Institute of Cartography and Geoinformatics | Leibniz Universität Hannover
Trajectory Analysis
Analyzing Trajectories in a Soccer Context
M.Sc. Udo Feuerhake
[email protected]
Outline
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Motivation
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The Tool
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Basic Analysis Tasks
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Advanced Analysis Tasks
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Conclusion & Outlook
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Motivation and Application Scenarios
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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
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Existing services/applications (especially in soccer domain)
provide just the basic analysis tasks
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The Tool
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Implemented in Java, at the moment extension to a
framework
Purposes:
 Testing
 Visualization of the results
 Comparison of results
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Basic Analysis Tasks
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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
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Basic Analysis Tasks
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Use of event-based approach
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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
Game Interruption Event
Game Resume Event
Movement observer
Active
Inactive
t
Movement Events
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Basic Analysis Tasks
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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
Team B in possession
Ball is free
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Basic Analysis Tasks
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Detection of passes
 Framed by a ‘ball kick event’ and a ‘ball stop event’
a_ball
 Ball possessing players are sender and receiver
Completed pass Bad pass
 Bad passes have no or wrong receiver
Whole team
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One player
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Basic Analysis Tasks
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Further tasks are solved similarly:
 Goals
 Sprints
 Ball contacts
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Advanced Analysis Tasks
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‚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
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Advanced Analysis Tasks
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Extraction of group movement patterns
 Approach is based on constellations (vector of relative
player 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
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Advanced Analysis Tasks
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Conclusion
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Tool for observing and analyzing trajectories in a soccer
context
Basic analysis tasks
 basic statistical values, hotspots
 Ball possession, contacts
 Passes, goals, sprints
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Advanced analysis tasks
 Passes graph
 Group movement pattern recognition
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Outlook
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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)
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Implementation of graph analysis methods for the pass graph
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Extension of the pattern recognition approach
 Use of more detailed and specific knowledge
 Use of a database for comparison issues
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!STRONG NEED FOR DATASETS!
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Thank you for your attention!
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