The Use of Usage Rates

Ryan Knaus

When you read Rotoworld player news, you’ll often see references to ‘usage rate’. It’s an estimate of how many offensive possessions are ‘used’ by a given player while they are on the court. More shot attempts, free throws and turnovers lead to a higher usage rate, so it’s no surprise that James Harden leads the league by a wide margin – during his 550 minutes on the court this season, 39.3% of Houston’s possessions have ended with the ball in Harden’s hands.

Other obvious, high-volume players in the top-10 for usage include Giannis Antetokounmpo, Trae Young, Bradley Beal and Joel Embiid. You’ll also see Paul George and Kawhi Leonard in the top four, both above 34% usage, though that should inevitably dip. During their first game together, against Boston on Wednesday, Kawhi had 28.9% usage (second-lowest of the season) and George had 29.7% (lowest of the season). Lou Williams is also ranked 20th in usage at 28.8%, and they all shared the court for long stretches on Sunday, so something had to give. Even though Sweet Lou was excellent vs. the Celtics (and had the team’s highest usage), I expect him to take the biggest hit as the season progresses.

 

 

I should emphasize that a rise or fall in usage does not mean that in every instance a player will do better or worse for fantasy value. This is a broad-based analysis that doesn’t account for countervailing forces. If Trae Young gets even more usage, for instance, any increased scoring may be offset by a dip in assists and/or FG%. His usage rise could also be primarily from turnovers, an obvious negative in 9-cat and (most) points leagues. Similarly, if Kawhi’s usage drops a few points with Paul George active, he might offset the dip with increased efficiency and greater energy to expend on defense.

Here are the correlations of usage to 8-cat, 9-cat and official Points-league values (used by FanDuel, Yahoo and NBA.com). The highlighted number is the multiple correlation coefficient, or Pearson's r, and the higher it is the stronger the correlation. A 1.0 would mean that usage and value were inextricably, perfectly linked. (Note: You can calculate usage without turnovers, but I’m using the far more pervasive version that does have turnovers. When you hear about ‘usage’ it almost always has them.)

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correlations

In other words, when usage goes up or down, fantasy values tend to go up or down – especially in points leagues and 8-cat. Turnovers are a bad event for players, of course, so it’s no surprise that the positive correlation weakens with 9-cat leagues. That’s a key takeaway here – usage is only modestly useful as a predictor of fantasy success in 9-cat, where turnovers equate to 1/9 of all value. If you are punting turnovers, as many owners do anyway, then you can focus on the stronger 8-cat correlation. If you drafted James Harden or Giannis Antetokounmpo, for instance, you probably went the rest of your draft without giving much thought to turnovers. You just wanted to win counting stats, bolster your defensive numbers and maybe work on percentages.

The same holds true during the season. If you are consistently winning turnovers, then pure usage won’t be the key to identifying waiver wire gems or evaluating trades. If you are always losing turnovers, but still winning 5-4, 6-3 and 7-2 most weeks anyway, then usage is a metric you ignore at your own risk. Especially in a league where near-constant injuries (and suspensions, sigh) routinely change the landscape, it’s good to identify high-usage players who might step into bigger roles on a moment’s notice. (Minutes are the No. 1 predictor of fantasy value in every format, not usage, but that’s a conversation for another column.)

Here is a chart showing the distribution of usage rates across the top-200 players for 8-cat (based on my ranking from a column last week).

usage chart

 

If you’re efficient, hit 3-pointers and/or rack up defensive stats (things usage doesn’t capture) you can still have impressive value with low usage. The guys lingering at the bottom all fit the bill – big men who rebound and block shots without many offensive looks (Robert Williams, Dwight Howard, Clint Capela) and swingmen in three-and-D mold (P.J. Tucker, OG Anunoby, Royce O’Neale). I’m surprised Jonathan Isaac’s usage isn’t higher, which just speaks to his upside. He’s already top-20 despite taking under 10 shots per game – Dillon Brooks is getting 12.1 shots per game. He’s also getting fewer shots than 10 rookies: Ja Morant, Kendrick Nunn, RJ Barrett, Coby White, Eric Paschall, Rui Hachimura, Tyler Herro, Jordan Poole, Darius Garland and De’Andre Hunter. Now that Nikola Vucevic is dealing with a sprained right ankle, we’re likely to see Isaac’s offensive role spike in the coming days. It’s an exciting prospect.

Low-end players with top-100 usage but modest playing time include Moritz Wagner, Frank Jackson, Jordan Clarkson, Nickeil Alexander-Walker, Grayson Allen, Theo Pinson and Dennis Smith Jr. Most of those guys didn’t make the cut for the chart above. A move to the starting lineup would also mean more competition for touches, but that doesn’t negate their green-light status when they’re on the court. These are the type of guys I’ll put on a ‘watch list’, monitor through the year, and pounce on if a key teammate gets injured. For a guy like Wagner, who is already top-100 in a mere 19.3 minutes per game, I might even float a trade offer – if he were to luck into 30 minutes, or simply earn more run from coach Scott Brooks, he might erupt.

On a more micro level, you can deploy ‘usage-adjacent’ stats to find specialists in any category. Looking for blocks? A simple list of blocks per game is the place to start, but that will only tell you the raw counting stats. The block rate, however, tells you what percentage of a team’s blocks went to an individual. For instance, Luke Kornet has had 70% of the Bulls’ blocks during his 116 minutes on the court this season. If Wendell Carter Jr. got hurt and you needed swats…bingo. Goga Bitadze is just under 70% for block rate, Nerlens Noel is at 64.7%, and so on. For perspective, Mitchell Robinson is second in the NBA at 73.3%, so the players just mentioned have been legitimately great in the blocks department.

The same method to find hidden gems can apply to steals rate (De’Anthony Melton leads the league at 54.5%, ahead of Dejounte Murray and Kris Dunn), assist rate (Tim Frazier is third in the league on a team desperate for playmaking), rebound rate (Jordan Bell leads the NBA), etc. There are also odd things you can learn, like the fact that Chris Silva is top-5 in the league for both block rate and block rate against – he’s swatting shots and being swatted in equal measure. Add usage and other ‘rate’ measures to your fantasy toolbag, and you’ll find yourself ahead of the curve when evaluating player values and locating fantasy potential. You can find the list, and much more, right here on NBA.com.

That’s all the time I have for this week’s deep-dive into usage rates and how they correlate with fantasy values! If you have any insights or questions, you can always find me on Twitter @Knaus_RW.