Many years ago when I was stat-tracking games, I first started tinkering with the concept of “Offensive Load,” or how much a player “directly” contributes to an individual possession. The idea was simple: Traditional “usage” looks at how much a player shoots or turns the ball over, but some shooters warp defenses and make plays while others are the beneficiaries of such plays.
Usage has value in its own way, but it doesn’t necessarily capture who drives the most offense. Thanks to optical tracking, analysts are now extending the concept to represent who is “involved” more in the offense, but that information is only available since 2014. Historically, playmakers who create for others are underrepresented by usage, but now that we can measure creation with the box score, we can calculate an offensive load estimate that incorporates passing and creation all the way back to 1978.
Calculating Offensive Load
If we want to measure meaningful offensive actions, we need to define what constitutes a “meaningful” action. Let’s define them as:
- Turning it over (while attempting to shoot, create or pass)
Usage covers half of this equation. The question is how to fill in the other half.
Shooting and turnovers are given equal weight in the classic usage formula. Since creating an opportunity is an integral part of many shot attempts, let’s give creation equal weight as well. That leaves “passing” (i.e. assists) as the final component of the formula, but this part is a bit trickier.
It turns out that 38 percent of opportunities created are also assists, so the first step is to remove those from the assist component to avoid double counting. Of the remaining “non-creation” assists, a percentage are from “capitalization” assists — the original or extra pass in an offensive advantage — another chunk are Rondo Assists (a more idle, basic pass where the receiver does most of the work) and the remainder are quality passes that exploit weaknesses in the defense. (These are the riskiest of the bunch.)
Some of these assists are mere happenstance, and some of them require solid decision making. In 2017, 23.9 percent of assists were hockey assists, so as a simple, ad hoc adjustment, one quarter of non-creation assists are removed from the Offensive Load calculation. Thus, the four components for offensive load are true shot attempts, a creation estimate, turnovers and non-creation assists. Using per 100 data, the final formula is:
Offensive Load: (Assists-(0.38*Box Creation))*0.75)+FGA+FTA*0.44+Box Creation+Turnovers
This allows us to compare who has carried the largest load at times for the last 40 seasons. Unsurprisingly, it’s Russell Westbrook’s unique 2017 season, an outlier at 74 percent and one of only three seasons above 60 percent. (Since load is a per 100 rate statistic, “percent” here refers to the percentage of plays that the player was “meaningfully involved” in while on the court.) The median load since 1978 is 27.1 percent, and everything above 32.4 percent falls in the top quartile.
The beauty of the stat is illustrated at the team level, where the partitioning of responsibility is more accurately reflected. Take a player like Steve Nash, who had the third-highest usage rate among Phoenix’s starters in 2005, but led them in Offensive Load by a landslide, which makes sense, because he directed the offense most of the time:
Thus, usage can more accurately be thought of as a team’s “shot distribution,” whereas load is reflective of who is responsible for the heavy lifting. Using Offensive Load, perimeter players with large ball-handling and playmaking responsibilities (like Nash) are no longer underrepresented, as they are in traditional usage. And now that we have load, we can come up with a more accurate estimate of turnover percentage as well.
Adjusted Turnover Percentage (cTOV%)
Traditional turnover rates are based off of usage, which, as previously mentioned, is mostly about scoring attempts. Because of this, playmakers are hammered in turnover percentage. In Phoenix, Nash’s turnover percentages were in the low 20s, whereas a scoring-centric player like Carmelo Anthony had rates between 8.9 and 12.7 percent for the heart of his career. By these accounts, Nash looks like a butterfingery Jeff George while Anthony a trusted gatekeeper of possessions. But this is simply a reward for Anthony throwing the ball at the rim a lot instead of setting up teammates.
Instead, if we use Offensive Load — which incorporates critical non-shooting functions — we can see a more accurate representation of how turnover-prone each player really was. Adjusting turnovers, which I’ll denote as cTOV% (creation-based turnover percentage), is a basic calculation:
cTOV% = Turnovers per 100 / Offensive Load
Now we can compare Anthony and Nash on a level playing field, one that accounts for the turnovers incurred when playmaking and passing:
As you can see, they now look quite similar. And, I suppose, it could still be argued that this adjustment is too small since taking pull-up jumpers is less likely to result in turnovers than any creation endeavors. But we’ll leave that for another time and place.
Either way, Offensive Load gives us a far more accurate representation of responsibilities than traditional usage, and adjusting turnovers based on it a fairer gauge of how turnover prone players really are.