by Zach Binney

In a sport as chaotic and violent as the NFL, the goal of predicting which players are going to get injured is both a Holy Grail and, to date, wholly unattainable with public data. While we have written many articles that identify risk factors for injuries -- weight, prior injury history, position and age, to name a few -- we have not written about a model that allows us to successfully predict which individual players will stay healthy or get hurt. That's partly because if we had such a model it would be too valuable to share freely, but also because our efforts to date have come up short.

Prediction is difficult because getting hurt in the NFL is the product of a complex web of risk factors. Here's a simplified version of what one such web might look like for an ACL tear:

Figure 1. Simplified web of risk factors for an ACL injury in the NFL.

There are player-level factors that contribute to injuries, such as their age and injury history. But there are also team-level factors, such as the amount of training players are subjected to and the related fatigue players experience. Stadium-level factors, such as weather and the type of turf, also play a role. There's also a role for plain old bad luck, though more optimistic analysts might call luck "factors to be named later."

To begin teasing apart this web, Ron Yurko (a PhD student at Carnegie Mellon and co-author of a computer program for scraping and analyzing NFL play-by-play data) and I wanted to look at how much player-, team-, and stadium-level factors contribute to NFL injury risk.

We used data from the Football Outsiders injury database on 3,694 players and 3,874 game-loss injuries from the 2012 to 2016 regular seasons. A game-loss injury is an injury that causes the player to miss one or more future games. We looked at how strongly each level (player, team, and stadium) impacted a player's risk of suffering such an injury each week. Because "team-level" factors are partly driven by the franchise (owner) and partly by the coaching staff, we looked at how each team/head coach combination impacted injury risk. While the head coach is only a proxy for the team's full staff (for example, the team physicians often carry over from coach to coach), historical data on medical and training staff is harder to find. Due to the uncertainty in a single NFL game, we also generated 1,000 simulations of the model to get a range of effects for player, team/coach, and stadium factors. We're going to gloss over the other technical details of the model, but they are available here if you're curious.

Before we get to the results, a quick word about why you should care: if we can show that coaching staffs and stadiums impact injury risk, we can identify which ones are safest and mimic their practices (for coaches) or designs and maintenance procedures (for stadiums). This may also help us identify new risk factors for injury we had not considered before.

What We Found

Player-level factors -- such as age, injury history, and playing style -- are by far the most important in determining a player's injury risk. The orange curve in Figure 2 shows this: on median, a higher-risk player (for example, Arian Foster) has nearly three times the odds of injury of a lower-risk player (for example, Frank Gore). "On median" simply means that half the time the difference is bigger than that, and half the time it is smaller.

Compare this with team/head coach-level factors (the blue curve in Figure 2), which might include practice load and intensity, roster construction strategies (more young players vs. veterans), in-game player usage, and so forth. Here, a player moving from a low-risk team/coach combination to a high-risk combination can expect, on median, a 21 percent increase in his odds of injury. If I were a player (or owner), that would be a big enough effect to worry me.

Stadium-level effects from things like turf and climate are a bit smaller but still substantial: on median, a player playing in a higher-risk stadium faces a 13 percent increase in his odds of getting hurt. If you're an NFL owner, is a 13 percent increase in injuries enough to make you think carefully about your stadium design and turf?

Figure 2. How Strongly Each Type of Factor Impacts Injury Risks.

Let's look at each of these levels in a bit more detail. Here are the five highest- and lowest-risk players at each position:

Figure 3. Highest and Lowest-Risk Players.

There's a lot to unpack here, but we can point out a few things. The safest quarterback and running back are Eli Manning and Frank Gore, respectively, whose ironman reputations precede them. Among tight ends, Greg Olsen is considered the second-safest player. Keep in mind we only used data from 2012 to 2016 in this model, a period during which Olsen was as dependable as they come. The odds of each of these players getting injured is 50 percent lower than the average player. Obviously, anything that you can do to be more like these players in terms of health, you should do. But that's not especially illuminating.

Instead, let's move on to teams and coaches:

Figure 4. Highest and Lowest-Risk Team-Coach Combinations.

A few interesting things jump out here. First, Philadelphia and Miami each have two different coaches among the ten safest. That suggests something smart being done at the franchise level that transcends specific coaches. That could be the product of strong investments in sports science and analytics, careful monitoring of player workload, or any number of other factors. If you're looking to keep your team healthier, though, I'd start by talking to the hard-working folks of the Eagles and Dolphins. Playing for one of these low-risk combinations is associated with a 20 percent reduction in your odds of injury versus an average team and coach.

Then we have John Fox. Fox was among the safest coaches to play for in Denver, but one of the most dangerous in Chicago. The reason for this is unclear. It's possible some of Fox's habits that impact a player's injury risk changed drastically between Denver and Chicago, but there are other non-Fox-centric explanations: perhaps Chicago's roster carried a higher injury risk than Denver's, or there were differences in the effectiveness of the medical and training staffs or injury prevention efforts of the two franchises. It would be interesting to talk with Fox to get his thoughts.

Finally, Jeff Fisher was the third-safest coach in our model. So he's got that going for him.

Lastly, let's look at which stadiums are the safest and most dangerous:

Figure 5. Highest and Lowest-Risk Stadiums.

Playing a game in a safer stadium, such as San Francisco, versus a more dangerous one, such as Seattle, is worth about a 20 percent decrease in your odds of injury. Turf type may be the single biggest factor at play here; it accounts for about 30 percent of the variation in injury risk between stadiums.

The curves above are colored by stadium turf. The first thing you might notice is that stadiums with natural grass (green curves) tend to be safer than those with artificial turf (other colors). The 10 safest stadiums to play in all use natural grass, while the six most dangerous feature some form of artificial turf. One particular brand, FieldTurf, was installed exclusively in five NFL stadiums from 2012 to 2016; three of them (Seattle, Indianapolis, and Detroit) were among the eight most dangerous to play in. Turf type isn't the whole story, however. How that turf, including artificial turf, is maintained is at least as important. This is all consistent with an earlier analysis of turf and injuries here at FO.

Pittsburgh and Washington are considered by many to be two of the worst surfaces to play on in the NFL, but they are two of the three safest stadiums in our model. Despite being muddier these fields may actually be softer, lessening the forces on players' knees and ankles. That is just a guess without additional data, though.

San Francisco, meanwhile, has maintained a low injury risk across two different stadiums. Other teams might be wise to learn more about their grounds crew and maintenance procedures, though their climate helps, as well.

We noted above that about 30 percent of the variation across stadiums is due to turf type. The other 70 percent is due to other factors such as climate and, potentially, how risky it is to play the stadium's home team.

Is Seattle's stadium so dangerous not because of the turf or the weather, but because of the Legion of Boom (remember, the data are from 2012 to 2016)? The answer is a resounding "probably not." Here are the risks of offensive injuries when each opposing defense is on the road, to separate stadium from defensive effects:

Figure 6. Offensive Injury Risks by Opposing Defense When Offense is at Home.

Seattle ranks in bottom third of "injurious" defenses, so the Legion of Boom does not seem to be why Seattle's stadium looks bad. The defense that saw the most offensive injuries across the field was Washington, whose stadium ranked as the safest in our model. Miami and Philadelphia were the next most injurious defenses; their stadiums ranked as about average and the fifth-most dangerous in our model. Overall there does not seem to be a strong correlation between injurious defenses and dangerous stadiums.

Conclusions

NFL injuries are complicated and difficult to predict. We've shown that a player's team, coach, and even the stadium he's playing in combine with his own injury proneness and a healthy dose of luck to determine whether he makes it through the week. Hopefully we have also identified some coaches and stadiums that may be worth mirroring to reduce injury risk. After all, the NFL is always more fun with Aaron Rodgers, Allen Robinson, and Deshaun Watson than without them.