Can social media opinions add value to historical data?: A study for T20I cricket match outcome prediction using machine learning

Predicting the outcome of cricket matches prior to their start is challenging due to the inability to generate real-time information from the thousands of data points generated by each match. Twitter (now “X”) and other social media sites have demonstrated their capacity to produce content in real time. This study proposes a machine learning-based method for predicting the results of T20 international matches (before they begin) using historical and Twitter data. A sentiment analysis was carried out to create a feature by considering three leading methods, revealing that the fine-tuned RoBERTa-based model outperformed those using LSTM and VADER with an F1 score of 95.3%. Three datasets were formulated, with historical, Twitter, and both data types. The XGBoost classifier produced the best models for all three datasets, and it was found that removing correlated and least important variables enhances their performance. Models using Twitter data outperformed those using historical data, while models using both data (with a 73.7% F1 score) outperformed both individual data models. Moreover, this best model performed better than the bookmakers’ predictions and could generate a profit by accurately predicting 11 matches out of 14 for the T20I World Cup 2022 data.