Performance Prediction of Cricket Player Using Blockchain Enabled HMM Model
- DOI
- 10.2991/978-94-6463-252-1_47How to use a DOI?
- Keywords
- Hidden Markov Model (HMM); Blockchain; InterPlanetary File System (IPFS); Cricket; Player Performance Prediction
- Abstract
Sports play an essential role for human life which keeps physical and mental fitness. Competition in sports is rising day by day. In sports giving opportunity to right talented person is a challenging task. In countries like India, team sport like cricket faces huge competition. This paper introduces the performance prediction of cricket player using blockchain enabled Hidden Markov Model (HMM). In the proposed work, the time series data of matches played by batsmen during past one year is collected, analyzed and performance of a batsman is predicted by using HMM. The data obtained including the predicted results are stored in the proposed blockchain with IPFS based system architecture for data management, decentralization, data security and immutable. It focuses on the players rather than the whole match outcome so best players can team up.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Sai Radha Krishna G AU - Sandeep Kalakota AU - Regula Harshavardhan PY - 2023 DA - 2023/11/09 TI - Performance Prediction of Cricket Player Using Blockchain Enabled HMM Model BT - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023) PB - Atlantis Press SP - 438 EP - 451 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-252-1_47 DO - 10.2991/978-94-6463-252-1_47 ID - G2023 ER -