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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Trajectory Analysis Analyzing Trajectories in a Soccer Context

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Trajectory analysis analyzing trajectories in a soccer context

Trajectory AnalysisAnalyzing Trajectories in a Soccer Context


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

      • Nodesplayers

      • Edgespasses

      • Edge weight frequency of passesbetweenpair ofplayers

    • 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


Advanced analysis tasks2

Advanced Analysis Tasks


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!


Trajectory analysis analyzing trajectories in a soccer context

Thank you for your attention!


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