Predicting transfer fees in professional European football before and during COVID-19 using machine learning

References

• Ante, L. (2019). Determinants of transfers fees: Evidence from the five major European football leagues. https://www.researchgate.net/profile/Lennart-Ante/publication/331929212
   Google Scholar
• Carmichael, F., Forrest, D., & Simmons, R. (1999). The labour market in association football: Who gets transferred and for how much? Bulletin of Economic Research, 51(2), 125–150. https://doi.org/10.1111/1467-8586.00075
   Google Scholar
• Carmichael, F., & Thomas, D. (1993). Bargaining in the transfer market: Theory and evidence. Applied Economics, 25(12), 1467–1476. https://doi.org/10.1080/00036849300000150
   Web of Science ®Google Scholar
• Carrieri, V., Principe, F., & Raitano, M. (2018). What makes you ‘super-rich’? New evidence from an analysis of football players’ wages. Oxford Economic Papers, 70(4), 950–973. https://doi.org/10.1093/oep/gpy025
   Web of Science ®Google Scholar
• Coates, D., & Parshakov, P. (2021). The wisdom of crowds and transfer market values. European Journal of Operational Research, 301(2), 523–534. https://doi.org/10.1016/j.ejor.2021.10.046
   Web of Science ®Google Scholar
• Depken, C. A., & Globan, T. (2021). Football transfer fee premiums and Europe's big five. Southern Economic Journal, 87(3), 889–908. https://doi.org/10.1002/soej.12471
   Web of Science ®Google Scholar
• Dobson, S., & Gerrard, B. (1999). The determination of player transfer fees in English professional soccer. Journal of Sport Management, 13(4), 259–279. https://doi.org/10.1123/jsm.13.4.259
   Web of Science ®Google Scholar
• Dobson, S., Gerrard, B., & Howe, S. (2000). The determination of transfer fees in English nonleague football. Applied Economics, 32(9), 1145–1152. https://doi.org/10.1080/000368400404281
   Web of Science ®Google Scholar
• Drewes, M., Daumann, F., & Follert, F. (2021). Exploring the sports economic impact of COVID-19 on professional soccer. Soccer & Society, 22(1-2), 125–137. https://doi.org/10.1080/14660970.2020.1802256
   Web of Science ®Google Scholar
• Fasiolo, M., Nedellec, R., Goude, Y., Capezza, C., Wood, S. N., & Fasiolo, M. M. (2020). Package ‘mgcViz’. Visualisations for generalised additive models. Available from: https://cran.r-project.org/web/packages/mgcViz/index.html
   Google Scholar
• Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R., & Goude, Y. (2020). Fast calibrated additive quantile regression. Journal of the American Statistical Association, 116(535), 1402–1412. https://doi.org/10.1080/01621459.2020.1725521
   Web of Science ®Google Scholar
• Feess, E., Frick, B., & Muehlheusser, G. (2004). Legal restrictions on buyout fees: Theory and evidence from German soccer. https://ssrn.com/abstract = 562445
   Google Scholar
• Feuillet, A., Terrien, M., Scelles, N., & Durand, C. (2020). Determinants of coopetition and contingency of strategic choices: The case of professional football clubs in France. European Sport Management Quarterly, 21(5), 748–763. https://doi.org/10.1080/16184742.2020.1779776
   Web of Science ®Google Scholar
• FIFA. (2022). Global transfer market report 2021. https://digitalhub.fifa.com/m/2b542d3b011270f/original/FIFA-Global-Transfer-Report-2021-2022-indd.pdf
   Google Scholar
• Fort, R., Lee, Y. H., & Oh, T. (2019). Quantile insights on market structure and worker salaries: The case of major league baseball. Journal of Sports Economics, 20(8), 1066–1087. https://doi.org/10.1177/1527002519851152
   Web of Science ®Google Scholar
• Frick, B. (2007). The football players’ labor market: Empirical evidence from the major European leagues. Scottish Journal of Political Economy, 54(3), 422–446. https://doi.org/10.1111/j.1467-9485.2007.00423.x
   Web of Science ®Google Scholar
• Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). Springer.
   Google Scholar
• Garcia-del-Barrio, P., & Pujol, F. (2020). Recruiting talent in a global sports market: Appraisals of soccer players’ transfer fees. Managerial Finance, 47(6), 789–811. https://doi.org/10.1108/MF-04-2020-0213
   Web of Science ®Google Scholar
• Gerrard, B., & Dobson, S. (2000). Testing for monopoly rents in the market for playing talent–evidence from English professional football. Journal of Economic Studies, 27(3), 142–164. https://doi.org/10.1108/01443580010326049
   Google Scholar
• Gyimesi, A., & Kehl, D. (2021). Relative age effect on the market value of elite European football players: A balanced sample approach. European Sport Management Quarterly, 1–17. https://doi.org/10.1080/16184742.2021.1894206. in press.
   Web of Science ®Google Scholar
• Hair, J. F. (2009). Multivariate data analysis. Prentice Hall.
   Google Scholar
• Hastie, T. J., & Tibshirani, R. J. (2017). Generalized additive models. Routledge.
   Google Scholar
• Hawkins, R. D., Hulse, M., Wilkinson, C., Hodson, A., & Gibson, M. (2001). The association football medical research programme: An audit of injuries in professional football. British Journal of Sports Medicine, 35(1), 43–47. https://doi.org/10.1136/bjsm.35.1.43
   PubMed Web of Science ®Google Scholar
• Hägglund, M., Waldén, M., & Ekstrand, J. (2006). Previous injury as a risk factor for injury in elite football: A prospective study over two consecutive seasons. British Journal of Sports Medicine, 40(9), 767–772. https://doi.org/10.1136/bjsm.2006.026609
   PubMed Web of Science ®Google Scholar
• Herberger, T. A., & Wedlich, F. (2017). Does selection bias matter in football players’ valuation? A crowdsourced valuation approach on players’ athletic characteristics. Journal of Global Sport Management, 2(3), 196–214. https://doi.org/10.1080/24704067.2017.1350593
   Google Scholar
• Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
   Web of Science ®Google Scholar
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). Springer.
   Google Scholar
• Kharrat, T., McHale, I. G., & Peña, J. L. (2020). Plus–minus player ratings for soccer. European Journal of Operational Research, 283(2), 726–736. https://doi.org/10.1016/j.ejor.2019.11.026
   Web of Science ®Google Scholar
• Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(1), 1–26. https://doi.org/10.18637/jss.v028.i05
   PubMedGoogle Scholar
• Landers, R. N., Brusso, R. C., Cavanaugh, K. J., & Collmus, A. B. (2016). A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research. Psychological Methods, 21(4), 475–492. https://doi.org/10.1037/met0000081
   PubMed Web of Science ®Google Scholar
• Leeds, M. A. (2014). Quantile regression for sports economics. International Journal of Sport Finance, 9(4), 346–359.
   Web of Science ®Google Scholar
• Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
   Google Scholar
• Liu, G., Luo, Y., Schulte, O., & Kharrat, T. (2020). Deep soccer analytics: Learning an action-value function for evaluating soccer players. Data Mining and Knowledge Discovery, 34(5), 1531–1559. https://doi.org/10.1007/s10618-020-00705-9
   Web of Science ®Google Scholar
• McHale, I. G., & Holmes, B. (2022). Estimating transfer fees of professional footballers using advanced performance metrics and machine learning. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2022.06.033
   Web of Science ®Google Scholar
• McHale, I. G., & Relton, S. (2018). Identifying key players in soccer teams using network analysis and pass difficulty. European Journal of Operational Research, 268(1), 339–347. https://doi.org/10.1016/j.ejor.2018.01.018
   Web of Science ®Google Scholar
• Müller, O., Simons, A., & Weinmann, M. (2017). Beyond crowd judgments: Data-driven estimation of market value in association football. European Journal of Operational Research, 263(2), 611–624. https://doi.org/10.1016/j.ejor.2017.05.005
   Web of Science ®Google Scholar
• O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6
   Web of Science ®Google Scholar
• Parnell, D., Bond, A. J., Widdop, P., & Cockayne, D. (2021). Football worlds: Business and networks during COVID-19. Soccer & Society, 22(1-2), 19–26. https://doi.org/10.1080/14660970.2020.1782719
   Web of Science ®Google Scholar
• Pfirrmann, D., Herbst, M., Ingelfinger, P., Simon, P., & Tug, S. (2016). Analysis of injury incidences in male professional adult and elite youth soccer players: A systematic review. Journal of Athletic Training, 51(5), 410–424. https://doi.org/10.4085/1062-6050-51.6.03
   PubMed Web of Science ®Google Scholar
• Quansah, T., Frick, B., Lang, M., & Maguire, K. (2021). The importance of club revenues for player salaries and transfer expenses – How does the coronavirus outbreak (COVID-19) impact the English Premier League? Sustainability, 13(9), 5154. https://doi.org/10.3390/su13095154
   Google Scholar
• Quansah, T. K., Lang, M., & Frick, B. (2022). Color blind – Investigating customer-based discrimination in European soccer. Working Paper, October 4, 2022, Université de Lausanne. http://doi.org/10.2139/ssrn.4237624
   Google Scholar
• Ramos-Filho, L., & Ferreira, M. P. (2021). The reverse relative age effect in professional soccer: An analysis of the Brazilian national league of 2015. European Sport Management Quarterly, 21(1), 78–93. https://doi.org/10.1080/16184742.2020.1725089
   Web of Science ®Google Scholar
• Reilly, B., & Witt, R. (1995). English league transfer prices: Is there a racial dimension? Applied Economics Letters, 2(7), 220–222. https://doi.org/10.1080/135048595357302
   Web of Science ®Google Scholar
• Ruijg, J., & van Ophem, H. (2015). Determinants of football transfers. Applied Economics Letters, 22(1), 12–19. https://doi.org/10.1080/13504851.2014.892192
   Web of Science ®Google Scholar
• Speight, A., & Thomas, D. (1997a). Arbitrator decision-making in the transfer market: An empirical analysis. Scottish Journal of Political Economy, 44(2), 198–215. https://doi.org/10.1111/1467-9485.00053
   Web of Science ®Google Scholar
• Speight, A., & Thomas, D. (1997b). Football league transfers: A comparison of negotiated fees with arbitration settlements. Applied Economics Letters, 4(1), 41–44. https://doi.org/10.1080/758521830
   Web of Science ®Google Scholar
• Wood, S., & Wood, M. S. (2015). Package ‘mgcv’. Available from: https://cran.uib.no/web/packages/mgcv/mgcv.pdf
   Google Scholar