NBA Player Usage Rate by Lineup
See how a player's usage rate changes based on who else is on the court. Select a team, choose a sample window, then drag players between On Court, Off Court, and Available zones to filter by lineup context.
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Upgrade to FTA+ →| Player | MIN | USG% | PTS | REB | AST | FGM | FGA | FTM | FTA | TOV | STL | BLK | 3PM |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Shai Gilgeousalexander
|
279.2 | 29.4% | 210 | 29 | 87 | 76 | 151 | 51 | 55 | 14 | 0 | 0 | 7 |
Chet Holmgren
|
279.2 | 24.4% | 164 | 66 | 13 | 62 | 124 | 24 | 31 | 19 | 0 | 0 | 16 |
Isaiah Hartenstein
|
279.2 | 17.5% | 120 | 73 | 34 | 50 | 79 | 20 | 28 | 21 | 0 | 0 | 0 |
Cason Wallace
|
279.2 | 13.6% | 104 | 25 | 21 | 41 | 77 | 2 | 3 | 9 | 0 | 0 | 20 |
Luguentz Dort
|
279.2 | 12.4% | 83 | 36 | 6 | 27 | 72 | 7 | 8 | 4 | 0 | 0 | 22 |
Recent plays involving these players on court
Gilgeous-Alexander Free Throw Technical (23 PTS)
HARTENSTEIN
GILGEOUSALEXANDER
DORT
HOLMGREN
WALLACE
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02/27
Gilgeous-Alexander Free Throw Technical (23 PTS)
HARTENSTEIN
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DORT
HOLMGREN
WALLACE
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02/27
MISS Wallace 3' Running Layup
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DORT
HOLMGREN
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02/27
MISS Wallace 3' Running Layup
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02/27
MISS Gilgeous-Alexander 1' Driving Reverse Layup
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02/27
MISS Gilgeous-Alexander 1' Driving Reverse Layup
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DORT
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02/27
Hartenstein REBOUND (Off:2 Def:4)
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02/27
Hartenstein REBOUND (Off:2 Def:4)
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DORT
HOLMGREN
WALLACE
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02/27
MISS Holmgren 25' 3PT Jump Shot
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02/27
MISS Holmgren 25' 3PT Jump Shot
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02/27
What Is Usage Rate (USG%) in Basketball?
Usage Rate (USG%) measures the percentage of team possessions a player uses while on the court. A "possession used" occurs when a player takes a field goal attempt, gets to the free throw line, or commits a turnover. The standard formula, developed by basketball statistician Dean Oliver, is:
The 0.44 coefficient for free throw attempts accounts for the fact that not all free throws end a possession — and-one free throws, technical foul shots, and three-shot fouls don't consume a full possession the way a standard two-shot foul does. This coefficient was empirically derived to estimate the number of possessions that end with free throw attempts.
~20%
Average Player USG%
(5 players on court at all times)
25-30%
Second Option USG%
(Key contributors, secondary scorers)
30-35%+
Star Player USG%
(Luka, SGA, Giannis, Tatum)
Why Lineup Context Matters for Usage Rate
Traditional usage rate is calculated across all of a player's minutes, regardless of who else is on the court. This is a significant blind spot. A player's usage rate can change dramatically depending on lineup context — when a team's primary ball handler sits, other players absorb those possessions, and usage shifts across the board.
Example: Shai Gilgeous-Alexander (OKC)
Possessions distributed across starting 5
SGA absorbs more possessions without Chet
This lineup-filtered approach gives you the real picture: not just how many possessions a player uses in general, but how their role changes based on who they share the court with. This is critical for DFS roster construction, player prop betting, and understanding how teams will adjust to injuries.
How the On/Off Usage Filter Works
Our lineup-filtered usage tool processes every play-by-play event from every NBA game this season. We track substitutions in real time to know exactly which five players are on the court for each team at any given moment, down to the second. This creates a timeline of "lineup stints" — continuous periods where the same five players share the floor.
Drag players here to filter for stints where they were on the court together. The starting lineup is loaded by default.
Drag players here to exclude stints where they were present. Use this to simulate injuries or bench-heavy lineups.
Players in this zone are not filtered. Their on-court status doesn't affect the results.
Drag players between the three zones to build any lineup filter. The results update automatically. The sample size indicator shows you exactly how many minutes and games your filter covers, so you can judge how reliable the data is.
DFS Application
When a high-usage star is ruled out, the remaining players absorb those extra possessions. Filter that player to OFF and see which remaining players see the biggest usage spike.
These players are often underpriced on DraftKings and FanDuel because the market hasn't fully adjusted to the lineup change. This is where you find edge.
Player Props Application
Player prop lines are typically set based on season averages. If you know a player's usage jumps from 22% to 28% without a specific teammate, their points, assists, and rebounds lines may all be set too low.
This creates profitable over opportunities on sportsbooks like DraftKings, FanDuel, BetMGM, and Caesars.
Rolling Window Analysis
Season-long usage rates are stable but can miss recent trends. A player who was the third option early in the season may have become the second option after a trade or injury. The rolling window filters let you capture these shifts.
L1
Last game only
L3
3-game trend
L5
Short-term trend
L10
Medium-term trend
Season
Full season data
Combining rolling windows with lineup filters is particularly powerful. For example, "SGA's usage in the last 5 games with Caruso ON and Dort OFF" tells you exactly how the team has been operating recently in that specific configuration — much more actionable than a season-long average.
Understanding Sample Size
Every query shows you the total minutes and game count that went into the calculation. This is essential for interpreting the results correctly.
200+ min, 15+ games
Highly reliable. Suitable for season-long analysis and trend identification. This is your bread and butter.
50-200 min, 5-15 games
Directionally useful. Good for identifying patterns but expect some variance from game to game.
Under 50 min
Small sample. Treat as preliminary — one hot or cold shooting streak can skew the numbers significantly.
What Makes This Tool Different
Most NBA stats sites show you season-long usage rates or basic on/off splits. Our tool goes deeper by letting you combine any number of lineup filters simultaneously:
Multi-Player Filtering
Drag players between On Court, Off Court, and Available zones. Want to see usage when SGA and Caruso share the court but Dort is off? Just drag them.
Second-Level Accuracy
We track every substitution from NBA.com's official play-by-play feed, not just box score data. Every second of court time is accounted for.
Transparent Sample Sizes
Every result shows exactly how many minutes and games back the calculation. No guessing about data reliability.
Rolling Windows + Lineup Context
Combine recency (L1, L3, L5, L10) with lineup filters to see exactly what's happening NOW, not just what happened in October.
WOWY Analysis: With Or Without You
In NBA analytics, WOWY (With Or Without You) analysis measures how a team or player performs with or without a specific teammate. Our lineup filter is a generalized version of WOWY that lets you stack multiple conditions.
This is particularly useful for evaluating player impact beyond the box score. A player who doesn't score a lot but raises everyone else's efficiency when on court might not show up in traditional stats but will be obvious when you toggle them ON vs OFF and compare the team's usage distribution.
Scouts, bettors, and fantasy analysts use WOWY data to separate individual skill from team context. Is a role player producing because they're talented, or because they play all their minutes next to a star who creates open looks? Filter the star to OFF and find out.
Data Source & Methodology
All play-by-play data is sourced from the official NBA.com stats API (PlayByPlayV3 endpoint). Lineup stints are computed by tracking every substitution event throughout each game, ensuring second-level accuracy in on-court tracking. Usage rates are calculated using the standard Dean Oliver formula described above. Data is updated daily throughout the 2025-26 NBA season, covering both regular season and playoff games.