Victor Wembanyama

Historical measurements from this snapshot.

Corpus measurements: 3 seasons in the snapshot, 179 games, 5,429.9 minutes; snapshot 2026-04-12

Assists per 36 minutes, corpus seasons: 4.2
Percentile rank 66 among 482 measured profiles
pct 66 of 482 measured
Field-goal percentage, corpus seasons: 48.3%
Percentile rank 71 among 482 measured profiles
pct 71 of 482 measured
Three-point percentage, corpus seasons: 34.2%
Percentile rank 42 among 482 measured profiles
pct 42 of 482 measured
Free-throw percentage, corpus seasons: 81.7%
Percentile rank 70 among 482 measured profiles
pct 70 of 482 measured
Games played, corpus seasons: 179
Percentile rank 70 among 482 measured profiles
pct 70 of 482 measured
Minutes played, corpus seasons: 5429.9
Percentile rank 81 among 482 measured profiles
pct 81 of 482 measured
Points per 36 minutes, corpus seasons: 27.8
Percentile rank 98 among 482 measured profiles
pct 98 of 482 measured
Rebounds per 36 minutes, corpus seasons: 13.1
Percentile rank 96 among 482 measured profiles
pct 96 of 482 measured
Seasons in corpus: 3
Percentile rank 64 among 482 measured profiles
pct 64 of 482 measured

Stored percentiles rank measured rows in this published pack; higher is only higher, not a quality judgement.

What stands out

The 13.1 rebounds per 36 minutes is the highest rebounding rate among the stars profiled in this pack.
He carries a 27.8-points-per-36 scoring load while stretching to the three-point line at 34.2 percent.
The sample is still young: 179 games across three seasons.

Reading note

Numbers are per-36-minute rates over a 3-season sample (seasons_played: 3), not per-game and not a full career; the manifest is descriptive_only, with no adjustment for opponent, teammates, role, or shot difficulty and a career_min_minutes>=800 inclusion floor. The rebounding comparison is only against the 20 star players in this pack, not the full league.

Also measured elsewhere

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

Team win rate with/without this player

SAS: active 0.7812 vs missed 0.7059 (delta +0.0754)

n_active=64, n_missed=17, ci95_offset=[-0.2391, 0.2391]

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 advantage claim.

Read analysis

Form curve mover -- top_movers_risers #1

delta=+19.821

n_qual_games=179

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)

Read analysis

Box Value Index -- top_30 #4

box_value_index_per36=26.261

games=179

NOT RAPM/xRAPM (no stint-level PBP + ridge solve); NOT EPM/DARKO/LEBRON (both trained to predict RAPM); NOT Basketball-Reference BPM (no team pace/position/team-record adjustment terms); NOT defense-tracking-informed (box counting stats only; blk/stl are the only defensive ingredients)

Read analysis

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