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
  • Introduction
  • Data Structure
  • Method
  • Experimental Results
  • Conclusions and Future Work
introduction
Introduction
  • 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
  • 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
  • Soccer game records(provided for research purpose by DataStadium Inc., Japan)
data structure1
Data Structure
  • Field geometry and Pass sequence

5346

Y

IN GOAL

PASS start

X

t

-3500

3500

-5346

pass sequence clustering problems
Pass sequence clustering: Problems
  • 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

Trajectory Mining

Segmentation and Generation of Multiscale Trajectories

Segment Hierarchy Trace and Matching

Calculation of Dissimilarities

Clustering of Trajectories

method multiscale matching

segment

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 (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

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)

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

    • 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
  • 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
  • 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

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’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’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’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
  • 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
  • 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
  • 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
  • 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
  • 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

Trajectory Mining

Segmentation and Generation of Multiscale Trajectories

Segment Hierarchy Trace and Matching

Calculation of Dissimilarities

Clustering of Trajectories

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