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Computational Estimation of Football Player Wages Cover
By: L. Yaldo and  L. Shamir  
Open Access
|Jul 2017

References

  1. Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1):37–66.
  2. Aldous, D. (1993). The continuum random tree III. The Annals of Probability, 248–289.10.1214/aop/1176989404
  3. Arnedt, R. B. (1998). European union law and football nationality restrictions: the economics and politics of the bosman decision. Emory International Law Review, 12, 1091.
  4. Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally weighted learning for control. In Lazy learning (pp. 75-113). Springer Netherlands.10.1007/978-94-017-2053-3_3
  5. Bishop, C. M. (2006). Pattern recognition and machine learning. Machine Learning, 128, 1–58.
  6. Bryson, A., Rossi, G., & Simmons, R. (2014). The migrant wage premium in professional football: a superstar effect? Kyklos, 67(1), 12–28.10.1111/kykl.12041
  7. Castellano, J., Alvarez-Pastor, D., & Bradley, P. S. (2014). Evaluation of research using computerised tracking systems (amisco r and prozone r) to analyse physical performance in elite soccer: A systematic review. Sports Medicine, 44(5), 701–712.10.1007/s40279-014-0144-3
  8. Cleary, J. G., Trigg, L. E., et al. (1995). K*: An instance-based learner using an entropic distance measure. In Proceedings of the 12th International Conference on Machine learning, 5, 108–114.10.1016/B978-1-55860-377-6.50022-0
  9. Dasarathy, B. V. (1994). Minimal consistent set (mcs) identification for optimal nearest neighbor decision systems design. IEEE Transactions on Systems, Man, and Cybernetics, 24(3), 511–517.10.1109/21.278999
  10. Dejonghe, T. & Van Opstal, W. (2010). Competitive balance between national leagues in european football after the bosman case. Rivista di Diritto ed Economia dello Sport, 6(2), 41–61.
  11. Feess, E., Gerfin, M., & Muehlheusser, G. (2010). The incentive effects of long-term contracts on performance-evidence from a natural experiment in european soccer. Technical Report, Mimeo: Berlin.
  12. Frank, E., Hall, M., & Pfahringer, B. (2002). Locally weighted naive bayes. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 249–256.
  13. Frick, B. (2006). Salary determination and the pay-performance relationship in professional soccer: Evidence from germany. Sports Economics After Fifty Years: Essays in Honour of Simon Rottenberg. Oviedo: Ediciones de la Universidad de Oviedo, 125–146.
  14. Frick, B. (2007). The football player’s labor market: Empirical evidence from the major european leagues. Scottish Journal of Political Economy, 54(3), 422–446.10.1111/j.1467-9485.2007.00423.x
  15. Frick, B. (2011). Performance, salaries, and contract length: empirical evidence from german soccer. International Journal of Sport Finance, 6(2), 87.
  16. Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378.10.1016/S0167-9473(01)00065-2
  17. Garcia-del Barrio, P., & Pujol, F. (2007). Pay and performance in the spanish soccer league: who gets the expected monopsony rents. Technical report, University of Navarra, Spain
  18. Garcia-del Barrio, P., & Pujol, F. (2009). The rationality of under-employing the bestperforming soccer players. Labour, 23(3), 397–419.10.1111/j.1467-9914.2009.00457.x
  19. Giulianotti, R. (2012). Football. Wiley Online Library.10.1002/9780470670590.wbeog213
  20. Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.10.1109/34.709601
  21. Jensen, M. M., Grønbæk, K., Thomassen, N., Andersen, J., and Nielsen, J. (2014). Interactive football-training based on rebounders with hit position sensing and audio-visual feedback. Intentional Journal of Computer Science in Sport, 13(1), 57–68.
  22. Kase, K., De Hoyos, I. U., Sanchis, C. M., & Breton, M. O. (2007). The proto-image of real madrid: implications for marketing and management. International Journal of Sports Marketing and Sponsorship, 8(3), 7–28.10.1108/IJSMS-08-03-2007-B004
  23. Kohavi, R. (1995). The power of decision tables. In European Conference on Machine Learning, 174–189.10.1007/3-540-59286-5_57
  24. Lames, M., McGarry, T., Nebel, B., & Roemer, K. (2011). Computer science in sportspecial emphasis: Football (dagstuhl seminar 11271). Dagstuhl Reports, 1(7).
  25. Markovits, A. S., & Green, A. I. (2017). FIFA, the video game: a major vehicle for soccer’s popularization in the United States. Sport in Society, 20(5-6), 716-734.10.1080/17430437.2016.1158473
  26. Muller, J. C., Lammert, J., & Hovemann, G. (2012). The financial fair play regulations of uefa: An adequate concept to ensure the long-term viability and sustainability of european club football? International Journal of Sport Finance, 7(2), 117.
  27. O’Donoghue, P. & Robinson, G. (2009). Validity of the prozone3 r player tracking system: A preliminary report. International Journal of Computer Science in Sport, 8(1), 37–53.
  28. Orejan, J. (2011). Football/Soccer: History and tactics. McFarland. Jefferson, NC, USA.
  29. Prasetio, D. (2016). Predicting football match results with logistic regression. In International Conference on Advanced Informatics: Concepts, Theory And Application, 1–5.10.1109/ICAICTA.2016.7803111
  30. Robnik-Sikonja, M. & Kononenko, I. (1997). An adaptation of relief for attribute estimation in regression. In Machine Learning: Proceedings of the Fourteenth International Conference, 296–304.
  31. Rohde, M. & Breuer, C. (2016). Europes elite football: Financial growth, sporting success, transfer investment, and private majority investors. International Journal of Financial Studies, 4(2), 12.10.3390/ijfs4020012
  32. Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 287–294.10.1145/130385.130417
  33. Shin, J. & Gasparyan, R. (2014). A novel way to soccer match prediction. Technical Report, Stanford U., CA. USA.
  34. Siegle, M., Stevens, T., & Lames, M. (2013). Design of an accuracy study for position detection in football. Journal of Sports Sciences, 31(2), 166–172.10.1080/02640414.2012.72313122994162
  35. Torgler, B. & Schmidt, S. L. (2007). What shapes player performance in soccer? empirical findings from a panel analysis. Applied Economics, 39(18), 2355–2369.10.1080/00036840600660739
  36. Torgler, B., Schmidt, S. L., & Frey, B. S. (2006). Relative income position and performance: an empirical panel analysis.10.2139/ssrn.889328
  37. Wicker, P., Prinz, J., Weimar, D., Deutscher, C., & Upmann, T. (2013). No pain, no gain? effort and productivity in professional soccer. International Journal of Sport Finance, 8(2), 124.
Language: English
Page range: 18 - 38
Published on: Jul 22, 2017
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2017 L. Yaldo, L. Shamir, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.