Sports result prediction using data mining techniques in comparison with base line model

Praphula Kumar Jain (), Waris Quamer () and Rajendra Pamula ()
Additional contact information
Praphula Kumar Jain: Indian Institute of Technology (ISM)
Waris Quamer: Indian Institute of Technology (ISM)
Rajendra Pamula: Indian Institute of Technology (ISM)

OPSEARCH, 2021, vol. 58, issue 1, No 3, 54-70

Abstract: Abstract Sports prediction is one of the recent growing areas of interest entailing good prediction accuracy. Coaches require models in order to assess their players, analyse opponent teams and formulate winning strategies. Generation of comprehensive statistical data in sports has enabled data mining (DM) techniques to be applied to it in order to extract underlying predictive information. In this paper, an approach based on data mining is proposed for result prediction in sports. The work includes pre-processing of data, feature extraction, attribute selection and application of DM algorithms as a learning strategy. To validate our proposed model, a case study concerning prediction of the results of Indian Premier League (IPL) matches is illustrated. The constructed models are based on the performance of teams in past matches, player performance indices, opposition team information and external factors, and therefore, relevant features are engineered to indicate the same. The best prediction accuracy was found to be 70.58%.

Keywords: Data mining; Sports prediction; IPL; Classification; Cricket (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s12597-020-00470-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:opsear:v:58:y:2021:i:1:d:10.1007_s12597-020-00470-9

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/12597

DOI: 10.1007/s12597-020-00470-9

Access Statistics for this article

OPSEARCH is currently edited by Birendra Mandal

More articles in OPSEARCH from Springer, Operational Research Society of India
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().