Longitudinal analysis of performance indicators in winning and losing teams in rugby union

Abstract

Analyzing performance indicators in rugby is essential for improving training programs and optimizing team strategies. A key method involves comparing these indicators between winning and losing teams to identify factors critical to success. However, much of the existing research tends to focus on single tournaments or short periods, lacking a systematic examination of long-term trends. This study analyzes performance indicator trends of winning and losing teams in English Premiership Rugby over multiple competitive seasons. Data from 1,001 matches spanning eight full seasons (2016–17 to 2023–24) were analyzed. Absolute (e.g., Score, Tries, Meters Run, Offloads, Turnovers Conceded) and relative (e.g., success rates for rucks, mauls, scrums, lineouts, tackles) indicators were recorded for winning and losing teams. Statistical methods included descriptive statistics, Mann– Whitney U test, Cohen’s d effect size, and linear regression analysis to identify significant differences and trends. Results revealed that scoring actions (Score, Tries, Conversions) were the most significant indicators consistently differentiating winning from losing teams over the eight seasons, demonstrating high statistical significance and large effect sizes (Cohen’s d = 1.0–1.6). Meters Run was also consistently higher for winning teams (significant in 7/8 seasons), while Turnovers Conceded were consistently higher for losing teams (significant in 6/8 seasons). Notably, a statistically significant increasing trend in the difference for Offloads between winning and losing teams was observed over the analyzed period (R2=0.71, p<0.05). Some indicators, like Total Lineouts, showed no consistent differences across all seasons. This study identified both stable and evolving performance indicators that differentiate winning and losing teams in elite rugby over an extended period. The findings offer practical value for coaches, analysts, and sports scientists in strategic planning and highlight the dynamic evolution of the game, particularly the increasing importance of offloads, underscoring the need for continuous updates in training methods and performance monitoring systems.