Cricket Score Prediction Using Deep Learning

Cricket is a sport characterized by its wealth of data and intricacies, making outcome prediction a fascinating challenge for analysts, broadcasters, and fans alike. The emergence of T20 leagues, particularly the Indian Premier League (IPL), has significantly increased the demand for advanced, real-time analytical tools. Traditional score prediction methods in cricket often depend on fixed metrics like average run rates, which do not adequately reflect the game's dynamic nature. This project introduces a deep learning approach to forecast the final score of a team batting first in a T20 match, utilizing historical match data and relevant contextual features. The proposed system employs a neural network model created in Python with TensorFlow and Keras, trained on an IPL dataset that includes match specific details such as venue, batting and bowling teams, batsmen, bowlers, current score, overs bowled, and wickets lost. Feature engineering is used to generate additional metrics like balls remaining, wickets in hand, and current run rate, which enhance the understanding of the match context. These features are then encoded, scaled, and input into a multilayer dense neural network to predict the expected final score. The model's performance is assessed using standard metrics like Mean Absolute Error (MAE) and R² Score to ensure accuracy and reliability. For real time interaction, the system is implemented with two user interfaces: a Jupyter notebook-based widget for exploratory analysis and a Flask web application that enables users to enter match details and receive immediate score predictions.