Shot Selection & Analytics
The math that reshaped basketball
Basketball Shot Selection and Analytics is part of Game Intelligence, BasketballGraph's level 5 basketball curriculum. This lesson explains points per shot, the four factors, per-possession thinking.
01Points per shot
The revolution starts with one number: expected points per shot = (points × make rate). A 35% three yields 1.05 points per attempt; a 45% long two yields 0.90. The 'worse' shooter taking threes produces more points than the 'better' shooter taking long twos.
That's why modern shot charts concentrate on the rim, the free-throw line, and the three-point arc — and why the long mid-range shot became a shot defenses happily concede. (Nuance survives: a great mid-range shooter beats a bad three every time. The math ranks *your* options, not a dogma.)
02The four factors
Dean Oliver's Four Factors explain winning better than any stat line:
1. Shooting (effective FG%: eFG% = (FGM + 0.5×3PM) / FGA — it credits threes properly)
2. Turnovers (turnover rate — possessions thrown away)
3. Rebounding (offensive rebound % — extra possessions)
4. Free throws (getting to the line)
…each measured for offense and defense. Teams that win three of the four factors almost always win the game.
03Per-possession thinking
Raw points mislead: a fast team scores 85 by playing more possessions, not by being efficient. Serious analysis is per-possession: offensive rating (points per 100 possessions), defensive rating, and pace. A 'high-scoring' team can have a bad offense; a 'low-scoring' one can be elite.
For a coach, analytics answers questions film can't: *what shots do we actually generate? whom should we foul? which lineups defend?* But numbers propose — film disposes. You'll see both sides in the Scouting topic next.
See it in action
The math behind threes vs. mid-range vs. layups (Basketball on Paper)
The Test
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