A COMPARISON BETWEEN DIFFERENT CLASSIFIERS FOR TENNIS MATCH RESULT PREDICTION
Open accessAbstract
Tennis is a very popular sport in the world. Many researchers have worked in the fields of forecasting the outcome of tennis matches using past statistical data records. This paper mainly investigates the comparison between three different classifiers namely decision tree, learning vector quantization and support vector machine. The research study aims to predict the result of tennis singles matches using eight UCI databases of grand slam tennis tournaments and evaluate the classification performance using various measures such as the root-mean-square error, accuracy, false positive rate, true positive rate, kappa statistic, recall, precision, and f-measure. All these performance measures confirm the supremacy of the decision tree classification algorithm compared to the others.
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The interpretability, simplicity, low computational cost, and graphical representability of decision trees have contributed to their increased use for classification tasks.
“The interpretability and simplicity of DT, its low-slung computational cost and the ability to represent them graphically have contributed to the increase in its use for classification task [50].”
From A comprehensive evaluation of ensemble learning for stock-market prediction (2020) · cites this paper as [50]
Section 4.1 describes decision trees as flowchart-like and says pruned trees are simpler and classify faster; the Conclusion says decision trees are easy to understand. The paper does not establish generally low computational cost or say these features caused an increase in decision-tree use. Page numbers are unavailable in our copy. (LLM, conf=0.84)
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References 24 · Cited by 20
Research works printed but not linked: 3
- [16]16. A. Somboonphokkaphan, S. Phimoltares, and C. Lursinsap, "Tennis Winner Prediction based on Time-Series History with Neural Modeling", in proceedings of the International MultiConference of Engineers and Computer Scientists IMECS 2009, Hong Kong, March 18 -20, 2009.
- [19]19. X. Wei, P. Lucey, S. Morgan, and S. Sridharan, ""Sweet-Spot": Using Spatiotemporal Data to Discover and Predict Shots in Tennis", in proceedings of the 7th Annual MIT Sloan Sports Analytics Conference 2013, Boston Convention and Research Center, March 1-2, 2013.
- [21]21. D. Buursma, "Predicting sports events from past results Towards effective betting on football matches", in proceedings of the 14th Twente Student Conference on IT, Enschede, University of Twente, The Netherlands, Copyright 2010, January 21, 2011.
- Authors
- Ghosh, S., Sadhu, S., Biswas, S., Sarkar, D., & Sarkar, P. P.
- Venue
- Malaysian Journal of Computer Science
- Publisher
- Univ. of Malaya
- Published
- 2019-04-26
- Volume
- 32
- Issue
- 2
- Pages
- 97-111
- ISSN
- 0127-9084 (ISSN-L)
- OpenAlex
- W2943805632
- Cited by
- 20
- References
- 24
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 7 · 2023: 2 · 2022: 3 · 2021: 2 · 2020: 2
- FWCI
- 2.19
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Malaysian Journal of Computer Science (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Sports Analytics and Performance
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- tennis match outcome prediction, decision tree, learning vector quantization, support vector machine, classifier comparison, Grand Slam tournaments, sports prediction
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