Understanding the relative contribution of technical and tactical performance to match outcome in Australian Football

References

• Ashker, S. E. (2011). Technical and tactical aspects that differentiate winning and losing performances in boxing. International Journal of Performance Analysis in Sport. Taylor & Francis, 11(2), 356–364.
   Web of Science ®Google Scholar
• Champion Data. (2019). Australia’s leading provider of sports statistics, television broadcast and new media services. Available at: https://www.championdata.com/index.php/sport/afl.html.
   Google Scholar
• Cotta, C., Mora, A. M., Merelo, J. J., & Merelo-Molina, C. (2013). A network analysis of the 2010 FIFA world cup champion team play. Journal of Systems Science and Complexity, 26(1), 21–42.
   Web of Science ®Google Scholar
• Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.
   Google Scholar
• Duch, J., Waitzman, J. S., & Amaral, L. A. N. (2010). Quantifying the performance of individual players in a team activity. PloS One, 5(6), e10937.
   PubMed Web of Science ®Google Scholar
• Garganta, J. (2009). Trends of tactical performance analysis in team sports: Bridging the gap between research, training and competition. Revista Portuguesa De Ciências Do Desporto, 9(1), 81–89. Available at: http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=s3h&AN=52218158&site=ehost-live&scope=site
   Google Scholar
• Grund, T. (2012). Network structure and team performance: The case of English Premier League soccer teams. Social Networks, 34(4), 682–690.
   Web of Science ®Google Scholar
• Higham, D. G., Hopkins, W. G., Pyne, D. B., & Anson, J. M. (2014). Performance indicators related to points scoring and winning in international rugby sevens. Journal Of Sports Science & Medicine, 13(2), 358.
   PubMed Web of Science ®Google Scholar
• Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in performance analysis. Journal Of Sports Sciences, 20(10), 739–754.
   PubMed Web of Science ®Google Scholar
• Hughes, M. D., Caudrelier, T., James, N., Donnelly, I., Kirkbride, A., & Duschesne, C. (2012). Moneyball and soccer – an analysis of the key performance indicators of elite male soccer players by position. Journal of Human Sport and Exercise Universidad de Alicante. Área de Educación Física y Deporte, 7(2), 402–412. .
   Google Scholar
• John, G., & Langley, P. (1995) Estimating continuous distributions in Bayesian classifiers, in P. Besnard & S. Hanks (eds) Proceedings of the Eleventh conference on Uncertainty in artificial intelligence. Monetral: Morgan Kaufmann Publishers Inc, pp. 338–345.
   Google Scholar
• Johnston, R. D., Black, G. M., Harrison, P. W., Murray, N. B., & Austin, D. J. (2018). Applied sport science of Australian football: A systematic review. Sports Medicine. Springer International Publishing, 1673–1694. doi:10.1007/s40279-018-0919-z.
   PubMed Web of Science ®Google Scholar
• Liu, H., Hopkins, W. G., & Gómez, M.-A. (2016). Modelling relationships between match events and match outcome in elite football. European Journal of Sport Science. Taylor & Francis, 16(5), 516–525.
   PubMed Web of Science ®Google Scholar
• Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., & Karmakar, C., ... & Venkatesh, S. (2016). Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. Journal of medical Internet research, 18(12), e323.
   Google Scholar
• 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), Informa UK Limited, 128–140.
   Google Scholar
• Mclean, S., Salmon, P. M., Gorman, A. D., Stevens, N. J., & Solomon, C. (2018). A social network analysis of the goal scoring passing networks of the 2016 European Football championships. Human Movement Science, 57, 400–408.
   PubMed Web of Science ®Google Scholar
• Memmert, D., Lemmink, K. A. P. M., & Sampaio, J. (2017). Current approaches to tactical performance analyses in soccer using position data. Sports Medicine. Springer, 47(1), 1–10.
   PubMed Web of Science ®Google Scholar
• Memmert, D., & Raabe, D. (2018). Data analytics in football: Positional data collection, modelling and analysis. New York, NY: Routledge.
   Google Scholar
• Michalopoulou, M., & Antoniou, P. (2005). Computer analysis of the technical and tactical effectiveness in Greek beach volleyball. International Journal of Performance Analysis in Sport. doi:10.1080/24748668.2005.11868314
   Google Scholar
• O’Brien, R. M. (2017). Dropping highly collinear variables from a model: Why it typically is not a good idea. Social Science Quarterly, 98(1), Blackwell Publishing Ltd, 360–375.
   Web of Science ®Google Scholar
• O’Donoghue, P. (2009). Research methods for sports performance analysis (1st ed.). London, England: Routledge. doi:10.4324/9780203878309
   Google Scholar
• Praça, G. M., Soares, V. V., Matias, C. J. A. D. S., Costa, I.T. D., & Greco, P. J. (2015). Relationship between tactical and technical performance in youth soccer players. Revista Brasileira De Cineantropometria & Desempenho Humano. SciELO Brasil, 17(2), 136–144.
   Google Scholar
• Quinlan, J. (2014) C4. 5: programs for machine learning. Available at: https://books.google.com.au/books?hl=en‎&id=b3ujBQAAQBAJ&oi=fnd&pg=PP1&dq=Quinlan+JR.+C4.+5:+programs+for+machine+learning,+Elsevier%3B+1993.&ots=sQ7tQNJmDa&sig=RVGNwB5Y7b140KuzG460QndL6qE.
   Google Scholar
• Robertson, S., Back, N., & Bartlett, J. D. (2016). Explaining match outcome in elite Australian rules football using team performance indicators. Journal of Sports Sciences, 34(7), Routledge, 637–644.
   PubMed Web of Science ®Google Scholar
• Robertson, S., Gupta, R., & McIntosh, S. (2016). A method to assess the influence of individual player performance distribution on match outcome in team sports. Journal of Sports Sciences, 34(19), 1893–1900.
   PubMed Web of Science ®Google Scholar
• Sargent, J., & Bedford, A. (2013). Evaluating Australian football league player contributions using interactive network simulation. Journal of Sports Science & Medicine, 12(1), 116–121. Accessed: 28 August 2019. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3761758/.
   PubMed Web of Science ®Google Scholar
• Sarmento, H., ., Marcelino, R., Anguera, M. T., CampaniÇo, J., Matos, N., & LeitÃo, J. C. (2014). Match analysis in football: A systematic review. Journal of Sports Sciences. Routledge, 32(20), 1831–1843.
   PubMed Web of Science ®Google Scholar
• Vidranski, T., Sertić, H., & Jukić, J. (2015). Technical and tactical aspects that differentiate winning and losing performances in Elite male karate fighter. Collegium Antropologicum. Hrvatsko antropološko društvo, 39(Supplement 1), 95–102.
   PubMedGoogle Scholar
• Wickham, H. (2016) ‘plyr: Tools for splitting, applying and combining data. R package version 1.8. 4ʹ.
   Google Scholar
• Wickham, H., & Chang, W. (2016) ‘ggplot2: Create elegant data visualisations using the grammar of graphics. R package version 2.2. 1ʹ.
   Google Scholar
• Witten, I. I., Frank, E. (2016). Data mining: Practical machine learning tools and techniques. 4th, Burlington: Kaufmann. doi:10.1016/C2015-0-02071-8/
   Google Scholar
• Yang, G., Leicht, A. S., Lago, C., & Gómez, M.-Á. (2018). Key team physical and technical performance indicators indicative of team quality in the soccer Chinese super league. Research in Sports Medicine. Taylor & Francis, 26(2), 158–167.
   PubMed Web of Science ®Google Scholar
• Yang, P., Zhou, B., Yang, J., & Zomaya, A. (2013). Stability of feature selection algorithms and ensemble feature selection methods in bioinformatics. In M. Elloumi & Y. Zomaya (Eds.) Biological knowledge discovery handbook: Preprocessing, mining and postprocessing of biological data (pp. 333–352). Hoboken, New Jersey: John Wiley and Sons.
   Google Scholar
• Young, C. M., Luo, W., Gastin, P., Lai, J. (2019b). Understanding effective tactics in Australian football using network analysis. International Journal of Performance Analysis in Sport. Taylor & Francis, 19(3), 331–341. .
   Web of Science ®Google Scholar
• Young, C. M., Luo, W., Gastin, P., Tran, J., & Dwyer, D. (2019a). Modelling match outcome in Australian Football: Improved accuracy with large databases. International Journal of Computer Science in Sport, 18(1), 80–92.
   Google Scholar
• Young, C. M., Luo, W., Gastin, P., Tran, J., & Dwyer, D. B. (2019c). The relationship between match performance indicators and outcome in australian football. Journal Of Science and Medicine in Sport, 22(4), 467–471.
   PubMed Web of Science ®Google Scholar