Capturing the Complex Relationship Between Internal and External Training Load: A Data-Driven Approach.

Background: Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling.

Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics.

In this study, we investigated the relationships between training load, perceived wellness and match performance in professional volleyball by applying the machine learning techniques XGBoost, random forest regression and subgroup discovery. Physical load data were obtained by manually logging all physical activities and using wearable sensors. Daily wellness of players was monitored using questionnaires.

Personalized machine learning approach to injury monitoring in elite volleyball players.

We implemented a machine learning approach to investigate individual indicators of training load and wellness that may predict the emergence or development of overuse injuries in professional volleyball. In this retrospective study, we collected data of 14 elite volleyball players (mean ± SD age: 27 ± 3 years, weight: 90.5 ± 6.

Training Distribution in 1500-m Speed Skating: A Case Study of an Olympic Gold Medalist.

Unlabelled: At the Olympic level, optimally distributing training intensity is crucial for maximizing performance.

Purpose: The authors evaluated the effect of training-intensity distribution on anaerobic power as a substitute for 1500-m speed-skating performance in the 4 y leading up to an Olympic gold medal.

Methods: During the preparation phase of the speed-skating season, anaerobic power was recorded periodically (n = 15) using the mean power (in watts) with a 30-s Wingate test.