Evaluating the efficiency of the association football transfer market using regression based player ratings

In recent times, the use of quantitative methods to improve decisions within sports has increased. In association football, large amounts of match data has become available. This work first shows how simple match data describing the players on pitch and the time for events such as goals and red cards, can be used to derive an objective player rating. The rating is based on solving a large linear regression model. The resulting player ratings are in turn used as input to a regression model for analyzing transfer fees. It is shown that the performance of players, as reflected in the player ratings, is an important predictor of transfer fees. At the same time, several other important factors that determine the size of transfer fees are identified

The collection, analysis and exploitation of footballer attributes: A systematic review

© 2022 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Non Commercial License (CC BY-NC 4.0)There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers. Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes. This resulted in the selection of 132 relevant papers published between 1999 and 2020. From these we have compiled a comprehensive list of player attributes, categorising them as static, such as age and height, or dynamic, such as pass completions and shots on target. To indicate their accuracy, we classified each attribute as objectively or subjectively derived, and finally by their implied accessibility and their likely personal and club sensitivity. We assigned these attributes to 25 logical groups such as passing, tackling and player demographics. We analysed the relative research focus on each group and noted the analytical methods deployed, identifying which statistical or machine learning techniques were used. We reviewed and considered the use of character trait attributes in the selected papers and discuss more formal approaches to their use. Based upon this we have made recommendations on how this work may be developed to support elite clubs in the consideration of transfer targets.Peer reviewedFinal Published versio

A Dimension Reduction Approach to Player Rankings in European Football

Player performance evaluation is a challenging problem with multiple dimensions. Football (soccer) is the largest sports industry in terms of monetary value and it is paramount that teams can assess the performance of players for both financial and operational reasons. However, this is a difficult task, not only because performance differs from position to position, but also it is based on competition, time played and team play-styles. Because of this, raw player statistics are not comparable across players and must be processed to facilitate a fair performance evaluation. Furthermore, teams may have different requirements and a generic player performance evaluation does not directly serve the particular expectations of different clubs. In this study, we provide a generic framework for estimating player performance and performing player-fit-to-criteria assessment, under different objectives, for left and right backs from competitions worldwide. The results show that the players who have ranked high have increased their transfer values and they have moved to suitable teams. Global nature of the proposed methodology expands the analyzed player pool, facilitating the search for outstanding players from all available competitions

Forecasting football match results - A study on modeling principles and efficiency of fixed-odds betting markets in football

Objectives of the study This thesis is about the statistical forecasting of (European) football match results. More specifically, the purpose of this thesis is to assess how a statistical forecast model that uses only publicly available information fares against public market odds in forecasting football match outcomes. Academic background and methodology The forecasting of sports results has been widely researched because it provides important insight into how betting markets operate. Football and betting associated with it has been the most popular topic because of the global popularity of the sport and because the betting markets associated with it capture large annual turnover. In spite of research by numerous authors, there is still room for improvement in terms of developing more accurate forecast models. Therefore, we contribute to existing literature by developing a regression model for forecasting football results. We assess the model's performance with forecast accuracy measurements and betting simulations. The principal idea of the model is based on the ELO rating system which assigns relative performance ratings to teams. Findings and conclusions In terms of accuracy measurements and betting simulations, the model developed in this thesis is able to match or surpass the results of existing statistical models of similar build. The measurements also indicate that the model can on average match the accuracy of the forecasts implied by the publicly quoted odds. However, the model is unable to generate positive betting returns. Together these results indicate that the publicly quoted odds for extensively betted football matches are slightly inefficient, but that this inefficiency does not make statistical betting algorithms consistently profitable. The results also indicate that historical league match results are the most important components of a statistical football forecast model, and that supplementing these components with other data yields only modest improvements to forecast accuracy