dc.contributor.author |
Hatharasinghe, Manuka Maduranga |
|
dc.contributor.author |
Poravi, Guhanathan |
|
dc.date.accessioned |
2020-05-24T08:17:07Z |
|
dc.date.available |
2020-05-24T08:17:07Z |
|
dc.date.issued |
2019 |
|
dc.identifier.citation |
Hatharasinghe, M M and Poravi, G (2019) ’Data Mining and Machine Learning in Cricket Match Outcome Prediction: Missing Links’ In:2019 IEEE 5th International Conference for Convergence in Technology (I2CT) Pune, India. 29 -31 March 2019. pp. 1-4. IEEE 10.1109/I2CT45611.2019.9033698 |
en_US |
dc.identifier.uri |
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9033698 |
|
dc.identifier.uri |
http://dlib.iit.ac.lk/xmlui/handle/123456789/409 |
|
dc.description.abstract |
Using Computer Intelligence to analyze and model the game of Cricket is a promising research area. The increased popularity and financial benefits have made Cricket an interesting sport to be subjected to statistical analysis and machine learning. The dynamic nature of Cricket, complex rules governing Cricket makes the task a challenging one. The various approaches taken and what has been disclosed from available work is neither very clear nor properly documented due to the differences in the approaches. If the good and the drawbacks of the existing work is properly analyzed and documented, it will assist in future researches. This paper presents an analysis of the existing work related to match outcome prediction in the Cricket domain. This paper is a result of an ongoing research, by the end of the research we hope to address the missing links and the drawbacks that will be explored in this paper. |
en_US |
dc.subject |
Data Mining |
en_US |
dc.subject |
Sabermetrics |
en_US |
dc.subject |
Data models |
en_US |
dc.subject |
Predictive models |
en_US |
dc.title |
Data Mining and Machine Learning in Cricket Match Outcome Prediction: Missing Links |
en_US |
dc.type |
Article |
en_US |