Predicting First Innings Scores in IPL: Using Machine Learning

this study is to forecast the initial scores in Indian Premier League (IPL) cricket matches by employing machine learning methodologies. Through the analysis of past match data we create model to analysis this score prediction

Methodology:

1. Data Collection: historical IPL match data, Collecting elements such as team, venue, toss outcome, and player statistics.
2. Data Pre-processing: These steps include cleaning the dataset by removing any inconsistencies or errors, handling missing values by either imputing or removing them, and encoding categorical variables to numerical values for further
3. Feature Selection: Determining the most pertinent features for forecasting first innings
4. Model Development: Incorporating regression algorithms including Linear Regression, Decision Tree, and Random
5. Model Evaluation: Assessing the performance of a model by utilising metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

The findings indicate that the Random Forest model yields the most precise forecasts for initial innings scores. This study showcases the capacity of machine learning in sports analytics and offers valuable insights for teams and analysts to enhance their strategic planning.