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A Skellam regression model for quantifying positional value in soccer
| Content Provider | Scilit |
|---|---|
| Author | Pelechrinis, Konstantinos Winston, Wayne |
| Copyright Year | 2021 |
| Abstract | Soccer is undeniably the most popular sport world-wide and everyone from general managers and coaching staff to fans and media are interested in evaluating players’ performance. Metrics applied successfully in other sports, such as the (adjusted) +/− that allows for division of credit among a basketball team’s players, exhibit several challenges when applied to soccer due to severe co-linearities. Recently, a number of player evaluation metrics have been developed utilizing optical tracking data, but they are based on proprietary data. In this work, our objective is to develop an open framework that can estimate the expected contribution of a soccer player to his team’s winning chances using publicly available data. In particular, using data from (i) approximately 20,000 games from 11 European leagues over eight seasons, and, (ii) player ratings from the FIFA video game, we estimate through a Skellam regression model the importance of every line (attackers, midfielders, defenders and goalkeeping) in winning a soccer game. We consequently translate the model to expected league points added above a replacement player (eLPAR). This model can further be used as a guide for allocating a team’s salary budget to players based on their expected contributions on the pitch. We showcase similar applications using annual salary data from the English Premier League and identify evidence that in our dataset the market appears to under-value defensive line players relative to goalkeepers. |
| Related Links | https://www.degruyter.com/downloadpdf/journals/jqas/ahead-of-print/article-10.1515-jqas-2019-0122/article-10.1515-jqas-2019-0122.pdf |
| Ending Page | 201 |
| Page Count | 15 |
| Starting Page | 187 |
| ISSN | 21946388 |
| e-ISSN | 15590410 |
| DOI | 10.1515/jqas-2019-0122 |
| Journal | Journal of Quantitative Analysis in Sports |
| Issue Number | 3 |
| Volume Number | 17 |
| Language | English |
| Publisher | Walter de Gruyter GmbH |
| Publisher Date | 2021-09-27 |
| Access Restriction | Open |
| Subject Keyword | Journal of Quantitative Analysis in Sports Mathematical and Computational Biology Positional Value Skellam Regression Soccer Win Probability Journal: Journal of Quantitative Analysis in Sports, Vol- 17, Issue- 1 |
| Content Type | Text |
| Resource Type | Article |
| Subject | 3300/3301 1800/1801 |
