Visual Analysis of Pressure in Football

Modern movement tracking technologies enable acquisition of high quality data about movements of the players and the ball in the course of a football match. However, there is a big difference between the raw data and the insights into team behaviors that analysts would like to gain. To enable such insights, it is necessary first to establish relationships between the concepts characterizing behaviors and what can be extracted from data. This task is challenging since the concepts are not strictly defined. We propose a computational approach to detecting and quantifying the relationships of pressure emerging during a game. Pressure is exerted by defending players upon the ball and the opponents. Pressing behavior of a team consists of multiple instances of pressure exerted by the team members. The extracted pressure relationships can be analyzed in detailed and summarized forms with the use of static and dynamic visualizations and interactive query tools. To support examination of team tactics in different situations, we have designed and implemented a novel interactive visual tool “time mask”. It enables selection of multiple disjoint time intervals in which given conditions are fulfilled. Thus, it is possible to select game situations according to ball possession, ball distance to the goal, time that has passed since the last ball possession change or remaining time before the next change, density of players’ positions, or various other conditions. In response to a query, the analyst receives visual and statistical summaries of the set of selected situations and can thus perform joint analysis of these situations. We give examples of applying the proposed combination of computational, visual, and interactive techniques to real data from games in the German Bundesliga, where the teams actively used pressing in their defense tactics

What’s next in complex networks? Capturing the concept of attacking play in invasive team sports

The evolution of performance analysis within sports sciences is tied to technology development and practitioner demands. However, how individual and collective patterns self-organize and interact in invasive team sports remains elusive. Social network analysis has been recently proposed to resolve some aspects of this problem, and has proven successful in capturing collective features resulting from the interactions between team members as well as a powerful communication tool. Despite these advances, some fundamental team sports concepts such as an attacking play have not been properly captured by the more common applications of social network analysis to team sports performance. In this article, we propose a novel approach to team sports performance centered on sport concepts, namely that of an attacking play. Network theory and tools including temporal and bipartite or multilayered networks were used to capture this concept. We put forward eight questions directly related to team performance to discuss how common pitfalls in the use of network tools for capturing sports concepts can be avoided. Some answers are advanced in an attempt to be more precise in the description of team dynamics and to uncover other metrics directly applied to sport concepts, such as the structure and dynamics of attacking plays. Finally, we propose that, at this stage of knowledge, it may be advantageous to build up from fundamental sport concepts toward complex network theory and tools, and not the other way around.info:eu-repo/semantics/acceptedVersio

Functional space-time properties of team synergies in high-performance football

This thesis aimed to investigate the performance of high-level teams in football, through the analysis of the interactions of their players in the context of the game, as these interactions result in functional effects that could not otherwise be achieved (synergies). From a spatial point of view, we argue that the understanding of collective “payoffs” emerging from players’ interactions and their behavioural patterns, can be accomplished through ”Delaunay triangulations” and consequent ”Voronoi diagrams”. Analysing the positional data (22 players and the ball) in 20 games of the French premier league, in this thesis we essentially sought to focus on territorial dominance as a variable that potentially captures the spatial affordances perceived by players. Whether from a collective global point of view or from a perspective of the local interactions that arise in the game landscape. Supported by the ecological dynamics and the synergism hypothesis, in this thesis we begin by demonstrating the existing connection between the territorial dominance of a team and the offensive effectiveness, as well as the absence of temporal overlap between the ball possession status and territorial dominance. Similarly, we also demonstrated that the space dominance of each player, which contributes to the territorial dominance of the team as a whole, is constrained by the team’s formation and the role assumed by each player in this collective framework. In order to understand the dynamics of interactions between players and the functional effects that come from it, we then focus on two tasks that are related to collective performance: the pass and the shot. Reflecting on the need to find methods that capture how the distribution of players on the pitch influences the functional degrees of freedom of a team as a whole and the passing opportunities that emerge from it. And, at the level of finishing situations, how the dominance of space can be included in the quantification of the value that each player assigns to occupy a certain place in the game landscape, and which is at the basis of their decision-making (shoot or pass the ball to another teammate possibly better ”positioned”). In sum, through the initial conceptual framework and the applied studies, we argue that the analysis of team performance should focus on the functional synergies that result from interactions between players. In this way, we demonstrate, through some examples, how the methods and conclusions taken from this thesis can be applied in practice by football coaches.Esta tese teve como objetivo investigar a performance de equipas de alto nível no futebol, através da análise das interações dos seus jogadores no contexto do jogo pois daí resultam efeitos funcionais que apenas são atingidos através dessas mesmas interações (sinergias). De um ponto de vista espacial, defendemos que o estudo glocal das interações entre os jogadores para a compreensão do rendimento coletivo, pode ser realizado através de “triangulações de Delaunay” e consequentes “diagramas de Voronoi”. Analisando os dados posicionais dos 22 jogadores e da bola, em 20 jogos da primeira liga francesa, nesta tese procurámos essencialmente nos focar sobre o domínio territorial enquanto variável que capta potencialmente as affordances espaciais percebidas pelos jogadores. Seja de um ponto de vista global coletivo, seja numa perspetiva das interações locais que surgem na paisagem de jogo. Suportados pela dinâmica ecológica e pela hipótese do sinergismo, nesta tese começamos por demonstrar a ligação existente entre o domínio territorial das equipas e a sua efetividade ofensiva, bem como a inexistência de uma sobreposição temporal entre a posse de bola e esse domínio. De igual forma, também demonstrámos que o domínio do espaço de cada jogador, que contribui para o domínio territorial da equipa no seu todo, é constrangido pelo sistema de jogo das equipas e pelo papel assumido por cada jogador neste referencial coletivo. No sentido de compreender a dinâmica das interações entre os jogadores e os efeitos funcionais que daí advêm, focamo-nos seguidamente em duas tarefas que estão relacionadas com a performance coletiva: o passe e o remate. Refletindo sobre a necessidade de encontrar métodos que captem de que forma a distribuição dos jogadores em campo influencia os graus de liberdade funcionais de uma equipa no seu todo e as oportunidades de passe que daí emergem. E, ao nível das situações de finalização, de que forma o domínio do espaço poderá ser incluído na quantificação do valor que cada jogador atribui a ocupar um determinador espaço na paisagem de jogo e que está na base da sua tomada de decisão (rematar ou passar a bola para outro colega eventualmente melhor “posicionado”). Em suma, através do enquadramento conceptual inicial e dos estudos aplicados, defendemos que o estudo da performance das equipas deverá se centrar nas sinergias funcionais que resultam das interações entre os jogadores. Desta forma, demonstramos, através de alguns exemplos, como é que os métodos e ilações retirados desta tese poderão ser aplicados na prática pelos treinadores de futebol

Complex networks analysis in team sports performance: multilevel hypernetworks approach to soccer matches

Humans need to interact socially with others and the environment. These interactions lead to complex systems that elude naïve and casuistic tools for understand these explanations. One way is to search for mechanisms and patterns of behavior in our activities. In this thesis, we focused on players’ interactions in team sports performance and how using complex systems tools, notably complex networks theory and tools, can contribute to Performance Analysis. We began by exploring Network Theory, specifically Social Network Analysis (SNA), first applied to Volleyball (experimental study) and then on soccer (2014 World Cup). The achievements with SNA proved limited in relevant scenarios (e.g., dynamics of networks on n-ary interactions) and we moved to other theories and tools from complex networks in order to tap into the dynamics on/off networks. In our state-of-the-art and review paper we took an important step to move from SNA to Complex Networks Analysis theories and tools, such as Hypernetworks Theory and their structural Multilevel analysis. The method paper explored the Multilevel Hypernetworks Approach to Performance Analysis in soccer matches (English Premier League 2010-11) considering n-ary cooperation and competition interactions between sets of players in different levels of analysis. We presented at an international conference the mathematical formalisms that can express the players’ relationships and the statistical distributions of the occurrence of the sets and their ranks, identifying power law statistical distributions regularities and design (found in some particular exceptions), influenced by coaches’ pre-match arrangement and soccer rules.Os humanos necessitam interagir socialmente com os outros e com o envolvimento. Essas interações estão na origem de sistemas complexos cujo entendimento não é captado através de ferramentas ingénuas e casuísticas. Uma forma será procurar mecanismos e padrões de comportamento nas atividades. Nesta tese, o foco centra-se na utilização de ferramentas dos sistemas complexos, particularmente no contributo da teoria e ferramentas de redes complexas, na Análise do Desempenho Desportivo baseado nas interações dos jogadores de equipas desportivas. Começámos por explorar a Teoria das Redes, especificamente a Análise de Redes Sociais (ARS) no Voleibol (estudo experimental) e depois no futebol (Campeonato do Mundo de 2014). As aplicações da ARS mostraram-se limitadas (por exemplo, na dinâmica das redes em interações n-árias) o que nos trouxe a outras teorias e ferramentas das redes complexas. No capítulo do estadoda- arte e artigo de revisão publicado, abordámos as vantagens de utilização de outras teorias e ferramentas, como a análise Multinível e Teoria das Híperredes. No artigo de métodos, apresentámos a Abordagem de Híperredes Multinível na Análise do Desempenho em jogos de futebol (Premier League Inglesa 2010-11) considerando as interações de cooperação e competição nos conjuntos de jogadores, em diferentes níveis de análise. Numa conferência internacional, apresentámos os formalismos matemáticos que podem expressar as relações dos jogadores e as distribuições estatísticas da ocorrência dos conjuntos e a sua ordem, identificando regularidades de distribuições estatísticas de power law e design (encontrado nalgumas exceções estatísticas específicas), promovidas pelos treinadores na preparação dos jogos e constrangidas pelas regras do futebol

Automated Detection of Complex Tactical Patterns in Football—Using Machine Learning Techniques to Identify Tactical Behavior

Football tactics is a topic of public interest, where decisions are predominantly made based on gut instincts from domain-experts. Sport science literature often highlights the need for evidence-based research on football tactics, however the limited capabilities in modeling the dynamics of football has prevented researchers from gaining usable insights. Recent technological advances have made high quality football data more available and affordable. Particularly, positional data providing player and ball coordinates at every instance of a match can be combined with event data containing spatio-temporal information on any event taking place on the pitch (e.g. passes, shots, fouls). On the other hand, the application of machine learning methods to domain-specific problems yields a paradigm shift in many industries including sports. The need for more informed decisions as well as automating time consuming processes—accelerated by the availability of data—has motivated many scientific investigations in football analytics. This thesis is part of a research program combining methodologies from sports and data science to address the following problems: the synchronization of positional and event data, objectively quantifying offensive actions, as well as the detection of tactical patterns. Although various basic insights from the overall research program are integrated, this thesis focuses primarily on the latter one. Specifically, positional and event data are used to apply machine learning techniques to identify eight established tactical patterns in football: namely high-/mid-/low-block defending, build-up/attacking play in the offense, counterpressing and counterattacks during transitions, and patterns when defending corner-kicks, e.g. player-/zonal- or post-marking. For each pattern, we consolidate definitions with football experts and label large amounts of data manually using video recordings. The inter-labeler reliability is used to ensure that each pattern is well-defined. Unsupervised techniques are used for the purpose of exploration, and supervised machine learning methods based on expert-labeled data for the final detection. As an outlook, semi-supervised methods were used to reduce the labeling effort. This thesis proves that the detection of tactical patterns can optimize everyday processes in professional clubs, and leverage the domain of tactical analysis in sport science by gaining unseen insights. Additionally, we add value to the machine learning domain by evaluating recent methods in supervised and semi supervised machine learning on challenging, real-world problems

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

Modelling Players' Interactions in Football: A Multilevel Hypernetworks Approach.

Na presente tese procura-se avançar com fundamentação teórica e prática, assim como com demonstrações empíricas referentes à reconceptualização das equipas de futebol enquanto redes sociais complexas. Estas redes evidenciam comportamentos sinérgicos emergentes e auto-organizados cuja complexidade, enraizada nas redes de interações dos jogadores, pode ser discernida através da análise de redes sociais. Não obstante, as técnicas tradicionais de rede exibem algumas limitações que podem levar a dados imprecisos e falaciosos. Essas limitações estão relacionadas com a exagerada ênfase que é colocada nos comportamentos de ataque das equipas, negligenciando-se as ações defensivas. Tal leva a que: a troca de informações incida maioritariamente nos comportamentos de passe; a variabilidade do comportamento dos jogadores seja, na maioria dos casos, desconsiderada; e a maioria das métricas usadas para modelar as interações dos jogadores se baseiem em distâncias geodésicas. Assim, as hiperredes multiníveis são aqui propostas enquanto nova abordagem metodológica capaz de superar aquelas limitações. Esta abordagem multinível caracteriza-se por um conjunto de conceitos e ferramentas metodológicas coerentes com a análise da dinâmica relacional subjacente aos processos sinergísticos evidenciados durante a competição. Por um lado, estes processos foram capturados na dinâmica de alteração das configurações táticas exibidas pelas equipas durante a competição, pela quantificação do tipo de simplices (interações de grupos de jogadores, e.g., 2vs.1) atendendo à localização da bola, e na dinâmica de interação, transformação dos simplices em determinados eventos do jogo. Por outro lado, a aplicação das hiperredes multiníveis permitiu, de igual modo, capturar as tendências de sincronização local (nível meso) emergentes em contextos de prática. Esta tese destacou o valor da adoção de uma abordagem de hiperredes multiníveis para melhorar a compreensão sobre os processos sinérgicos dos jogadores e equipas de futebol emergentes durante a prática e a competição. Estas poderão vir a revelar-se ferramentas promissoras na análise da performance desportiva, tendo igualmente um papel relevante na monitorização e controlo do treino.PALAVRAS-CHAVE: FUTEBOL, CIÊNCIA DAS REDES, HIPERREDES MULTINÍVEL, DINÂMICA DA EQUIPA, ANÁLISE DA PERFORMANCEThis thesis aims to advance practical and theoretical understanding, as well as empirical evidence regarding the re-conceptualisation of Football teams as complex social networks. These networks display synergetic, emergent and self-organised behaviour and the complexity rooted in the networks of players' interactions can be discerned through analysis of social networks. Notwithstanding, traditional network techniques display some limitations that can lead to inaccurate and misleading data. Such limitations are related with an over-emphasis on network attacking behaviours thus neglecting the defensive actions of the opposing team. This leads to: information exchange mainly analysed through passing behaviours; the variability of players' performance is in most cases disregarded; most metrics used to model players' interactions are based on geodesic distances. Thus, multilevel hypernetworks are proposed as a novel methodological approach capable of overriding such limitations. This multilevel approach is characterised by a set of conceptual and methodological tools consistent with analysis of the relational dynamics underlying the synergistic processes evidenced during competition. On the one hand, these processes were captured in the changing dynamics of tactical configurations of teams during competition, by the quantification of the type of simplices (interactions between sub-groups of players, e.g., 2vs.1) in relation to ball location, and in the dynamics of simplices' interactions and transformations in certain game events. On the other hand, the application of multilevel hypernetworks allowed to capture local (meso level) synchronisation tendencies in practice contexts. This thesis highlighted the value of adopting a multilevel hypernetworks approach for enhancing understanding about the synergistic processes of players and football teams emerging during practice and competition. These tools may prove to be promising in the analysis of sports performance, also having an important role in the monitoring and control of training

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

The Analysis of Team Tactical Behaviour in Football Using GNSS Positional Data

Tactical analysis in football is an emerging field focused on assessing the collective movement of teams. Advanced player tracking technology systems facilitate the data collection for tactical analysis. GNSS tracking systems is currently the most popular player tracking technology in football application and is mainly used in physical monitoring. It also captures players positional information as geographic coordinates (i.e., latitude and longitude coordinates) which requires extra data pre-processing for tactical analysis as opposed to Cartesian coordinates (i.e., X, Y coordinates). Given the lack of a comprehensive workflow on pre-processing raw GNSS positional data for calculating tactical measures in previous publications, this thesis aimed to present a workflow that provides exemplar data, processing steps, potential issues, and corresponding solutions. With the presented workflow, not only sport scientists but also practitioners are able to engage in tactical analysis using GNSS tracking systems and bring in their own understanding and perspective. In other words, GNSS tracking systems could play an important role in both physical and tactical analysis in real-world application. Collective movements and actions vary as the match progresses along. The second objective was to use GNSS positional data to compare team tactical behaviour in different phases of a competitive match. The presented workflow was applied in data pre-processing of this analysis as a proof of concept. Although team tactical behaviour in football has been widely studied in recent years, there is no previous study that analyses team tactical behaviour in phase of attack, defence, and transition, based on tactical measures measured by positional data. In this thesis, effective playing time of a professional football match was divided into phase of in possession (IP), attack-to- defence transition (ADT), out of possession (OOP), and defence-to-attack transition (DAT). Team length, width, length per width ratio (LpW ratio), surface area, stretch indices, and interpersonal distance were calculated and compared to explore the difference of team tactical behaviour between phases. The findings showed that the team tactical behaviour during each phase was in line with the offensive and defensive tactical principles. The team presented a more dispersed and wider formation while in possession than other phases. The difference of all team tactical behaviour between IP and DAT indicated the potentiality of distinguishing defence-to-attack transition from in possession when analysing offensive tactical behaviour. Moreover, there was no significant difference across all tactical measures between defence-to-attack transition and defence, which implied that a short period of time was required for the team to switch to attack mode. In the future, the difference between transitions, attack, and defence should be valued in tactical analysis. Combining multi-type data with multi-disciplinary knowledge could inform stakeholders of dynamic team moving pattern and benefit decision making process. However, data quality (e.g., positional data and synchronisation of positional data and event data) should be prioritised in this type of study