Descriptive conversion of performance indicators in rugby union

Journal of Science and Medicine in Sport
Mark BennettLiam P Kilduff

Abstract

The primary aim of this study was to examine whether accuracy of rugby union match prediction outcomes differed dependent on the method of data analysis (i.e., isolated vs. descriptively converted or relative data). A secondary aim was to then use the most appropriate method to investigate the performance indicators (PI's) most relevant to match outcome. Data was 16 PI's from 127 matches across the 2016-17 English Premiership rugby season. Given the binary outcome (win/lose), a random forest classification model was built using these data sets. Predictive ability of the models was further assessed by predicting outcomes from data sets of 72 matches across the 2017-18 season. The relative data model attained a balanced prediction rate of 80% (95% CI - 75-85%) for 2016-17 data, whereas the isolated data model only achieved 64% (95% CI - 58-70%). In addition, the relative data model correctly predicted 76% (95% CI - 68-84%) of the 2017-18 data, compared with 70% (95% CI - 63-77%) for the isolated data model. From the relative data model, 10 PI's had significant relationships with game outcome; kicks from hand, clean breaks, average carry distance, penalties conceded when the opposition have the ball, turnovers conceded, total metr...Continue Reading

Citations

Jan 16, 2020·Sports Medicine - Open·Carmen M E ColomerBenjamin G Serpell
Jun 4, 2020·Scandinavian Journal of Medicine & Science in Sports·Stephanie L LazarczukNeil E Bezodis
Aug 21, 2020·Journal of Athletic Training·Stephen W WestKeith A Stokes
Mar 17, 2020·European Journal of Sport Science·Mark BennettLiam P Kilduff
Apr 17, 2021·Journal of Science and Medicine in Sport·Luke Nicholas WoodhouseMark Waldron
Jul 24, 2021·The Journal of Sports Medicine and Physical Fitness·Kenji DomaJonathan D Connor

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