How AI is revolutionising athletic recovery and performance

Individual data shows that while sleep and soreness matter, the key to recovery differs for each athlete.
Machine learning reveals that recovery predictors in athletes vary significantly, highlighting the need for personalised strategies.

Researchers at the University of Auckland’s Sports Performance Research Institute New Zealand have used machine learning to delve into athletic recovery. They tracked 43 endurance athletes, gathering extensive data on sleep, diet, heart-rate variability, and workout routines. The study revealed that while certain factors like sleep quality and muscle soreness broadly influence recovery, the most effective predictors vary from person to person.

For instance, sleep data might be a strong indicator for one athlete, while for another, protein intake and muscle soreness could be more relevant. A simpler model using just a few variables performed nearly as well as more complex ones, emphasising that not all factors are equally important for every athlete. However, the effectiveness of predictions significantly improved when tailored to individual data.

The study also examined heart-rate variability (HRV) but found that predicting HRV changes based on controllable factors, like training load and diet, proved challenging. Although HRV is often used as a gauge for readiness to train, the researchers concluded that its predictive value might be limited.

Ultimately, the research underscores the importance of personalised recovery strategies. While broad patterns exist, the best approach to recovery seems to hinge on understanding the unique factors that impact each athlete individually.