On the Development of a Soccer Player Performance Rating System for the English Premier League

Transfer fees in European football have experienced a rapid increase in the past years. Simultaneously, an increasing number of domestic and recently foreign investors — who are assumed to further increase team spending in European football — have entered the football market by becoming club owners. In light of these developments, fears associated with an increasing influence of foreign (majority) investors from the financial as well as the emotional fan perspective have increased. Given the rather limited number of empirical studies focusing on the impact of investors on transfer fees, we shed further light on this topic. Based on a data sample including transfer fees, player characteristics, player performance and team performance from 2012–2013 to 2018–2019 for the English Premier League, we estimate OLS regressions and quantile regressions to analyze the effects of ownership concentration and investor origin on the amount of individual transfer fees. While we do not find strong evidence that ownership concentration increases the willingness to pay, we find fairly consistent results that foreign investors are willing to pay a premium compared to domestic investors. Our results also indicate that especially foreign investors who own a majority share of a club have a positive effect on transfer fees for the upper quantiles.

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Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries

Journal of Quantitative Analysis in Sports ◽

10.1515/jqas-2012-0036 ◽

2013 ◽

Vol 9 (1) ◽

pp. 37-50 ◽

Cited By ~ 18

Author(s):

Anthony Costa Constantinou ◽

Norman Elliott Fenton

Keyword(s):

Simple Technique ◽

Simple Approach ◽

Profit Margin ◽

Rating System ◽

Association Football ◽

Premier League ◽

Football Betting ◽

English Premier League ◽

Academic Study

AbstractA rating system provides relative measures of superiority between adversaries. We propose a novel and simple approach, which we call pi-rating, for dynamically rating Association Football teams solely on the basis of the relative discrepancies in scores through relevant match instances. The pi-rating system is applicable to any other sport where the score is considered as a good indicator for prediction purposes, as well as determining the relative performances between adversaries. In an attempt to examine how well the ratings capture a team’s performance, we have a) assessed them against two recently proposed football ELO rating variants and b) used them as the basis of a football betting strategy against published market odds. The results show that the pi-ratings outperform considerably the widely accepted ELO ratings and, perhaps more importantly, demonstrate profitability over a period of five English Premier League seasons (2007/2008–2011/2012), even allowing for the bookmakers’ built-in profit margin. This is the first academic study to demonstrate profitability against market odds using such a relatively simple technique, and the resulting pi-ratings can be incorporated as parameters into other more sophisticated models in an attempt to further enhance forecasting capability.

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The impact of different standards of opponents on observed player performance in the English Premier League

Journal of Human Sport and Exercise ◽

10.4100/jhse.2012.72.01 ◽

2012 ◽

Vol 7 (2) ◽

pp. 341-355 ◽

Cited By ~ 8

Author(s):

Athalie Redwood-Brown ◽

Christopher Bussell ◽

Harmeet Singh Bharaj

Keyword(s):

Premier League ◽

Player Performance ◽

The Impact ◽

English Premier League

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Correction: Estimation of injury costs: financial damage of English Premier League teams’ underachievement due to injuries

BMJ Open Sport & Exercise Medicine ◽

10.1136/bmjsem-2019-000675corr1 ◽

2020 ◽

Vol 6 (1) ◽

pp. e000675corr1

Keyword(s):

Premier League ◽

English Premier League

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A Skellam regression model for quantifying positional value in soccer

Journal of Quantitative Analysis in Sports ◽

10.1515/jqas-2019-0122 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Konstantinos Pelechrinis ◽

Wayne Winston

Keyword(s):

Regression Model ◽

Video Game ◽

Soccer Player ◽

Performance Metrics ◽

Optical Tracking ◽

General Managers ◽

Annual Salary ◽

Using Data ◽

English Premier League ◽

Positional Value

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.

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