Offensive Load and Adjusted TOV%

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:

  • Shooting
  • Creating
  • Passing
  • 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.


How Valuable is Creating Open Shots for Teammates?

Since we now have a good way to measure creation historically, I wanted to explore the relationship between creating shots for teammates and performance. Theoretically, we’d expect there to be some positive relationship between creation and the scoreboard — the more a team can breakdown a defense, the more higher-efficiency looks they’ll have. Using Box Creation, we can test this hypothesis.

Sure enough, there is a moderately strong relationship between a team’s creation rate and its offensive rating.* In 2006, the league started moving toward its current pace and space, 3-point centric game. Since then, the correlation between Box Creation and a team’s offensive rating was a healthy 0.66. (It was 0.56 since 1980.) For some perspective, turnovers have about a 0.4 correlation with offensive rating and effective field goal percentage has about a 0.8 correlation.

Remember, a team’s creation rate is not an estimate of the percentage of open shots a team takes — teams will end up with open shots when the defense breaks down, in transition or even just from setting a bunch of screens and forcing the defense to concede a deep jumper. Instead, Box Creation is a pace-adjusted estimation of how often a team created an opportunity (per 100 possessions) that led to an open shot. So why isn’t the relationship super strong?

First, creation is about drawing defensive attention and moving defenders as a reaction to a threat. But the ball still needs to find an open shot for this to be counted as an opportunity created, and that doesn’t always happen. Poor spacing or a slow pass (or ball stoppers!) can terminate the offense’s advantage, failing to capitalize on an opening that the creator provided. In this sense, passing is a separate but related component. While it’s the next step in creation, good passing, in general, is about capitalizing on or exploiting an advantage that already exists. (That advantage can come from creation or some defensive error.) So creation rates are not entirely independent of teammate quality.

Second, teams that excel in isolation, at offensive rebounding or by screening for long shots do not rely as strongly on their creators. This speaks to one of the wonderful parts of basketball; there are many ways to skin the cat! Because of that, we wouldn’t expect the relationship between shots created and offensive performance to be that strong. However, as you can glean from the plot above, the majority of historically great offenses create a lot of shots for each other. Fourteen of the top 15 creating teams since 1978 have finished with offensive ratings at least five points better than league average.

There’s a similar, moderate relationship for individuals between Box Creation and Offensive Adjusted Plus-Minus (ORAPM). Using Jeremias Engelmann’s 2006-2011 single-year prior-informed set, the correlation between creation and ORAPM is 0.52 for individual players. Again, this is expected — being a good creator helps, but it’s not the only way to defeat defenses.

Still, the moderately strong relationship between creation and performance reflects the importance of having centerpieces on the roster who can generate easier shots for players who can’t create for themselves.

*Because of the way basketball-reference data is organized, note that this method underestimates teams that made trades. A team swapping two strong creators will be severely underestimated.