Weighed, Measured, and Found Wanting

There’s a scene in the anachronistic, pre-9/11 Heath Ledger vehicle A Knight’s Tale where Ledger’s William Thatcher is shit-talking his adversary, Count Adhemar, played with prickish flair by Rufus Sewell, tells him he’s going to beat his ass the next time they joust. Adhemar responds coolly. “Please. You have been weighed, you have been measured, and you have been found wanting.” 

It’s a scene and a sentiment I think about a fair amount lately, as it relates to something that was and to a degree, still is, a passion of mine, which is quantifying and understanding basketball with statistics. Being a stats enthusiast, I was the type to derisively sneer at people who say that an over-obsession with statistics sucks away the joy from the game, because hey, man, I love basketball and I love thinking about it and part of thinking about it is, for me and others like me, thinking about it in a quantitative sense. It seemed like an easy way for people with empirically bad opinions to try to shut me and those like me (which is to say, nerds who are better at thinking and talking about basketball than actually playing it) down.

But as I’ve gotten older, I’ve come to see the issue with a bit more nuance especially as it relates to the current and former players’ perspective on “analytics” and “advanced statistics” (both of these terms adding way more weight to most of the math involved than is really deserved). A professional basketball player in 2020 is at work  24 hours a day, 365 days of the year, given the extent of  physical maintenance necessary to be one of the 400 or so best basketball players in the world. Pairing that tedious, all consuming lifestyle with a cacophony of would-be Twitter GMs who have no idea what it actually takes to get where you are running their mouths  about the ways in which you have been found wanting has to be irritating and exhausting. Count Adhemar is the villain, after all.  

As I’ve aged, I’ve gotten more experience in the working world under the thumb of corporate America in the absolute peak of late capitalist management hell and as it turns out, being monitored for bottom line performance for every second you’re at work fucking sucks. It’s also not even particularly effective! Being treated like a cog in a machine is demonstrably bad for mental health and performance. 

There’s also the matter of Goodhart’s Law.  When something goes from being an indicator of success to something that is measured it ceases to be a good indicator of success. Only shooting threes and layups is good if you get good versions of those shots, but if the process for generating those shots leads to low quality shots, you aren’t actually going to have success. Also, if you’re min-maxing all of the time, you might not have the necessary counters when you run into a team that’s good enough to force you away from the shots you want. (looking at you, James Harden).

There is also the matter of pure enjoyment. Always worrying about who is the best player or whether a player is actually good or not (rather than fun and entertaining) or marginally better than another player is amusing for a time, but eventually that shit is boring as hell. Basketball is a cool game, with interesting people and personalities, and so much of the conversation still seems to be driven by what amounts to boiling a player down to ELITE or BAD and flattening all of the individual little things that make a player what they are, in aggregate. The drive to boil players down to a single number (whether that’s ESPN’s Real Plus-Minus, 538’s RAPTOR, Player Impact Plus-Minus, or my own super simple metric on the same scale, DRE) is understandable, but it has started to feel massively reductive and counterproductive. All of those metrics are fairly well-calibrated for their purposes, but they are all, at bottom, estimates of performance or value or whatever with fairly massive error bars. The ranks they provide are best viewed as clusters, as opposed to absolute certainties, and anyone who actually knows anything about their construction and their blindspots or flaws will tell you the same. 

The interesting thing about such numbers is that they ultimately group players into largely the buckets or clusters you would probably expect based on watching a handful of games or looking at the minutes per game of the best and worst teams and identifying whose driving success or failure. You can feel better about the numbers than your own eye test, as far as the clustering goes, mostly because they capture all of the games, as opposed to whichever ones you happen to be able to get in front of your eyeballs. But, like, get as many of those games in front of your eyeballs as you can, man, because it’s the world’s best game! (After you’ve reckoned with the moral baggage of this particular version of basketball we’re getting at this moment in time, of course.)

Perhaps the biggest problem with the drive to quantify every aspect of the game and boil it down to cold, hard math is that it leads to what might be called Slamm’d Up brain. There’s been a poisoning of the brains of too many fans for a long time to think of themselves  as would-be GMs. This creates an obsession with bargains (management speak for not paying players what they’re actually worth) and avoiding albatross contracts (paying players “too much,” according to a dollars-to-production-matrix ). The temptation is obvious, especially for partisans of a particular team. In a salary capped league, maximizing wins per dollar spent is close to something like how you build a championship team. 

But that’s not actually how it works. The league’s salary cap is porous and easy to spend over if you really want to win and most title teams of recent vintage spent well over the salary cap and into the luxury tax to get the job done. Worrying about maximizing value instead of focusing on having a fun team with cool players that wins a decent number of games every year or close to it is how you get things like Hinkie’s bloodless Process, which was a perfectly analytically sound way of maximizing around the edges that lead to some of the ugliest basketball in the history of the world. 

It makes sense in a world where only championships matter, where min-maxing your results has some inherent moral value (a mindset that drives the operation of private equity, where more and more new NBA owners made their ducats) and the cost of throwing three years of basketball in the garbage bin is treated as negligible because there’s no difference between winning half your games and bowing out early in the playoffs and winning 20-25 games. But, friends, as someone who has watched the Chicago Bulls throw three years of basketball in the bin rather than winning 40-50 games every year with Jimmy Butler in his prime, let me tell you: there is a big difference!

When Corbin asked me to do some stats guy writing for the blog, I was pretty excited, mainly because I love the podcast and the community of people around it and also because I haven’t written in a while. So I’ll be doing some writing  and there will be stats because that’s the thing I’m probably best at. But ahead of all of that, I wanted to say that I want to use the things I’ve learned about statistics in the last probably 15 years of thinking too much about this stuff to make the stories a little more interesting, provide a little context, and ultimately to have fun with my pals talking about my favorite sport. I want to use what I know to find more joy and appreciation in the NBA and its characters, not less, especially given how well, you know, the rest of the world looks right now. 

So let’s endeavor to avoid Slamm’d Up brain, despite dabbling in the quantitative. We can use the numbers to better understand things, without making the leap to salary scolding. Leave that to Brett and Randy.

Kodla Math

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