How coaches and sports teams are using computer vision to predict injuries and provide tailored workouts and practice drills to reduce the risk of injury
Eric Niiler / Wall Street Journal : Tweets: @stefaniei , @wsjscience , @edgeimpulse , and @chrismattmann Tweets: Stefanie Ilgenfritz / @stefaniei : “There are athletes that are treating their body like a business, and they've started to leverage data and information to better manage themselves.” The Future of Everything looks at the use of AI to prevent injury in athletes. https://www.wsj.com/... via @WSJ @wsjscience : Coaches and elite athletes are betting on new technologies that combine artificial intelligence with video to predict injuries before they happen and provide highly tailored workouts and practice drills that reduce the risk of getting hurt https://www.wsj.com/... @edgeimpulse : Computer vision will change the game in real-time analysis of athletes and sharpen training prescriptions, analytics experts say. https://www.wsj.com/... Chris Mattmann / @chrismattmann : “We will see way more athletes playing far longer and playing at the highest level far longer as well,” says one data expert who works with pro teams https://www.wsj.com/...
Context & Ripple Effects
This piece lands mid-arc in a slow build: machine learning had already been mapped across sports two years earlier for talent assessment and injury prediction (an early overview of ML in sports), and pro surfing was already using biomechanics data alongside wave forecasting to keep riders healthy (technology's role in professional surfing). What changed by mid-2022 is that computer vision moved from scouting novelty to a coaching tool — video plus AI now drives both injury prediction and individually tailored workouts and drills.
First-order effects
- Coaches and trainers at teams adopting these systems shift from generic conditioning programs to per-athlete prescriptions generated from video analysis, with athletes — described as treating their body like a business — gaining data leverage over their own training decisions.
Second-order effects
- Vendors of camera-based analysis become procurement targets for leagues, a path validated a year later when MLB partnered with Uplift Labs to flag player flaws and injury risk from two iPhone cameras (MLB's Uplift Labs partnership); wearable-first rivals face pressure to add vision capabilities or lose the coaching workflow.
Third-order effects
- Injury-prevention analytics follows the same adoption curve as hospital AI, where US hospitals already run predictive models to prioritize at-risk ER and ICU patients (predictive triage models in US hospitals) — suggesting a future where roster decisions and insurance pricing lean on algorithmic risk scores, and where consumer fitness obsessives building their own dashboards (hyperpersonalized AI training tools) pull the same tooling downmarket.
The trend: Elite sport is shifting from reactive injury treatment to camera-and-AI-driven prevention, moving from experimental pilots toward league-wide procurement and eventually consumer-grade training tools.