DOI: 10.5061/dryad.5x69p8df7
title: Importance of linebreaks in rugby union: The case of a winning team

description: 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.
description: # Importance of Linebreaks In Rugby Union : The Case of a Winning Team
 [https://doi.org/10.5061/dryad.5x69p8df7](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