Speaker
Description
The rapid growth of sports analytics has enabled researchers to use data-driven techniques to understand performance patterns and predict outcomes in competitive sports. Cricket, particularly the Indian Premier League (IPL), generates a large amount of structured match data that can be analyzed to study team strategies, player performance, and match dynamics. This project focuses on analyzing IPL match data and predicting match outcomes using statistical analysis and machine learning techniques implemented in the R programming environment.
The study utilizes a ball-by-ball IPL dataset that provides detailed information about every delivery in a match, including the batsman, bowler, runs scored, extras, wickets, overs, and match context. The dataset is first cleaned and preprocessed in RStudio to ensure accuracy and consistency. Data preprocessing includes handling missing values, organizing variables, and preparing the dataset for further analysis. Exploratory Data Analysis (EDA) is then performed to identify patterns and relationships between match variables and match results.
Key concepts of this project include sports data analytics, exploratory data analysis, feature engineering, and predictive modeling. Important match features such as run rate, wickets remaining, overs completed, powerplay performance, and death-over scoring trends are extracted to understand the factors that influence match outcomes. Visualization techniques using R packages such as ggplot2, dplyr, and tidyr are applied to examine team performance, venue scoring patterns, and player contributions across different stages of the match.
Machine learning models including Logistic Regression and Random Forest are implemented to predict the probability of a team winning under different match situations. These models are trained using historical IPL data to analyze how different match conditions influence the final result. The project demonstrates how statistical analysis and machine learning techniques in R can be applied to sports datasets to generate meaningful insights.
Overall, this study highlights the importance of sports analytics in modern cricket and shows how historical IPL data can be used to build predictive models and support data-driven decision-making in cricket analysis.
If you used AI tools or services to support the preparation of this submission, please state the name and reason for using each of them.
AI tools were used only for language refinement and editing of the abstract. The analysis and project concept were developed independently.
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Machine Learning, Sports Analytics, IPL Data Analysis, Predictive Modeling, R Programming |
|---|---|
| Virtual Option | This submission is for pre-recorded virtual presentation only |
| Video Recording | Please don't share recordings of my talk |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |