New Sports Performance Technologies: The 4 Key Questions Teams Need to Ask

I’ve had some thoughts on this topic for awhile, and then this article from a couple scientists with, I believe, the San Antonio Spurs came out in the Strength and Conditioning Journal. It does a great job at summarizing the key issues, but I wanted to try and distill it down to a 4-question framework that I use when evaluating a new sports performance technology for teams that I consult for.

So here are the four simple but broad questions that I ask with any new technology, in the order in which I ask them. I’ll use a single example of a hypothetical portable tool that measures forces acting on the shoulder joint of baseball pitchers to illustrate my four questions.

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Just How Dangerous IS the NFL vs. Other Sports?

Turns out, yeah, pretty dangerous, actually.

As I’m working on my dissertation literature review I figured I’d put a small piece of it to good use for someone other than my committee. 4 people would normally read this, so I’m really hoping we can double that.

There is a wide-ranging belief that the NFL has the highest injury rates of the major North American sports, but by how much? Because game injury rates (rather than injury rates in practices) tend to be easier to calculate (and more available across studies) than those incorporating practices, we’ll focus exclusively on those.

We’ll need to introduce one concept to make the analyses below make sense: the idea of an “athlete-exposure” (AE). This is simply defined as 1 athlete participating in a practice or, in the case of this article, a game. Thus a single NFL game where every available player plays in the game would count for 92 athlete exposures – the 46 guys on the active game day roster on each of the two teams. All the injury rates below are presented per 1,000 AEs.

The studies compared below all have differences in how exactly injuries are defined (namely how much practice/competition time a player has to miss for something to count as an “injury”) and the years they covered, but in an effort to ensure high-quality data I’ve limited the studies to official league injury surveillance systems wherever possible.

Injury Rates Across Sports

Among the “Big 4” North American sports, the NFL has by far the highest game injury rate at 75.4 per 1,000 AEs. This means for every 1,000 players playing in a single game, 75.4 will suffer some sort of injury. So in a single game with 92 AEs, you might expect about 75.4 x 92/1,000 = ~7 injuries.

Compared to the other Big 4 North American sports, the NFL thus has roughly 4-5 times the in-game injury rate of the NBA, MLB, and NHL.

What about more international sports (MLS statistics weren’t readily available, so soccer goes here, stop complaining)? The NFL also has roughly 4 times the in-game injury rate of soccer and, interestingly, Australian rules football.

The only sport that comes close to touching the in-game injury rate of the NFL is rugby. The data are variable, but in general rugby seems to have either a similar or higher injury rate to the NFL. I wanted to look into concussions specifically in the NFL vs. rugby, but I’ve found some conflicting data and I want to do more research on this before saying anything.

I’ll spare you the gory details of the data sources unless you want to click below the jump, where I’ve included everything you should need to evaluate these on your own if you want!

UPDATE ~6 hours after original post:

I purposefully simplified or ignored quite a few issues in writing this post to try to communicate my main point, but some good questions from friends have come up and I want to briefly address a couple shortcomings of this analysis:

  1. This post only considers one possible way to measure injury rates: per player-game. The results would likely look substantially different if we used another, more precise denominator such as player-hours. The NHL numbers, in particular, would look higher relative to the NBA and MLB in such an analysis since hockey players on average play a lower proportion of each game than baseball or basketball players. So this is not the be-all end-all answer for which sport is the “most dangerous.”
  2. Along those same lines, although the risk of being hurt in any given NFL game is way higher athletes play way more games in the NBA, NHL, and MLB. So the differences across sports in an individual athlete’s risk of being injured in a season are going to be much less than the differences in injury rates I’ve shown here. Maybe I’ll take a stab at calculating risks in another post!

In a nutshell: this is just one way of measuring which sport is more “dangerous,” and it’s probably the way that casts the NFL in the most negative light. Injury rates per player-hour or 1-season risks would likely make football look less dramatically bad.

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Checking a Reported Drop in Starting QB Injuries in 2016: Definitions and Counts

I saw a remarkable stat reported in Pro Football Talk (PFT) this morning: that starting QBs missed only 35 games in 2016, vs. 76/77/59 in 2013-15, respectively. The claim is sourced to Peter King’s MMQB column this morning, but unfortunately I haven’t been able to find a source document for the actual numbers. Per King, the NFL’s Competition Committee views these data as evidence the new QB protection rules are having their intended effect.

Now that seemed like a huge drop to me. I did some quick calculations – Teddy Bridgewater (16 games) plus Tony Romo (10 games) plus Jay Cutler (11 games) alone gets us to 37 games missed by starting QBs in 2016. That’s already more than the number reported by King.

Epidemiologists spend a large chunk of our time just counting things. As it turns out, that’s not grade school math. It’s really, really hard, and a correct count relies on counting a.) the right things and b.) doing so in a consistent manner. So I wanted to go into my injury data, sourced from Pro-Football-Reference (pro-football-reference.com), and see if I could replicate the numbers reported by King (and, ostensibly, the NFL). Spoiler alert: I couldn’t really.

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The “Post-Probable” Injury Report Era: Full-Season Update

Hey, long time. Been awhile. How are the kids? Childish? That’s good.

I’ve been a bit distracted with side projects lately – buying a house, co-teaching a high-level statistics course, my dissertation…you know, little things – so sorry for not updating this blog that no one reads for a few months.

BUT! I’m back with a very exciting post: I’m updating my prior investigation into the effects of the NFL’s decision to remove “Probable” from its injury report this past season, now that we have a full season to see how teams adapted (the original analysis had only weeks 1-8). Let me tell you, it’s been miserable for NFL injury analysts and honestly…probably pretty much fine for everyone else.

Since my previous two posts lay out all the relevant background, methods, and data sources in detail, we’re gonna skip right to the results update!

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Is it Really Too Soon to Judge Roberto Aguayo?

I threatened to do this occasionally in my introductory post, but steel yourselves for a football analytics post that has nothing to do with injuries. You have ESPN’s Bill Barnwell and my friend and colleague Daniel Adler to blame for this.

On Bill’s December 5th show the two were discussing Roberto Aguayo, the kicker for the Tampa Bay Buccaneers that the team drafted in the 2nd round this year. Due to his high draft position Aguayo’s struggles – he is currently just 15/22 on field goals in the NFL – have been highly publicized. I’m heavily paraphrasing, but they basically came to the conclusion that it’s obviously far too soon to judge whether Roberto Aguayo is, in fact, a good, bad, or mediocre kicker.

Now Bill and Daniel are super smart guys, but I wondered if the statistics would bear them out…

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Projecting “Questionable” Players in the Post-Probable Era

In my last post I began looking at the effects of removing “Probable” from the game status portion of the NFL Injury Report this year. This left only three categories for players to fall into: “Questionable,” “Doubtful,” and “Out”.

“Out,” like it always did, means the player is certain to not play. Per our data, “Doubtful” continues to mean essentially the same thing.

“Questionable” is where things get interesting. According to my analysis, about 1/3 of players who would have previously been marked “Probable” in earlier years are now marked “Questionable,” while the other 2/3 simply aren’t listed (i.e. they’re considered “not injured”). This has altered what “Questionable” means in terms of how likely a player is to suit up on game day – in previous years 60-65% played in the next game, but so far in 2016 it’s 73%!

That means the “Questionable” players – already a hard-to-predict group – got even more heterogeneous. But can we look a little deeper and try and identify those more or less likely to suit up for their next game? I’m going to stratify by teaminjury type, and practice status to try and find out!

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The Effects of Eliminating “Probable” from the NFL Injury Report

Less than a month before the season began, the NFL announced a few substantial changes to how it handled injuries. The biggest one – at least from a fan(tasy football) perspective – was a modification to the game status report component of the NFL injury report: eliminating the “Probable” designation for how likely players are to play in their upcoming game.1

I wasn’t sure how this change would affect NFL injury reports, so I’ve been eagerly waiting to amass enough data to examine this rule change. Now that we’ve got a half season let’s take a look at the data!

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