Scoring and Usage
In basketball, playing time is one of the key component to measure how good a player is. If players are able to stay on the court, they will get more opportunity to perform and to prove themselves. And of course, they will have better basketball statistics.
In this analysis, we compared NBA players’ 2014-15 regular season points per game stats according to their playing time and usage rate. For clarity, usage is the term describing a player’s role in the offense, by explaining how many of his team’s possessions a player is personally responsible for ending (in terms of shoots, free thrown awarded foul drawns and turnovers) while he is on the floor.
In the following graphs, we took into account players who played at least 10 minutes per game and played at least 10 games in the regular season.
Just for introduction, you can find below this seasons points per game, playing time and usage rate leaders with the league average (for players with at least 10+ games and 10+ minutes per game).
Points per Game vs. Playing Time
Both in theory and in reality, higher playing time results in higher probability to score; but not in a linear way. In the below graph, you can see players distribution with their playing time on horizontal axis and their points per game average on vertical axis.
The trend is traced with yellow dot line. We traced this line concordant to the polynomial distribution. Players marked above this trend line can be evaluated as above average scorers relative their playing time and who marked below can be commented as blow average scorers.
The closest (the perpendicular) line from the players’ marks to the trend line displays how this players scoring*playing time duplex differentiates from the average.
From this graph, we can extract a few insights:
- All players who have 20+ points per game, get at least 30 minutes per game playing time. And similarly, all players having 15+ points per game get 25+ minutes per game.
- But the converse is a little bit more complex, there are players who can’t or don’t score much (10- ppg) even with 30 points per game playing time.
- At the upper end of the line, we see some players who have extraordinary offensive skills. Their scoring ability is not only dependent to their playing time as they are far above of the trend line. Examples: Westbrook, Harden, Durant, Curry.
- Jimmy Butler is the league leader for average playing time, but he is a below average scorer.
Points per Game vs. Usage Rate
Playing time is a good indicator for scoring opportunities. But there is a better one: the usage rate. Below graph shows players’ distribution with their usage rate on horizontal axis and their points per game average on vertical axis.
Similarly, the trend (also polynomial) is traced with yellow dot line. And, players marked above this trend line have above average scoring efficiency and who marked below can be evaluated as blow average efficient at scoring.
Here some key points from this graph:
- Russell Westbrook has sky-high usage rate with 37% of Thunder possessions ended with his shoots or turnovers. But comparing the league average, despite leading the ppg of the regular season, he should score more with this usage rate.
- In addition to Westbrook, Cousins, Bryant, Wade, Rose and Wroten are other players with 30+% usage rate but below average scoring efficiency.
- On the other hand, Harden, James, Durant, Davis, Aldridge, Anthony, Curry are examples of efficient scorers.
- Jimmy Butler is the only player having below 25% usage rate and have 20+ ppg. In this perspective, he is the league most efficient scorer.
Playing Time vs. Usage Rate
Up to now, we saw that there is absolute positive correlation between points per game and playing time or usage rate. But what about the relationship between playing time and usage rate? Our expectation should be that more playing time means higher usage rate, but it is not as straightforward as we think.
The following graph represents players’ distribution with their usage rate on the horizontal axis and their average playing time on the vertical axis. This time the trend line is depicted as linear. And we’re noticing more dispersed distribution.
Players marked above this trend line are less ball users. And below the line, there are players with more ball usage comparing to the average.
Here some insights of this this graph:
- There is a weak positive correlation between playing time and usage rate.
- Russell Westbrook is a one-man army.
- Some players’ usage rate is really sky-high despite their limited playing time. For instance, proportionally to the playing time, Villanueva or Speights use as much ball as Irving, Lillard, Elllis, Gay etc.
- Contrary, some players’ usage rate really low comparing to their playing time. The examples are Butler, D. Jordan, Ariza or Batum.