Enhanced Predictive Modeling of Cricket Game Duration Using Multiple Machine Learning Algorithms
2020 International Conference on Data Science and Engineering (ICDSE)
Cricket has the second-largest fan-base after football. Interest in any game is a factor of quality of the game which in turn depends on the quality of players. It is therefore important to have good players and that they are paid well. Sports industry largely relies on the advertising sector for sponsorship and financing of games. Advertisement companies spend a fortune to acquire the best slots during a game to catch the maximum viewership. This implies that advertising companies have a lot of interest in the duration of a match. Indian Premier League (IPL) has a huge fan-base and is one of the major events where companies spend a large amount of money to advertise their products. Due to this, a short game, which ends prior than expected, results in loss of opportunity in terms of time-slots lost and hence revenue and fan interest. The prediction of duration of a game will be beneficial for both sport and advertisement industry. In this paper, we use machine learning algorithms to predict the duration of a match in terms of the number of balls expected to be delivered in the match. The work introduces four different approaches, using historical data, to predict the number of balls in a match.
Machine Learning, Duckworth–Lewis–Stern, Statistics, Singular value decomposition, Collaborative Filtering, Logistic Regression, Cricket
Applied Data Science
Shivam Tyagi, Rashmi Kumari, Sarath Chandra Makkena, Swayam Swaroop Mishra, and Vishnu S. Pendyala. "Enhanced Predictive Modeling of Cricket Game Duration Using Multiple Machine Learning Algorithms" 2020 International Conference on Data Science and Engineering (ICDSE) (2020). https://doi.org/10.1109/ICDSE50459.2020.9310081