Importance of linebreaks in rugby union: The case of a winning team
Data files
Feb 12, 2025 version files 4.70 KB
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data_article.csv
2.63 KB
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README.md
2.06 KB
Abstract
The evolving landscape of rugby union, driven by rule changes that promote faster and more dynamic gameplay, has altered the significance of performance indicators (KPIs) in evaluating team success. In this study, we analyze 34 rugby games from the 2018/2019 season, focusing on a consistently successful team, to examine the impact of linebreaks as a critical KPI in modern rugby. We differentiate across dynamic linebreaks, occurring in open and unstructured phases of play, and static linebreaks, which emerge from set pieces like scrums and lineouts. Using detailed match data and video analysis, we found that dynamic linebreaks significantly contributed to larger score differences between opposing teams, regardless of match location, highlighting their impact on the team’s victories. Conversely, static linebreaks proved more effective during home games, indicating a potential advantage in executing structured plays in familiar settings. These findings emphasize the importance of dynamic play in modern rugby and offer practical insights for coaches and performance analysts to refine strategies that maximize offensive opportunities. By focusing on dynamic phases, teams can enhance their chances of success in all match environments, providing a strategic edge in professional rugby.
https://doi.org/10.5061/dryad.5x69p8df7
Description of the data and file structure
The dataset contains key performance indicators KPI collected on 34 rugby union matches. From each single match we calculated the differences between opposing teams in the value of each KPI. Those differences are indicated in the dataset as "XXX_diff" for each KPI! The dataset also contains the differences in points scored (the response variable) and a categorical variable indicating the site (home vs away).
Files and variables
File: data_article.csv
Description:
Variables
- place: home vs away
- point_diff: the differences in score
- ruck: the number of ruck performed by a team
- ruck_opp: the number of ruck performed by the opponent
- ruck*diff: ruck - ruck_*opp
- lbk: the number of linebreaks performed by a team
- lbk_opp: the number of linebreaks performed by the opponent
- lbk*diff: lbk - lbk_*opp
- static_play_lbk: the number of statics linebreaks performed by a team
- static_play_lbk_opp: the number of statics linebreaks performed by the opponent
- static_play_lbk_diff: static_play_lbk - static_play_lbk_opp
- dynamic_play_lbk: the number of dynamics linebreaks performed by a team
- dynamic_play_lbk_opp: the number of dynamics linebreaks performed by the opponent
- dynamic_play_lbk2_diff: dynamic_play_lbk - dynamic_play_lbk2_diff
- ball_lost: the number of ball lost by a team
- ball_lost_opp: the number of ball lost by the opponent
- ball_lost_diff: ball_lost - ball_lost_opp
- foul: the number of fouls of a team
- foul_opp: the number of fouls of the opponent
- foul_diff: foul - foul_opp
- perc_poss: the percentage of possessions of a team
- perc_poss_opp: the percentage of possession of the opponent
- perc*possdiff: perc_poss - perc_*poss_opp
Access information
Other publicly accessible locations of the data:
- none
Data was derived from the following sources:
- video analysis of matches