Identifying key players in soccer teams using network analysis and pass difficulty

We use a unique dataset to identify the key members of a football team. The methodology uses a statistical model to determine the difficulty of a pass from one player to another, and combines this information with results from network analysis, to identify which players are pivotal to each team in the English Premier League during the 2012–13 season. We demonstrate the methodology by looking closely at one game, whilst also summarising player performance for each team over the entire season. The analysis is hoped to be of use to managers and coaches in identifying the best team lineup, and in the analysis of opposition teams to identify their key players

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

Analyzing passing networks in association football based on the difficulty, risk, and potential of passes

This paper investigates the use of network analysis to identify key players on teams, and patterns of passing within teams, in association football. Networks are constructed based on passes made between players, and several centrality measures are investigated in combination with three different methods for evaluating individual passes. Four seasons of data from the Norwegian top division are used to identify key players and analyze matches from a selected team. The networks examined in this work have weights based on three different aspects of the passes made: their probability of being completed, the probability that the team keeps possession after the completed pass, and the probability of the pass being part of a sequence leading to a shot. The results show that using different metrics and network weights leads to the identification of key passers in different phases of play and in different positions on the pitch. Keywords: network analysis, pagerank, centrality, sportpublishedVersio

A framework for the analytical and visual interpretation of complex spatiotemporal dynamics in soccer

Pla de Doctorat Industrial de la Generalitat de CatalunyaSports analytics is an emerging field focused on the application of advanced data analysis for assessing the performance of professional athletes and teams. In soccer, the integration of data analysis is in its initial steps, primarily due to the difficulty of making sense of soccer's complex spatiotemporal relationships and effectively translating findings to practitioners. Recently, the availability of spatiotemporal data has given rise to applying statistical approaches to address problems such as estimating passing and scoring probability, or the evaluation of players' mental pressure. However, most of these approaches focus on isolated aspects of the sport, while coaches tend to focus on the broader interplay of all 22 players on the pitch. To address the non-stop flow of questions that coaching staff deal with daily, we identify the need for a flexible analysis framework that allows us to answer these questions quickly, accurately, and in a visually-interpretable way while capturing the complex spatial and contextual factors that rule the game. We propose developing such a comprehensive framework through the concept of the expected possession value (EPV). First introduced in basketball, EPV constitutes an instantaneous estimate of the expected points to be scored at the end of a possession. However, aside from a shared high-level goal, our focus on soccer necessitates a drastically different approach to account for the sport's nuances, such as looser notions of possession, the ability of passes to happen at any location, and space-time dependent turnover evaluation. Following this, we propose modeling EPV in soccer by addressing the question, "can we estimate the expectation of a team scoring or conceding the next goal at any time in the game?" From here, we address a series of derived interrogations, such as how should the EPV expression be structured so coaches can more easily interpret it? Can we produce calibrated and interpretable estimates for each of its components? Can we develop representative and soccer-specific features with the aid of coaches? Is it possible to learn complex features from raw level spatiotemporal data? Finally, and most importantly, can we produce compelling practical applications? These questions are successfully addressed in this thesis, where we present a series of contributions for both the machine learning and soccer analytics fields related to the modeling and practical interpretation of complex spatiotemporal dynamics. We propose a decomposed modeling approach where a series of foundational soccer components can be estimated separately and then merged to provide a single EPV estimation, providing flexibility to this integrated model. From a practical standpoint, we leverage several function approximation approaches to exploit complex relationships in spatiotemporal tracking data. An essential contribution of this work is the proposal of SoccerMap, a flexible deep learning architecture capable of producing accurate and visually-interpretable probability surfaces in a broad range of problems. Based on a large set of spatial and contextual features developed, we model and provide accurate estimates for each of the components of the EPV components. The flexibility and interpretation capabilities of the proposed model allow us to produce a broad set of practical applications related to on-ball performance, off-ball performance, and match analysis in soccer, and open the door for its future adaption to other sports. This thesis was developed under an Industrial Ph.D. program and carried out entirely at Fútbol Club Barcelona, which promoted a close collaboration with professional coaches. As a result, a vast part of the ideas developed in this thesis is now part of the club's daily player and team performance analysis pipeline.Sports analytics es una área de investigación de gran crecimiento y que se encuentra enfocada en la aplicación de análisis avanzado de datos para la evaluación del rendimiento de equipos y deportistas profesionales. En el fútbol, la integración del análisis de datos se encuentra en una etapa incipiente, principalmente dado la dificultad de evaluar los complejos factores espacio-temporales del juego, y de traducir los hallazgos al lenguaje de los entrenadores. La reciente disponibilidad de datos espacio-temporales ha dado pie a la aplicación de métodos estadísticos para explorar problemas tales como la estimación de la probabilidad de pasar o rematar exitosamente, o la evaluación de la presión mental durante el juego, entre muchos otros. Sin embargo, la mayoría de los estudios hasta la fecha se han enfocado en aspectos aislados del juego, mientras que el análisis de los entrenadores suele tomar una óptica más integral en la que considera la interacción de los 22 jugadores en el campo. En base a todo esto, identificamos la necesidad de contar con un completo sistema (framework) de análisis que permite responder al contínuo flujo de preguntas de los cuerpos técnicos de forma ágil y visualmente interpretable, y que al mismo tiempo permita capturar los complejos fenómenos espaciales y contextuales que rigen al fútbol. Proponemos el desarrollo de este sistema a través del concepto del valor esperado de la posesión (EPV, por sus siglas en inglés). El EPV, que fue introducido inicialmente en el baloncesto, constituye la estimación segundo a segundo de los puntos que se esperan obtener al final de una posesión de balón. Sin embargo, su adaptación al fútbol requiere de un enfoque completamente diferente para poder captar conceptos esenciales tales como que los pases pueden ir a cualquier ubicación en el campo, una definición menos rígida de la posesión de balón, y los efectos de perder el balón de acuerdo al espacio y tiempo en que este ocurre. En base esto, proponemos modelar el EPV enfocándonos en responder la siguiente pregunta ¿podemos estimar la esperanza de que un equipo marque o reciba el próximo gol, en cualquier instante del partido? A partir de aquí, desarrollamos una serie de preguntas derivadas relacionadas con la capacidad de proveer flexibilidad e interpretabilidad a nuestro modelo, así como desarrollar aplicaciones prácticas de forma ágil. Estas interrogantes son desarrolladas con éxito en esta tesis, donde presentamos una serie de contribuciones tanto al área de machine learning como a la de sports analytics. Proponemos un novedoso enfoque en el que se descompone el EPV en una serie de componentes esenciales, que pueden ser estimados de forma separada y luego integrados para producir una estimación única del EPV, dotando de mayor flexibilidad a este modelo integrado. Desde un punto de vista práctico, nos apoyamos en una serie de métodos de aproximación de funciones para sacar provecho de relaciones complejas en datos espacio-temporales de tracking. Derivado de esto, proponemos SoccerMap, una flexible arquitectura de deep learning capaz de producir superficies de probabilidad precisas y visualmente interpretables. Adicionalmente, nos apoyamos en una larga serie de variables espaciales y contextuales, desarrolladas en este trabajo, para modelar y proveer estimaciones acuradas de cada uno de los componentes del EPV. La flexibilidad de este modelo nos permite producir una vasta cantidad de aplicaciones prácticas relacionadas al rendimiento con y sin balón, y al análisis de partidos en fútbol, y marca un camino para su integración en otros deportes. Esta tesis fue desarrollada con el apoyo del Plan de Doctorados Industriales del Departamento de Investigación y Universidades de la Generalitat de Catalunya, y llevado a cabo en el Fútbol Club Barcelona, contando con la colaboración de entrenadores y profesionales del club.Postprint (published version

Predicting wins, losses and attributes' sensitivities in the soccer World Cup 2018 using neural network analysis

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes' sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model's output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15-20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance

Discovering Key Players and Key Groups in a Soccer Team Using Centrality Measures

In this thesis, I introduce that passing performance is crucial skill in the soccer game. I provide network centrality approaches to discover key players and key groups in a soccer team. The Utility Model of game theory evaluates each soccer player’s contribution to his team outcome. The approach of finding key players is to implement soccer passing network data with the combination of Nash Equilibrium with Bonacich Centrality Measure. We identify the key player by finding the top individual Inter-Centrality Measure, and also identify the key group of players that match better together in the game. The results verification will use 2013 market values, media attention, and team unbeaten probability by his appearance/absence

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports