Predicting biathlon shooting performance using machine learning

References

• Baca, A., & Kornfeind, P. (2012). Stability analysis of motion patterns in biathlon shooting. Human Movement Science, 31(2), 295–302.
   PubMed Web of Science ®Google Scholar
• Ball, K. A., Best, R. J., & Wrigley, T. V. (2003). Body sway, aim point fluctuation and performance in rifle shooters: Inter- and intra-individual analysis. Journal of Sports Sciences, 21(7), 559–566.
   PubMed Web of Science ®Google Scholar
• Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1), 105–139.
   Web of Science ®Google Scholar
• Begg, R., & Kamruzzaman, J. (2005). A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. Journal of Biomechanics, 38(3), 401–408.
   PubMed Web of Science ®Google Scholar
• Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.
   Web of Science ®Google Scholar
• Chen, T., & He, T. (2017). xgboost: Extreme gradient boosting (Version 0.6-4) [Computer software]. Retrieved from https://cran.r-project.org/web/packages/xgboost
   Google Scholar
• Era, P., Konttinen, N., Mehto, P., Saarela, P., & Lyytinen, H. (1996). Postural stability and skilled performance–A study on top-level and naive rifle shooters. Journal of Biomechanics, 29(3), 301–306.
   PubMed Web of Science ®Google Scholar
• Gilovich, T., Vallone, R., & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive Psychology, 17(3), 295–314.
   Web of Science ®Google Scholar
• Groslambert, A., Candau, R., Hoffman, M. D., Bardy, B., & Rouillon, J. D. (1999). Validation of simple tests of biathlon shooting ability. International Journal of Sports Medicine, 20(3), 179–182.
   PubMed Web of Science ®Google Scholar
• Hawkins, R. (2011). Identifying mechanic measures that best predict air-pistol shooting performance. International Journal of Performance Analysis in Sport, 11(3), 499–509.
   Web of Science ®Google Scholar
• Hoffman, M. D., Gilson, P. M., Westenburg, T. M., & Spencer, W. A. (1992). Biathlon shooting performance after exercise of different intensities. International Journal of Sports Medicine, 13(3), 270–273.
   PubMed Web of Science ®Google Scholar
• Hoffman, M. D., & Street, G. M. (1992). Characterization of the heart rate response during biathlon. International Journal of Sports Medicine, 13(5), 390–394.
   PubMed Web of Science ®Google Scholar
• Hyndman, R. J., & Athanasopoulos, G. (2013). Forecasting: Principles and practice. Melbourne: OTexts.
   Google Scholar
• Kuhn, M. (2017). caret: Classification and regression training (Version 6.0-77) [Computer software]. Retrieved from https://cran.r-project.org/web/packages/caret
   Google Scholar
• Luchsinger, H., Kocbach, J., Ettema, G., & Sandbakk, Ø. (2017). Comparison of performance-levels and sex on sprint race performance in the biathlon world cup. International Journal of Sports Physiology and Performance. Advance online publication.
   Web of Science ®Google Scholar
• Mężyk, E., & Unold, O. (2011). Machine learning approach to model sport training. Computers in Human Behavior, 27(5), 1499–1506.
   Web of Science ®Google Scholar
• Nitzsche, K. (1998). Biathlon: Leistung - Training - Wettkampf: ein Lehrbuch für Trainer, Übungsleiter und Aktive [Biathlon: performance - training - competition: a textbook for coaches, instructors and athletes]. Wiebelsheim: Limpert.
   Google Scholar
• Novatchkov, H., & Baca, A. (2013). Artificial intelligence in sports on the example of weight training. Journal of Sports Science and Medicine, 12(1), 27–37.
   PubMed Web of Science ®Google Scholar
• Ofoghi, B., Zeleznikow, J., Dwyer, D., & Macmahon, C. (2013). Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques. Journal of Sports Sciences, 31(9), 954–962.
   PubMed Web of Science ®Google Scholar
• Ofoghi, B., Zeleznikow, J., Macmahon, C., Rehula, J., & Dwyer, D. B. (2016). Performance analysis and prediction in triathlon. Journal of Sports Sciences, 34(7), 607–612.
   PubMed Web of Science ®Google Scholar
• R Core Team. (2016). R: A language and environment for statistical computing [Computer software]. Vienna: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
   Google Scholar
• Sattlecker, G., Buchecker, M., Gressenbauer, C., Müller, E., & Lindinger, S. J. (2017). Factors discriminating high from low score performance in biathlon shooting. International Journal of Sports Physiology and Performance, 12(3), 377–384.
   PubMed Web of Science ®Google Scholar
• Sattlecker, G., Buchecker, M., Müller, E., & Lindinger, S. J. (2014). Postural balance and rifle stability during standing shooting on an indoor gun range without physical stress in different groups of biathletes. International Journal of Sports Science & Coaching, 9(1), 171–184.
   Web of Science ®Google Scholar
• Skattebo, Ø., & Losnegard, T. (2017). Variability, predictability and race factors affecting performance in elite biathlon. International Journal of Sports Physiology and Performance. Advance online publication.
   PubMed Web of Science ®Google Scholar
• Venables, W. N., & Ripley, B. D. (2016). nnet: Feed-forward neural networks and multinomial log-linear models (Version 7.3-12) [Computer software]. Retrieved from https://cran.r-project.org/web/packages/nnet
   Google Scholar
• Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 1–23.
   PubMed Web of Science ®Google Scholar
• Wickham, H. (2016). ggplot2: Create elegant data visualisations using the grammar of graphics (Version 2.2.1) [Computer software]. Retrieved from https://cran.r-project.org/web/packages/ggplot2
   Google Scholar