Trajectory Analysis Analyzing Trajectories in a Soccer Context

1. Trajectory AnalysisAnalyzing Trajectories in a Soccer Context

2. Outline • Motivation • The Tool • Basic Analysis Tasks • Advanced Analysis Tasks • Conclusion & Outlook

3. Motivation and Application Scenarios • Application scenarios: • Monitoring of performance in the training/competition • Enables an adjusted training and better performance of the individual player and the whole team • Analysis of the opponent • Better/easier preparation of the competition • Existing services/applications (especially in soccer domain) provide just the basic analysis tasks

4. The Tool • Implemented in Java, at the moment extension to a framework • Purposes: • Testing • Visualization of the results • Comparison of results

5. Basic Analysis Tasks • Determination (measurement) of basic statistical values of a player or a whole team • Total covered distance • (Distribution of) velocities / accelerations • Min./mean/ max. values • Heat/intensity maps

6. Basic Analysis Tasks • Use of event-based approach • Different kinds of events • ‘Game events’ may be given attached to the dataset (annotations) • Match is started / interrupted / finished • Control of movement observer • ‘Movement events’ are generated by the observer from the data Game Start Event Movement observer Game Interruption Event Active Inactive Game Resume Event t Movement Events

7. Basic Analysis Tasks • Determining the ball possession (per team) • Nearest player (body part) is possessor (up to an upper boundary) • E.g. 0.3m (depends on the data accuracy) • Ball possession change event, if possessor changes • Possession time = time between two possession events t Team A in possession Ball Possession Change Event Ball is free Team B in possession

8. Basic Analysis Tasks • Detection of passes • Framed by a ‘ball kick event’ and a ‘ball stop event’ • Ball possessing players are sender and receiver • Bad passes have no or wrong receiver a_ball Completed pass Bad pass Whole team One player

9. Basic Analysis Tasks • Further tasks are solved similarly: • Goals • Sprints • Ball contacts

10. Advanced Analysis Tasks • ‚Pass graph‘ • Generation of a graph structure • Nodes players • Edges passes • Edge weight frequency of passes between pair of players • Visual analysis is possible via the stroke width of the edges • Analysis via graph based algorithms, e.g. frequent pass sequences

11. Advanced Analysis Tasks • Extraction of group movement patterns • Approach is based on constellations (vector of relativeplayer positions) • Sequence of constellations is recorded during the observation time • Clustering of constellations to determine their similarities • Use of sequence mining algorithm to extract patterns from the sequence of clusters (clustered constellations) • Example pattern (occurred twice during the observation time): time step: subsequence subsequence

12. Advanced Analysis Tasks

13. Conclusion • Tool for observing and analyzing trajectories in a soccer context • Basic analysis tasks • basic statistical values, hotspots • Ball possession, contacts • Passes, goals, sprints • Advanced analysis tasks • Passes graph • Group movement pattern recognition

14. Outlook • Further planned features: • Detection of goal kicks (distinction of kicks and passes) • Detection of corner kicks, free kicks, penalties, throw-ins • Detection of physical interactions of players (e.g. fouls) • Implementation of graph analysis methods for the pass graph • Extension of the pattern recognition approach • Use of more detailed and specific knowledge • Use of a database for comparison issues • !STRONG NEED FOR DATASETS!

15. Thank you for your attention!