Traditional Machine Learning Algorithms and Deep Learning for ODI Cricket Prediction

dc.contributor.advisorHoare, Terry
dc.contributor.authorTirthe, Priti
dc.date.accessioned2024-04-02T15:25:18Z
dc.date.available2024-04-02T15:25:18Z
dc.date.issued2024
dc.description.abstractA comparative study between traditional machine learning and a deep neural network approach is presented for predicting winning teams in for One Day International (ODI) cricket games. Data is extracted from the espncricinfo website covering the years 1971 to 2022 for model training. Features include team performance and match conditions. Model performance is evaluated on 2023 match results. Both small (2010–2022) and large datasets (1971-2022) are used for training for comparative purposes. The deep neural ANN achieves an accuracy of 85.4%, outperforming the conventional techniques including ensemble techniques such as random forests and gradient boosting. The deep neural ANN model is shown to outperform in identifying nuances and intricate patterns, demonstrating an ability to use large amounts of historical data to increase accuracy. This study builds upon earlier work to add significant insights to improve ODI cricket result predictions.
dc.identifier.citationTirthe, P. (2024). Traditional Machine Learning Algorithms and Deep Learning for ODI Cricket Prediction. Masters Thesis, Dublin Business School.
dc.identifier.urihttps://hdl.handle.net/10788/4525
dc.language.isoen
dc.publisherDublin Business School
dc.rights.holderCopyright: The author
dc.rights.urihttp://esource.dbs.ie/copyright
dc.subjectCricket
dc.subjectMachine learning
dc.subjectDeep learning
dc.titleTraditional Machine Learning Algorithms and Deep Learning for ODI Cricket Prediction
dc.typeThesis
dc.type.degreelevelMSc
dc.type.degreenameMSc in Data Analytics
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