Team Performance Indicators That Predict Match Outcome in Rugby Union

Year 2024, , 203 - 216, 29.04.2024

Oleh Kvasnytsya , Valeria Tyshchenko , Mykola Latyshev , Iryna Kvasnytsya , Mykola Kirsanov , Oleg Plakhotniuk , Maksym Buhaiov

https://doi.org/10.54141/psbd.1342340

Abstract

The aim of the study is to identify the most significant indicators of the national team's performance at the European Rugby Championships 15 and to design a model for predicting the outcomes of matches. Data was collected from teams’ performance at the European Rugby 15 Championships 2021, 2022 and 2023 for the analysis. The total number of matches was 41. All indicators presented in the official reports were taken: 22 for the home and away teams. The analysis of the team results was carried out according to all indicators: mean value, standard deviation, and test were used to compare the performance indicators of the winning and losing teams. Machine learning techniques were utilized to develop a predictive model for match outcomes. On one hand, 15 indicators (68.2%) are higher for teams that won (winning teams). On the other hand, 7 (31.8%) indicators are higher for teams that lost. The difference between the teams' means varies from -56.46% (the minus indicates that this indicator is higher for the teams that lost) to 273.68%. Based on the results, the Random Forest Classifier and Extra Trees Classifier algorithms have the best prediction accuracy (0.92). The most significant indicators of team performance that affect the final result of the match are tries (196.3% – the difference between the average values of winning and losing teams), conversions (176.7%), missed tackles (- 56.46%), offload (126.3%). Based on the data obtained, refining the team training process in Rugby 15 is possible.

Keywords

European Championship, Indicator, Machine learning, Prediction, Rugby

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