How clutch decisions are valued

How is a coach's end-game shot decision scored?

On expectation at the moment of the decision, never on what happened next. With 1, 2, 3 points to make up and at most 35 seconds left in the fourth, the model compares the win probability of a two against a three using that team's own conversion rates. 1364 such decisions across 30 teams.

The outcome never enters

score_decision is never handed whether the shot went in. tests/test_hindsight_guard.py re-scores every decision with the outcome flipped and asserts an identical result, so a well-chosen shot that missed scores the same as one that fell.

Where the error actually is

How often the three was the better choice, against how often it was taken, by deficit
Down byDecisionsThree was optimalThree was chosen
1 point4077.6%57.5%
2 points46783.3%55.5%
3 points49099.8%54.7%

This, not the team table, is the finding: what teams should do swings from 7.6% to 99.8% across the deficit while what they choose barely moves. Down one they shoot the three far too often; down three, too rarely.

Why neighbouring ranks are ties

With ~45 decisions per team the intervals are wide enough that a 1-to-30 ranking would assert precision the data does not have. Teams resolve into 2 tiers, from TOR at 0.97 to ATL at 3.23 points of win probability given up per decision. A "distinguishably worse than optimal" test is deliberately not reported as a finding: the metric is non-negative by construction, so all 30 teams would pass it.

What it is not

Decision expected-value, not a coach rating; negative ETM means lower expected win probability than the alternative given that team's own conversion rates.

Over Expected is a basketball shot-value platform covering NBA, EuroLeague, EuroCup, Liga ACB, Lega Basket Serie A, Basketball Bundesliga, Greek Basket League, ABA Liga, WNBA.