There is a lot of science and data that goes into measuring injury risk.
Particularly in hockey, a prime example is the hip adduction-to-abduction strength ratio. A popular study from the early 2000’s showed that an adductor-to-abductor strength ratio below 0.8 increased the risk of groin injury by 17x. These findings have influenced how we measure injury risk in hockey players.
But believing that we understand injury risk based on a single number or test is an illusion. Here’s two reasons why.
#1 People create flawed stories to make sense of the world.
This is the narrative fallacy. We like to simplify narratives which aren’t always representative of the actual series of events. We also often only consider the events that happened, but not the ones that didn’t happen.
The Tyler study has led to a narrative that hip adduction-to-abduction strength ratio can show who is at risk of injury. Yet, other researchers have found that acute:chronic workload ratios have been related to risk of soft tissue injury. A more recent study found that all players with groin pain had adduction-to-abduction strength ratios greater than 0.8. Not to mention other important factors in injury such as bad luck, illness, stress and social factors. See where I’m going with this?
#2 The “I-knew-it-all-along” effect.
Hindsight is 20/20. Cliche but it’s true. It’s common to look back and point out the factors that led to poor outcomes. It’s easy to look at a player’s strength scores and say that he was weak and that’s why he got hurt. But is a strength ratio of 0.75 enough to hold an athlete back from playing because of his “increased risk of injury”? Or is a strength ratio of 0.82 enough to clear an athlete? With our poor ability to create narratives about the past, our outlook on the future is also flawed. With this hindsight bias, we can be too conservative or too risky when it comes to the idea of injury risk.
Hanging your hat on a single number to try to predict future injury risk seems too risky to me. Rather, having a set of criteria made up of different risk-factors will help with your decision-making process.
“What’s most important isn’t knowing the future, it’s knowing how to react appropriately to the information available at each point in time.” – Ray Dalio
Data shouldn’t be used as a predictor of success or failure, it should be used as a guide. It gives you a snapshot of the current state of an athlete. Collecting information over time will give you a profile of who they are and where they are going.
To better understand injury risk, we need to understand what factors influence injury. Once we identify these factors, we can track them as indicators of potential injury risk.
Here are a few ways to better understand an athlete’s injury risk.
Tip #1: Serial Monitoring. Having one instance of a test doesn’t give us much information. When we have more data points, we can follow the trends in internal and external changes.
Tip #2: Set thresholds. Next we need to set a threshold. Often, a 10%-20% reduction in a score is a flag. Not necessarily a red flag, but an indication to dive deeper into why things changed. This should lead to a conversation with the athlete to get to the bottom of things.
Tip #3: Adjust. Finally, once we identify the cause of change in a metric, we can make the necessary adjustments. Maybe it’s an extra rest day, maybe it’s extra work to restore their baseline strength levels.
Remember, with little information it’s easier to create a flawed narrative. When we look at trends in data, we can better understand an athlete and their risk of injury risk.