[Aoki, R., Assuncao, R. M., & de Melo, P. O. S. (2017). Luck is hard to beat: The difficulty of sports prediction. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1367–1376).10.1145/3097983.3098045]Search in Google Scholar
[Beretta, L., & Santaniello, A. (2016). Nearest neighbor imputation algorithms: a critical evaluation. BMC Medical Informatics and Decision Making, 16(3), 197–208.10.1186/s12911-016-0318-z495938727454392]Search in Google Scholar
[Bishop, C. M. (2006). Pattern recognition and machine learning (1st ed.). Springer.]Search in Google Scholar
[Bounsiar, A., & Madden, M. G. (2014). Kernels for one-class support vector machines. In 2014 International Conference on Information Science & Applications (ICISA) (pp. 1–4).10.1109/ICISA.2014.6847419]Search in Google Scholar
[Brefeld, U., & Zimmermann, A. (2017). Guest editorial: Special issue on sports analytics. Data Mining and Knowledge Discovery, 31(6), 1577–1579.10.1007/s10618-017-0530-1]Search in Google Scholar
[Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231.10.1214/ss/1009213726]Search in Google Scholar
[Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 15.10.1145/1541880.1541882]Search in Google Scholar
[Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 27.10.1145/1961189.1961199]Search in Google Scholar
[Chawla, N. V, Japkowicz, N., & Kotcz, A. (2004). Special issue on learning from imbalanced data sets. ACM Sigkdd Explorations Newsletter, 6(1), 1–6.10.1145/1007730.1007733]Search in Google Scholar
[Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.]Search in Google Scholar
[Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning (pp. 233–240).10.1145/1143844.1143874]Search in Google Scholar
[Forsman, H., Gråstén, A., Blomqvist, M., Davids, K., Liukkonen, J., & Konttinen, N. (2016). Development and validation of the perceived game-specific soccer competence scale. Journal of Sports Sciences, 34(14), 1319–1327.10.1080/02640414.2015.112551826708611]Search in Google Scholar
[Goldstein, M., & Uchida, S. (2016). A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS One, 11(4), 1–31.10.1371/journal.pone.0152173483673827093601]Search in Google Scholar
[He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.10.1109/TKDE.2008.239]Search in Google Scholar
[Jolliffe, I. T. (1986). Principal component analysis (1st ed.). Springer.10.1007/978-1-4757-1904-8_1]Search in Google Scholar
[Knobbe, A., Orie, J., Hofman, N., van der Burgh, B., & Cachucho, R. (2017). Sports analytics for professional speed skating. Data Mining and Knowledge Discovery, 31(6), 1872–1902.10.1007/s10618-017-0512-3]Search in Google Scholar
[Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). John Wiley & Sons.10.1002/9781119013563]Search in Google Scholar
[Louzada, F., Maiorano, A. C., & Ara, A. (2016). iSports: A web-oriented expert system for talent identification in soccer. Expert Systems with Applications, 44, 400–412.10.1016/j.eswa.2015.09.007]Search in Google Scholar
[Macnamara, Á., & Collins, D. (2011). Development and initial validation of the psychological characteristics of developing excellence questionnaire. Journal of Sports Sciences, 29(12), 1273–1286.10.1080/02640414.2011.58946821812724]Search in Google Scholar
[Merigó, J. M., & Gil-Lafuente, A. M. (2011). Decision-making in sport management based on the OWA operator. Expert Systems with Applications, 38(8), 10408–10413.10.1016/j.eswa.2011.02.104]Search in Google Scholar
[Narasimhan, H., & Agarwal, S. (2013). A structural SVM based approach for optimizing partial AUC. In Proceedings of the 30th International Conference on Machine Learning (pp. 516–524).]Search in Google Scholar
[Nieuwenhuis, C. F., Spamer, E. J., & Rossum, J. H. A. van. (2002). Prediction function for identifying talent in 14-to 15-year-old female field hockey players. High Ability Studies, 13(1), 21–33.10.1080/13598130220132280]Search in Google Scholar
[O’Connor, D., Larkin, P., & Mark Williams, A. (2016). Talent identification and selection in elite youth football: An Australian context. European Journal of Sport Science, 16(7), 837–844.10.1080/17461391.2016.115194526923813]Search in Google Scholar
[Ofoghi, B., Zeleznikow, J., MacMahon, C., & Raab, M. (2013). Data mining in elite sports: a review and a framework. Measurement in Physical Education and Exercise Science, 17(3), 171–186.10.1080/1091367X.2013.805137]Search in Google Scholar
[Papić, V., Rogulj, N., & Pleš, V. (2009). Identification of sport talents using a web-oriented expert system with a fuzzy module. Expert Systems with Applications, 36(5), 8830–8838.10.1016/j.eswa.2008.11.031]Search in Google Scholar
[Power, P., Ruiz, H., Wei, X., & Lucey, P. (2017). Not All Passes Are Created Equal: Objectively Measuring the Risk and Reward of Passes in Soccer from Tracking Data. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1605–1613).10.1145/3097983.3098051]Search in Google Scholar
[Reilly, T., Williams, A. M., Nevill, A., & Franks, A. (2000). A multidisciplinary approach to talent identification in soccer. Journal of Sports Sciences, 18(9), 695–702.10.1080/0264041005012007811043895]Search in Google Scholar
[Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.10.1162/08997660175026496511440593]Search in Google Scholar
[Silver, N. (2003). Introducing PECOTA. Baseball Prospectus, 2003, 507–514.]Search in Google Scholar
[Smith, L., Lipscomb, B., & Simkins, A. (2007). Data mining in sports: Predicting cy young award winners. Journal of Computing Sciences in Colleges, 22(4), 115–121.]Search in Google Scholar
[Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., … Altman, R. B. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics, 17(6), 520–525.10.1093/bioinformatics/17.6.52011395428]Search in Google Scholar
[Williams, A. M., & Reilly, T. (2000). Talent identification and development in soccer. Journal of Sport Science, 18(9), 657–667.10.1080/0264041005012004111043892]Search in Google Scholar
[Woods, C. T., Raynor, A. J., Bruce, L., McDonald, Z., & Robertson, S. (2016). The application of a multi-dimensional assessment approach to talent identification in Australian football. Journal of Sports Sciences, 34(14), 1340–1345.10.1080/02640414.2016.114266826862858]Search in Google Scholar
[Youngstrom, E. A. (2013). A primer on receiver operating characteristic analysis and diagnostic efficiency statistics for pediatric psychology: we are ready to ROC. Journal of Pediatric Psychology, 39(2), 204–221.10.1093/jpepsy/jst062393625823965298]Search in Google Scholar