r/NBAanalytics

I'm a basketball coach and developer, and I've been working on a basketball analytics platform called Statly.

One thing I've always found interesting is how often coaches talk about "basketball IQ" - but there aren't many practical ways to actually train or test decision-making.

So I started building a small experiment.

The idea is simple:

You watch a real basketball possession.

The video pauses at a key moment.

You have to decide what the best read is before seeing what actually happens.

After answering, you get the correct read, an explanation of the cue you should have noticed, and your score.

It's still a very early public beta and right now there's only one challenge. I'm mostly trying to figure out whether players and coaches actually find this kind of training useful before building much more.

You can try it here: https://play.statly-analytics.com

I'd genuinely appreciate any feedback, especially from basketball players, coaches, or anyone who has worked on decision-making training in sports.

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.

r/NBAanalytics

I built a basketball IQ trainer that pauses real plays and asks you what happens next

[OC] How I built an NBA + WNBA rating system using deep reinforcement learning (based on my MIT Sloan 2026 Best Overall Paper)