Best strategy to win a match: an analytical approach using hybrid machine learning-clustering-association rule framework

DOI10.1007/s10479-022-04541-6OpenAlexW4221012579MaRDI QIDQ6115868

Lalitha Dhamotharan, Ajay Kumar, Praveen Ranjan Srivastava, Ashish Kumar Jha, Prajwal Eachempati

Publication date: 13 July 2023

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10479-022-04541-6

zbMATH Keywords

neural networkclusteringmachine learningrandom forestsports analyticsapriori algorithmensemble gradient boost predictive modelmatch result prediction

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Applications of statistics (62Pxx) Multivariate analysis (62Hxx)

Related Items (1)

Understanding the effect of contextual factors and decision making on team performance in Twenty20 cricket: an interpretable machine learning approach

Cites Work

• Sports result prediction using data mining techniques in comparison with base line model
• Comparing league formats with respect to match importance in Belgian football
• An analytics approach to the FIFA ranking procedure and the World Cup final draw
• Inverse optimal value problem on minimum spanning tree under unit \(l_{\infty}\) norm
• Robust fuzzy clustering of time series based on B-splines
• Atheoretical regression trees for classifying risky financial institutions
• Trimmed fuzzy clustering of financial time series based on dynamic time warping
• When to declare the third innings of a test cricket match?
• Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis
• Contextual batting and bowling in limited overs cricket

This page was built for publication: Best strategy to win a match: an analytical approach using hybrid machine learning-clustering-association rule framework