League and team characteristics that determine disciplinary action

This study investigates the causes of disciplinary action taken by referees in Portuguese football through three separate approaches. The first approach analyses how characteristics of different leagues and teams can impact player disciplinary action. The second approach focuses on characteristics specific to matches and how disciplinary action can vary from game to game. The third approach analyses football players on an individual level and aims to understand the player-specific characteristics that impact their fouling behaviour. Through these unique approaches, this study arrives at a comprehensive set of insights and recommendations that will support the development of Portuguese referees

Sports, Inc. Volume 3, Issue 1

The ILR Cornell Sports Business Society magazine is a semester publication titled Sports, Inc. This publication serves as a space for our membership to publish and feature in-depth research and well-thought out ideas to advance the world of sport. The magazine can be found in the Office of Student Services and is distributed to alumni who come visit us on campus. Issues are reproduced here with permission of the ILR Cornell Sports Business Society.https://digitalcommons.ilr.cornell.edu/sportsinc/1003/thumbnail.jp

Artificial intelligence and automation in human resources

The football industry has developed significantly over the past few decades and evolved into something more complicated than merely entertainment or sports. SL Benfica is one of the most successful sports organizations in Portugal and Europe, however the Club is not indifferent to the disruption and trends of the labor market. The goal of this dissertation was to identify approaches that may be used to address the Club's difficulties in a variety of contexts, with an emphasis on the corporate framework and a propensity to highlight best practices in the different business sector

Business model CANVAS Spirax

Treball Final de Grau en Administració d'Empreses. Codi: AE1049. Curs 2019/202

Focal Spot, Summer 1989

https://digitalcommons.wustl.edu/focal_spot_archives/1052/thumbnail.jp

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

Creating a model for expected Goals in football using qualitative player information

The field of sports analytics has been growing a lot in recent years. Sports like baseball and basketball were among the first to embrace it, but football has also taken big steps in that direction. One of the causes is that data analysis allows for the development of new advanced metrics which can provide a competitive advantage. This project presents a new version of one of these advanced metrics applied to football, the Expected Goals. The metric estimates how likely it is for a shot to end up becoming a goal. We present two different approaches for building the predictors: one that uses player qualitative information and another player agnostic. We then reflect on the importance of the calibration of the probabilities yielded by the models, as well as their possible interpretations, and present some of the applications that can be used to evaluate team and player performance. We also show the impact each feature has on the models to make their outputs interpretable and to demonstrate that the addition of the player qualitative information is important for the performance of the model

Does sacking a coach really help? Evidence from a Difference-in-Differences approach

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis project looks to evaluate if football clubs should or should not change their coach in order to improve their performance in the national league. For this analysis I selected, three of the most important European football leagues, La Liga (Spain), Serie A (Italy) and Premier League (England). The data used in this project was taken from the transfermarkt website, a large football platform. The data period is from season 2005-06 to season 2019-20 and has information about individual games results and squad value by player. The steps before the analysis were a data cleaning and consolidation of the information, creation of new features as a performance measure and selection of cases of interest for this analysis based on club and coach profile. Numeric variables were standardized to be on the same scale and make different seasons comparable. A K-means was applied to identify clubs according to their investments which has a proportional correlation with performance. Finally, a difference in differences analysis was applied to evaluate if a club would obtain a performance gain if they decided to sack their coach between game twelve and twenty-six of the season after a poor performance in consideration to squad price. As a general conclusion, it is possible to consider that on average the clubs in the treatment group and comparison group recover their performance after a period of underperforming, but the recovery of the clubs that sack their coach is lower compared with the clubs that keep them