Dr. Otto Kolbinger
Dr. Otto Kolbinger currently works as a post-doctoral research associate at the Chair of Performance Analysis and Sport Informatics at the Technical University of Munich. His current projects focus on the evolution of rules in game sports with emphasis on trivial offenses and technological officiating aids. Further, he serves as consultant for several data science projects of a leading German sports magazine.
Invited Session: Text Mining
In sport science – and performance analysis in particular – there are various scenarios in which valuable information is provided as unstructured text data: Professional scouts provide extensive scouting reports for promising athletes, experts provide elaborated previews for upcoming events, and huge crowds share their opinions and feelings about on-going events. Deriving information from such unstructured text corpora in an effective manner is possible by deploying different text mining techniques, which already builds a significant part of the methodological spectrum in various research areas (e.g. economics, public health). In this invited session, we want to highlight how different text mining approaches have already been applied in performance analysis to detect and predict different events as well as to evaluate technological officiating aids. In addition, we give an outlook how else this innovative methodology could be deployed to provide support for practitioners in sport.
Dr. Albrecht Zimmermann
Albrecht Zimmermann ist mâitre de conférences (associate professor) at the Greyc laboratory of the University of Caen. He is co-organizer of the on-going workshop series on Machine Learning and Data Mining for Sports Analytics (MLSA). His main work is in pattern mining but his research interests span a wide range of machine learning and data mining topics.
Invited Session: Using machine learning to assess and compare athletes in team sports
Obviously, a valuable player in team sports is someone who helps their team win. At a fine-grained level, this means that such players perform actions that improve their team’s chance of winning and avoid doing those that decrease these chances. But what are good and bad actions is far from obvious, even for experts and aficionados of the sport, and looking at all actions a player is performing, and grading them, quickly becomes an insurmountable task. Machine learning allows to create models that allow deciding actions’ values, turning subjective and sporadic assessments into data driven ones. This allows assessing players in turn, whether they are valuable, particularly good (or bad) at something, and how similar they are to other players. In this session, we will discuss how different machine learning techniques have been employed to address those tasks. The resulting information can be used for game-planning, to decide which players to acquire or replace, or for selective training.
Professor Nic James and Dr Nimai Parmar
London Sport Institute, Middlesex University, London, UK. Chris Connelly, GB Boxing, English Institute of Sport, UK.
Nic is a Professor of Sport and Exercise Science and Head of Research in the London Sport Institute at Middlesex University. He has worked with England squash, GB Olympic and Paralympic teams, International dancers, professional rugby and football teams as well as coaches and athletes from other sports in multiple European countries. His research interests include notational analysis, statistics and motor learning, all of which inform his teaching on the MSc Performance Analysis. Nic has supervised 11 PhDs to completion and currently supervises 6, two of whom work for Liverpool FC and two Leicester City FC. Dr Nimai Parmar is a Senior Lecturer in Performance Analysis and Programme Leader for the MSc in Sport Performance Analysis at Middlesex University in London. Nimai has led on projects and funded studentships with multiple professional and elite sports in the UK and currently supervises 7 PhD students in performance analysis and sports science. Chris has previously provided PA support to GB Handball, Port Vale FC, Stoke City FC, The Premier League and UK Goalball. Chris now works as senior performance analyst for GB boxing as well as a Technical Lead for the English Institute of Sport. During his time with GB Boxing he has provided PA support at multiple World and European Championships, Commonwealth Games and the Rio Olympics. Chris is now leading PA provision in preparation for Tokyo Olympics. In addition to these roles, Chris heads up performance analysis support to Heavyweight World Champion boxer, Anthony Joshua.
This talk will cover the challenges associated with speaking the same language between computer scientists, performance analysts and coaches. In particular, identifying achievable and interesting research questions. Exemplars of projects within Squash and Boxing will be provided and the talk will discuss how these language barriers have been overcome resulting in successful outcomes for the projects and explore the lessons learned.
Professor Nic James
Interactive Panel Discussion: Building research groups involving computer scientists and performance analysts (90 mins)
Opportunities to develop research groups with performance analysts and computer scientists are growing. The collection and application of sport performance data is ever increasing, providing pathways for impactful research projects to be developed. This panel will be chaired by Prof James and will explore computer science solutions to performance data. Experts will present solutions and applied professionals will present their thoughts on how well these methods fit their data analysis goals. This panel session will invite topics from the audience prior to the conference to maximise audience engagement.