Football-specific validity of TRACAB’s optical video tracking systems

Abstract

The present study aimed to validate and compare the football-specific measurement accuracy of two optical tracking systems engineered by TRACAB. The "Gen4" system consists of two multi-camera units (a stereo pair) in two locations either side of the halfway line, whereas the distributed "Gen5" system combines two stereo pairs on each side of the field as well as two monocular systems behind the goal areas. Data were collected from 20 male football players in two different exercises (a football sport-specific running course and smallsided games) in a professional football stadium. For evaluating the accuracy of the systems, measures were compared against simultaneously recorded measures of a reference system (VICON motion capture system). Statistical analysis uses RMSE for kinematic variables (position, speed and acceleration) and the difference in percentages for performance indicators (e.g. distance covered, peak speed) per run compared to the reference system. Frames in which players were obviously not tracked were excluded. Gen5 had marginally better accuracy (0.08 m RMSE) for position measurements than Gen4 (0.09 m RMSE) compared to the reference. Accuracy difference in instantaneous speed (Gen4: 0.09 m�s-1 RMSE; Gen5: 0.08 m�s-1 RMSE) and acceleration (Gen4: 0.26 m�s-2 RMSE; Gen5: 0.21 m�s-2 RMSE) measurements were significant, but also trivial in terms of the effect size. For total distance travelled, both Gen4 (0.42 ± 0.60%) and Gen5 (0.27 ± 0.35%) showed only trivial deviations compared to the reference. Gen4 showed moderate differences in the low-speed distance travelled category (-19.41 ± 13.24%) and small differences in the high-speed distance travelled category (8.94 ± 9.49%). Differences in peak speed, acceleration and deceleration were trivial (<0.5%) for both Gen4 and Gen5. These findings suggest that Gen5's distributed camera architecture has minor benefits over Gen4's single-view camera architecture in terms of accuracy. We assume that the main benefit of the Gen5 towards Gen4 lies in increased robustness of the tracking when it comes to optical overlapping of players. Since differences towards the reference system were very low, both TRACAB's tracking systems can be considered as valid technologies for football-specific performance analyses in the settings tested as long as players are tracked correctly.

Scanning, Contextual Factors, and Association With Performance in English Premier League Footballers: An Investigation Across a Season

“…Optical tracking data (e.g., TRACAB ® ; Linke et al, 2020 ) could have provided a higher resolution alternative, but it was not available in our dataset. It is important to note that there is no ultimate “ground-truth” for positional data, because as yet there is no tracker inside the football (or universally worn by all players) to accurately measure their pitch position in real-game situations.…”

Using Optical Tracking System Data to Measure Team Synergic Behavior: Synchronization of Player-Ball-Goal Angles in a Football Match

“…The high rate and high resolution of this type of kinematic data enables the creation of large datasets which can capture subtle changes in performance [ 1 ]. When collected over time, these data can feed modern tools of pattern behavior classification, such as machine learning [ 2 ].…”

“…Spatiotemporal positional data were used from one match of the 2018 FIFA WORLD CUP RUSSIA. Data were provided by the Portuguese Football Federation (FPF) and obtained by the TRACAB Optical Tracking System (ChyronHego), which was recently validated and is acknowledged as one of the market leaders in the field [ 1 ]. The dataset consisted of positional data of every player (n = 28) and the ball (n = 1), captured at 25 Hz with spatial resolution of 0.01 m. For CPA measures, each cluster (team) was constituted by the players on the pitch (n = 11).…”

“…The use of electronic performance-tracking systems to obtain spatiotemporal data in association football (football, for simplicity) is becoming a generalized practice for performance analysis. These systems include global positioning systems, radio-based local positioning systems and video camera-based systems, which provide big data about the individual performances of players and tactical behaviors of teams [ 1 , 2 , 3 ].…”

“…Optical-based camera systems have the advantage of being non-invasive to players, using multi-camera units and monocular systems around the pitch. These systems are able to provide high rate and high spatial resolution data from the position of the players and the ball, opening a door for the increasing development of football analytics [ 1 , 4 ]. Football clubs and national teams are increasingly investing in sports analytics departments, specialized in processing the data, computing compound variables and performance indicators, and visually presenting them to coaches, players and others.…”