Performance Indicators in Rugby Union

In 2012, Michael T Hughes, Michal M Hughes, Jason Williams, Nic James, Goran Vuckovic and Duncan Locke wrote an insightful academic journal discussing the performance indicators in rugby union during the 2011 World Cup. They gathered various materials from professional analysts working for coaches and player at the World Cup event, and verified the reliability and accuracy of their data against video footage from different matches.

READ FULL JOURNAL HERE

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This research study analyses the influence of the following key performance indicators in the final outcome of a game:

Scoring Indicators: 

  • Points scored

    • Total points scored in WWC 2011
    • Points scored per game
    • Points scored agains Tier A teams
    • Points scored per game against Tier A teams
  • Tries scored

    • Total tries scored in WWC 2011
    • Tries scored per game
    • Tries scored from set pieces
    • Percentage of tries scored from set piece
    • Tries scored from set pieces per game
    • Tries scored from broken play
    • Percentage of tries scored from broken play
    • Tries scored from broken play per game

Quality Indicators:

  • Total Possession - Times and Productivity

    • Minutes that ball is in play in the match
    • Rest minutes in the match
    • Minutes with possession in the match
    • Percentage of time with possession
    • Number of possessions in the match
    • Minutes per possession
    • Minutes of possession per point scored
    • Number of possessions per point scored
    • Minutes of possession per try scored
    • Number of possessions per try scored
    • Total number of line breaks
    • Total number of line breaks per game
    • Minutes of possession per line break
    • Number of possessions per line break
    • Total number of set piece line breaks
    • Total number of set piece line breaks per game
    • Percentage of set piece line breaks
    • Total number of broken play line breaks
    • Total number of broken play line breaks per game
    • Percentage of broken play line breaks
    • Number of phases in the match
    • Percentage of phases per possession
    • Attacking penalties won
  • Attacking Possession

    • Number of possessions in opposition's 22 line
    • Number of converted possessions in opposition's 22 line
    • Percentage of converted possessions in opposition's 22 line
    • Number of points from opposition's 22 line
    • Number of points from opposition's 22 line per game
    • Number of points per possession in opposition's 22 line
  • Kicking game

    • Total number of kicks at goal
    • Total number of kicks converted
    • Percentage of kicks converted
    • Penalties conceded

While these key performance indicators of a rugby union game or tournament can be useful to summarize the some elements of a team's performance, what M. Hughes et al (2012) found was the there was little correlation between each individual metric, or set of metrics, with the final outcome of the World Cup 2011 tournament. For example, France was identified as one of the worst teams in most of these metrics, though they were the runners-up of the tournament.

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The paper also touches on the challenges individual player performance analysis in rugby union. Due to the nature of the sport, a specific position on the field will require its own set of performance indicators. The study suggests to analyse an individuals performance against common key performance indicators and use that individual's performance profile to run intra-position comparisons (Hughes et al, 2012). This also leads to the creation of position profiles, where strengths and weaknesses of players playing in each position can be identified. It is also suggested that the individual player profiles should be based in the context of the team's profile as well as the opposition team's strengths and weaknesses, as these elements will impact a player's performance profiling.

Similarly to most team sports, randomness and luck can play a big part in the final outcome of a rugby union match. Therefore, predicting the performance of a team based on a few data points might not be enough to correlate it to the final performance achieved by that team. There are many complex interactions that occur during a rugby union game between teammates and oppositions which are difficult to account for through today's available statistics. However, studies like the one carried out by Hughes et al (2012) are another step towards narrowing down the best procedures to follow to successfully apply analytics to rugby performance predictions and team sports in general.