Yeah Thibs is kind of exonerated by this since he’s constantly overplaying his guys haha

Interesting findings. I tore my Achilles the first local league game back post COVID after doing nothing but drinking and getting fat for basically two years, then stretching only the day-of. Seems to align with the scientifically proven results.

It’s r/nba, we react to the titles and not to the content. Get on with the times man.

That’s only half true. “Cumulative career demands can compromise tendon durability”.

I thought similarly that a consistently high workload might be better than a sudden spike when reading the title, but the results also show a positive correlation between cumulative workload over a season and injury.

actually it suggests BOTH. correlation =/= causation

It is still part of load management, in my opinion, returning gradually to play NBA games.

Sitting them down, though they are 100% healthy?

No, that’s bad.

(If you follow what the research is suggesting)

The science behind tendon loading tells us both progressive loading, and recovery are both important.

Tendon collagen synthesis requires high mechanical tension and prolonged recovery windows, differing fundamentally from rapid muscle protein synthesis. While muscles recover quickly due to rich blood flow, tendons adapt slowly through specialized cellular loading

When you load tendons, it typically takes 48hrs to recover. If you just smash your body long term, the load will still cause problems for tendons, that’s how you end up with tendinopathy.

Commenting on this for visibility.

I have a PhD in Biostatistics. I’m an Associate Professor of Biostatistics and Data Science at a medical research university. Doing these types of analyses and reviewing these types of papers is my day job. For the record, I have 70+ peer-reviewed medical research papers, and I’m on the editorial board of several medical journals as a statistical reviewer.

Tl;dr: these findings are incredibly misleading. The statistics are shit. There’s massive confounding not accounted for. There’s almost no argument of causality. This paper should not have been published on the basis of poor of statistical rigor and reporting. This is my professional opinion as someone who does this for a living.

This paper is seriously flawed.

At best, this is a curiosity that makes me think about how this question could be studied better. It does not convince in the slightest of any underlying epidemiological relationship between workload and Achilles rupture. I’m highly, highly skeptical.

The sample size is small. 19 ruptures over more than a decade. The study design fundamentally cannot answer the question the paper wants to answer.

Every player in the analysis tore his Achilles. The analysis conditioned on the outcome. That’s a big red flag from the start. There is no uninjured comparison group, no properly sampled control periods, and no denominator representing the total amount of time players were actually at risk. They didnt estimate whether high minutes/workload increases the risk of Achilles rupture. They only described playing-time patterns among 19 people who eventually ruptured their achilles. Those are not the same thing. They’re quite different actually

The big problem is exposure to what? What is the exposure opportunity? It is time spent playing on court. You cannot rupture your Achilles while sitting on the bench. More minutes means more opportunities to rupture occurs.

Suppose 2 players have exactly the same risk of rupture during every minute they play. One plays 36 minutes and the other plays 18. The first player has twice as much opportunity for a rupture to occur. That does not mean playing 36 minutes biologically weakened his tendon. It may simply mean he spent twice as long in the environment where the event was capable of occurring.

The authors did exclude the injury game itself. But that does not solve the exposure problem because playing time is highly correlated across nearby games. A player who plays high minutes in the injury game was probably also playing high minutes during the preceeding games. Starters generally stay starters. Injuries to teammates can increase someone’s role for several week. Therefore, even if Achilles ruptures occurred with a completely constant random probability during every minute played, conditioning on the games in which ruptures occurred would naturally select players who were in higher minute roles. Because those roles persist across games, their previous 10 or 15 games would also tend to show higher minutes. That produces the appearance of a “preinjury workload increase” without requiring workload to have any effect whatsoever on the biological integrity of the tendon.

This also explains why the effect can tail off as the authors move farther backward in time. The injury game is most strongly related to the games immediately surrounding it. Minutes five games earlier will generally be more correlated with the injury-game role than minutes 30 or 40 games earlier. When the authors average across progressively longer windows, the selected high-minute period gets diluted.

In other words, the pattern they interpret as accumulating fatigue can arise naturally from exposure weighted event sampling and ordinary persistence in player roles. Their 10 and 15-game findings are also not meaningfully independent results. The windows overlap almost completely. Based on the reported averages, the 10-game window represents approximately 16 total minutes above the season-average expectation. The 15-game window represents approximately 18 total minutes above expectation. They are basically detecting the same small block of excess playing time twice and presenting it through two nested summaries. They report these as separate findings in a way the authors want to use as compounding evidence. They’re effectively the same finding reported twice that the authors are using as evidence.

And the P-values do not mean much. Their tests are effectively asking whether recent minutes differ from season average minutes under a null assumption that injured players recent minutes should be centered at the season average. But that is the wrong null distribution. Under a constant per min injury probability, players are already more likely to experience the event during high exposure periods. If minutes are correlated across games (they are), those event-selected periods will also have higher preceding minutes. The expected recent-minus-season difference may therefore already be positive even when workload has no causal effect.
A small P-value only says the data are inconsistent with the statistical null tested. It does not say the null they tested was scientifically right. And it’s not.

There are also extensive multiple-testing problems. With only 19 cases, the authors examine several time windows, player characteristics, subgroups, correlations, ANOVAs, and paired comparisons. They explicitly do not adjust for multiple comparisons, yet highlight P-values such as .03 and .0498. This is just flat out bad statistical practice. Again, disingenuous. When you run enough exploratory tests, some will fall below .05 by random chance alone. A P-value of .05 in a tiny dataset after a bunch of overlapping analyses should not be presented as evidence. There’s an XKCD comic about green jelly beans causing acne exactly showing this flawed reasoning (Google it). Calling the analyses hypothesis-generating is reasonable at best. Repeatedly describing the findings as statistically significant is just wrong

The subgroup conclusions are even worse. The authors discuss factors such as age, experience, position, role, and specialization in relation to Achilles rupture. But everyone in the dataset had an Achilles rupture. They did not model rupture occurrence. They compared characteristics among people who ruptured it.

You cannot conclude that position is unrelated to rupture risk by comparing the positions of 19 injured players. That’s not how stats work. You need to know the positions and exposure histories of all the players who DID NOT rupture. The same applies to age and experience.

Their claim that older or more experienced players ruptured under heavier workloads while younger players ruptured under lighter workloads is also related to conditioning on the outcome. Once you restrict the analysis to people who experienced the event, relationships can appear among possible causes even when those relationships do not exist in the full population. This is a classic selection bias. Like stats/epi 101 selection bias. Honestly maybe the most embarrassing mistake in this paper riddled with mistakes.

There is also potential for time varying confounding. Recent minutes are influenced by playoff status, changing roles, injuries to teammates, return from a previous injury, competitiveness, travel, practices, underlying Achilles issues, and many other factors. Even minutes themselves are a rough measure of the relevant exposure. 30 minutes involving repeated explosive cuts, jumps, and accelerations are not necessarily equivalent to 30 minutes of low intensity play time. The study has no information on movement, load practice exposure, preexisting tendon symptoms, or many of the variables that would be required to interpret workload biologically.

Finally, there appears to be pretty basic problems with the uncertainty reporting. The table reports values such as 29 plus minus 2.1 minutes and labels the 2.1 as a standard deviation, while the reported 95% confidence interval is far wider than would be compatible with a standard deviation of 2.1 in a sample of 18 or 19 players. Literally a data integrity error. The confidence interval appears much closer to what would result if 2.1 were a standard error. At minimum, either the variability statistic is mislabeled or the confidence interval was calculated wrong. That is not a subtle causal inference disagreement. It is basic stats reporting which draw into question the integrity of the analysis as a whole (which, see above, it’s pretty poor).

The proper analysis would require a population at risk (all NBA players), player-minutes or another meaningful exposure denominator, and appropriate comparison time frames. One possible way would be a full cohort model estimating rupture rates per minute of exposure while examining whether prior workload changes the subsequent per-minute hazard. Another would be a carefully designed case-crossover analysis using control windows sampled in a way that accounts for playing-time exposure and the time-varying structure of workload. What they shouldn’t do is select only the players who ruptured, look backward , and interpret elevated recent playing time as evidence that workload caused the rupture. (But that’s what they reported and what I’m tearing apart in this comment).

The honest conclusion supported by this study is something like: “Among 19 NBA players who sustained Achilles ruptures, average playing time during certain recent-game windows was modestly higher than their season averages.” That it. No causality. Lots of room for error and nitpicking. It is NOT evidence whatsoever that workload increase cause Achilles ruptures, and it does not justify recommendations that NBA teams reduce workloads to prevent them.

The paper repeatedly acknowledges that it cannot establish workload as a risk factor, but then uses causal and language anyway. Big no no. Bad practice. Bad statistics. That is grossly misleading.

> “Proactive workload management… may help preserve player health and career longevity in the NBA.”

Proactive workload management is the definition of load management

Volume is the just primary contributor to fatigue and injuries. Everybody that knows what they’re talking about in the lifting and bodybuilding space has known this for years. Volume is the clear driver of fatigue and muscle damage. You can’t out-train this damage and fatigue. There are diminishing returns and excessive volume is the number one way to get injured.

Looks like the suggested approach is to ramp up towards the end of the season instead of limiting their minutes

Kind of? The idea is more like gradual ramp up > sudden and quick ramp up.

As the season progresses, guys need to be playing more and more minutes but not do it suddenly with 10-15 games to go.

No they should gradually shorten them instead of having a guy play 30 mpg and maybe only 20 in the last game to 40 mph average in the playoffs.

Thibs: Don’t worry about it. You’re playing all game.

Coaches should have short rotations all year if they want short rotations in the playoffs.

Or not to load manage during the year thinking players will be fresh and available to play larger workloads than they are used to come playoff time.

These conversations always remind me of an important article from my sports med training days: “The training—injury prevention paradox: should athletes be training smarter and harder?”

Link (I love Figure 6 as a summary about acute-to-chronic workload):
https://pmc.ncbi.nlm.nih.gov/articles/PMC4789704/

So Michael Malone and David Adelman are literally basketball terrorists?

Steve Kerr vindicated again

Its suggesting Thibs is a medical genius

I think it means the optimal way is to gradually transition into the playoff rotation starting in the second half of the regular season

I think the paper is too inconclusive to make that claim. The big limitation that they acknowledge is that they aren’t correcting for intensity. It is likely that in the playoffs, where minutes ramp-up, you also are playing harder. Tough to quantify, but I would imagine this is a significant risk factor for Achilles injury.

Correlation=/=causation

I don’t understand this part:

whereas younger players ruptured tendons under lighter usage.

So even with lighter usage they got injury?

Ya….weird that they didn’t start from 2013 when Kobe, the most famous Achilles injury, hurt his.

This is how the Spurs have done load management for over a decade, which has deflated the stars counting stats. Most star players would rather miss games and keep their high per game averages.

They don’t want their per game averages to drop. NBA should market stats as totals like baseball and football and hockey do

A LOT of medical study does not abide the law of large numbers bc the large numbers do not exist. Case-based examination is fairly frequent. For example when kids get medical conditions that almost never appear in children, a paper might be written only about them (happened to someone I know, was published in The Lancet!). “NBA players that get Achilles tears” is a similarly small population.

So you might be right but for this stuff its often just “do the best you can to investigate” because there’s no available better method. & no better method shouldnt mean “wait until there’s enough data” because that may take a literal century.

Coming from a sports medicine background. Its playing style. In the nfl its the same thing. ACL tears are more common because the nfl is more open field/passing game oriented now than it was. In the nba its more about spacing. So there is inevitably going to be more chance of players having soft tissue injuries than before.

The paper is saying that load management into a large workload demand causes these issues.

The most recent Achilles injuries happened in the playoffs where load management isn’t really a thing

I think a much larger issue here is the miles put on players before they even enter the league

If the cause is an increased amount of minutes in a short preceding time period, load management would not help with that at all. You load manage so players can play huge minutes in the playoffs … ie a sudden increase in minutes. So based on that, going from load management to the playoffs would potentially (based on this article) increase risk of injury. If that is true, then load management is actually hurting the players, not helping. But the sample size is not large enough to make any meaningful conclusions

Medical staff are different from S&C, and have different roles.

Ramping up load in a progressive and controlled manner is proven to reduce injury over time. Ramping up too quickly (what this post is about) is being pointed to. If your baseline is too low (“load management”) and now it’s playoff time or a big game and you play 40 mins at high intensity you’re more likely to get hurt.

Some guys have degenerative issues like Kahwi, and that is its own thing and now more medical territory, but guys that sit too much and don’t build that overall endurance / durability will not have it for when it matters.

The issue this professional is identifying is that guys aren’t playing enough then ramping up too quickly. It’s the exact opposite.

Total games played over the season doesn’t matter as much but guys who quickly spike their minutes are more likely to get hurt.

Load management should mean playing consistently all season but coming on for fewer minutes each game. Players don’t want that because it tanks their averages, so we get to the present situation where they rest half the season and go super hard in the rest, and that’s what causes these injuries.

i mean i thinks its the opposite it means you cant ramp up quickly just in time for playoffs and ramping up risks tearing acl. If you play a ton in the regular season the extra playoff minutes mean less risk.

How? This literally says season long games played doesn’t matter as much as excessive workload within a short amount of time.

If anything, it completely invalidates the load management strategy of “Let’s sit him out 30 games during the season so he’s 100% in the playoffs and can carry a huge load in critical games”.

The common approach to load management of resting then a spike in activity is more in line with this proposed cause for achilles tears than not doing the benching at all. Some players resting spiking the minutes of others is much of the same. Just maintain consistent minutes and theyre fine.

Alternatively, a feather in the cap for Thibs. Can’t have sudden spikes in workload when you play 48 minutes every night

Yea I don’t think it’s possible to have any real conclusions when you take into account the amount of players who didn’t tear their Achilles. Which is probably 5 times the amount per team. And just using the 16 playoff team over 11 years is obviously a lot

We need a t-shirt

Yeah calling 1.6 minutes extra per game “significant” is hyperbole

Yep. Marathon training was the first thing that came to mind. This makes sense. You train for probably 5 months, slowly increasing mileage every week, probably peaking at ~70km/week about a month prior to the marathon.

Can’t just train lightly and then all of a sudden run 42km. That’s begging for an injury

Likewise, can’t load manage all year, then play 40mins/game every other day and expect to be fine

Reading it the same way you are I think it becomes a bit useless data as without knowing the number of players that regularly experience this same work load increases without major injury the numbers are kind of meaningless. Because I would bet at least 2 player on each team see this type of workload increase on every team every year so we’re talking about 19 of 30×2×11=660 these are just made up numbers by me but realistically I can believe this work load increase is happening even more often than this.

Some players like back to backs. Because they’re often followed by 2-3 days off. Playing every other night throughout the season doesn’t give you those soft reset breaks…unless you take rest days and sit out games.

A lot of them are after negative steps. But is there any alternative footwork to explode from stationary?

Three of the seven Achilles tears in 2024-2025 were Pacers players. 😭

But this says it happens when players ramp up, and play more minutes, in theory you would actually want to shorten the playoffs, when rotations are shorter and guys are playing more minutes. Or the alternative is that coaches need to keep playing their guys in the allotted minutes, and not ramp up their starters minutes come playoff time.