Ever since there has been sport, there have been medical practitioners trying to keep those participating in sport healthy. From the ancient Greek physician Galen to modern-day sports scientists and everyone in between, the goal has been to identify causes of injury and prescribe interventions to avoid orthopedic ailments.

Much of the research to date has focused on describing specific injuries, identifying individual variables contributing to them, and examining the effectiveness of certain interventions at preventing the injuries in the first place.

This linear thought process—injury X is caused by variable Y and can be prevented by intervention Z—is rather intuitive, and the early academic findings were fruitful. The FIFA 11 program and others of its ilk have been shown to reduce ACL injuries in soccer athletes, in part by stimulating the knees’ proprioceptive capabilities. Likewise, Nordic curls are championed as a key component of any training program aimed at reducing hamstring injuries, as the eccentric forces robustly develop the strength of the muscle group. There are myriad other examples.

Yet, while the research has shown that many of these interventions are effective in relatively controlled settings—it’s virtually impossible to develop true double-blind, placebo-controlled randomized controlled trials in the exercise sciences for a plethora of reasons—the same results have failed to translate fully into the field. Injury occurrences are, at the very least anecdotally, increasing at alarming rates across multiple sports and at multiple levels, such as youth baseball and American professional women’s basketball, though quality epidemiological data is hard to come by for many sports.

It is my opinion that, as an industry, we should shift our focus from trying to isolate the influence of individual variables on injury occurrence to how these variables interact. Enter nonlinear systems.

Nonlinear systems are a branch of mathematics that, as the name implies, studies the seemingly random interactions between multiple variables (inputs) and how they give rise to seemingly random results (outputs) that aren’t necessarily proportional to the inputs. Perhaps the most well-known example of a nonlinear system is chaos theory, specifically the butterfly effect. Take it away, Dr. Ian Malcolm!

In short, in a linear system, A + B = C; increased medial elbow torque + increased pitcher fatigue = UCL tear. However, in a nonlinear system, to boil it down much too simply, A (increased medial elbow torque) + B (increased pitcher fatigue) might equal L (no UCL damage). And then the next time, A + B might equal Z (still no UCL injury). And then finally, the third time A + B might equal C (UCL tear). And the reason why each of these outputs is different is, on the surface, seemingly random.

What is interesting about nonlinear systems centers on why I used the adverb “seemingly” when describing the randomness at play. Because when these interactions are examined in granular detail, patterns emerge. And anytime a pattern or patterns are found, theoretically, predictions can be made. (More on this in a second.)

I say theoretically because, as of this writing, very little empirical research has been conducted on the use of nonlinear systems in athletic injury occurrence, though the field has been used quite extensively in other forms of epidemiology, most notably the spread of infectious disease.

(Quick aside: I’ve been using nonlinear systems, chaos theory, and the just-now linked agent-based modeling somewhat interchangably, but technically speaking they aren’t. The overarching category under which all these fall is termed complex systems. Nonlinear systems are a subcategory of complex systems, and chaos theory is a subcategory of nonlinear systems. Agent-based modeling is a simulation technique used to model complex systems.)

Chaos theory is a structural underpinning for weather prediction. A reason why meteorologists can predict that there is a 75% chance of rain on Friday is that their models, which have been fine-tuned over decades, account for all of the variables that contribute to the development of a storm and the myriad ways in which they interact. These models then form a prediction with a given confidence interval based on the underlying patterns that emerge from the simulation of such interactions.

All of this is to say, why can’t a similar approach be taken to predict athletic injury? This is something that my research team and I are examining, and we believe it is time that others in the field do so as well.

Take baseball pitching, for example. Below is a list of (some) variables that have been tied to UCL injuries in some way, shape, or form:

- Pitch count
- Medial elbow torque
- Reduced arm slot
- UCL elasticity
- Forearm muscle strength and fatigue
- Gapping between the bones of the inner elbow
- UCL thickening
- Point in the season
- Percentage of pitches that are fastballs
- Fastball velocity
- Degree of elbow flexion at ball release
- Humeral rotation velocity
- Shoulder abduction angle at foot strike
- Maximum shoulder external rotation range of motion at maximum layback
- Lateral ball release position
- Hip muscle weakness
- Decreased stride length compared to height
- Reduced trunk rotation range of motion
- Reduced shoulder range of motion
- Presence of scapular dyskinesis

Addressing each of these variables individually or in groups has not caused UCL injuries to fall. Instead, UCL injuries remain a significant burden in amateur and professional baseball. Instead of taking a linear approach to injury prevention—which MLB teams are insistently wanting to do, according to multiple league sources—perhaps a more fruitful endeavor would be to approach the UCL epidemic (and other sport injuries) nonlinearly.

Lucas Seehafer is an assistant professor in the Exercise Science department at Bethany Lutheran College in Mankato, Minnesota. He also holds adjunct lab instructor positions in the Doctor of Physical Therapy programs at Tufts University, Boston and the Medical University of South Carolina. He holds a PhD in Kinesiology and is a Doctor of Physical Therapy.