Workload Woes

How advancements in motion capture and other tech will bring in a new era of workload management

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Load management.

Team execs, league execs, and fans alike shudder just hearing the term. That’s because it’s become synonymous- at least in the public eye- with unnecessarily taking days off.

And for fans wanting to see stars play, or league offices eyeing sustained growth via long-term fan engagement, players resting when they could play is bad for business.

But what if we moved beyond equating load management with rest days?

What if load management was simply a desire to create situations where each athlete could better manage the physical demands placed on them throughout the season?

What if load management involved:

• A deeper understanding of workload,

• Continuous monitoring- looking not only at headline numbers, but also at more granular metrics from 3D player tracking, and

• A data driven approach to preparation.

Understanding Workload

Definitionally, overuse injuries occur when workloads are too high. Still, given the adaptability of the human body, it is unclear if reducing workload actually lowers the risk of overuse injuries…because it is unclear how workload is actually defined.

In baseball, we could be talking about innings pitched, pitches thrown, effort on individual throws, time between appearances, etc.

In basketball, we have the same issue. A minute played is not simply a minute played, with different situations and different athletes causing wide variation in stress per minute.

This lack of clarity results in the industry taking a surface level view of workload, which creates reasonable skepticism we’re managing it correctly.

This skepticism includes 1) an NBA report finding “no link between load-managed players and a decreased risk of injury,” and 2) a Twitter thread from Whoop’s Justin Perline (formerly a Pirate’s Senior Analyst) arguing that workload has “no evidence of meaningfulness”.

To be clear- that doesn’t mean monitoring workload won’t yield better results. It just means we aren’t looking at the right things.

Fortunately, today’s tools can help us better understand the stress athletes put on their bodies.

And stress, not minutes played or innings pitched, is what teams should be concerned about.

Continuous Monitoring with 3D Player Tracking

Major League Baseball and the NBA each have Hawk-Eye systems installed in every stadium, tracking 29 key points on every athlete throughout the game. (The NHL and NFL will likely follow suit.)

With today’s technology, MLB front offices can dive into a pitcher’s delivery, understanding how biomechanics impacts injury risk (and performance).

NBA franchises can do the same, analyzing a player’s sprinting, cutting, and jumping patterns, measuring each movement’s impact on fatigue, stress, and injury risk- all down to the body part.

Tracking how long an athlete was in a game used to be a reasonable stress indicator. It no longer is.

A Data Driven Approach to Preparation

While in-game monitoring unlocks seemingly endless possibilities, allowing us to dig into movement efficiency, spot deviations, and track workload, it is only part of the process.

On top of in-game metrics, we should have a data driven approach to practice. Whether it be individual teams tracking fitness metrics throughout the year, league-wide initiatives to better understand injury risk, or external tech companies like Springbok Analytics offering insights into an athlete’s muscle composition, we need to add this data to what we see in-game to guide organizational decision making- helping us decide 1) what to do in the weight room 2) how to practice 2) when to rest.

Where Do We Go From Here?

We (the royal we) are not going to “solve” injuries. The world’s best athletes are also the world’s fiercest competitors. As long as this is true, overuse injuries will occur- as they are the cost of max effort over time.

With that said, as technology improves, we can (and need to) do more to enhance workload monitoring. Specifically, we can:

• Stop living at the extremes. Rest isn’t inherently good. Nor is it bad. If players push their bodies beyond their capabilities, injuries will follow. But that can happen due to too much work, or from a lack of preparation.
  

• Start personalizing recommendations. Now that we have the ability to 1) quantify stress on various body parts 2) spot deviations as signs of fatigue, and 3) understand an athlete’s unique ability to handle stress, workload monitoring best practices need to evolve.
  
  By making use of both game and practice data, we can move beyond a “less is more” philosophy and co-develop personalized plans with the athlete.
  

• Control what we can control. Blaming velocity for pitching injuries is the same as claiming NBA players get hurt because they run fast and jump high. While technically true, pointing to the exact things that correlate with winning is not helpful- at least not at the individual level.
  
  Teams are best off with healthy, high-performing athletes. Since this is what the athlete wants as well, they need to work together to build a plan 1) the athlete supports and 2) that aids in both performance increases and injury risk decreases.
  
  (Yes, this is easier said than done. And it is why tech can’t replace coaches- the experts who can marry the art and the science. Rather, tech should be designed to empower them.)
  

• Realize we can’t control everything. Despite entering a world of endless data, there are still no guarantees. Players will test limits. They will get hurt.
  
  While coaches, trainers, and front offices can’t prevent this, it is their job to improve the odds as much as possible. And it is our job- and every tech company’s job- to make that task just a bit easier.