, Off Reb, Def Reb, Def Actions, Def Presence, Off Presence, Turnovers. Answers WHY a player's RAPM is what it is (Draymond = Playmaking + Def Reb, Gobert = Def Presence + Def Reb, etc.).
Presence Impact (V₀ on-off with cluster-robust CIs)
Same on-off idea as RAPM, but built on V-Rating's V₀ instead of realized points. Adds cluster-robust confidence intervals. Possessions within a game are correlated, so naive on-off SEs undersell variance by ~4×. We group by game and use sandwich-formula SEs. Every rating on this metric ships with a 95% CI, so you can tell "elite" from "we don't know yet."
What makes this different
- Every profile shows all 5 metrics side by side, with an agreement flag. When they disagree, that's the interesting signal.
- Coverage is deeper than most sites: 5.5M NBA possessions (2000-01 through 2025-26) and 855K WNBA possessions (2002 through 2026).
- Free, no login, no paywall. Trade Analyzer, Team Builder, Championship Odds, and per-game Kalman-filtered career trajectories are all included.
Site: [hoopq.dev](https://hoopq.dev)
- Free, no login, no paywall. Trade Analyzer, Team Builder, Championship Odds, and per-game Kalman-filtered career trajectories are all included.
Paper: [Deep Reinforcement Learning for NBA Player Valuation (MIT Sloan 2026)](https://www.sloansportsconference.com/research-papers/deep-reinforcement-learning-for-nba-player-valuation-a-temporal-difference-approach-with-shapley-attribution)
Happy to answer questions about any of the methods.