Clustering of trajectory data obtained from soccer game record a first step to behavioral modeling
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Clustering of Trajectory Data obtained from Soccer Game Record -A First Step to Behavioral Modeling                      . Shoji Hirano Shusaku Tsumoto [email protected] [email protected] Dept of Medical Informatics, Shimane Univ. School of Medicine, Japan. Outline. Introduction

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Clustering of trajectory data obtained from soccer game record a first step to behavioral modeling

Clustering of Trajectory Data obtained from Soccer Game Record -A First Step to Behavioral Modeling                      

Shoji Hirano Shusaku [email protected]@computer.org

Dept of Medical Informatics,

Shimane Univ. School of Medicine, Japan


Outline
Outline Record -A First Step to Behavioral Modeling                      

  • Introduction

  • Data Structure

  • Method

  • Experimental Results

  • Conclusions and Future Work


Introduction
Introduction Record -A First Step to Behavioral Modeling                      

  • Clustering of Spatio-temporal Data

    • Provides a way to discover interesting characteristics about the motion of targets

    • Related field: meteorology, medical image analysis, sports, crime research etc.

    • Approaches

      • Spatial clustering + temporal continuity trace (e.g. tracking of moving object)

      • Spatial clustering based on temporal correlation (e.g. fMRI analysis)

      • Spatial clustering + observation of the temporal changes of the clusters (e.g. Observation of the climate regimes)


Objective
Objective Record -A First Step to Behavioral Modeling                      

  • Development of a clustering method for trajectories with multiscale structural comparison scheme

    • Compare trajectories according to both local and global views.

    • Visualize common characteristics of trajectories

  • Application: Clustering of trajectories of passes in soccer game records

    • Discovery of interesting spatio-temporal patterns of passes which may reflect the strategy and tactics of the team

      • Globally similar passes: strategy of the team -ex. Attack from right side

      • Locally similar passes: tactics of the ream -x. Frequent use of one-two passes


Data structure
Data Structure Record -A First Step to Behavioral Modeling                      

  • Soccer game records(provided for research purpose by DataStadium Inc., Japan)


Data structure1
Data Structure Record -A First Step to Behavioral Modeling                      

  • Field geometry and Pass sequence

5346

Y

IN GOAL

PASS start

X

t

-3500

3500

-5346


Pass sequence clustering problems
Pass sequence clustering: Problems Record -A First Step to Behavioral Modeling                      

  • Irregularly-sampled spatio-temporal sequence

    • Data point is generated when a player takes an interaction with a ball

    • High interaction -> Dense DataLow interaction -> Sparse Data

  • Need for Multiscale Observation

    • Strategy -> global pass featureTactics -> local pass feature

    • Both exist concurrently

      It is required to partly change comparison scale according to the granularity of data and type of events

Dense

Sparse


Trajectory mining

Preprocessing Record -A First Step to Behavioral Modeling                      

Trajectory Mining

Segmentation and Generation of Multiscale Trajectories

Segment Hierarchy Trace and Matching

Calculation of Dissimilarities

Clustering of Trajectories


Method multiscale matching

segment Record -A First Step to Behavioral Modeling                      

MatchedPairs

Method: Multiscale Matching

  • A pattern matching method that compares structural similarity of planar curves across multiple observation scales

    • Able to compare objects by partly changing observation scales

    • Simultaneously compare both global and local similarities

Scale s

Sequence A

Sequence B


Multiscale description witkin et al 1984 mokhatan et al 1986
Multiscale Description Record -A First Step to Behavioral Modeling                      (Witkin et al 1984, Mokhatan et al. 1986)

  • Describe convex/concave structure at multiple scales

  • Sequence description:

    t : course parameter

  • Sequence x(t) at scale s :

  • Scale s controls the degree of smoothing

    • s = small: local feature, s = large: global feature

Scale s


Multiscale matching based on convex concave structure of segments ueda et al 1990
Multiscale Matching based on Convex/Concave Structure of Segments (Ueda et al. 1990)

  • Segment: Partial sequence between adjacent inflection points

  • Curvature K (t, s) at scale s

  • Inflection point:

  • Represent a sequence as a set of segments

Scale s


Matching procedure

IN GOAL Segments

B4(1)

B6(0)

B2(2)

B3(1)

B5(0)

B1(2)

B2(1)

B4(0)

Sequence B

B2(0)

B1(1)

B3(0)

B0(1)

B0(2)

B1(0)

B0(0)

Inflection Points

IN GOAL

A4(0)

A2(1)

A2(2)

A3(0)

A1(1)

A1(2)

A2(0)

Sequence A

t

A0(2)

A0(1)

A1(0)

A0(0)

Scale 0

Scale 1

Scale 2

Matching Procedure


Segment dissimilarity

Segment bi(j) Segments

Segment ai(k)

Segment Dissimilarity

  • Dissimilarity of Segments

  • Dissimilarity of sequences

Max( , )

Rotation Angle

Length

P: the number of matched pairs


Indiscernibility based clustering overview

  • Iterative refinement of initial ERs Segments

    • For each pair of objects, count the ratio of ERs that have ability to discriminate them (indiscernibility degree)

    • If the number is small, assume that these ERs give too fine classification and disable their discrimination ability

    • Iterate step2 until the clusters become stable

Indiscernibility-based Clustering: Overview

  • Assignment of initial equivalence relations (ERs)

    • Assign an initial ER to each of the N objects.

    • An ER independently performs binary classification, similar or dissimilar, based on the relative proximity.

    • Indiscernible objects under all of the N ERs form a cluster.


Experiments
Experiments Segments

  • Data

    • Game records of FIFA WorldCup 2002 (64 games, including all heats and finals)

    • Number of goals: 168 (own goals excluded)

  • Procedure

    • Select series containing ‘IN GOAL’ event, and generate a total of 168 trajectories of 2-D ball location.

    • For every possible pair of the trajectories, calculate dissimilarity by using multiscale matching.

    • Group the trajectories by using the obtained dissimilarities and indiscernibility-based clustering


Experimental results
Experimental Results Segments

  • Cluster Constitution

Note: 55.2% (7839/14196) of triplet in the dissimilarity matrix

did not satisfy the triangular inequality due to matching failure


Experimental results cont d

Italy vs Korea Segments

Turkey vs Japan

Experimental Results (cont’d)

  • Cluster 1 (87 cases)

Corner Kick – Goal

Matching Result

IN GOAL

Europe: 45, South America: 24, Asia: 9


Experimental results cont d1
Experimental Results (cont Segments ’d)

  • Cluster 2 (24 cases)

Complex Pass – Side attack- Goal

Matching Result

IN GOAL

Germany vs Cameroon

Poland vs Portugal

Europe: 13, South America: 7, Asia: 3


Experimental results cont d2
Experimental Results (cont Segments ’d)

  • Cluster 4 (16 cases)

Side Change – Centering/Dribble – Goal

Matching Result

IN GOAL

Slovenia vs Paraguay

China vs Turkey

Europe: 10, South America: 4, Africa: 2


Experimental results cont d3
Experimental Results (cont Segments ’d)

  • Cluster 3 (17 cases)

Side Change – Centering/Dribble – Goal

(Intermediate cases between Cluster 2 and 4)

Europe: 10,

South America: 2,

Africa: 2

Asia 2


Summary of experimental results
Summary of Experimental Results Segments

  • Goal success patterns can be classified into 4 major groups (with 8 minor patterns)

  • Patterns: complexity of pass sequences

  • With additional information

    • Dribble/Centering/Side change: European Style

      • However, the differences are not statistically significant.

  • Key is “Side Change”

    • Players (Defenders) should take care of the other side of the ball movement.

    • The higher complexity of pass transactions, the higher rate of goal success gains by side change.


Conclusions
Conclusions Segments

  • Presented a new scheme of spatio-temporal data mining

    • Grouped similar patterns using multiscale comparison and indiscernibility-based clustering techniques.

    • Visualized similar patterns using matching results.

    • Application to real World Cup data:

      • Grouping and visualization of interesting pass patterns:ex. Complex pass -> side attack -> goal


Future work
Future Work Segments

  • Technical Issues

    • Numerical Evaluation

    • Validation and improvement of segment dissimilarity measure; inclusion of event type to dissimilarity

  • Apply the proposed method to all path series including non-‘IN GOAL’ series

    • Differences between success and failure are very small.

    • This suggests that the patterns of soccer attack are simple.

  • Apply the proposed method to medical environment

    • Trajectories of Laboratory Examinations (IEEE ICDM06)

    • Trajectories of Patients’ Movement: Patient Safety


Matching criteria
Matching Criteria Segments

  • Criteria for determining the best set of segment pairs

    • Complete match; original sequence should be correctly formed by concatenating the selected segments without any overlaps or gaps

    • Minimization of total segment difference

Overlap

Gap

a2

a1

a4

a3

A

a5

b2

b4

P : Number of matched segment pairs

b1

b3

b5

B

:dissimiarity of segments


Matching failure problem in msm
Matching Failure Problem in MSM Segments

  • Theoretically, any sequence can finally become a single segment at enough high scales. Therefore, any pair of sequences should be successfully matched.

  • Practically, there should be an upper limit of scales in order to reduce computational complexity. Therefore, the number of segments can be different even at the highest scales.

  • If matching is not successful, the method should return infinite dissimilarity or a magic value that indicates matching failure.

match

Scale n

Scale 2

no-match

Scale 1


Trajectory mining1

Preprocessing Segments

Trajectory Mining

Segmentation and Generation of Multiscale Trajectories

Segment Hierarchy Trace and Matching

Calculation of Dissimilarities

Clustering of Trajectories


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