Descriptive only — no edge claimed. These are career per-36 rates, not projections.

NBA players · NBA · as of 2026-04-12

Myles Turner

Descriptive card · conservative measured rates.

seasons played

Higher than 64% of 482 in pack

games

215

Higher than 94% of 482 in pack

minutes

6005.4

Higher than 88% of 482 in pack

PTS /36

19.2

Higher than 78% of 482 in pack

REB /36

8.1

Higher than 74% of 482 in pack

AST /36

1.8

Higher than 9% of 482 in pack

FG %

49.0

Higher than 74% of 482 in pack

3P %

38.4

Higher than 81% of 482 in pack

FT %

76.4

Higher than 43% of 482 in pack

Bars show where this card sits within its own pack (482 nba players), by simple rank of the measured value. Higher is only higher -- no quality judgement is implied.

What stands out

38.4 percent from three from the five spot.
The rebounding rate, 8.1 per 36, is modest for the position -- the cost of standing outside.
Scoring sits at 19.2 per 36 on 49 percent shooting.

Reading note

Per-36-minute rates over a 3-season sample (seasons_played: 3), not per-game and not full-career figures. The manifest is descriptive_only: no adjustment for opponent, teammates, role, or shot difficulty, and a career_min_minutes>=800 inclusion floor applies. Games and minutes measure availability, not how well those minutes were used.

Also measured elsewhere

Joined onto 330 of 482 player cards; the leaderboard badges cover far fewer.

Team win rate with/without this player

MIL: active 0.4225 vs missed 0.2000 (delta +0.2225)

n_active=71, n_missed=10, ci95_offset=[-0.2733, 0.2733]

WITH/WITHOUT = ROSTER CONFOUND: this is NOT a causal player-impact estimate. Why a player missed games is entangled with injuries clustering, rest-game scheduling, blowout pulls, and a different replacement lineup -- none controlled for. DESCRIPTIVE_ONLY, not a predictive or edge claim.

Form curve mover -- top_movers_fallers #1

delta=-11.043

n_qual_games=214

NOT a live hot/cold streak or predictive signal (net-change over the whole span); NOT BPM/EPM/RAPM/DARKO (box-composite only, same limits as box_value_index.py); NOT age/opponent/role adjusted (raw box rates only); NOT player_id-deduped (accented names split across ids upstream -- carried through)