Forecasting IPL Score Using Machine Learning Techniques

The IPL, which stands for Indian Premier League, has become one of the most widespread and fought over Twenty20 cricketers in the world, which borrows the interest of its audience with its ever-changing and chaotic nature. In this research paper, the researchers aim to employ various machine learning techniques to predict the cricket scores of an IPL match, recording valuable insights aimed at enhancing the understanding of the sport and providing useful insights for the sporting teams and the fans as well as the analysts. The work aims to develop and test computational intelligence models appropriate for estimating IPL match total scores using modern technologies such as machine learning and artificial intelligence based on historical match scores, player statistics and match context. Among the methods used were ensemble techniques, such as neural and regression networks, to investigate complex patterns within the dataset and increase accuracy in predictive performance. As part of modelling, we harnessed a dataset that covers several seasons of the IPL that includes performance indicators of players, match characteristics, and necessary background information like the weather and venue of the game. Models are evaluated using the same dataset, but the target for prediction was never seen during training, thus ensuring that the model was accurate, recall, precision, and overall performance were optimal during training. Standard metrics such as MAE-Mean Absolute Error, MSE-Mean Square Error and RMSE Root Mean Squared Error measure the predictability of models. Lastly, we compared the algorithms to identify those that better predict IPL cricket scores. Knowledge derived from our research is rich since it hails from cricket match result factors highlighting the need to consider a player's form, teamwork, and extra influences from the weather. From its ability to demonstrate the viability and effectiveness of machine learning to predict scores in the game of cricket's IPL, the work gives grounds for real practical applications for use within the cricketing realm, like enhanced strategy through the game, systems in fan engagement, etc.