Nylon Calculus: Iron-Men 2018 and how to survive the NBA

Every year when I tabulate the iron-man numbers, I worry that the group will be sadly small — or nonexistent entirely. The 2018 season was one of excessive tanking thanks to a strong draft class and, unfortunately, a wide breakout of injuries that crippled many teams. Were there any survivors?

The inconceivable iron-man from last season, Corey Brewer, missed a game due to the humbling DNP-coach’s decision. He was allegedly okay with the decision where he even declined his coach’s suggestion at putting him in for three seconds to keep the streak going, and normally I’m fine with the inclusion of a player who misses a game for a non-health related ailment. But ultimately he missed so many games I could not justify putting him on the list with an asterisk. This is actually a real concern for iron-men as they age: their skills and services erode to the point where they simply run out of talent. To be an iron-man you not only have to have incredible health and durability but you have to be good enough you stay on the court. Brewer’s final count was 317 regular season games in a row with 339 including the playoffs, and those balloon to 486 and 521, respectively, if you ignore a game he missed due to the birth of his son.

Corey Brewer wasn’t the only notable player whose streak ended. Robin Lopez, who has had a decent streak before, was part of the great tanking apocalypse of Chicago, and he missed numerous DNP-coach’s decision games as well. There were too many to give him a pass for this one; he missed 18 games. Tanking is a weird way to end his streak of consecutive games played — 254 with 265 including the post-season — but don’t ask him about it directly: “I’m not familiar with military artillery.”

Then there was Mason Plumlee, a sterling example of how durable players need not play below the rim, who missed a few games due to a calf injury. He was otherwise healthy and the streak was snapped at a remarkable 324 games. Even a sophomore missed a game: Jamal Murray missed his first game since early in his high school days due to the concussion protocol. Then there was Gorgui Dieng, who had played over two seasons in a row without missing a game until spraining a finger. But the Timberwolves still have representation.

The NBA iron-man is now, perhaps surprisingly, Karl-Anthony Towns, who has 246 games played in a row. A careful observer will note 246 is a multiple is 82, so yes, he’s gone through an entire three seasons without missing a game. Close behind is Andrew Wiggins with 239 games. He’s only had one missed game, and it was back in the 2016 season with a sore knee.

However, there is a valid argument for another player at the top of the list. Bismack Biyombo — who sadly became the butt of jokes during the depressing three-way trade that also involved Timofey Mozgov and Julyan Stone – has only missed one game over the past three seasons. How did the unsinkable Bismack miss a game? It was just a suspension at the beginning of the 2017 season due to the accumulation of flagrant fouls. With the Raptors, he picked up a flagrant foul in the last game of the 2016 Cavaliers series for elbowing Kevin Love in the face. Since his team lost, and that was his fourth flagrant foul “point” of that post-season, the suspension was rolled over to the next season. That post-season, coincidentally, was also peak-Biyombo, as the young big man has seen his role decline precipitously to the point where he became a punch-line during the off-season.

Table: Consecutive games played streaks

[i] Suspended for a game due to excessive number of flagrant fouls.

[ii] Marcin Gortat missed a game at the very end of 2016 due to rest — several starters missed that game gearing up for the playoffs.

[iii] Missed one game to “recalibrate.”

[iv] Missed two games due to grandmother’s death.

While one can argue about the merits of an “iron-man” who missed a mere one game due to a suspension, Biyombo’s streak is now in danger not because of his health but because he may not have the on-court value to appear in every game. He could be passed by Karl-Anthony Towns soon or his teammate, Andrew Wiggins. Joe Ingles isn’t far away either, and he, like Biyombo, has an interesting case too. Ingles, the full-time forward and secret math teacher, missed a game back in December of 2015 due to a scheduled oral surgery. I ultimately decided that’s too much of a stretch to dismiss the missed game because it’s a physical ailment, but if you ignored that he’d have 274 regular season games in a row.

The players who qualified, as you can see in that above table, are truly a random smattering of players with little in common. There are lots of big men and guards; there are young guys and some older ones, like Raymond Felton, no one’s prime example of a fitness nut. There are athletic guys, and then there are guys who play above the rim — and that’s the pattern every year I’ve done this. Is there anything predictable we can learn about the iron-men?

To define a long-term survivor, I’m going to flip the script here. Let’s look at who plays the most future minutes instead of games missed. This is more of a continuous variable, and it addresses one issue: not all games played are equal. You should get more credit for logging multiple 3000 minute seasons.

Considering all those factors, I set up a simple but effective model. Grab every player and randomly select one of their 24- to 27-year old seasons. Then try to predict how many more minutes they’ll play in their careers. Obviously, this means I had to remove current players — all seasons before 2000 were omitted.

At this point, you could see how one could argue you’d need a minutes filter here, excising all low minute seasons or players who never played again. But that’s a blatant selection bias. You don’t want to only study high-minute players.

Also, the most important predictor of playing time in the future is playing time in the past. But how do you adjust for guys who came out of high school, played few minutes at first, versus guys who debuted at 22-years-old and had no large playing time gaps? Or guys who stop playing when they’re older just because they’ve lost a lot of their abilities? I’ve adjusted playing time numbers for guys whose BPM was below 0 (average), or below 15 PER for players before 1974. A season with 1000 minutes played with a BPM of -2, generally regarded as replacement level, is equivalent to 3000 minutes at a higher level.

How do you predict the players who will log heavy minutes when they’re older? The most important factors are how often they played when they were younger — healthy, full seasons are better — and how good they are. Obviously, it’s easier to last longer in the league if you’re a star, partly because people give you the benefit of the doubt and reputations linger. Age is obviously a factor too, depending on how you set up the model. But after that things get noisy.

Do skill-based players last longer? By using a proxy of assists and free-throw percentage, I found little evidence that’s true. Obviously, the method would be improved with better data, but tracking statistics debuted when Jamal Crawford, for instance, was still in his early 30’s, so it’s impossible for them to study the aging and longevity of NBA players. Box-score stats have to be relied upon for this study, but even more creative stats failed. Should height be a factor? Weakly, it is, but it wasn’t significant enough to include in the model, even when I adjusted height by position.  Weight didn’t work either, and neither did things like blocks or steals (when filtering out seasons before those stats were tracked.) And, of course, all these variables were tested with a step regression and with a few modifications here and there, testing combinations of variables in case assists, for instance, only worked if position were considered too.

The results have been frustrating, to say the least. After a lot of tinkering with algorithms like step regression with replacement and extensive testing, there are very few powerful, relevant predictors for players who log heavy minutes when they’re older. That’s disappointing, but it actually equates to previous observations about iron-men. There are no strong patterns for players who stay healthy and play heavy minutes besides having a history of clean health and being good enough to stay on the court.

The one exception was the season: this was treated as a variable by itself, and it was positively correlated. For every 10 years into the future, the player was expected to have roughly 660 more minutes played in their future. And by season I mean the season of study, so it’s like a Kevin McHale season in 1984 versus Glen Rice in 1994.

Want to see the patterns for yourself? The table below shows the top players by their positive residuals. In other words, after adjusting for the handful of factors discussed above, here are the players who most outperformed expectations. Elvin Hayes is out in first thanks to some impressive longevity for his time, and because his advanced stats were never great — remember you get a boost the better you are, since you’ll stay on the court longer. There’s no strong pattern below. There are shooters, yes, like Steve Nash, Dale Ellis, and Reggie Miller, but it’s not iron-clad. Plus, you can see the players run the full range of sizes from the Lilliputian Avery Bradley to the towering Kareem-Abdul Jabbar, who may have been under-selling his height too.

Table: Players with highest longevity

*Past MP% is the percentage of total possible minutes played in previous seasons, adjusted for BPM or PER

On the flip side, you can see the players who fell the most below their expected minutes totals. It’s a list of injured stars and regretful arcs headlined by Bill Walton. Many of these players were already injury-prone (surprise, surprise) but a few players like Mahmoud Abdul-Rauf (Chris Jackson) were relatively healthy before they got older. Contrary to popular belief, the list isn’t dominated by super-massive players either. It’s a nice healthy mix. However, the last player is a surprise: Michael Jordan, who missed several seasons when he was older due to retirements and had a high minutes expectation thanks to his advanced stats (i.e. BPM.) Overall, there’s not much of a strong pattern at all besides a general avalanche of injuries.

Table: Players with worst longevity

*Past MP% is the percentage of total possible minutes played in previous seasons, adjusted for BPM or PER

Being an iron-man is, unfortunately, strongly controlled by luck, whether by the chaotic misfortune of falling at the wrong time or the hidden luck of genetics. For Karl-Anthony Towns, or Bismack Biyombo based on how you want to quality things, nothing is guaranteed. This still needs another strong investigation. Perhaps next time I’ll just focus on age curves, or I’ll constrain the time period and introduce more stats with more robust statistical methods. I’m using the wrong weapons, archaic box-score stats and basic demographic information, but that’s all I have for such a wide time period.  But maybe I’m just fighting against the entropy of the universe, and searching for patterns is a fool’s errand and akin to emptying the ocean with a colander one scoop at a time. For promising young stars, that’s the ultimate fear, the randomness of the game and the lack of fairness in who stays healthy and who doesn’t. But that’s their reality, and it’s the kind of risk we all share in navigating this twisting, weird life.

Until next year….