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@article{170257,
        author = {Podilapu Saiprakash and Dr P Srinivasa Rao and Ramachandran Vedantham},
        title = {Forecasting Results in a Cricket Match using Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3270-3275},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170257},
        abstract = {The growing availability of sports data and advancements in machine learning (ML) have led to significant interest in using these technologies to predict match outcomes in team sports. This review aims to examine the current landscape of ML applications in predicting match results across various team sports such as football, basketball, and rugby. Numerous techniques, including supervised learning algorithms like decision trees, support vector machines, and deep neural networks, have been applied to analyze historical data, player statistics, and team dynamics to forecast outcomes. Additionally, it discusses the role of feature selection in improving model performance, with factors such as player form, team strategies, and game context playing a significant role. The integration of more sophisticated data sources, like real-time sensor data and video analytics, shows promise in enhancing predictive accuracy. 
The review further explores the role of deep learning in match result prediction, particularly through recurrent neural networks (RNNs) and convolutional neural networks (CNNs), which can capture temporal dependencies and spatial relationships within data. The increasing complexity of models necessitates advanced computational resources, yet the potential for improved predictive capabilities is significant. Moreover, the incorporation of realtime data feeds into machine learning models represents a promising frontier, enabling live predictions and ingame analytics.},
        keywords = {machine learning (ML), recurrent neural networks (RNNs) and convolutional neural networks (CNNs),},
        month = {November},
        }