3-point rate-15 pts
Larsson's 3-point rate is down 15 points over his 40 games since Jan 11 (29% vs 44%). No clear cause shows up in our data; it could be an injury, a coaching choice or the schedule. Probably real: about 70% should hold.
Last season (2025-26)
What changed?
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.
Biggest real changes
Minutes+7.8
Larsson's playing time is up 7.8 minutes a game over his 40 games since Jan 11 (29.8 vs 22.0). No clear cause shows up in our data; it could be an injury, a coaching choice or the schedule. Probably real: about 80% should hold.
3-point rate+11 pts
Wiggins's 3-point rate is up 11 points over his last 25 games of the season (48% vs 37%). The likely reason: Norman Powell (31 MPG before) played only 17 of 35 games. Probably real: about 70% should hold.
- Teammate outNorman Powell (31 MPG before) played only 17 of 35 games
3-point rate+11 pts
Mitchell's 3-point rate is up 11 points over his last 25 games of the season (50% vs 39%). The likely reason: Norman Powell (31 MPG before) played only 13 of 27 games. Probably real: about 70% should hold.
- Teammate outNorman Powell (31 MPG before) played only 13 of 27 games
Points-4.7
Powell's scoring is down 4.7 points a game over his last 25 games of the season (19.1 vs 23.8). Much of it is playing time: his minutes went from 30.8 to 28.0 a game. Also shooting efficiency: his TS% went from 62.8% to 58.1%. Probably real: about 75% should hold.
- MinutesHis minutes went from 30.8 to 28.0 a game (about 46% of the change)
- EfficiencyHis TS% went from 62.8% to 58.1% (about 33% of the change)
- Shot volumeHis shots per 36 minutes (counting free throw trips) went from 22.1 to 21.1 (about 21% of the change)
TS%+5.6 pts
Wiggins's TS% is up 5.6 points over his last 25 games of the season (62.3% vs 56.7%). Most of it is shot-making: his TS% was 4.5 points above what his shot spots predict (0.0 points below before), with Norman Powell out. Probably real: about 65% should hold.
- Shot-makingHis TS% was 4.5 points above what his shot spots predict (0.0 points below before) (about 82% of the change)
- Teammate outNorman Powell (31 MPG before) played only 17 of 35 games
- Shot dietHis above-the-break 3 share of shots went from 28% to 35% (about 18% of the change)
Above-the-break 3 share+11 pts
Jakučionis's above-the-break 3 share is up 11 points over his last 25 games of the season (50% vs 39%). The likely reason: Norman Powell (31 MPG before) played only 13 of 27 games. Probably real: about 65% should hold.
- Teammate outNorman Powell (31 MPG before) played only 13 of 27 games
Above-the-break 3 share+10 pts
Mitchell's above-the-break 3 share is up 10 points over his last 25 games of the season (44% vs 34%). The likely reason: Norman Powell (31 MPG before) played only 13 of 27 games. Probably real: about 65% should hold.
- Teammate outNorman Powell (31 MPG before) played only 13 of 27 games
TS%-4.7 pts
Powell's TS% is down 4.7 points over his last 25 games of the season (58.1% vs 62.8%). Most of it is shot-making: his TS% was 0.5 points below what his shot spots predict (2.7 points above before). Also shot diet: his above-the-break 3 share of shots went from 37% to 42%. Probably real: about 70% should hold.
- Shot-makingHis TS% was 0.5 points below what his shot spots predict (2.7 points above before) (about 68% of the change)
- Shot dietHis above-the-break 3 share of shots went from 37% to 42% (about 32% of the change)
Rim share+9 pts
Larsson's rim share is up 9 points over his 40 games since Jan 11 (45% vs 35%). No clear cause shows up in our data; it could be an injury, a coaching choice or the schedule. Probably real: about 65% should hold.
TS%+4.9 pts
Larsson's TS% is up 4.9 points over his 40 games since Jan 11 (62.3% vs 57.4%). Much of it is shot diet: his rim share of shots went from 35% to 45%. Also shot-making: his TS% was 0.4 points below what his shot spots predict (2.6 points below before). Partly real: about 60% should hold.
- Shot dietHis rim share of shots went from 35% to 45% (about 56% of the change)
- Shot-makingHis TS% was 0.4 points below what his shot spots predict (2.6 points below before) (about 44% of the change)
Corner-3 share-9 pts
Larsson's corner-3 share is down 9 points over his 40 games since Jan 11 (6% vs 15%). No clear cause shows up in our data; it could be an injury, a coaching choice or the schedule. Probably real: about 60% should hold.
TS%+4.9 pts
Mitchell's TS% is up 4.9 points over his last 25 games of the season (61.4% vs 56.5%). Most of it is shot-making: his TS% was 6.8 points above what his shot spots predict (1.0 points above before), with Norman Powell out. Partly real: about 55% should hold.
- Shot-makingHis TS% was 6.8 points above what his shot spots predict (1.0 points above before) (about 100% of the change)
- Teammate outNorman Powell (31 MPG before) played only 13 of 27 games
How this works
Windows
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.
Is it big?
A change counts as big when it reaches:
- Points: 4 points a game
- Minutes: 5 minutes a game
- Usage: 4 points
- TS%: 4 points
- 3P%: 5 points
- 3-point rate: 8 points
- Rim share: 8 points
- Midrange share: 8 points
- Corner-3 share: 8 points
- Above-the-break 3 share: 8 points
- Assists: 2 assists a game
- Rebounds: 2.5 rebounds a game
Players need 15+ minutes a game on one side. Shooting needs 40+ threes or 60+ shots on each side.
Is it real?
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.
Causes
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.
What we measured
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.
What it cannot see
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.