Evaluation of soccer players under the Moneyball concept

References

• Ade, J., Fitzpatrick, J., & Bradley, P. S. (2016). High-intensity efforts in elite soccer matches and associated movement patterns, technical skills and tactical actions. Information for position-specific training drills. Journal of Sports Sciences, 0414(August), 1–10.
   Google Scholar
• Aggio, M. (2015). How do basketball teams win championships? A quantitative analysis on factors determining wins (Bachelor's thesis). Università Ca’Foscari Venezia, Venezia, Italia.
   Google Scholar
• Ahmed, M. (2016). Can Artificial intelligence modelling approaches assist football clubs in identifying transfer targets, while maintaining a fair transfer market using player performance data? (Doctoral dissertation). Cardiff Metropolitan University, Cardiff, Wales.
   Google Scholar
• Ahtiainen, S. (2018). Top 5 European football leagues–The association between financial performance and sporting success. Finland: Aalto University School of Business.
   Google Scholar
• Alamar, B., & Mehrotra, V. (2011). Beyond ‘Moneyball’: The rapidly evolving world of sports analytics, Part I. Analytics Magazine.
   Google Scholar
• Anderson, C. K., & Sally, D. (2013). Os Números do Jogo: Por que tudo o que você sabe sobre futebol está errado. São Paulo: Paralela.
   Google Scholar
• Andrade, G. N. D., & Sant’Anna, A. P. (2013). Composição probabilística e indice de Malmquist para avaliação de eficiência em distribuidoras de energia elétrica. In Simpósio Brasileiro de Pesquisa Operacional XLV SBPO (Vol.2013, pp. 937–948). Natal.
   Google Scholar
• Anton, H. (2019). In-game strategy recommendations in association football: A study based on network theory (Master’s Thesis). Åbo Akademi University, Finland.
   Google Scholar
• Apostolou, K. (2018). Sports Analytics algorithms for performance prediction (Master’s Thesis). International Helenic University, Greece.
   Google Scholar
• Asif, R., Zaheer, M. T., Haque, S. I., & Hasan, M. A. (2016). Football (soccer) analytics: A case study on the availability and limitations of data for football analytics research. International Journal of Computer Science and Information Security, 14(11), 516.
   Google Scholar
• Baerg, A. (2017). Big data, sport, and the digital divide: Theorizing how athletes might respond to big data monitoring. Journal of Sport and Social Issues, 41(1), 3–20.
   Web of Science ®Google Scholar
• Baughman, A. K., Bogdany, R. J., McAvoy, C., Locke, R., O’Connell, B., & Upton, C. (2015). Predictive cloud computing with big data: Professional golf and tennis forecasting [application notes]. IEEE Computational Intelligence Magazine, 10(3), 62–76.
   Web of Science ®Google Scholar
• Beer, D. (2015). Productive measures: Culture and measurement in the context of everyday neoliberalism. Big Data & Society, 2(1), 1–12.
   Web of Science ®Google Scholar
• Bonomo, F., Durán, G., & Marenco, J. (2014). Mathematical programming as a tool for virtual soccer coaches: A case study of a fantasy sport game. International Transactions in Operational Research, 21(3), 399–414.
   Web of Science ®Google Scholar
• Bradley, P. S., Lago-Peñas, C., Rey, E., & Gomez Diaz, A. (2013). The effect of high and low percentage ball possession on physical and technical profiles in English FA premier league soccer matches. Journal of Sports Sciences, 31, 12.
   Web of Science ®Google Scholar
• Bradley, P. S., Lago-Peñas, C., Rey, E., & Sampaio, J. (2014). The influence of situational variables on ball possession in the English premier league. Journal of Sports Sciences, 0414(May2014), 1–7.
   Google Scholar
• Brand, G. (2015). How a transfer works - from the scouting to the signing. Retrieved from http://www.skysports.com/football/news/15117/9921213/how-a-transfer-works-from-the-scouting-to-the-signing
   Google Scholar
• Calder, J. M. (2013). Player performance evaluation in rugby using stochastic multi-criteria acceptability analysis with simplified uncertainty formats (Master’s Thesis). University of Cape Town.
   Google Scholar
• Castellano, J., & Casamichana, D. (2015). What are the differences between first and second divisions of Spanish football teams? International Journal of Performance Analysis in Sport, 15(1), 135–146.
   Web of Science ®Google Scholar
• Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica: Journal of the Econometric Society, 50, 1393–1414.
   Web of Science ®Google Scholar
• Coles, J. (2015). The science of recruitment and team selection: Practitioner methodology in performance analytics (Master’s Thesis). Cardiff Metropolitan University.
   Google Scholar
• Constantinou, A., & Fenton, N. E. (2017). Towards smart-data: Improving predictive accuracy in long-term football team performance. Knowledge-Based Systems, 124, 93-104.
   Web of Science ®Google Scholar
• Dendir, S. (2016). When do soccer players peak? A note. Journal of Sports Analytics, 2(2), 89–105.
   Google Scholar
• Deutscher, C., & Buschemann, A. (2016). Does performance consistency pay off financially for players? Evidence from the Bundesliga. Journal of Sports Economics, 17(1), 27–43.
   Web of Science ®Google Scholar
• Dixon, P. M., Weiner, J., Mitchell-Olds, T., & Woodley, R. (1987). Bootstrapping the Gini coefficient of inequality. Ecology, 68(5), 1548–1551.
   Web of Science ®Google Scholar
• Eldridge, R. (2010). Measuring efficiency in the National Basketball Association: A “Moneyball” approach (Master’s Thesis). NYU - Stern School of Business.
   Google Scholar
• Ellefsrød, M. B. (2013). The betting machine: Using in-depth match statistics to compute future probabilities of football match outcomes using the Gibbs sampler (Master Thesis). Norwegian University of Science and Technology.
   Google Scholar
• Fare, R., Färe, R., Fèare, R., Grosskopf, S., & Lovell, C. A. K. (1994). Production frontiers. London: Cambridge University Press.
   Google Scholar
• Ferri, L., Macchioni, R., Maffei, M., & Zampella, A. (2017). Financial versus sports performance: The missing link. International Journal of Business and Management, 12(3), 36.
   Google Scholar
• Filho, L. A. D. O. R., & Ferreira, M. P. (2016). Gestão na formação de atletas: Proposta de avaliação técnica para o futebol. Revista Conbrad, 1(3), 238–253.
   Google Scholar
• Gai, Y., Volossovitch, A., Lago, C., & Gómez, M.-Á. (2019). Technical and tactical performance differences according to player’s nationality and playing position in the Chinese football super league. International Journal of Performance Analysis in Sport, 19(4), 632–645.
   Web of Science ®Google Scholar
• Gavião, L. O., & Lima, G. B. A. (2017). Decision support in athtlete acquisition: An application of the composition of probabilistic preferences to football [in Portuguese]. Beau Bassin: Novas Edições Acadêmicas.
   Google Scholar
• Gavião, L. O., Principe, V. A., Lima, G. B. A., & Sant’Anna, A. P. (2017). Aplicação da Composição Probabilística de Preferências e do Índice de Gini à escolha de jogadores da Liga Inglesa de Futebol. In II Seminário Internacional de Estatística com R (pp. 11). Niterói-RJ: UFF.
   Google Scholar
• Gavião, L. O., Sant’Anna, A. P., Lima, G. B. A., & Garcia, P. A. D. A. (2018). CPP: Composition of probabilistic preferences. R package version 0.1.0. Vienna: R Core Team. Retrieved from https://cran.r-project.org/package=CPP
   Google Scholar
• Gerrard, B. (2010). Analysing sporting efficiency using standardised win cost: Evidence from the FA premier league, 1995–2007. International Journal of Sports Science & Coaching, 5(1), 13–35.
   Web of Science ®Google Scholar
• Gerrard, B., & Howard, D. (2007). Is the Moneyball approach transferable to complex invasion team sports? International Journal of Sport Finance, 2(4), 214–230.
   Web of Science ®Google Scholar
• Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric statistical inference (5th ed.). Boca Raton, FL: CRC Press.
   Google Scholar
• Gini, C. (1921). Measurement of inequality of incomes. The Economic Journal, 31(121), 124–126.
   Google Scholar
• Gómez, M.-Á., Lago, C., Gómez, M.-T., & Furley, P. (2019). Analysis of elite soccer players’ performance before and after signing a new contract. PLoS One, 14(1), e0211058.
   PubMed Web of Science ®Google Scholar
• Green, C., Lozano, F., & Simmons, R. (2015, May). Rank-order tournaments, probability of winning and investing in talent: Evidence from Champions’ League qualifying rule. National Institute Economic Review, 232(1), 30–40.
   Web of Science ®Google Scholar
• Guimarães, J. H. M. M. (2018). Data analytics applied to football and football players (Doctoral dissertation). Universidade Católica Portuguesa, Porto, Portugal.
   Google Scholar
• Håland, E. M., Wiig, A. S., Stålhane, M., & Hvattum, L. M. (2019). Evaluating passing ability in association football. IMA Journal of Management Mathematics. https://doi.org/10.1093/imaman/dpz004.
   Web of Science ®Google Scholar
• Hauke, J., & Kossowski, T. (2011). Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae, 30(2), 87–93.
   Google Scholar
• Hoffmann, R. (2016). Desigualdade da distribuição da renda no Brasil: A contribuição de aposentadorias e pensões e de outras parcelas do rendimento domiciliar per capita. Economia E Sociedade, 18(1), 213–231.
   Google Scholar
• Hughes, M., Caudrelier, T., James, N., Redwood-Brown, A., Donnelly, I., Kirkbride, A., & Duschesne, C. (2012). Moneyball and soccer - An analysis of the key performance indicators of elite male soccer players by position. Journal of Human Sport and Exercise, 7(2), 402–412.
   Google Scholar
• Ingle, S. (2013, January 20). Football scouts used to trust intuition. Now they also use spreadsheets. Retrieved from https://www.theguardian.com/football/blog/2013/jan/20/football-data-scouts-michu-transfer
   Google Scholar
• Jackson, P. (2005). The last season: A team in search of its soul. Los Angeles: Penguin Group USA.
   Google Scholar
• Jarvandi, A., Sarkani, S., & Mazzuchi, T. (2013). Modeling team compatibility factors using a semi-Markov decision process: A data-driven approach to player selection in soccer. Journal of Quantitative Analysis in Sports, 9(4), 347–366.
   Google Scholar
• Jhanwar, M. G. (2017). Quantitative assessment of player performance and winner prediction in ODI cricket (Master’s Thesis). International Institute of Information Technology Hyderabad, India.
   Google Scholar
• Kioussis, G. N. (2018). Can a manager dope? Match analysis in the digital age. International Review for the Sociology of Sport, 53(7), 824-836. doi:10.1177/1012690216684761
   Web of Science ®Google Scholar
• Klaiber, J. D. (2016). Soccer player performance rating systems for the German Bundesliga (Master’s Thesis). Ghent University.
   Google Scholar
• Lago-Peñas, C., Gómez-Ruano, M.-Á., Owen, A. L., & Sampaio, J. (2016). The effects of a player dismissal on competitive technical match performance. International Journal of Performance Analysis in Sport, 16(3), 792–800.
   Web of Science ®Google Scholar
• Lago-Peñas, C., Gómez-Ruano, M., & Yang, G. (2017). Styles of play in professional soccer: An approach of the Chinese soccer super league. International Journal of Performance Analysis in Sport, 17(6), 1073–1084.
   Web of Science ®Google Scholar
• Lewis, M. (2004). Moneyball: The art of winning an unfair game. New York: WW Norton & Company.
   Google Scholar
• Magalhães, L. B., Castroneves, T., Chaves, M. C. D. C., Gomes, C. F. S., & Pereira, E. R. (2016). Estudo de apoio à decisão: A escolha do “Camisa 10” ideal baseado no método MACBETH. Revista Brasileira De Futsal E Futebol, 8(29), 113–128. Retrieved from http://www.rbff.com.br/index.php/rbff/article/viewFile/44/44
   Google Scholar
• Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos De Estadistica Y De Investigacion Operativa, 4(2), 209–242.
   Google Scholar
• Martínez, J. A., & Martínez, L. (2011). A stakeholder assessment of basketball player evaluation metrics. Journal of Human Sport & Exercise, 6(1), 153–183.
   Google Scholar
• Martín-Martín, A., Orduna-Malea, E., Thelwall, M., & López-Cózar, E. D. (2018). Google scholar, web of science, and scopus: A systematic comparison of citations in 252 subject categories. Journal of Informetrics, 12(4), 1160–1177.
   Web of Science ®Google Scholar
• Mason, D. S. (2006). Moneyball as a supervening necessity for the adoption of player tracking technology in professional hockey. International Journal of Sports Marketing and Sponsorship, 8(1), 41–55.
   Google Scholar
• McGuinness, N. (2016). How a player gets signed in the transfer window: A scout’s perspective. Retrieved from http://bleacherreport.com/articles/2657153-how-a-player-gets-signed-in-the-transfer-window-a-scouts-perspective
   Google Scholar
• McHale, I. G., & Relton, S. D. (2018). Identifying key players in soccer teams using network analysis and pass difficulty. European Journal of Operational Research, 268(1), 339–347.
   Web of Science ®Google Scholar
• McHale, I. G., Scarf, P. A., & Folker, D. E. (2012). On the development of a soccer player performance rating system for the English Premier League. Interfaces, 42(4), 339–351.
   Web of Science ®Google Scholar
• McIntosh, S., Kovalchik, S., & Robertson, S. (2018). Validation of the Australian football league player ratings. International Journal of Sports Science & Coaching, 13(6), 1064–1071.
   Web of Science ®Google Scholar
• Moorsteen, R. H. (1961). On measuring productive potential and relative efficiency. The Quarterly Journal of Economics, 75(3), 451–467.
   Web of Science ®Google Scholar
• Mourao, P. (2012). The indebtedness of Portuguese soccer teams–Looking for determinants. Journal of Sports Sciences, 30(10), 1025–1035.
   PubMed Web of Science ®Google Scholar
• Mourao, P. R. (2016). Soccer transfers, team efficiency and the sports cycle in the most valued European soccer leagues–Have European soccer teams been efficient in trading players? Applied Economics, 48(56), 5513–5524.
   Web of Science ®Google Scholar
• Nicholls, S. B., & Worsfold, P. R. (2016). The observational analysis of elite coaches within youth soccer: The importance of performance analysis. International Journal of Sports Science & Coaching, 11, 1–7.
   Web of Science ®Google Scholar
• Nørstebø, O., Bjertnes, V. R., & Vabo, E. (2016). Valuing individual player involvements in Norwegian Association Football (Master’s Thesis). NTNU.
   Google Scholar
• Nsolo, E. (2018). Prediction models for soccer sports analytics. Sweden: Linköping University.
   Google Scholar
• Nüesch, S. (2009). A note on the endogeneity of the pay-performance relationship in professional soccer. Economics Bulletin, 29(3), 1850–1855.
   Google Scholar
• Orchard, J. W. (2009). On the value of team medical staff: Can the “Moneyball” approach be applied to injuries in professional football? British Journal of Sports Medicine, 43(13), 963–965.
   PubMed Web of Science ®Google Scholar
• Peeters, T. (2018). Testing the wisdom of crowds in the field: Transfermarkt valuations and international soccer results. International Journal of Forecasting, 34(1), 17–29.
   Web of Science ®Google Scholar
• Pereira, T., Ribeiro, J., Grilo, F., & Barreira, D. (2019). The golden index: A classification system for player performance in football attacking plays. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 233 (4), 1754337119851682.
   Web of Science ®Google Scholar
• Phatak, A., & Gruber, M. (2019). Keep your head up—correlation between visual exploration frequency, passing percentage and turnover rate in elite football midfielders. Sports, 7(6), 139.
   Web of Science ®Google Scholar
• Pomerol, J.-C., & Barba-Romero, S. (2012). Multicriterion decision in management: Principles and practice. New York: Springer.
   Google Scholar
• Pradhan, S. (2018). Ranking regular seasons in the NBA’s modern era using grey relational analysis. Journal of Sports Analytics, 4(1), 31–63.
   Google Scholar
• R-Core-Team. (2019). R: A language and environment for statistical computing. Vienna, Austria. Retrieved from http://www.R-Project.org
   Google Scholar
• Rezende, B. R. D. (2007). Cartas a um jovem atleta. Rio de Janeiro: Elsevier.
   Google Scholar
• Ruijg, J., & van Ophem, H. (2015). Determinants of football transfers. Applied Economics Letters, 22(1), 12–19.
   Web of Science ®Google Scholar
• Sant’Anna, A. P., Gomes, L. F. A. M., Costa, F. F. D., Rangel, L. A. D., Faria, M. J. D. S., Ferreira, R. G., … Senna, V. D. (2012). Análise multicritério baseada em probabilidades de preferência. In V. F. de Oliveira, V. Cavenaghi, & F. S. Másculo (Eds.), Tópicos emergentes e desafios metodológicos em Engenharia de Produção: Casos, experiências e proposições - Volume V (pp. 258). Rio de Janeiro: ABEPRO.
   Google Scholar
• Sant’Anna, A. P. (2015). Probabilistic composition of preferences, theory and applications. New York: Springer.
   Google Scholar
• Sant’Anna, A. P., Costa, H. G., & Pereira, V. (2015). CPP-TRI: A sorting method based on the probabilistic composition of preferences. International Journal of Information and Decision Sciences, 7(3), 193–212.
   Google Scholar
• Sant’Anna, A. P., & Sant’Anna, L. A. F. P. (2001). Randomization as a stage in criteria combining. In International Conference on Industrial Engineering and Operations Management - VII ICIEOM (pp. 248–256). Salvador.
   Google Scholar
• Sarkar, S. (2018). Paradox of crosses in association football (soccer)–A game-theoretic explanation. Journal of Quantitative Analysis in Sports, 14(1), 25–36.
   Web of Science ®Google Scholar
• Sarkar, S., & Chakraborty, S. (2018). Pitch actions that distinguish high scoring teams: Findings from five European football leagues in 2015–16. Journal of Sports Analytics, 4(1), 1–14.
   Google Scholar
• Schuth, G., Carr, G., Barnes, C., Carling, C., & Bradley, P. S. (2016). Positional interchanges influence the physical and technical match performance variables of elite soccer players. Journal of Sports Sciences, 34(6), 501–508.
   PubMed Web of Science ®Google Scholar
• Siu, V. (2013). Proliferation of data-driven analysis in soccer (Part 1: The scouts). SportTechie. Retrieved from http://www.sporttechie.com/2013/11/20/sports/soccer/the-proliferation-of-data-driven-analysis-in-soccer-part-one-the-scouts/
   Google Scholar
• Stewart, M., Mitchell, H., & Stavros, C. (2007). Moneyball applied: Econometrics and the identification and recruitment of elite Australian footballers. International Journal of Sport Finance, 2(4), 231–248.
   Web of Science ®Google Scholar
• Torgler, B., & Schmidt, S. L. (2007). What shapes player performance in soccer? Empirical findings from a panel analysis. Applied Economics, 39(18), 2355–2369.
   Web of Science ®Google Scholar
• Tunaru, R., & Viney, H. P. (2010). Valuations of soccer players from statistical performance data. Journal of Quantitative Analysis in Sports, 6(2), 410–1559.
   Google Scholar
• Van Haaren, J., Dzyuba, V., Hannosset, S., & Davis, J. (2015). Automatically Discovering Offensive Patterns in Soccer Match Data. In: Fromont E., De Bie T., van Leeuwen M. (eds), Advances in Intelligent Data Analysis XIV. IDA 2015. Lecture Notes in Computer Science, vol 9385. Springer, Cham.
   Google Scholar
• Vilain, J.-B., & Kolkovsky, R. L. (2016). Estimating individual productivity in football [Report]. Sciences Po International University. Sciences Po Department of Economics, Paris, France.
   Google Scholar
• Weimar, D., & Wicker, P. (2017). Moneyball revisited effort and team performance in professional soccer. Journal of Sports Economics, 18(2), 140–161.
   Web of Science ®Google Scholar
• West, J. (2018). A review of the key demands for a football goalkeeper. International Journal of Sports Science & Coaching, 13(6), 1215–1222.
   Web of Science ®Google Scholar
• Whiting, S. W., & Maynes, T. D. (2016). Selecting team players: Considering the impact of contextual performance and workplace deviance on selection decisions in the National Football League. Journal of Applied Psychology, 101(4), 484.
   PubMed Web of Science ®Google Scholar
• Yu, W. (1992). ELECTRE Tri: Aspects méthodologiques et manuel d’utilisation [Report]. Document du LAMSADE. Université de Paris-Dauphine, Paris, France, LAMSADE.
   Google Scholar