Data is used to measure and compare human beings in many ways in the world we live in. We get accustomed at a very young age through the school system to being tracked, scored, assessed and ultimately judged by numbers and figures. This typically continues well into our adult lives – sales reps get ranked based on performance to quota, employees get their annual performance review, authors and professors get rated online, etc.

These numbers and figures can be related to different kinds things:

  • They can be based on our levels of activity – how much did we do something?
  • They can be the subjective opinions of others – what did someone or some group of people think of us?
  • or they can be some objective measure of results, performance, or output – what was the result of our efforts?

High achievers and competitive people can react pretty strongly to news about poor performance scores, no matter what the metric. That fact was on display this week, when NBA star LeBron James of the Cleveland Cavaliers was told by Jason Lloyd of The Atlantic that he’s recording the slowest average speed of anyone on the floor so far in the Eastern Conference finals series being played against the Boston Celtics. This metric is based on the NBA’s new player tracking system, and the updated stats tables for all players can be found here.

Is the Best Player Really the Slowest?

Technically, Lloyd was right, at least as much as we trust the accuracy of the player tracking system. It’s actually worse than just the Eastern Conference series, too. As amazing as he is, LeBron is, in fact, tied with one other player for dead last out of the 60 players who have played 8 or more games with player tracking activated in this year’s NBA playoffs.

So what was James’s reaction to this information?

“That’s the dumbest shit I’ve ever heard. That tracking bullshit can kiss my ass. The slowest guy? Get out of here.”

So, basically he didn’t like it. He didn’t stop there:

“Tell them to track how tired I am after the game, track that shit. I’m No. 1 in the NBA on how tired I am after the game.”

Thou Dost Protest Too Much

What I find most interesting is that he didn’t object along the lines that I thought would be most obvious – to point to his league-leading scoring statistics, his freakishly high efficiency and game impact metrics, or his team’s incredible play of late. Those would be objections about the use of an activity metric (how fast was he running up and down the court) instead of an output metric (how much was he actually contributing and helping his team to win).

He could have just laughed and said “imagine what I could do if I actually ran hard.” But no – he took exception to a metric that seemed to indicate he wasn’t trying hard. He appealed to something else entirely – how tired he felt after the game – to counteract that implication.

Is Average Speed a Bogus Metric?

So is it the “dumbest shit” to use this particular metric to track basketball player performance in the first place? Is average speed over the course of a game a good performance indicator of that player’s contribution to the outcome of the game? Some of my Twitter followers don’t think so:

So is there a better way to measure a player’s impact on a game? It turns out there are a bunch of different ways to measure this. An interesting way to measure player contribution is known as PIE – Player Impact Estimate – and it seeks to measure “a player’s overall statistical contribution against the total statistics in games they play in.” Or, “in its simplest terms, PIE shows what % of game events did that player or team achieve.” You can find the formula on the NBA stats page here.

Of course no one would be surprised to find out that LeBron has the highest PIE of any player in the playoffs, and it’s not even close. LeBron is involved in 23.4 percept of game events thus far in the 2018 playoffs. The next closest player is Victor Oladipo of the Indiana Pacers with a PIE of 19.3. When it comes to impacting the game come playoff time, there’s no doubt that LeBron is king.

So how does average speed relate to PIE? If LeBron is last in the former and first in the latter, we’d guess that there’s not a strong positive correlation. And we’d guess right. If we correlate average speed with PIE, we see that there’s a very weak correlation (the coefficient of determination, R^2, is only 0.056):

Screen Shot 2018-05-24 at 11.50.50 AM

What’s interesting is that this view shows that LeBron is way up in the top left corner of this chart – he has a low average speed and a high player impact estimate compared to other players. Turns out he’s in really good company in this top-left quadrant, with 10 of the 12 remaining All-Stars also in this space. You can see that the player with whom he’s tied for the slowest average speed is James Harden, and no one is challenging Harden’s performance. Especially not Draymond Green.

In Conclusion

I’ll wrap it up by sharing what someone said to me about this situation – we need to get buy-in from stakeholders before sharing performance metrics with them. I think there’s a lot of wisdom in that. People tend to take measurements of their effort and performance very personally. I know I do. We’d do well to relax a little about that, but it’s human nature.

We should also take care to put the emphasis on the metrics that actually matter. If a metric doesn’t matter, we shouldn’t use it to gauge performance. And activity and opinion metrics are one thing, but they should always be secondary in importance to output or performance scores. Just measuring how much people do something will simply prompt them to increase the volume on that particular activity. Just measuring how much someone else approves of them will lead them to suck up to that person. We all want to contribute to a winning team, and our personal performance metrics should reflect that.

At the same time, though, data is data, and tracking things can help in interesting ways. Perhaps the training staff could use the average speed data to track a player’s recovery from an injury. Or perhaps a certain, ahem, all-star player later in his career could benefit from keeping average speed down to conserve energy for the final round. Or perhaps a coaching staff could evaluate their team’s performance when they play an “up-tempo” style versus running the game a slower place. Who knows?

In other words, data is only “the dumbest shit you’ve ever heard” when it’s used for the wrong things.

Thanks for reading,