Performance Analysis of Basketball Referees by Machine Learning Techniques

Sheng-Wei Wang, Wen-Wei Hsieh

2016

Abstract

Basketball referees are important in a basketball game. In this paper, we analyze the performance of basketball referees in a game from history data and using the machine learning techniques. The data are collected from Taiwan Super Basketball League games. We first observed that the teamwork is a key factor to the performance of referee teams. Furthermore, the degree of teamwork are more important than the personal capabilities. Then, we derived some classifiers by machine learning algorithms to further analyze the data set. Among the three classifiers, a classifier named linear classifier using pocket algorithm, which is able to classify the data points with most correct rate, performs better than the other two classifiers. The classifier also proved the importance of teamwork is much larger than that of personal capability. In the future, the classifier can be used to predict the performance of a referee team in a basketball game.

Paper Citation

in Harvard Style

Wang S. and Hsieh W. (2016). Performance Analysis of Basketball Referees by Machine Learning Techniques . In Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS, ISBN 978-989-758-205-9, pages 165-170. DOI: 10.5220/0006031501650170

in Bibtex Style

@conference{icsports16,
author={Sheng-Wei Wang and Wen-Wei Hsieh},
title={Performance Analysis of Basketball Referees by Machine Learning Techniques},
booktitle={Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,},
year={2016},
pages={165-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006031501650170},
isbn={978-989-758-205-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,
TI - Performance Analysis of Basketball Referees by Machine Learning Techniques
SN - 978-989-758-205-9
AU - Wang S.
AU - Hsieh W.
PY - 2016
SP - 165
EP - 170
DO - 10.5220/0006031501650170