This study investigates the accuracy of the tracking system LPM (Local Position Measurement). The goal was to determine detailed error values of the system in the context of sports performance analyses. Six moderately trained male soccer players (amateur level) performed 276 runs on three different courses at six different speeds. Additionally, ten small-sided game plays were carried out. All runs and game plays were recorded with the LPM tracking system and the motion capture system VICON simultaneously. VICON served as the reference system. The absolute error of all LPM position estimations was on average 23.4±20.7 cm. The estimation for average velocities varied between 0.01 km h⁻¹ and 0.23 km h⁻¹, the maximum speed estimations differed by up to 2.71 km h⁻¹. In addition, the results showed that the accuracy of the LPM system is highly dependent on the instantaneous dynamics of the player and decreases in the margins of the observation field. These dependencies were quantified. Considering commonly used applications of position tracking systems in sports (Leser, Ogris, & Baca, 2011 Leser, R. Analyses of tacklings in football. Presented at the 8th International Symposium of the International Association of Computer Science in Sports, Shanghai, China. 22 September [Google Scholar] ), the accuracy of LPM is acceptable for position and velocity estimations. The system provides valuable results for average velocities but seems to be far less reliable when dealing with high dynamic movements and measuring instantaneous velocities.

Position data of players and athletes are widely used in sports performance analysis for measuring the amounts of physical activities as well as for tactical assessments in game sports. However, positioning sensing systems are applied in sports as tools to gain objective information of sports behavior rather than as components of intelligent spaces (IS). The paper outlines the idea of IS for the sports context with special focus to game sports and how intelligent sports feedback systems can benefit from IS. Henceforth, the most common location sensing techniques used in sports and their practical application are reviewed, as location is among the most important enabling techniques for IS. Furthermore, the article exemplifies the idea of IS in sports on two applications.

The main aim of this study was to determine the accuracy of the ultra‐wideband (UWB)‐based positioning system Ubisense, which is used for time‐motion analysis in sports. Furthermore, some alternatives for positioning the system's transponders on the atheletes, as well as the accuracy depending on the location of measurement, were tested. Therefore, in a pre‐study, some basic issues were examined (measurement assumptions and consistency and location of the system's transponder used for position detection), and position measurements at the borders and in the centre of a basketball field were performed. In the main study, 13 male basketball players (15.8 years ± 0.6; 187.9 height ± 3.4; 77.5 weight ± 3.7), equipped with a Ubisense transponder mounted on top of their heads, handled a trundle wheel during simulated match play. The players with the trundle wheel participated passively in the match by following one of the ten competing players. The distance measurements of the trundle wheel were used as reference values and compared to the Ubisense distance estimations. Best results were found with the measurements of a single mounted transponder on top of the athlete's heads. No differences were detectable in the accuracy between measurements in the centre and at the borders of the basketball field. The (Ubisense) system's difference to the (trundle wheel) reference was 3.45 ± 1.99%, resulting in 95% limits of agreement of −0.46–7.35%. The study indicates the examined system's sufficient accuracy for time‐motion analysis in basketball.

Most existing game analysis attempts do not consider situational context information sufficiently for the assessments of tactical behaviour. The purpose of this case study was to apply a new analysis approach, so far only used for 1-versus-1 analyses in soccer, to small-sided games and to identify and analyse therewith the efficiency of tactical patterns in 3-versus-2 game play. Therefore, 279 games, performed by an elite youth soccer team, were recorded by video and a position tracking system. The data was processed by the new approach, including unsupervised machine learning routines and artificial intelligence methods. Two crucial moments were used to analyse the occurring tactical patterns for the specific game: T0 (time instant of the shot) and T1 (time instant of the last action before the shot). For both moments typical tactical patterns, considering the spatio-temporal context of the situations were found. By combining the patterns of both moments, hidden tactical strategies were identified and their efficiencies calculated. The most significant result is that offence attempts have the highest scoring probability when played via the wings near to the goal line and the final pass, before the shot, is given against the playing direction into the centre of the field.