Last season (2025-26)
Players whose game really moved: more minutes, a bigger role, a new shot diet. Each change comes with the likely reason and how much of it should hold.
The 2026-27 season does not have enough games yet, so this shows 2025-26: each player's last 25 games (or his games since a role change or a trade) against the rest of his season. Once players have about 20 games this season, the page switches to this season by itself.
Sensabaugh's 3-point rate is down 13 points over his 44 games since Jan 1 (48% vs 61%). The likely reason: Walker Kessler (31 MPG before) missed all 50 games, plus 3 more key players out. Also a new teammate: John Konchar joined the rotation on Feb 7 and played 26.2 MPG. Probably real: about 70% should hold.
George's playing time is down 9.4 minutes a game over his 8 games since Jan 30 (25.1 vs 34.5). The likely reason: John Konchar joined the rotation on Feb 7 and played 24.0 MPG. Partly real: about half should hold.
Williams's corner-3 share is down 13 points over his 19 games since Mar 5 (10% vs 23%). The likely reason: Lauri Markkanen (34 MPG before) missed all 20 games, plus 4 more key players out. Also the starting role change: he started 19 of 19 games, up from 22 of 48. Partly real: about 60% should hold.
Konchar's corner-3 share is down 13 points over his 26 games with UTA (20% vs 32%). The likely reason: he moved from MEM to UTA (first game Feb 7). Partly real: about 55% should hold.
Williams's 3-point rate is down 10 points over his 19 games since Mar 5 (21% vs 31%). The likely reason: Lauri Markkanen (34 MPG before) missed all 20 games, plus 4 more key players out. Also the starting role change: he started 19 of 19 games, up from 22 of 48. Probably real: about 70% should hold.
Markkanen's playing time is down 7.6 minutes a game over his 9 games since Jan 27 (28.4 vs 36.0). The likely reason: John Konchar joined the rotation on Feb 7 and played 22.2 MPG. Partly real: about 55% should hold.
Markkanen's scoring is down 5.6 points a game over his 9 games since Jan 27 (22.3 vs 27.9). Most of it is playing time: his minutes went from 36.0 to 28.4 a game, after John Konchar joined the rotation. Also shooting efficiency: his TS% went from 61.6% to 57.6%. Partly real: about 55% should hold.
Williams's rim share is down 9 points over his 19 games since Mar 5 (32% vs 42%). The likely reason: Lauri Markkanen (34 MPG before) missed all 20 games, plus 4 more key players out. Also the starting role change: he started 19 of 19 games, up from 22 of 48. Probably real: about 60% should hold.
Konchar's 3-point rate is down 8 points over his 26 games with UTA (47% vs 55%). The likely reason: he moved from MEM to UTA (first game Feb 7). Probably real: about 65% should hold.
Sensabaugh's corner-3 share is down 9 points over his 44 games since Jan 1 (11% vs 20%). The likely reason: Walker Kessler (31 MPG before) missed all 50 games, plus 3 more key players out. Also a new teammate: John Konchar joined the rotation on Feb 7 and played 26.2 MPG. Probably real: about 60% should hold.
Mykhailiuk's playing time is down 5.9 minutes a game over his 8 games since Feb 3 (18.1 vs 24.0). Most of it is the starting role change: he started 0 of 8 games, down from 41 of 42, after John Konchar joined the rotation. Partly real: about half should hold.
Williams Jr.'s playing time is down 5.5 minutes a game over his 8 games since Jan 31 (16.1 vs 21.6). The likely reason: John Konchar joined the rotation on Feb 7 and played 22.2 MPG. Partly real: about half should hold.
Every night we split each player's regular season (8+ games on each side) in one of three ways, in this order: the day he joined a new team; the game after which his minutes and usage shifted the most (a simple two-part split); or, if neither moved much, his last 10 games. Until the new season has enough games (about 20 each for most players) we use last season and its last 25 games, which catches the roles that changed at the trade deadline.
A change counts as big when it reaches:
Players need 15+ minutes a game on one side. Shooting needs 40+ threes or 60+ shots on each side.
Both windows are pulled toward the player's whole season by a fixed amount of weight M, the sample size where a stat is half skill and half luck: (window x n + season x M) / (n + M). A change is shown only if the pulled gap keeps at least half of the raw gap: Probably real keeps 60% or more, Partly real keeps 50 to 59%, anything less is Mostly noise and stays off the feed.
M values: 3P% 501 threes, TS% 218 shots and points 9.7 games, from Is it real?; minutes 9.4, usage 17, assists 18.9, rebounds 18.1, 3-point rate 13.9, rim share 18.2, midrange 16.8, corner 3 21.6 and above the break 15.4 games, measured on 1,766 pairs of back-to-back 10-game stretches in 2025-26. That is why 3P% changes almost never make it: threes are mostly luck until a player has taken hundreds.
Rules, each with a number. Points split exactly into minutes, shot volume and efficiency. Assists and rebounds split into minutes and per-minute rate. TS% and 3P% split into shot diet (league FG% from the spots he shot from) and shot-making (the rest). Context: a new team, a move into or out of the starting five (starting lineups from our stint data), key teammates (25+ minutes before) who missed most of the window, a new rotation player (20+ minutes), and opponents' defensive rating. We keep causes that explain 15%+ of the change and show the top three.
Backtest on 2025-26: we ran this as if the season stopped on 3 dates and checked each flag against the rest of the season. Probably real changes kept 71% of their size on average (we predicted 66%) and 88% kept going the same way (301 flags). Partly real kept 60% (predicted 55%). Mostly noise kept 39% (predicted 33%), so the pull is a little cautious, which is the safe side.
Latest run: 393 players checked, 447 probably real and 180 partly real changes, and 273 big swings left out as mostly noise.
Injuries a player played through, coaching decisions, matchups and schemes, and anything off the box score. Missing teammates are read from who played, not the injury report. Causes are rough splits, not proof; when nothing in our data fits we say so.
