Rugby game performances and weekly workload: Using of data mining process to enter in the complexity

Area of Science:

• Sports Science
• Data Mining
• Performance Analysis

Background:

• Understanding professional rugby match performance is complex.
• Key performance indicators and workload influences require detailed analysis.
• Data mining offers novel approaches to sports performance assessment.

Purpose of the Study:

• Identify key performance indicators in professional rugby.
• Define synthetic performance indicators.
• Analyze the impact of weekly workload (2WL) on match performance throughout a season.

Main Methods:

• Collected workload, locomotor activity, and rugby-specific actions from 14 professional players.
• Utilized mixed-linear models to compare player activity with competition results.
• Employed principal component analysis and threshold-based data mining models.

Main Results:

• Defensive skills were identified as a fundamental factor in team performance.
• Locomotor activity was effectively summarized by two dimensions: total distance and high-speed/metabolic efforts, plus sprint/acceleration counts.
• Chronic physical impacts positively influenced the ability to win offensive 1-on-1 duels (p<0.05).

Conclusions:

• Defensive capabilities are paramount for success in professional rugby.
• A significant portion of player locomotor data can be represented by key metrics.
• Understanding the relationship between workload, physical impacts, and performance is crucial for optimizing player readiness and match outcomes.