Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Optimising classification in sport: a replication study using physical and technical-tactical performance indicators to classify competitive levels in rugby league match-play

Optimising classification in sport: a replication study using physical and technical-tactical...    Determining key performance indicators and classifying players accurately between competitive levels is one of the classification challenges in sports analytics. A recent study applied Random Forest algorithm to identify important variables to classify rugby league players into academy and senior levels and achieved 82.0% and 67.5% accuracy for backs and forwards. However, the classification accuracy could be improved due to limitations in the existing method. Therefore, this study aimed to introduce and implement feature selection technique to identify key performance indicators in rugby league positional groups and assess the performances of six classification algorithms. Fifteen and fourteen of 157 performance indicators for backs and forwards were identified respectively as key performance indicators by the correlation-based feature selection method, with seven common indicators between the positional groups. Classification results show that models developed using the key performance indicators had improved performance for both positional groups than models developed using all performance indicators. 5-Nearest Neighbour produced the best classification accuracy for backs and forwards (accuracy = 85% and 77%) which is higher than the previous method’s accuracies. When analysing classification questions in sport science, researchers are encouraged to evaluate multiple classification algorithms and a feature selection method should be considered for identifying key variables. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Science and Medicine in Football Taylor & Francis

Optimising classification in sport: a replication study using physical and technical-tactical performance indicators to classify competitive levels in rugby league match-play

Download PDF

Optimising classification in sport: a replication study using physical and technical-tactical performance indicators to classify competitive levels in rugby league match-play

Abstract

   Determining key performance indicators and classifying players accurately between competitive levels is one of the classification challenges in sports analytics. A recent study applied Random Forest algorithm to identify important variables to classify rugby league players into academy and senior levels and achieved 82.0% and 67.5% accuracy for backs and forwards. However, the classification accuracy could be improved due to limitations in the existing method. Therefore,...
Loading next page...
 
/lp/taylor-francis/optimising-classification-in-sport-a-replication-study-using-physical-QBhYWf5fcx
Publisher
Taylor & Francis
Copyright
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
ISSN
2473-4446
eISSN
2473-3938
DOI
10.1080/24733938.2022.2146177
Publisher site
See Article on Publisher Site

Abstract

   Determining key performance indicators and classifying players accurately between competitive levels is one of the classification challenges in sports analytics. A recent study applied Random Forest algorithm to identify important variables to classify rugby league players into academy and senior levels and achieved 82.0% and 67.5% accuracy for backs and forwards. However, the classification accuracy could be improved due to limitations in the existing method. Therefore, this study aimed to introduce and implement feature selection technique to identify key performance indicators in rugby league positional groups and assess the performances of six classification algorithms. Fifteen and fourteen of 157 performance indicators for backs and forwards were identified respectively as key performance indicators by the correlation-based feature selection method, with seven common indicators between the positional groups. Classification results show that models developed using the key performance indicators had improved performance for both positional groups than models developed using all performance indicators. 5-Nearest Neighbour produced the best classification accuracy for backs and forwards (accuracy = 85% and 77%) which is higher than the previous method’s accuracies. When analysing classification questions in sport science, researchers are encouraged to evaluate multiple classification algorithms and a feature selection method should be considered for identifying key variables.

Journal

Science and Medicine in Football – Taylor & Francis

Published: Nov 16, 2022

Keywords: Performance analysis; team sport; rugby league; machine learning; feature selection

References

  • Ensemble-based logistic model trees for website phishing detection
  • Machine learning approach for the classification of corn seed using hybrid features
  • Phishing website detection: forest by penalizing attributes algorithm and its enhanced variations
  • Software defect prediction: analysis of class imbalance and performance stability
  • Impact of feature selection methods on the predictive performance of software defect prediction models: an extensive empirical study
  • Talent development in adolescent team sports: a review
  • Repeated measures random forests (RMRF): identifying factors associated with nocturnal hypoglycemia
  • Predictive modelling of training loads and injury in Australian football
  • Ensemble feature selection in medical datasets: combining filter, wrapper, and embedded feature selection results
  • Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review
  • Ensemble and deep-learning methods for two-class and multi-attack anomaly intrusion detection: an empirical study
  • Influence of playing standard on the physical demands of professional rugby league
  • Activity and recovery cycles and skill involvements of successful and unsuccessful elite rugby league teams: a longitudinal analysis of evolutionary changes in National Rugby League match-play
  • An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm
  • Correlation-based feature selection for machine learning
  • Sleep quality in elite athletes: normative values, reliability and understanding contributors to poor sleep
  • Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset
  • A comparison of physical and technical performance profiles between successful and less-successful professional rugby league teams
  • Bedload transport rate prediction: application of novel hybrid data mining techniques
  • Influence of feature selection on multi-layer perceptron classifier for intrusion detection system
  • Parameter tuning in KNN for software defect prediction: an empirical analysis
  • Sports analytics and the big-data era
  • A deep learning based DDoS detection system in software-defined networking (SDN)
  • Sports analytics for football league table and player performance prediction
  • Multi-layer intrusion detection system with extra trees feature selection, extreme learning machine ensemble, and softmax aggregation
  • A novel selective naïve Bayes algorithm
  • Importance of various training-load measures in injury incidence of professional rugby league athletes
  • The use of technical-tactical and physical performance indicators to classify between levels of match-play in elite rugby league
  • Whole, half and peak running demands during club and international youth rugby league match-play
  • The use of microtechnology to quantify the peak match demands of the football codes: a systematic review
  • Sports prediction and betting models in the machine learning age: the case of tennis
  • Nonparametric variable importance assessment using machine learning techniques
  • Transferring an analytical technique from ecology to the sport sciences 2
  • A comparison of game-play characteristics between elite youth and senior Australian National Rugby League competitions