Institute of Cartography and Geoinformatics | Leibniz Universität Hannover

M.Sc. Udo Feuerhakeudo.feuerhake@ikg.uni-hannover.de

Trajectory AnalysisAnalyzing Trajectories in a Soccer Context

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Outline► Motivation

► The Tool

► Basic Analysis Tasks

► Advanced Analysis Tasks

► Conclusion & Outlook

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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

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The Tool► 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► 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► 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

t

Game Start EventGame Interruption Event

Game Resume Event

Movement Events

Movement observerActive Inactive

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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

Ball Possession Change EventTeam A in possession

Team B in possession Ball is free

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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

Whole team One player

Bad passCompleted pass

a_ball

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Basic Analysis Tasks► Further tasks are solved similarly:

Goals

Sprints

Ball contacts

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Advanced Analysis Tasks► ‚Pass graph‘

Generation of a graph structure

• Nodes players• Edges passes• 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

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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

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Advanced Analysis Tasks

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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

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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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Thank you for your attention!