trajectory analysis analyzing trajectories in a soccer context
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Trajectory Analysis Analyzing Trajectories in a Soccer Context. Outline. Motivation The Tool Basic Analysis Tasks Advanced Analysis Tasks Conclusion & Outlook. Motivation and Application Scenarios. Application scenarios: Monitoring of performance in the training/competition

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Presentation Transcript
outline
Outline
  • Motivation
  • The Tool
  • Basic Analysis Tasks
  • Advanced Analysis Tasks
  • Conclusion & Outlook
motivation and application scenarios
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
the tool
The Tool
  • Implemented in Java, at the moment extension to a framework
  • Purposes:
    • Testing
    • Visualization of the results
    • Comparison of results
basic analysis tasks
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
basic analysis tasks1
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

basic analysis tasks2
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

basic analysis tasks3
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

basic analysis tasks4
Basic Analysis Tasks
  • Further tasks are solved similarly:
    • Goals
    • Sprints
    • Ball contacts
advanced analysis tasks
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
advanced analysis tasks1
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

conclusion
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
outlook
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!
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