Have a personal or library account? Click to login
Multifactorial analysis of factors influencing elite australian football match outcomes: a machine learning approach Cover

Multifactorial analysis of factors influencing elite australian football match outcomes: a machine learning approach

Open Access
|Dec 2019

References

  1. 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
  2. Australia Sports Betting. (2018). Historical AFL Results and Odds Data. Retrieved from http://www.aussportsbetting.com/data/
  3. 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.
  4. 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.
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. Department of Infrastructure, Regional Development and Cities. (2018). Australian Air Distances. Retrieved from https://bitre.gov.au/statistics/aviation/files/australian_air_distances.csv
  13. Elo, A. E. (1978). The rating of chessplayers, past and present: Batsford.
  14. 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
  15. 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
  16. 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
  17. 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
  18. Jackson, K. (2016). Assessing player performance in Australian football using spatial data. (Doctor of Philosophy), Swinburne University of Technology, Melbourne.
  19. 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
  20. 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
  21. Kuhn, M. (2017). caret: Classification and regression training (Version 6.0-76.). Retrieved from https://CRAN.R-project.org/package=caret
  22. Kuhn, M., & Johnson, K. (2016). Applied Predictive Modeling. (pp. 600). doi:10.1007/978-1-4614-6849-310.1007/978-1-4614-6849-3
  23. 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
  24. 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
  25. 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
  26. Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18-22.
  27. Milborrow, S. (2018). earth: Multivariate adaptive regression splines (Version 4.6.3). Retrieved from https://CRAN.R-project.org/package=earth
  28. 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
  29. Molnar, C. (2018). Interpretable Machine Learning. Retrieved from https://christophm.github.io/interpretable-ml-book/
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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/
  37. Ridgeway, G. (2017). gbm: Generalized boosted regression models (Version 2.1.3). Retrieved from https://CRAN.R-project.org/package=gbm
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. Therneau, T., & Atkinson, B. (2018). rpart: Recursive partitioning and regression trees (Version 4.1-13). Retrieved from https://CRAN.R-project.org/package=rpart
  44. Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (4 ed.). New York: Springer.10.1007/978-0-387-21706-2
  45. 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
  46. 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/
  47. 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
  48. 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
  49. 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
Language: English
Page range: 100 - 124
Published on: Dec 16, 2019
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2019 J. Fahey-Gilmour, B. Dawson, P. Peeling, J. Heasman, B. Rogalski, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.