Machine Learning In Team Sports

This brief highlights the application of performance analysis tools in data acquisition, and various machine learning algorithms for evaluating team performance as well as talent identification in beach soccer and sepak takraw. Numerous performance indicators and human performance parameters are considered based on their relevance to each sport. The findings presented here demonstrate that the key performance indicators as well as human performance parameters can be used in the future evaluation of team performance as well as talent identification in these sports. Accordingly, they offer a valuable resource for coaches, club managers, talent identification experts, performance analysts and other relevant stakeholders involved in performance assessments.

To understand the dynamic patterns of behaviours and interactions between athletes that characterize successful performance in different sports is an important challenge for all sport practitioners. This book guides the reader in understanding how an ecological dynamics framework for use of artificial intelligence (AI) can be implemented to interpret sport performance and the design of practice contexts. By examining how AI methodologies are utilized in team games, such as football, as well as in individual sports, such as golf and climbing, this book provides a better understanding of the kinematic and physiological indicators that might better capture athletic performance by looking at the current state-of-the-art AI approaches. Artificial Intelligence in Sport Performance Analysis provides an all-encompassing perspective in an innovative approach that signals practical applications for both academics and practitioners in the fields of coaching, sports analysis, and sport science, as well as related subjects such as engineering, computer and data science, and statistics.

This book constitutes the refereed post-conference proceedings of the 5th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018, colocated with ECML/PKDD 2018, in Dublin, Ireland, in September 2018. The 12 full papers presented together with 4 challenge papers were carefully reviewed and selected from 24 submissions. The papers present a variety of topics, covering the team sports American football, basketball, ice hockey, and soccer, as well as the individual sports cycling and martial arts. In addition, four challenge papers are included, reporting on how to predict pass receivers in soccer.

This book constitutes the refereed post-conference proceedings of the 7th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2020, colocated with ECML/PKDD 2020, in Ghent, Belgium, in September 2020. Due to the COVID-19 pandemic the conference was held online. The 11 papers presented were carefully reviewed and selected from 22 submissions. The papers present a variety of topics within the area of sports analytics, including tactical analysis, outcome predictions, data acquisition, performance optimization, and player evaluation.

The use of technology within sport is well established, most professional sport teams engage in the use of electronic performance and tracking systems. This book is the first to offer a deep and structured examination of these technologies and how they are used in a team sport setting. The Use of Applied Technology in Team Sport describes and assists researchers, academics and professionals with understanding the methodology around applied technology in sport, examining what systems track players’ performance and who are the manufacturers that provide these systems. This new volume goes on to describe how to apply the systems, highlights the ways of reporting analysis information and helps the reader to know and understand the future avenues of research and development. The Use of Applied Technology in Team Sport is considered an essential guide for researchers, academics and students as well as professionals working in the areas of Applied Sport Science, Coaching, and subjects relating to Physiology, Biomechanics, Sports Engineering, Sports Technology and Performance Analysis in Sport.

It seems that artificial intelligence (AI) is always just five years away, but it never arrives. Recently, however. developments have made the practical utility of game theory a genuine reality. Will sport provide the petri dish in which AI will prove itself? What do domain specialists like managers and coaches want to know that they can’t currently find out, and can AI provide the answer? What competitive advantages might AI provide for recruitment, performance and tactics, health and fitness, pedagogy, broadcasting, eSports, gambling and stadium design in the future? Written by leading experts in both sports management and AI, AI for Sports begins to answer these and many other questions on the future of AI for sports.

Data mining is the process of extracting hidden patterns from data, and it’s commonly used in business, bioinformatics, counter-terrorism, and, increasingly, in professional sports. First popularized in Michael Lewis’ best-selling Moneyball: The Art of Winning An Unfair Game, it is has become an intrinsic part of all professional sports the world over, from baseball to cricket to soccer. While an industry has developed based on statistical analysis services for any given sport, or even for betting behavior analysis on these sports, no research-level book has considered the subject in any detail until now. Sports Data Mining brings together in one place the state of the art as it concerns an international array of sports: baseball, football, basketball, soccer, greyhound racing are all covered, and the authors (including Hsinchun Chen, one of the most esteemed and well-known experts in data mining in the world) present the latest research, developments, software available, and applications for each sport. They even examine the hidden patterns in gaming and wagering, along with the most common systems for wager analysis.

This paper addresses the complex system of team sports outcome prediction, a burgeoning field of research with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies often exhibit limitations, including insufficient feature engineering, underutilization of advanced machine learning techniques, and limited practical application, such as in sports betting scenarios. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a comprehensive predictive system, using National Basketball Association (NBA) as an example to test extended framework.Our approach emphasizes a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits than game-winner prediction.Using machine learning algorithms, particularly LightGBM, our model shows a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 300 % on an initial investment of $100.Our findings not only contribute to the academic sphere, but they also have critical practical implications for sports betting. As a result, this study advances the understanding of team sports outcome prediction, simultaneously paving the way for future research and potential profitability in the sports betting market.

This book discovers the latest research and insights in sports performance analysis and computer science in sports with the 13th World Congress of Performance Analysis of Sport and 13th International Symposium on Computer Science in Sport joint conference proceedings. This comprehensive book features over 40 peer-reviewed scientific works, showcasing the latest developments in these areas. The book covers a wide range of topics, including data analytics in sports, performance tracking and monitoring, artificial intelligence and machine learning in sports, virtual and augmented reality in sports, sensor technology, sports biomechanics, and motor control. By reading this book, you'll gain a deeper understanding of how applied and research-based problems can, together, transform the world of sports, and how you can stay ahead of the curve in this rapidly evolving field. This means that whether you're a researcher, coach, athlete, or sports enthusiast, there is something for everyone in this book.