253 research outputs found

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

    Get PDF
    © 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

    Evaluating the effectiveness of styles of play in elite soccer

    Get PDF
    The aim of this study was to evaluate the effectiveness of styles of play in soccer and the influence of contextual variables (i.e. match status, venue and quality of opposition). Team possessions (n = 68,766) from the 380 matches of the 2015–2016 English Premier League season were collected for this study. The Possession Effectiveness Index, based on Expected Goals and Ball Movement Points metrics, was used to measure the effectiveness of team possessions. Linear mixed models were applied to analyse the influence of contextual variables on the effectiveness score for each style. Results showed that the effectiveness of Direct Play, Counterattack, Maintenance and Crossing significantly increased when teams were winning by two or more goals. Counterattack increased its effectiveness when teams were winning by one goal and reduced its effectiveness when losing by one goal. The effectiveness of Direct Play increased when losing by two goals or more. Playing away negatively affected the effectiveness of Direct Play, Maintenance and High Pressure. In addition, playing against a stronger opposition reduced the effectiveness of all styles of play. The results suggest that the effectiveness of styles of play changes under specific circumstances and that not all contextual variables affect them in the same way

    Heatmaps in soccer: event vs tracking datasets

    Full text link
    We investigate how similar heatmaps of soccer players are when constructed from (i) event datasets and (ii) tracking datasets. When using event datasets, we show that the scale at which the events are grouped strongly influences the correlation with the tracking heatmaps. Furthermore, there is an optimal scale at which the correlation between event and tracking heatmaps is the highest. However, even at the optimal scale, correlations between both approaches are moderate. Furthermore, there is high heterogeneity in the players' correlation, ranging from negative values to correlations close to the unity. We show that the number of events performed by a player does not crucially determine the level of correlation between both heatmaps. Finally, we analyzed the influence of the player position, showing that defenders are the players with the highest correlations while forwards have the lowest.Comment: 6 pages, 5 figure

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

    Get PDF
    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

    A visual analytics approach for passing strateggies analysis in soccer using geometric features

    Get PDF
    Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol
    corecore