This paper discusses the application of advance statistics in soccer. In order to valuate the performance and value of soccer players, we introduce more advance statistics and try to detect the effect on players’ rating and market value from their box score statics.
In section 2 of this paper, we introduce an advance statistic - Box Rating (BR) - to valuate the performance of a soccer player in a match. The derivation of BR comes from the rating given by Whoscored regressed by 26 series of box score statistics (including goals, assists, passes and so on) of 2024 players in European top 5 leagues last 3 competition seasons (2017-2020). We then analyse the ridge trace of the regression coefficient to test the robustness of the regression.
In section 3 of this paper, to avoid the confounders like age, injury and commercial value which have unobserved effect to players’ market value, we use 35 series of player ability ratings from FIFA20 as the In- strument Variable and do two stage least squre (2SLS), getting the true effect players’ box score statistics exerting on their market value.
In section 4 of this paper, we compute the correlation among ratings and market value, and do some hypotheses-test of our results. We also point out some limitation of players’ statistics.

Haoyang Yan. How Soccer Players’ Box Score Statistics Effect on their Rating and Market Value. The Frontiers of Society, Science and Technology (2020) Vol. 2 Issue 15:82-104. https://doi.org/10.25236/FSST.2020.021511.

[1] Brian Macdonald. Adjusted plus-minus for nhl players using ridge regres- sion with goals, shots, fenwick, and corsi. Journal of Quantitative Analysis in Sports, 8(3), 2012.
[2] Brian Macdonald. A regression-based adjusted plus-minus statistic for nhl players. Journal of Quantitative Analysis in Sports, 7(3), 2011.
[3] Shankar Ghimire, Justin A Ehrlich, and Shane D Sanders. Measuring in- dividual worker output in a complementary team setting: Does regularized adjusted plus minus isolate individual nba player contributions? PloS one, 15(8):e0237920, 2020.
[4] Jeremy Mertz, L Donald Hoover, Jean Marie Burke, David Bellar, M Lani Jones, Briana Leitzelar, and W Lawrence Judge. Ranking the greatest nba players: A sport metrics analysis. International Journal of Performance Analysis in Sport, 16(3):737–759, 2016.
[5] Jeremias Engelmann. A new player evaluation technique for players of the national basketball association (nba). In Proceedings of the MIT Sloan Sports Analytics Conference, 2011.
[6] Tarak Kharrat, Ian G McHale, and Javier López Peña. Plus–minus player ratings for soccer. European Journal of Operational Research, 283(2):726– 736, 2020.
[7] Francesca Matano, Lee F Richardson, Taylor Pospisil, Collin Eubanks, and Jining Qin. Augmenting adjusted plus-minus in soccer with fifa ratings. arXiv preprint arXiv:1810.08032, 2018.
[8] Masoomeh Zameni, Mahmood Fathy, and Amin Sadri. A low cost algorithm for expected goal events detection in broadcast soccer video. International Journal of Digital Content Technology and its Applications, 4(8):118–125, 2010.
[9] Andrew Rowlinson et al. Football shot quality: Visualizing the quality of soccer/football shots. 2020.
[10] Guiliang Liu, Yudong Luo, Oliver Schulte, and Tarak Kharrat. Deep soccer analytics: learning an action-value function for evaluating soccer players. Data Mining and Knowledge Discovery, pages 1–29, 2020.
[11] Paul Power, Aditya Cherukumudi, Sujoy Ganguly, Felix Wei, Long Sha, Jennifer Hobbs, Hector Ruiz, and Patrick Lucey. Trading places–simulating goalkeeper performance using spatial & body-pose data, 2019.
[12] V Barnett and S Hilditch. The effect of an artificial pitch surface on home team performance in football (soccer). Journal of the Royal Statistical Society: Series A (Statistics in Society), 156(1):39–50, 1993.