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

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-theart and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)

Situation Management with Complex Event Processing

With the broader dissemination of digital technologies, visionary concepts like the Internet of Things also affect an increasing number of use cases with interfaces to humans, e.g. manufacturing environments with technical operators monitoring the processes. This leads to additional challenges, as besides the technical issues also human aspects have to be considered for a successful implementation of strategic initiatives like Industrie 4.0. From a technical perspective, complex event processing has proven itself in practice to be capable of integrating and analyzing huge amounts of heterogeneous data and establishing a basic level of situation awareness by detecting situations of interests. Whereas this reactive nature of complex event processing systems may be sufficient for machine-to-machine use cases, the new characteristic of application fields with humans remaining in the control loop leads to an increasing action distance and delayed reactions. Taking human aspects into consideration leads to new requirements, with transparency and comprehensibility of the processing of events being the most important ones. Improving the comprehensibility of complex event processing and extending its capabilities towards an effective support of human operators allows tackling technical and non-technical challenges at the same time. The main contribution of this thesis answers the question of how to evolve state-of-the-art complex event processing from its reactive nature towards a transparent and holistic situation management system. The goal is to improve the interaction among systems and humans in use cases with interfaces between both worlds. Realizing a holistic situation management requires three missing capabilities to be introduced by the contributions of this thesis: First, based on the achieved transparency, the retrospective analysis of situations is enabled by collecting information related to a situation\u27s occurrence and development. Therefore, CEP engine-specific situation descriptions are transformed into a common model, allowing the automatic decomposition of the underlying patterns to derive partial patterns describing the intermediate states of processing. Second, by introducing the psychological model of situation awareness into complex event processing, human aspects of information processing are taken into consideration and introduced into the complex event processing paradigm. Based on this model, an extended situation life-cycle and transition method are derived. The introduced concepts and methods allow the implementation of the controlling function of situation management and enable the effective acquisition and maintenance of situation awareness for human operators to purposefully direct their attention towards upcoming situations. Finally, completing the set of capabilities for situation management, an approach is presented to support the generation and integration of prediction models for predictive situation management. Therefore, methods are introduced to automatically label and extract relevant data for the generation of prediction models and to enable the embedding of the resulting models for an automatic evaluation and execution. The contributions are introduced, applied and evaluated along a scenario from the manufacturing domain

Design of monitoring applications and prediction of key industrial metrics: IIoT + AI

The global industry has suffered deep changes in the last years because of the successful development and integration of new technologies. Industry 4.0 has emerged as a new standard for achieving efficiency and improving processes. Among the technologies used in Industry 4.0, Internet of Things applied to industry (IIoT) enable real-time, intelligent, and autonomous access, collection, analysis, communications, and exchange of process, product and/or service information, within the industrial environment, so as to optimize overall production value. Because of its importance, in this project, a methodology for extracting, analyzing and using the data gathered by IIoT devices is proposed in order to extract meaningful information and to predict industrial key metrics with Artificial Intelligence. In addition, for the complete validation of the proposed methodology, a practical implementation of all the mentioned aspects is carried out by developing a study of the industrial process in the wastewater treatment field using the data collected by an Industrial Internet of Things infrastructure and modelling key time series metrics, such as total organic carbon (TOC) and carbon removal performance (CRP) by using Machine Learning models XGBOOST Regressor, Multi-Layer Perceptron (MLP) Regressor and Support Vector Regressor (SVR) to implement a dashboard with an operational panel and a decision-making panel that helps anticipate possible deviations in the performance of the industrial process

Biosignals reflect pair-dynamics in collaborative work : EDA and ECG study of pair-programming in a classroom environment

Collaboration is a complex phenomenon, where intersubjective dynamics can greatly affect the productive outcome. Evaluation of collaboration is thus of great interest, and can potentially help achieve better outcomes and performance. However, quantitative measurement of collaboration is difficult, because much of the interaction occurs in the intersubjective space between collaborators. Manual observation and/or self-reports are subjective, laborious, and have a poor temporal resolution. The problem is compounded in natural settings where task-activity and response-compliance cannot be controlled. Physiological signals provide an objective mean to quantify intersubjective rapport (as synchrony), but require novel methods to support broad deployment outside the lab. We studied 28 student dyads during a self-directed classroom pair-programming exercise. Sympathetic and parasympathetic nervous system activation was measured during task performance using electrodermal activity and electrocardiography. Results suggest that (a) we can isolate cognitive processes (mental workload) from confounding environmental effects, and (b) electrodermal signals show role-specific but correlated affective response profiles. We demonstrate the potential for social physiological compliance to quantify pair-work in natural settings, with no experimental manipulation of participants required. Our objective approach has a high temporal resolution, is scalable, non-intrusive, and robust.Peer reviewe