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
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.
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.
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
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
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
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
Day, J., & Nguyen, R. (2018). fitzRoy: Easily scrape and process AFL data (Version 0.1.6.). Retrieved from https://github.com/jimmyday12/fitzRoy10.32614/CRAN.package.fitzRoy
Department of Infrastructure, Regional Development and Cities. (2018). Australian Air Distances. Retrieved from https://bitre.gov.au/statistics/aviation/files/australian_air_distances.csv
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
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
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
Jackson, K. (2016). Assessing player performance in Australian football using spatial data. (Doctor of Philosophy), Swinburne University of Technology, Melbourne.
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
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
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
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
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
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
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
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
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
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
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
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
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/
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
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
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
Therneau, T., & Atkinson, B. (2018). rpart: Recursive partitioning and regression trees (Version 4.1-13). Retrieved from https://CRAN.R-project.org/package=rpart
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
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/
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
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
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