About latent roles in forecasting players in team sports

Forecasting players in sports has grown in popularity due to the potential for a tactical advantage and the applicability of such research to multi-agent interaction systems. Team sports contain a significant social component that influences interactions between teammates and opponents. However, it still needs to be fully exploited. In this work, we hypothesize that each participant has a specific function in each action and that role-based interaction is critical for predicting players' future moves. We create RolFor, a novel end-to-end model for Role-based Forecasting. RolFor uses a new module we developed called Ordering Neural Networks (OrderNN) to permute the order of the players such that each player is assigned to a latent role. The latent role is then modeled with a RoleGCN. Thanks to its graph representation, it provides a fully learnable adjacency matrix that captures the relationships between roles and is subsequently used to forecast the players' future trajectories. Extensive experiments on a challenging NBA basketball dataset back up the importance of roles and justify our goal of modeling them using optimizable models. When an oracle provides roles, the proposed RolFor compares favorably to the current state-of-the-art (it ranks first in terms of ADE and second in terms of FDE errors). However, training the end-to-end RolFor incurs the issues of differentiability of permutation methods, which we experimentally review. Finally, this work restates differentiable ranking as a difficult open problem and its great potential in conjunction with graph-based interaction models. Project is available at: https://www.pinlab.org/aboutlatentrolesComment: AI4ABM@ICLR2023 Worksho

Ask not what economics can do for sports - ask what sports can do for economics

In this article we list the advantages of using sports data for economic research. We also provide a rich overview of economic literature that used sports data to test different fundamental economic theories as well as articles that presented divergences of economic decision making from neo-classical theories. Finally we present articles that were published in this special issue on behavioral economics and decision making in sports, all of which try to answer more general questions by means of sports data.publishedVersio

Big Data in Sports: A Bibliometric and Topic Study

Background: The development of the sports industry was impacted by the era of Big Data due to the rapid growth of information technology. Unfortunately, that has become an increasingly challenging Issue. Objectives: The purpose of the research was to analyze the scientific production of Big Data in sports and sports-related activities in two databases, Web of Science and Scopus. Methods/Approach: Bibliometric analysis and topic mining were done on 51 articles selected after four exclusion criteria (written in English, journal articles, the final stage of publication, and a detailed review of all full texts). The software tool used was Statistica Data Miner. Results: We found that the first articles appeared in Scopus in 2013 and WoS in 2014. USA and China are countries which produced the most articles. The most common research areas in WoS and Scopus are Public environmental and occupational health, Medicine, Environmental science ecology, and Engineering. Conclusions: We conducted that further research and literature review will be required as this is a broad and new topic

Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science

Although causal inference has shown great value in estimating effect sizes in, for instance, physics, medical studies, and economics, it is rarely used in sports science. Targeted Maximum Likelihood Estimation (TMLE) is a modern method for performing causal inference. TMLE is forgiving in the misspecification of the causal model and improves the estimation of effect sizes using machine-learning methods. We demonstrate the advantage of TMLE in sports science by comparing the calculated effect size with a Generalized Linear Model (GLM). In this study, we introduce TMLE and provide a roadmap for making causal inference and apply the roadmap along with the methods mentioned above in a simulation study and case study investigating the influence of substitutions on the physical performance of the entire soccer team (i.e., the effect size of substitutions on the total physical performance). We construct a causal model, a misspecified causal model, a simulation dataset, and an observed tracking dataset of individual players from 302 elite soccer matches. The simulation dataset results show that TMLE outperforms GLM in estimating the effect size of the substitutions on the total physical performance. Furthermore, TMLE is most robust against model misspecification in both the simulation and the tracking dataset. However, independent of the method used in the tracking dataset, it was found that substitutes increase the physical performance of the entire soccer team

Analítica de datos y su influencia sobre la gerencia deportiva

El exponencial crecimiento de los datos y de herramientas para aprovecharlos, hacen de estas tecnologías una ventaja competitiva para aquellos que sepan aprovecharlas. El fútbol, como cualquier otro negocio, está incursionando en maneras innovadoras de gerenciar las organizaciones. Es por esto, que la analítica de datos influye positivamente en las decisiones de los gerentes deportivos. Los equipos han evolucionado a nuevas metodologías apoyadas por herramientas innovadoras. Para demostrar esto, se hizo un análisis documental cuantitativo, donde se revisó la literatura investigativa del sector y se demostró la correlación que poseen las variables: analítica de datos y gerencia deportiva. Donde se encontró que los documentos vienen por parte investigadores aficionados al deporte.Resumen ; Introducción ; 1. Revisión de literatura ; 2. Metodología ; 3. Desarrollo ; Conclusiones ; Recomendaciones ; ReferenciasAdministrador de EmpresasPregrad

TRACKING FORMATION CHANGES AND ITS EFFECTS ON SOCCER USING POSITION DATA

This study investigated the application of advanced machine learning methods, specifically k-means clustering, k-Nearest Neighbors (kNN), and Support Vector Machines (SVM), to analyze player tracking data in soccer. The primary hypothesis posits that such data can yield a standalone, in-depth understanding of soccer matches. The study revealed that while k-means and spatial analysis are promising in analyzing player positions, kNN and SVM show limitations without additional variables. Spatial analysis examined each team’s convex hull and studied the correlation between team length, width, and surface area. Results showed team length and surface area have a strong positive correlation with a value of 0.8954. This suggested that teams with longer team length have a more direct style of play with players more spread out which led to larger surface areas. k-means clustering was performed with different k values derived from different approaches. The silhouette method recommended a k value of 2 and the elbow recommended a k value of 4. The context of the sport suggested additional analysis with a k value of 11. The results from k-means suggested natural data partitions, highlighting distinct player roles and field positions. kNN was performed to find similar players with the model of k = 19 showing the highest accuracy of 8.61%. The SVM model returned a classification of 55 classes which indicated a highly granular level of categorization for player roles. The results from kNN and SVM indicated the necessity of further contextual data for more effective analysis and emphasized the need for balanced datasets and careful model evaluation to avoid biases and ensure practical application in real-world scenarios. In conclusion, each algorithm offers unique perspectives and interpretations on player positioning and team formations. These algorithms, when combined with expert knowledge and additional contextual data, can significantly enrich the scope of analysis in soccer. Future work should consider incorporating event data and additional variables to enhance the depth of analytical insights, enabling a more comprehensive understanding of how formations evolve in response to various in-game situations

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

Football analytics: a literature analysis from 2010 to 2020

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe overall goal for the current study is to present a literature review of analytics, precisely machine learning (ML) reference authors in terms of methods and applicable scopes of study, in football where is a field that historically there are empirical decisions and the usage of analytics has been growing intensely. The research aims to list relevant academic contributions published between 2010 and 2020, performing a comparable picture per authors across the following subsets: player individual technical skills and team performance. Furthermore, the approach will provide a summary of studies for machine learning methods applied in football. Such outcomes of this study would contribute to the discussion about football analytics. Regarding that these summaries can drive researchers to have a deep dive into the fields of interest straight to references preview studied in the thesis. Results indicate that football analytics has broadly vast opportunities in terms of research, regarding machine learning methods and a high potential to have a deep exploration of team and player perspective. This study can leverage and pavement new further in-depth and targeted investigation toward football analytics

Tekoäly osana huippu-urheilun data-analytiikkaa

Tekoälyn rooli kasvaa läpi yhteiskunnan merkittävää vauhtia, eikä sen käytön kasvu näytä hidastuvan. Tekoälyä käyttäviä ohjelmistoja tai laitteita on hyödynnetty osana huippu-urheilutoimintaa jo useita vuosia ja niiden avulla kerätyn datan avulla joukkueet ja yksilöurheilijat pyrkivät saavuttamaan kilpailuetua. Tässä kandidaatintyössä aiheena on tarkastella tekoälyn käyttöä osana huippu-urheilun data-analytiikkaa. Tutkielmassa käsitellään sitä, miten tekoälyä sovelletaan eri urheilulajeissa ja tutkitaan, miten se toimii apuna suorituskyvyn ja joukkueen ottelustrategian parantamisessa. Tutkielman keskeiset havainnot ovat, että tekoälyllä on nykypäivänä merkittävä rooli huippu-urheilun data-analytiikassa ja sen käyttö voi tuoda sekä yksilölle sekä joukkueelle monia etuja. Tekoälyn käyttö mahdollistaa tarkemman ja erityisesti nopeamman datan analysoinnin ja sen hyödyntäminen tarjoa uusia tapoja tutkia urheilusuorituksia. Se myös auttaa valmentajia ja urheilijoita tekemään parempia päätöksiä perustuen objektiiviseen, dataan pohjautuvaan analyysiin ja sen pohjalta muodostettuihin ennusteisiin suorituskyvyn osalta. Tutkielman lopuksi esitetään joitakin haasteita ja rajoituksia tekoälyn käytölle urheiludatan analysoinnissa. Näitä ovat esimerkiksi datan laatu ja määrä sekä taloudelliset resurssit. Tekoälyn hyödyntäminen osana urheilun data-analytiikkaa vaatii aina laitteistoa ja osaamista. Etenkin pie nemmissä urheilulajeissa myös huipputasolla on tavallista, että taloudelliset resurssit ovat rajatut. Datan hankinta vaatii myös taloudellisia resursseja tai laitteistoja ja teknologioita, eikä välttämättä siltikään data ole halutunlaista tai tarpeeksi laadukasta analyysiä varten. Lisäksi urheilutoiminnan parista ei välttämättä löydy sisäistä osaamista analyysin harjoittamista varten, jolloin se tulisi ostaa palveluna

The influence of data analysis on football teams to increase sports´ performance

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceAs we know, football is the most popular sport among the fans all over the world and, in today’s world a very lucrative business for club owners and stakeholders, and sometimes its own supporters. With the board and supporters’ expectations being higher with the money spent on new players and conditions to attract valuable assets for the clubs, the teams tend to invest their money on infrastructures and other type of conditions for their players, including a better staff. The teams’ staff normally gather many data during the training sessions, other teams’ observation, and post-match observations, meaning that the investment is now increasing on hiring new data analysts. Additionally, there are scouting teams that gather data as well. With that, the question that arises is how can football teams increase their performance, using data analysis? The goal of this dissertation is to understand how the existing tools are helping teams improving their performance in and off the pitch and propose new ways on how future analysis can be conducted. To meet this goal, an extended systematic literature review will be taken, to present a discussion and conclusions on how data analysis can influence football clubs and players’ performance