[Apley, D. W. (2016). Visualizing the effects of predictor variables in black box supervised learning models. arXiv.org, 1-36. Retrieved from https://arxiv.org/abs/1612.08468]Search in Google Scholar
[Australia Sports Betting. (2018). Historical AFL Results and Odds Data. Retrieved from http://www.aussportsbetting.com/data/]Search in Google Scholar
[Bailey, M. (2000). Identifying arbitrage opportunities in AFL betting markets through mathematical modelling. Paper presented at the Proceedings of the Fifth Australian conference on Mathematics and Computers in Sport, University of Technology, Sydney.]Search in Google Scholar
[Bailey, M., & Clarke, S. R. (2004). Deriving a profit from Australian Rules football: A statistical approach. Paper presented at the Proceedings of the Seventh Australian conference on Mathematics and Computers in Sport, Massey University, Palmerston North.]Search in Google Scholar
[Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2]Search in Google Scholar
[Bunker, R. P., & Thabtah, F. (2017). A machine learning framework for sport result prediction. Applied Computing and Informatics. doi:10.1016/j.aci.2017.09.00510.1016/j.aci.2017.09.005]Open DOISearch in Google Scholar
[Carey, D. L., Crossley, K. M., Whiteley, R., Mosler, A., Ong, K.-L., Crow, J., & Morris, M. E. (2018). Modelling training loads and injuries: The dangers of discretization. Medicine & Science in Sports & Exercise, 50(11), 2267-2276. doi:10.1249/MSS.000000000000168510.1249/MSS.0000000000001685]Open DOISearch in Google Scholar
[Corke, T. (2016). Matter of Stats: what makes AFL finals games different from the regular season? The Guardian. Retrieved from https://www.theguardian.com/sport/2016/sep/07/matter-of-stats-what-makes-afl-finals-games-different-from-the-regular-season]Search in Google Scholar
[Coutts, A. J. (2014). In the age of technology, Occam’s razor still applies. International Journal of Sports Physiology and Performance, 9(5), 741. doi:10.1123/IJSPP.2014-035310.1123/IJSPP.2014-0353]Open DOISearch in Google Scholar
[Day, J., & Nguyen, R. (2018). fitzRoy: Easily scrape and process AFL data (Version 0.1.6.). Retrieved from https://github.com/jimmyday12/fitzRoy]Search in Google Scholar
[Delen, D., Cogdell, D., & Kasap, N. (2012). A comparative analysis of data mining methods in predicting NCAA bowl outcomes. International Journal of Forecasting, 28(2), 543-552. doi:10.1016/j.ijforecast.2011.05.00210.1016/j.ijforecast.2011.05.002]Open DOISearch in Google Scholar
[Department of Infrastructure, Regional Development and Cities. (2018). Australian Air Distances. Retrieved from https://bitre.gov.au/statistics/aviation/files/australian_air_distances.csv]Search in Google Scholar
[Elo, A. E. (1978). The rating of chessplayers, past and present: Batsford.]Search in Google Scholar
[Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22.10.18637/jss.v033.i01]Search in Google Scholar
[Gastin, P. B., Fahrner, B., Meyer, D., Robinson, D., & Cook, J. L. (2013). Influence of physical fitness, age, experience, and weekly training load on match performance in elite Australian football. Journal of Strength and Conditioning Research, 27(5), 1272-1279. doi:10.1519/JSC.0b013e318267925f10.1519/JSC.0b013e318267925f]Open DOISearch in Google Scholar
[Hagglund, M., Walden, M., Magnusson, H., Kristenson, K., Bengtsson, H., & Ekstrand, J. (2013). Injuries affect team performance negatively in professional football: An 11-year follow-up of the UEFA Champions League injury study. British Journal of Sports Medicine, 47(12), 738-742. doi:10.1136/bjsports-2013-09221510.1136/bjsports-2013-092215]Open DOISearch in Google Scholar
[Hvattum, L. M., & Arntzen, H. (2010). Using ELO ratings for match result prediction in association football. International Journal of Forecasting, 26(3), 460-470. doi:10.1016/j.ijforecast.2009.10.00210.1016/j.ijforecast.2009.10.002]Open DOISearch in Google Scholar
[Jackson, K. (2016). Assessing player performance in Australian football using spatial data. (Doctor of Philosophy), Swinburne University of Technology, Melbourne.]Search in Google Scholar
[James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (1 ed.). New York: Springer.10.1007/978-1-4614-7138-7]Search in Google Scholar
[Karatzoglou, A., Smola, A., Hornik, K., & Zeileis, A. (2004). kernlab - an S4 package for kernel methods in R. Journal of Statistical Software, 11(9), 1-20.10.18637/jss.v011.i09]Search in Google Scholar
[Kuhn, M. (2017). caret: Classification and regression training (Version 6.0-76.). Retrieved from https://CRAN.R-project.org/package=caret]Search in Google Scholar
[Kuhn, M., & Johnson, K. (2016). Applied Predictive Modeling. (pp. 600). doi:10.1007/978-1-4614-6849-310.1007/978-1-4614-6849-3]Open DOISearch in Google Scholar
[Kuhn, M., & Wickham, H. (2018). recipes: Preprocessing tools to create design matrices (Version 0.1.3.). Retrieved from https://CRAN.R-project.org/package=recipes]Search in Google Scholar
[Lazarus, B. H., Hopkins, W. G., Stewart, A. M., & Aughey, R. J. (2018). Factors affecting match outcome in elite Australian football: A 14-year analysis. International Journal of Sports Physiology and Performance, 13(2), 140-144. doi:10.1123/ijspp.2016-045010.1123/ijspp.2016-045028488906]Open DOISearch in Google Scholar
[Leicht, A. S., Gomez, M. A., & Woods, C. T. (2017). Team performance indicators explain outcome during women’s basketball matches at the Olympic Games. Sports, 5(4), 1-8. doi:10.3390/sports504009610.3390/sports5040096596902429910456]Open DOISearch in Google Scholar
[Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18-22.]Search in Google Scholar
[Milborrow, S. (2018). earth: Multivariate adaptive regression splines (Version 4.6.3). Retrieved from https://CRAN.R-project.org/package=earth]Search in Google Scholar
[Miljkovic, D., Gajic, L., Kovacevic, A., & Konjovic, Z. (2010). The use of data mining for basketball matches outcomes prediction. Paper presented at the IEEE 8th International Symposium on Intelligent Systems and Informatics, Subotica, Serbia.10.1109/SISY.2010.5647440]Search in Google Scholar
[Molnar, C. (2018). Interpretable Machine Learning. Retrieved from https://christophm.github.io/interpretable-ml-book/]Search in Google Scholar
[Molnar, C., Bischl, B., & Casalicchio, G. (2018). iml: An R package for interpretable machine learning. Journal of Open Source Software, 3(26), 786. doi:10.21105/joss.0078610.21105/joss.00786]Search in Google Scholar
[Mooney, M., O’Brien, B., Cormack, S., Coutts, A., Berry, J., & Young, W. (2011). The relationship between physical capacity and match performance in elite Australian football: A mediation approach. Journal of Science and Medicine in Sport, 14(5), 447-452. doi:10.1016/j.jsams.2011.03.01010.1016/j.jsams.2011.03.01021530392]Open DOISearch in Google Scholar
[Morley, B., & Thomas, D. (2005). An investigation of home advantage and other factors affecting outcomes in English one-day cricket matches. Journal of Sports Sciences, 23(3), 261-268. doi:10.1080/0264041041000173013310.1080/0264041041000173013315966344]Open DOISearch in Google Scholar
[Mullen, K., Ardia, D., Gil, D., Windover, D., & Cline, J. (2011). ‘DEoptim’: An R package for global optimization by differential evolution. Journal of Statistical Software, 40(6), 1-26.10.18637/jss.v040.i06]Search in Google Scholar
[O’Malley, J. A. (2008). Probability formulas and statistical analysis in tennis. Journal of Quantitative Analysis in Sports, 4(2). doi:10.2202/1559-0410.110010.2202/1559-0410.1100]Open DOISearch in Google Scholar
[Piggott, B. G., McGuigan, M. R., & Newton, M. J. (2015). Relationship between physical capacity and match performance in semiprofessional Australian rules football. Journal of Strength and Conditioning Research, 29(2), 478-482. doi:10.1519/JSC.000000000000076510.1519/JSC.000000000000076525627451]Open DOISearch in Google Scholar
[R Core Team. (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/]Search in Google Scholar
[Ridgeway, G. (2017). gbm: Generalized boosted regression models (Version 2.1.3). Retrieved from https://CRAN.R-project.org/package=gbm]Search in 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), 637-644. doi:10.1080/02640414.2015.106602610.1080/02640414.2015.106602626176890]Open DOISearch in 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. doi:10.1080/02640414.2016.114210610.1080/02640414.2016.114210626853070]Open DOISearch in Google Scholar
[Robertson, S., & Joyce, D. (2015). Informing in-season tactical periodisation in team sport: Development of a match difficulty index for Super Rugby. Journal of Sports Sciences, 33(1), 99-107. doi:10.1080/02640414.2014.92557210.1080/02640414.2014.92557224977714]Open DOISearch in Google Scholar
[Robertson, S., & Joyce, D. (2018). Evaluating strategic periodisation in team sport. Journal of Sports Sciences, 36(3), 279-285. doi:10.1080/02640414.2017.130031510.1080/02640414.2017.130031528266908]Open DOISearch in Google Scholar
[Ryall, R., & Bedford, A. (2010). An optimized ratings-based model for forecasting Australian Rules football. International Journal of Forecasting, 26(3), 511-517. doi:10.1016/j.ijforecast.2010.01.00110.1016/j.ijforecast.2010.01.001]Open DOISearch in Google Scholar
[Therneau, T., & Atkinson, B. (2018). rpart: Recursive partitioning and regression trees (Version 4.1-13). Retrieved from https://CRAN.R-project.org/package=rpart]Search in Google Scholar
[Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (4 ed.). New York: Springer.10.1007/978-0-387-21706-2]Search in Google Scholar
[Woods, C. T., Sinclair, W., & Robertson, S. (2017). Explaining match outcome and ladder position in the National Rugby League using team performance indicators. Journal of Science and Medicine in Sport, 20(12), 1107-1111. doi:10.1016/j.jsams.2017.04.00510.1016/j.jsams.2017.04.00528479281]Search in Google Scholar
[Woods, M. (2018). Finals experience no Demons barrier: Lewis. Newcastle Herald. Retrieved from https://www.theherald.com.au/story/5624387/finals-experience-no-demons-barrier-lewis/]Search in Google Scholar
[Yeo, I.-K., & Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. Biometrika, 87(4), 954-959.10.1093/biomet/87.4.954]Search in Google Scholar
[Young, C. M., Luo, W., Gastin, P., Tran, J., & Dwyer, D. B. (2018). The relationship between match performance indicators and outcome in Australian football. Journal of Science and Medicine in Sport. doi:10.1016/j.jsams.2018.09.23510.1016/j.jsams.2018.09.23530352743]Open DOISearch in Google Scholar
[Zimmermann, A. (2016). Basketball predictions in the NCAAB and NBA: similarities and differences. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(5), 350-364. doi:10.1002/sam.1131910.1002/sam.11319]Open DOISearch in Google Scholar