Speaker
Description
The increasing availability of structured sports datasets has created new opportunities for applying statistical analysis and predictive modeling techniques to sports analytics. The Indian Premier League (IPL) provides detailed match and ball-by-ball datasets that allow in-depth statistical exploration of match dynamics and performance patterns. This study applies statistical analysis and visualization techniques in the R programming environment to analyze scoring patterns, evaluate strategic factors influencing match outcomes, and develop predictive models using historical IPL data.
Exploratory analysis is first conducted to examine run distribution across overs in order to understand scoring behavior during different phases of an innings such as the powerplay, middle overs, and death overs. Visualizations generated using ggplot2 highlight how scoring rates evolve throughout an innings and how teams accelerate scoring during the final overs.
Venue-based analysis is also performed to compute average runs scored at different stadiums, allowing comparison of scoring patterns across venues. A chi-square hypothesis test is conducted to evaluate whether winning the toss significantly affects the probability of winning a match.
Finally, a logistic regression model is developed to predict match outcomes using variables such as competing teams, venue, toss winner, and first innings score. The project demonstrates how the R ecosystem enables effective statistical analysis, reproducible workflows, and predictive modeling for sports data analytics.
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | sports analytics, statistical modeling, hypothesis testing, data visualization, R |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
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| 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 |