Player projections
Every rotation player's projected counting stats for one day's slate, next to the pace and defense context behind them. Click a column header to sort. Deep-dive a single player →
2026-06-13 — 16 players
| Jalen Brunson | NYK @ SAS | 38.6 | 28.5 | 4.0 | 6.2 | 0.9 | 0.2 | 2.8 | 2.5 | 7.1 | 6.2 | 7.4 | 96.23 | 85.78 | 99.67 | 35.7 |
| Victor Wembanyama | SAS vs. NYK | 34.3 | 21.1 | 10.7 | 3.5 | 1.2 | 2.4 | 2.6 | 1.9 | 5.5 | 4.9 | 6.0 | 85.78 | 96.23 | 105.72 | 38.2 |
| Stephon Castle | SAS vs. NYK | 31.8 | 17.2 | 5.0 | 5.2 | 1.0 | 0.3 | 2.9 | 1.5 | 4.5 | 3.6 | 4.7 | 85.78 | 96.23 | 105.72 | 38.2 |
| De'Aaron Fox | SAS vs. NYK | 33.2 | 16.3 | 4.0 | 5.2 | 1.4 | 0.4 | 2.4 | 1.8 | 5.4 | 2.4 | 3.2 | 85.78 | 96.23 | 105.72 | 38.2 |
| OG Anunoby | NYK @ SAS | 35.3 | 15.6 | 5.4 | 1.7 | 1.2 | 0.8 | 1.5 | 2.4 | 5.9 | 3.3 | 4.0 | 96.23 | 85.78 | 99.67 | 35.7 |
| Karl-Anthony Towns | NYK @ SAS | 32.4 | 15.2 | 11.6 | 3.2 | 0.9 | 0.7 | 2.4 | 1.6 | 4.1 | 3.9 | 4.6 | 96.23 | 85.78 | 99.67 | 35.7 |
| Mikal Bridges | NYK @ SAS | 32.3 | 13.1 | 3.7 | 2.8 | 1.2 | 0.5 | 1.5 | 1.7 | 4.6 | 1.3 | 1.6 | 96.23 | 85.78 | 99.67 | 35.7 |
| Devin Vassell | SAS vs. NYK | 33.3 | 11.9 | 4.1 | 2.3 | 1.0 | 0.5 | 1.1 | 2.5 | 6.5 | 1.3 | 1.6 | 85.78 | 96.23 | 105.72 | 38.2 |
| Dylan Harper | SAS vs. NYK | 25.1 | 11.0 | 4.2 | 2.9 | 0.8 | 0.3 | 1.5 | 1.2 | 3.3 | 1.8 | 2.3 | 85.78 | 96.23 | 105.72 | 38.2 |
| Josh Hart | NYK @ SAS | 29.4 | 10.5 | 7.2 | 4.0 | 1.1 | 0.3 | 1.7 | 1.6 | 4.1 | 1.4 | 2.0 | 96.23 | 85.78 | 99.67 | 35.7 |
| Julian Champagnie | SAS vs. NYK | 28.9 | 9.7 | 4.7 | 1.5 | 0.9 | 0.5 | 0.9 | 2.6 | 6.7 | 1.0 | 1.2 | 85.78 | 96.23 | 105.72 | 38.2 |
| Landry Shamet | NYK @ SAS | 19.5 | 7.1 | 1.6 | 0.8 | 0.5 | 0.1 | 0.6 | 1.6 | 4.0 | 0.6 | 0.8 | 96.23 | 85.78 | 99.67 | 35.7 |
| Miles McBride | NYK @ SAS | 15.3 | 5.6 | 1.4 | 1.1 | 0.5 | 0.1 | 0.5 | 1.4 | 3.6 | 0.5 | 0.7 | 96.23 | 85.78 | 99.67 | 35.7 |
| Keldon Johnson | SAS vs. NYK | 13.1 | 5.4 | 2.5 | 0.9 | 0.3 | 0.1 | 0.5 | 0.8 | 2.1 | 0.9 | 1.1 | 85.78 | 96.23 | 105.72 | 38.2 |
| Mitchell Robinson | NYK @ SAS | 14.2 | 4.4 | 5.8 | 0.5 | 0.6 | 0.8 | 0.6 | 0.0 | 0.0 | 0.7 | 1.6 | 96.23 | 85.78 | 99.67 | 35.7 |
| Luke Kornet | SAS vs. NYK | 11.8 | 3.5 | 3.3 | 0.7 | 0.3 | 0.5 | 0.4 | 0.0 | 0.0 | 0.7 | 0.8 | 85.78 | 96.23 | 105.72 | 38.2 |
This slate is final: toggle between what the model predicted pregame and what actually happened. Context columns are rolling last-10-game values as of the game date: possessions per 48 (pace), opponent points allowed per 100 possessions (DRtg), and opponent three-point attempts conceded per game.
How the player projections work
What's being projected
For every rotation player on the slate, a separate player model projects points, rebounds, assists, steals, blocks, turnovers, threes and free throws for that night. The projection starts from the player's recent production and minutes, then adjusts for the matchup: how fast both teams play, and how stingy the opponent's defense has been lately.
Why the context columns matter
Pace is the number of possessions a team uses per 48 minutes. A fast game means more shots for everyone, so the same player tends to put up bigger raw numbers against a fast opponent. Defensive rating is points allowed per 100 possessions, which strips pace back out to measure how hard it is to score. Opponent threes allowed shows which defenses give up volume from deep. Together they explain most of the night-to-night swing in a player's projection.
Checking the projections
Once a slate is final, the table can switch from the pregame projections to what actually happened, so you can see where the model was close and where it missed. Single-game player stats are noisy by nature; for a fuller picture of how one player's projections have held up, open the player deep-dive.