Practical analysis on performance, selection, and decision-making—for coaches, analysts, and athletes.
Expected Goals (xG) models continue to reshape how we understand football performance. This insight breaks down a recent Premier League match using xG data to reveal the story beyond the scoreline.
A practical metric to quantify how quickly teams turn recognition into action—combining time-to-good-option, execution latency, and ΔxT so coaches can train faster, smarter decisions.
Midfielders cover the most distance on the pitch and are critical to both defensive and attacking transitions. Predicting their fatigue involves monitoring workload metrics such as total distance covered, high-intensity sprints, heart rate variability, and recovery times. By combining GPS tracking data, match video analysis, and biometric indicators, coaches can anticipate when a midfielder is approaching physical or cognitive fatigue. This helps in making timely substitutions, adjusting tactical roles, and designing recovery protocols to maintain peak performance throughout the match and across the season.
Guards carry heavy offensive and defensive loads, and fatigue can lower shooting accuracy late in games. By combining shot chart data, biometric monitoring, and predictive models, teams can forecast shooting declines and adjust rotations or training accordingly. Future AI-powered tools may even detect fatigue in real time to preserve efficiency in high-pressure moments