Abstract
Cricket is one of the most popular sports in the world. It is a dynamic game involving complex rules and strategies. There are many websites like Cricbuzz and Cricinfo that provide live text commentary using journalists who report from the ground or use live telecast of the match. This process of using journalists for typing out live text commentary is both labor and cost intensive. This research presents an approach that uses dynamic web scraping to scrape live scores and associated parameters like run rate, outcome of each ball, etc. which are then fed to a supervised learning algorithm that uses all these parameters and generates a commentary for the current ball event by selecting an appropriate commentary template from a pool of predefined commentary templates. We also compare results between the different supervised learning algorithms, i.e., Neural Network and Random Forest, in terms of accuracy metrics and prove that Random Forest performs better by having an accuracy of 92.7% in generating the appropriate commentary.
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Hegde, A.S., Jha, K., Suganthi, S., Honnavalli, P.B. (2022). Automating Live Cricket Commentary Using Supervised Learning. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 91. Springer, Singapore. https://doi.org/10.1007/978-981-16-6285-0_4
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DOI: https://doi.org/10.1007/978-981-16-6285-0_4
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