Martin Lames

On the search for reliable performance indicators in game sports

This article addresses the reliability of performance indicators in game sports. In this context, reliability is invariably treated from a technical point of view as a question of observer agreement i.e. high levels of agreement between observations. That the measurement process itself should yield reliable data, as defined, for sports performance is given. Our considerations of reliability, however, extend from the process of measurement to include the trait (i.e., the performance) being measured. From these considerations, we present the argument that the performance traits, as measured, are inherently unstable and that the performance indicators are therefore necessarily unreliable (or unstable). In this light, the ongoing search for reliable (or stable) measures of sports performance indicators is questioned. Instead, alternative approaches for performance analysis are offered that recognise the dynamic interactions that characterise game sports as key features of sport performance. This notion of dynamic interactions is compatible with sporting experiences and the way that sports practitioners think about sports performance. We conclude that performance analysis for purposes of theoretical advancement should make use of mathematical modelling and simulation techniques, and that performance analysis for practical purposes should include qualitative research methods to arrive at the necessary inferences for sports practice.

Computerized real-time analysis of football games

Computer systems support many coaching activities performed before and after competitions, such as strategy development and performance evaluation, but competent assistance during games is much more demanding. It requires real-time interpretation of sensor data, the recognition and classification of ball actions, and fast-action game analysis and assessment. Only recently has high-precision microwave technology enabled computer systems to simultaneously track the position of players and the ball. Consequently, real-time game analysis systems must be able to automatically recognize intentional activities in a multiagent system with continually acting agents. To meet these demands, weve developed the football interaction and process model and a software system that can acquire, interpret, and analyze this model. Our FIPM system can acquire models of player skills, infer action-selection criteria, and determine player and team strengths and weaknesses.

Survey on team tracking techniques applied to sports

Recent years have brought an increasing interest on analyzing efficiently the performance of sports players during training sessions and games. The information collected from such analysis is very valuable to educators and coaches since it allows them to better understand the difficulties of a trainee, a player or even an entire team and formulate adequate training and strategic plans accordingly. In order to perform this analysis in a consistent and systematic way, sophisticated sensory systems and data processing techniques are needed. This paper presents a survey on relevant work, current techniques and trends on the area of team tracking systems applied to sports. We propose a classification of these systems by distinguishing them into two main categories: intrusive and nonintrusive. Nonintrusive systems are further refined into outdoor and indoor sports applications. The specific characteristics of each system are itemized, including the identification of the strong points and limitations. Finally, the paper highlights some open issues and research opportunities on this area.

Coaching and computer science

Coaching is one of the earliest areas of application of computer science in sports. It may be divided into three different activities – preparation, control and debriefing of competition. These stages create different conditions for the support of coaching by computer science with the stage of competition control being most demanding, because data acquisition, data processing and data-based interventions have to take place during and at the site of the competition. The history of computer science and coaching was marked by continuous technological progress and growing conceptual insights into the process of coaching. Although there is considerable support in processing observational data including video data, it has to be acknowledged that central activities of coaching are not affected yet, e.g. analyses of strengths and weaknesses or the central task of strategy development and implementation. Conceptual advice for these tasks of coaching is obtained by qualitative research methodology, e.g. for assessment and intervention. Nevertheless, there are excellent perspectives for coaching and computer science. Real-time position analysis (RTPA) will allow support during competition. Analyses on more abstract levels than just positions will become possible using methods of artifi cial intelligence. This will result in new options with unforeseeable impact on the work of coaches.

Informatische Unterstützung des Coachings im Hochleistungssport

ZusammenfassungCoaching ist eines der frühesten Anwendungsfelder von Informatik im Sport.