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Biomechanical Analysis Methods to Assess Professional Badminton Players' Lunge Performance

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Identification of pattern mining algorithm for rugby league players positional groups separation based on movement

Victor Elijah Adeyemo^1,2,3,4, Anna Palczewska¹, Ben Jones^2,3,4,5,6

¹School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, United Kingdom.

|May 1, 2024

Summary

This summary is machine-generated.

This study found that closed contiguous movement patterns, extracted using the LCCspm algorithm, are best for profiling rugby players. These patterns significantly improve the accuracy of distinguishing player positions in sports big data analysis.

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Area of Science:

Sports Science
Data Mining
Machine Learning

Background:

Sports big data analysis offers opportunities for granular movement evaluation.
Player movement profiling is crucial for enhancing training specificity.

Purpose of the Study:

To identify optimal movement patterns for player profiling in professional rugby league.
To compare the effectiveness of three pattern mining algorithms (LCCspm, LCS, AprioriClose).
To quantify pattern similarity and classify player positions.

Main Methods:

Applied LCCspm, LCS, and AprioriClose algorithms to extract movement patterns from 319 professional rugby league matches.
Utilized Jaccard similarity to measure pattern congruence between algorithms.
Employed machine learning classification (Multi-layered Perceptron) to assess pattern utility for positional separation.

Main Results:

LCCspm and LCS patterns showed moderate similarity (Jaccard score 0.19); AprioriClose patterns showed negligible similarity.
Closed contiguous movement patterns extracted by LCCspm demonstrated superior ability in differentiating player positions.
The Multi-layered Perceptron model achieved 91.02% accuracy, with precision, recall, and F1 scores of 0.91.

Conclusions:

Closed contiguous movement patterns are recommended over non-consecutive or non-sequential patterns for player profiling.
LCCspm algorithm effectively extracts movement patterns for accurate positional classification in rugby.
Data-driven insights from pattern mining can significantly enhance sports performance analysis.