Predicting the Results of Cricket Matches Using Deep Learning Method

Consequently, being a multi-format and multi-variable game, cricket raises a lot of challenges in respect to making a perfect prediction of the match. The paper proposes a new deep learning-based prediction approach for One Day International (ODI) cricket, using Long Short-Term Memory Network combined with an attention mechanism. While traditional machine learning-based models have shown some promise, they still suffer from the inability to capture long-term dependencies within a cricket match sequential data. The LSTM architecture inherently tends to learn these temporal patterns really well; hence, it is particularly suited for this task. The attention mechanism further enhances this model, where the critical moments in the match are solely responsible for the outcome. Based on this, detection will be done with an extensive dataset covering more than matches of various cricket formats: ODI, T20I, and Test matches. The dataset is a collection from ESPNcricinfo and other official cricket databases, including ball-by-ball information with relevant contextual features: performance metrics from teams, player statistics, weather conditions, and venue information. With this great performance, it was much higher than the best performance of traditional machine learning approaches by as high as 94%. This work provides new insights into applying deep learning to sports analytics and opens up more possibilities toward innovation in predicting the results of cricket matches.