Expected number of goals depending on intrinsic and extrinsic factors of a football player. An application to professional Spanish football league

Sáez-Castillo, A. J.; Avi, José Rodríguez; Sánchez, José María Pérez

doi:10.1080/17461391.2011.589473

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Models have been used to rate players for specific tasks. For example, Sáez Castillo et al (2013) and McHale and Szczepański (2014) present methods to identify the scoring ability of footballers whereas López Peña and Touchette (2012), López Peña and Sánchez Navarro (2015), Brooks et al (2016) and Szczepański and McHale (2016) deal with the passing aspect. But identifying the overall contribution of a player to a team's success (or lack of it) has proven difficult in soccer.…”

Section: Introductionmentioning

confidence: 99%

Plus–minus player ratings for soccer

Kharrat

McHale

Peña

2020

European Journal of Operational Research

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The paper presents a plus-minus rating for use in association football (soccer). We first describe the standard plus-minus methodology as used in basketball and ice-hockey and then adapt it for use in soccer. The usual goal-differential plus-minus is considered before two variations are proposed. For the first variation, we present a methodology to calculate an expected goals plus-minus rating. The second variation makes use of in-play probabilities of match outcome to evaluate an expected points plus-minus rating. We use the ratings to examine who are the best players in European football, and demonstrate how the players' ratings evolve over time. Finally, we shed light on the debate regarding which is the strongest league. The model suggests the English Premier League is the strongest, with the German Bundesliga a close runner-up. * task is to estimate time-varying ratings for individuals which update following new information (the latest results). Elo ratings have been used for over half a century for rating chess players. Similarly, the Glicko rating system (Glickman, 2012) provides a more theoretically justified model for estimating time-varying ratings of individuals.More recently, attention has moved to using machine learning techniques to estimate player ratings. The TrueSkill rating (Herbrich et al., 2007) developed at Microsoft is a generalisation of the Elo ratings and is used for rating video game players.Rating players in sports teams is more problematic. Players often have different responsibilities with some concentrated on offence (i.e. aiding scoring), whilst others are specialised in defence (i.e. helping to prevent scores for the opposition). A commonly used approach is to assign a value to a set of actions considered to be 'of interest' and to reward the player taking them with the associated value. This method was used for example in the EA SPORTS Player Performance Indicator (McHale et al., 2012) and is still used by the English Premier League as the official player ratings system. Due to its additivity, the previous approach provides simple, user-friendly player ratings and rankings. However, a cost of the simplicity is the lack of context and a deeper understanding of the situations in which actions were committed. Further, the data requirement is not trivial.Models have been used to rate players for specific tasks. For example, Sáez Castillo et al. (2013) and McHale and Szczepański (2014) present methods to identify the scoring ability of footballers whereas López Peña and Touchette (2012), López Peña and Sánchez Navarro (2015), Brooks et al. (2016) and Szczepański and McHale (2016) deal with the passing aspect.But identifying the overall contribution of a player to a team's success (or lack of it) has proven difficult in soccer. However, the concept of the PM ratings provides hope.The concept of the PM rating is fundamentally different to the rating mechanisms discussed above. It directly measures the contribution a player has on a team's success as measured by (the differential) of a ta...

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Section: Introductionmentioning

confidence: 99%

Plus–minus player ratings for soccer

Kharrat

McHale

Peña

2020

European Journal of Operational Research

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“…In many sports, there is a great deal of literature related to the evaluation of player performance [18]. Various approaches have been proposed to rate players for specific tasks, for example, a Bayesian regression model to obtain an estimation of the number of goals scored of any soccer player [19]; and a soccer player ranking system based completely on the value of the passes completed [20]. The final value is computed based on the relationship between pass locations and shot opportunities generated.…”

Section: Player Evaluation Methodsmentioning

confidence: 99%

A Fuzzy Framework to Evaluate Players' Performance in Handball

Romero¹,

Angulo²,

Serrano-Guerrero³

et al. 2020

IJCIS

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The evaluation of the players' performance in sports teams is commonly based on the opinion of experts who do not always agree on the importance of the chosen indicators. This paper presents a novel approach based on fuzzy multi-criteria group decision-making tools for selecting those criteria that best represent the handball player's performance in a match and for setting their relevance weights. Our approach consists of a fuzzy model to aggregate expert judgments. This methodology overcomes some drawbacks of classical systems, including the definition of the relevance of each criteria using linguistic labels. A preliminary evaluation analyzes handball players' performance indicators and their application to a short tournament. Considering the obtained results, we can conclude that the proposal is relevant and provides useful insights regarding player performance in different matches. The proposed methodology has also been compared with a basic plus-minus rating methodology. This comparison illustrates the feasibility of our approach. Results suggest that plus-minus rating is not the best solution to represent the performance of specialized players who only play when their team attack or defense. Our approach demonstrates being more appropriate for sports such as handball because it includes the valuation of a full set of positive actions in defense and attack.

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“…The GWRM has been also considered as a form of mixed NB distribution to model the excess of variability in relation to the Poisson distribution (over-dispersion) [10]. Finally, it could also be mentioned a Bayesian version of the GWRM which permits an estimation of the posterior distribution of the proneness of footballers in relation to their ability to score goals [11]. …”

Section: Introductionmentioning

confidence: 99%

GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data

2016

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Understanding why a random variable is actually random has been in the core of Statistics from its beginnings. The generalized Waring regression model for count data explains that inherent variability is given by three possible sources: randomness, liability and proneness. The model extends the negative binomial regression model and it is not included in the family of generalized linear models. In order to avoid that shortcoming, we developed the R package for fitting, describing and validating the model. The version we introduce in this communication provides a new design of the modelling function as well as new methods operating on the associated fitted model objects, so that the new software integrates easily into the computational toolbox for modelling count data in R. The release of a plug-in in order to use the package from the interface R Commander tries to contribute to the spreading of the model among non-advanced users. We illustrate the usage and the possibilities of the software with two examples from the fields of health and sport.

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Expected number of goals depending on intrinsic and extrinsic factors of a football player. An application to professional Spanish football league

Cited by 5 publications

References 17 publications

Plus–minus player ratings for soccer

Plus–minus player ratings for soccer

A Fuzzy Framework to Evaluate Players' Performance in Handball

GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data

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