Team Performance Indicators That Predict Match Outcome in Rugby Union
Yıl 2024,
Cilt: 15 Sayı: 1, 203 - 216, 29.04.2024
Oleh Kvasnytsya
,
Valeria Tyshchenko
,
Mykola Latyshev
,
Iryna Kvasnytsya
,
Mykola Kirsanov
,
Oleg Plakhotniuk
,
Maksym Buhaiov
Öz
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.
Kaynakça
- Bennett, M., Bezodis, N., Shearer, D. A., Locke, D., & Kilduff, L. P. (2019). Descriptive conversion of performance indicators in rugby union. Journal of science and medicine in
sport, 22(3), 330-334. https://doi.org/10.1016/j.jsams.2018.08.008
- Bompa, T., & Buzzichelli, C. (2015). Periodization training for sports, 3e. Human kinetics.
- Bremner, S., Robinson, G., & Williams, M. D. (2013). A retrospective evaluation of team performance indicators in rugby union. International Journal of Performance Analysis in
Sport, 13(2), 461-473. https://doi.org/10.1080/24748668.2013.11868662
- Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied computing and informatics, 15(1), 27-33. https://doi.org/10.1016/j.aci.2017.09.00
- Bunker, R., & Susnjak, T. (2022). The application of machine learning techniques for predicting match results in team sport: A review. Journal of Artificial Intelligence Research, 73, 1285-1322. https://doi.org/10.48550/arXiv.1912.11762
- Colomer, C. M., Pyne, D. B., Mooney, M., McKune, A., & Serpell, B. G. (2020). Performance analysis in rugby union: a critical systematic review. Sports Medicine-Open, 6, 1-15. https://doi.org/10.1186/s40798-019-0232-x
- Dindorf, C., Bartaguiz, E., Gassmann, F., & Fröhlich, M. (2022). Conceptual structure and current trends in Artificial Intelligence, Machine Learning, and Deep Learning research in sports: A bibliometric review. International Journal of Environmental Research and Public Health, 20(1), 173. https://doi.org/10.3390/ijerph20010173
- Fontana, F. Y., Colosio, A. L., Da Lozzo, G., & Pogliaghi, S. (2017). Player’s success prediction in rugby union: From youth performance to senior level placing. Journal of science and medicine in sport, 20(4), 409-414. https://doi.org/10.1016/j.jsams.2016.08.017
- Gabbett, T. J., Jenkins, D. G., & Abernethy, B. (2011). Relative importance of physiological, anthropometric, and skill qualities to team selection in professional rugby league. Journal of Sports Sciences, 29(13), 1453-1461. https://doi.org/10.1080/02640414.2011.603348
- Hopkins, W. G., Hawley, J. A., & Burke, L. M. (1999). Design and analysis of research on sport performance enhancement. Medicine and science in sports and exercise, 31(3), 472-485. https://doi.org/10.1097/00005768-199903000-00018
- Horvat, T., & Job, J. (2020). The use of machine learning in sport outcome prediction: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5), e1380. https://doi.org/10.1002/widm.1380
- Jones, M. R., West, D. J., Harrington, B. J., Cook, C. J., Bracken, R. M., Shearer, D. A., & Kilduff, L. P. (2014). Match play performance characteristics that predict post-match creatine kinase responses in professional rugby union players. BMC sports science, medicine and rehabilitation, 6(1), 1-7. https://doi.org/10.1186/2052-1847-6-38
- Jones, N. M., Mellalieu, S. D., & James, N. (2004). Team performance indicators as a function of winning and losing in rugby union. International Journal of Performance Analysis in Sport, 4(1), 61-71. https://doi.org/10.1080/24748668.2004.11868292
- Latyshev, M., Latyshev, S., Korobeynikov, G. Kvasnytsya, O., Shandrygos, V., & Dutchak, Y. (2020). The analysis of the results of the Olympic free-style wrestling champions.
Journal of Human Sport and Exercise, 15 (2), 400-410. https://doi.org/10.14198/jhse.2020.152.14
- Latyshev, M., Tropin, Y., Podrigalo, L., & Boychenko, N. (2022). Analysis of the Relative Age Effect in Elite Wrestlers. Ido movement for culture. Journal of Martial Arts Anthropology, 22(3), 28-32. https://doi.org/10.15391/ed.2023-3.10
- McGarry, T. (2009). Applied and theoretical perspectives of performance analysis in sport: Scientific issues and challenges. International Journal of Performance Analysis in Sport, 9(1), 128-140. https://doi.org/10.1080/24748668.2009.11868469
- O’Donoghue, P., & Williams, J. (2004). An evaluation of human and computer-based predictions of the 2003 rugby union world cup. International Journal of Computer Science in Sport, 3(1), 5-22 https://doi.org/10.1515/ijcss-2016-0003.
- Ortega, E., Villarejo, D., & Palao, J. M. (2009). Differences in game statistics between winning and losing rugby teams in the Six Nations Tournament. Journal of sports science & medicine, 8(4), 523. PMID: 24149592
- Paolini, S., Bazzini, M. C., Rossini, M., De Marco, D., Nuara, A., Presti, P., ... & Fabbri-Destro, M. (2023). Kicking in or kicking out? The role of the individual motor expertise in predicting the outcome of rugby actions. Frontiers in Psychology, 14, 797. https://doi.org/10.3389/fpsyg.2023.1122236
- Parmar, N., James, N., Hughes, M., Jones, H., & Hearne, G. (2017). Team performance indicators that predict match outcome and points difference in professional rugby league. International Journal of Performance Analysis in Sport, 17(6), 1044-1056. https://doi.org/10.1080/24748668.2017.1419409
- Reed, D., & O’Donoghue, P. (2005). Development and application of computer-based prediction methods. International Journal of Performance Analysis in Sport, 5(3), 12-28.
https://doi.org/10.1080/24748668.2005.11868334
- Richter, C., O’Reilly, M., & Delahunt, E. (2021). Machine learning in sports science: challenges and opportunities. Sports Biomechanics, 1-7. https://doi.org/10.1080/14763141.2021.1910334
- Rizi, R. M., Yeung, S. S., Stewart, N. J., & Yeung, E. W. (2017). Risk factors that predict severe injuries in university rugby sevens players. Journal of science and medicine in sport, 20(7), 648-652. https://doi.org/10.1016/j.jsams.2016.11.022
- Romanenko, V., Piatysotska, S., Tropin, Y., Rydzik, Ł., Holokha, V., & Boychenko, N. (2022). Study of the reaction of the choice of combat athletes using computer technology. Slobozhanskyi Herald of Science & Sport, (4), 97-103. https://doi.org/10.15391/snsv.2022-4.001
- Sampaio, J., & Leite, N. (2013). Performance indicators in game sports. In Routledge handbook of sports performance analysis (pp. 115-126). Routledge.
- Sasaki, K., Furukawa, T., Murakami, J., Shimozono, H., Nagamatsu, M., Miyao, M., Yamamoto, T., Watanabe, I., Yasugahira, H., Saito T., Ueno, Y., Katsuta, T.& Kono, I.
(2007). Scoring profiles and defense performance analysis in Rugby Union. International Journal of Performance Analysis in Sport, 7(3), 46-53. https://doi.org/10.1080/24748668.2007.11868409
- Schoeman, R., & Schall, R. (2019). Comparison of match-related performance indicators between major professional rugby competitions. International Journal of Sports Science & Coaching, 14(3), 344-354. https://doi.org/10.1177/1747954119848419
- Stekler, H. O., Sendor, D., & Verlander, R. (2010). Issues in sports forecasting. International Journal of Forecasting, 26(3), 606-621. https://doi.org/10.1016/j.ijforecast.2010.01.003
- Tee, J. C., Klingbiel, J. F., Collins, R., Lambert, M. I., & Coopoo, Y. (2016). Preseason Functional Movement Screen component tests predict severe contact injuries in professional rugby union players. Journal of strength and conditioning research, 30(11), 3194-3203. https://doi.org/10.1519/JSC.0000000000001422
- Thomas, J. R., Martin, P., Etnier, J. L., & Silverman, S. J. (2022). Research methods in physical activity. Human kinetics.
- Till, K., Cobley, S., O’Hara, J., Brightmore, A., Cooke, C., & Chapman, C. (2011). Using anthropometric and performance characteristics to predict selection in junior UK Rugby League players. Journal of Science and Medicine in Sport, 14(3), 264-269. https://doi.org/10.1016/j.jsams.2011.01.006
- Toselli, S., Merni, F., & Campa, F. (2019). Height prediction in elite Italian rugby players: A prospective study. American Journal of Human Biology, 31(5), e23288. https://doi.org/10.1002/ajhb.23288.
- Travassos, B., Davids, K., Araújo, D., & Esteves, T. P. (2013). Performance analysis in team sports: Advances from an Ecological Dynamics approach. International journal of
performance analysis in sport, 13(1), 83-95. https://doi.org/10.1080/24748668.2013.11868633
- Vahed, Y., Kraak, W., & Venter, R. (2016). Changes on the match profile of the South African Currie Cup tournament during 2007 and 2013. International Journal of Sports Science & Coaching, 11(1), 85-97. https://doi.org/10.1177/1747954115624826.
- Veal, A. J., & Darcy, S. (2014). Research methods in sport studies and sport management: A practical guide. Routledge. https://doi.org/10.4324/9781315776668
- Watson, N., Durbach, I., Hendricks, S., & Stewart, T. (2017). On the validity of team performance indicators in rugby union. International Journal of Performance Analysis in
Sport, 17(4), 609-621. https://doi.org/10.1080/24748668.2017.1376998
- Wunderlich, F., & Memmert, D. (2021). Forecasting the outcomes of sports events: A review. European Journal of Sport Science, 21(7), 944-957. https://doi.org/10.1080/17461391.2020.1793002
- Xu, X. Q., Korobeynikov, G., Han, W., Dutchak, M., Nikonorov, D., Zhao, M., & Mischenko, V. (2023). Analysis of phases and medalists to women’s singles matches in badminton at the Tokyo 2020 Olympic Games. Slobozhanskyi Herald of Science and Sport, 27(2), 64-69. https://doi.org/10.15391/snsv.2023-2.002
Yıl 2024,
Cilt: 15 Sayı: 1, 203 - 216, 29.04.2024
Oleh Kvasnytsya
,
Valeria Tyshchenko
,
Mykola Latyshev
,
Iryna Kvasnytsya
,
Mykola Kirsanov
,
Oleg Plakhotniuk
,
Maksym Buhaiov
Kaynakça
- Bennett, M., Bezodis, N., Shearer, D. A., Locke, D., & Kilduff, L. P. (2019). Descriptive conversion of performance indicators in rugby union. Journal of science and medicine in
sport, 22(3), 330-334. https://doi.org/10.1016/j.jsams.2018.08.008
- Bompa, T., & Buzzichelli, C. (2015). Periodization training for sports, 3e. Human kinetics.
- Bremner, S., Robinson, G., & Williams, M. D. (2013). A retrospective evaluation of team performance indicators in rugby union. International Journal of Performance Analysis in
Sport, 13(2), 461-473. https://doi.org/10.1080/24748668.2013.11868662
- Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied computing and informatics, 15(1), 27-33. https://doi.org/10.1016/j.aci.2017.09.00
- Bunker, R., & Susnjak, T. (2022). The application of machine learning techniques for predicting match results in team sport: A review. Journal of Artificial Intelligence Research, 73, 1285-1322. https://doi.org/10.48550/arXiv.1912.11762
- Colomer, C. M., Pyne, D. B., Mooney, M., McKune, A., & Serpell, B. G. (2020). Performance analysis in rugby union: a critical systematic review. Sports Medicine-Open, 6, 1-15. https://doi.org/10.1186/s40798-019-0232-x
- Dindorf, C., Bartaguiz, E., Gassmann, F., & Fröhlich, M. (2022). Conceptual structure and current trends in Artificial Intelligence, Machine Learning, and Deep Learning research in sports: A bibliometric review. International Journal of Environmental Research and Public Health, 20(1), 173. https://doi.org/10.3390/ijerph20010173
- Fontana, F. Y., Colosio, A. L., Da Lozzo, G., & Pogliaghi, S. (2017). Player’s success prediction in rugby union: From youth performance to senior level placing. Journal of science and medicine in sport, 20(4), 409-414. https://doi.org/10.1016/j.jsams.2016.08.017
- Gabbett, T. J., Jenkins, D. G., & Abernethy, B. (2011). Relative importance of physiological, anthropometric, and skill qualities to team selection in professional rugby league. Journal of Sports Sciences, 29(13), 1453-1461. https://doi.org/10.1080/02640414.2011.603348
- Hopkins, W. G., Hawley, J. A., & Burke, L. M. (1999). Design and analysis of research on sport performance enhancement. Medicine and science in sports and exercise, 31(3), 472-485. https://doi.org/10.1097/00005768-199903000-00018
- Horvat, T., & Job, J. (2020). The use of machine learning in sport outcome prediction: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5), e1380. https://doi.org/10.1002/widm.1380
- Jones, M. R., West, D. J., Harrington, B. J., Cook, C. J., Bracken, R. M., Shearer, D. A., & Kilduff, L. P. (2014). Match play performance characteristics that predict post-match creatine kinase responses in professional rugby union players. BMC sports science, medicine and rehabilitation, 6(1), 1-7. https://doi.org/10.1186/2052-1847-6-38
- Jones, N. M., Mellalieu, S. D., & James, N. (2004). Team performance indicators as a function of winning and losing in rugby union. International Journal of Performance Analysis in Sport, 4(1), 61-71. https://doi.org/10.1080/24748668.2004.11868292
- Latyshev, M., Latyshev, S., Korobeynikov, G. Kvasnytsya, O., Shandrygos, V., & Dutchak, Y. (2020). The analysis of the results of the Olympic free-style wrestling champions.
Journal of Human Sport and Exercise, 15 (2), 400-410. https://doi.org/10.14198/jhse.2020.152.14
- Latyshev, M., Tropin, Y., Podrigalo, L., & Boychenko, N. (2022). Analysis of the Relative Age Effect in Elite Wrestlers. Ido movement for culture. Journal of Martial Arts Anthropology, 22(3), 28-32. https://doi.org/10.15391/ed.2023-3.10
- McGarry, T. (2009). Applied and theoretical perspectives of performance analysis in sport: Scientific issues and challenges. International Journal of Performance Analysis in Sport, 9(1), 128-140. https://doi.org/10.1080/24748668.2009.11868469
- O’Donoghue, P., & Williams, J. (2004). An evaluation of human and computer-based predictions of the 2003 rugby union world cup. International Journal of Computer Science in Sport, 3(1), 5-22 https://doi.org/10.1515/ijcss-2016-0003.
- Ortega, E., Villarejo, D., & Palao, J. M. (2009). Differences in game statistics between winning and losing rugby teams in the Six Nations Tournament. Journal of sports science & medicine, 8(4), 523. PMID: 24149592
- Paolini, S., Bazzini, M. C., Rossini, M., De Marco, D., Nuara, A., Presti, P., ... & Fabbri-Destro, M. (2023). Kicking in or kicking out? The role of the individual motor expertise in predicting the outcome of rugby actions. Frontiers in Psychology, 14, 797. https://doi.org/10.3389/fpsyg.2023.1122236
- Parmar, N., James, N., Hughes, M., Jones, H., & Hearne, G. (2017). Team performance indicators that predict match outcome and points difference in professional rugby league. International Journal of Performance Analysis in Sport, 17(6), 1044-1056. https://doi.org/10.1080/24748668.2017.1419409
- Reed, D., & O’Donoghue, P. (2005). Development and application of computer-based prediction methods. International Journal of Performance Analysis in Sport, 5(3), 12-28.
https://doi.org/10.1080/24748668.2005.11868334
- Richter, C., O’Reilly, M., & Delahunt, E. (2021). Machine learning in sports science: challenges and opportunities. Sports Biomechanics, 1-7. https://doi.org/10.1080/14763141.2021.1910334
- Rizi, R. M., Yeung, S. S., Stewart, N. J., & Yeung, E. W. (2017). Risk factors that predict severe injuries in university rugby sevens players. Journal of science and medicine in sport, 20(7), 648-652. https://doi.org/10.1016/j.jsams.2016.11.022
- Romanenko, V., Piatysotska, S., Tropin, Y., Rydzik, Ł., Holokha, V., & Boychenko, N. (2022). Study of the reaction of the choice of combat athletes using computer technology. Slobozhanskyi Herald of Science & Sport, (4), 97-103. https://doi.org/10.15391/snsv.2022-4.001
- Sampaio, J., & Leite, N. (2013). Performance indicators in game sports. In Routledge handbook of sports performance analysis (pp. 115-126). Routledge.
- Sasaki, K., Furukawa, T., Murakami, J., Shimozono, H., Nagamatsu, M., Miyao, M., Yamamoto, T., Watanabe, I., Yasugahira, H., Saito T., Ueno, Y., Katsuta, T.& Kono, I.
(2007). Scoring profiles and defense performance analysis in Rugby Union. International Journal of Performance Analysis in Sport, 7(3), 46-53. https://doi.org/10.1080/24748668.2007.11868409
- Schoeman, R., & Schall, R. (2019). Comparison of match-related performance indicators between major professional rugby competitions. International Journal of Sports Science & Coaching, 14(3), 344-354. https://doi.org/10.1177/1747954119848419
- Stekler, H. O., Sendor, D., & Verlander, R. (2010). Issues in sports forecasting. International Journal of Forecasting, 26(3), 606-621. https://doi.org/10.1016/j.ijforecast.2010.01.003
- Tee, J. C., Klingbiel, J. F., Collins, R., Lambert, M. I., & Coopoo, Y. (2016). Preseason Functional Movement Screen component tests predict severe contact injuries in professional rugby union players. Journal of strength and conditioning research, 30(11), 3194-3203. https://doi.org/10.1519/JSC.0000000000001422
- Thomas, J. R., Martin, P., Etnier, J. L., & Silverman, S. J. (2022). Research methods in physical activity. Human kinetics.
- Till, K., Cobley, S., O’Hara, J., Brightmore, A., Cooke, C., & Chapman, C. (2011). Using anthropometric and performance characteristics to predict selection in junior UK Rugby League players. Journal of Science and Medicine in Sport, 14(3), 264-269. https://doi.org/10.1016/j.jsams.2011.01.006
- Toselli, S., Merni, F., & Campa, F. (2019). Height prediction in elite Italian rugby players: A prospective study. American Journal of Human Biology, 31(5), e23288. https://doi.org/10.1002/ajhb.23288.
- Travassos, B., Davids, K., Araújo, D., & Esteves, T. P. (2013). Performance analysis in team sports: Advances from an Ecological Dynamics approach. International journal of
performance analysis in sport, 13(1), 83-95. https://doi.org/10.1080/24748668.2013.11868633
- Vahed, Y., Kraak, W., & Venter, R. (2016). Changes on the match profile of the South African Currie Cup tournament during 2007 and 2013. International Journal of Sports Science & Coaching, 11(1), 85-97. https://doi.org/10.1177/1747954115624826.
- Veal, A. J., & Darcy, S. (2014). Research methods in sport studies and sport management: A practical guide. Routledge. https://doi.org/10.4324/9781315776668
- Watson, N., Durbach, I., Hendricks, S., & Stewart, T. (2017). On the validity of team performance indicators in rugby union. International Journal of Performance Analysis in
Sport, 17(4), 609-621. https://doi.org/10.1080/24748668.2017.1376998
- Wunderlich, F., & Memmert, D. (2021). Forecasting the outcomes of sports events: A review. European Journal of Sport Science, 21(7), 944-957. https://doi.org/10.1080/17461391.2020.1793002
- Xu, X. Q., Korobeynikov, G., Han, W., Dutchak, M., Nikonorov, D., Zhao, M., & Mischenko, V. (2023). Analysis of phases and medalists to women’s singles matches in badminton at the Tokyo 2020 Olympic Games. Slobozhanskyi Herald of Science and Sport, 27(2), 64-69. https://doi.org/10.15391/snsv.2023-2.002