This Data Dive explores the IPL Player Performance Dataset by Anova testing and building regression model.

This Data Dive explores hypothesis testing on IPL Player Performance Dataset

This Data dive analyzes the IPL Player Performance dataset by exploring meaningful relationships between two pairs of numeric variables, an original with a derived one to analyze bowling performance via correlation coefficients and confidence intervals. These methods offer a statistical understanding of how IPL bowlers perform across numerous numbers of innings.

This Data Dive examines the IPL Player Performance Dataset by exploring missing data patterns and analyzing how documentation shapes interpretation. Through categorical checks, missingness analysis, and an outlier study, the report highlights how data quality influences cricket analytics

The data dive explores how sampling variability changes with sample size by comparing three independent samples of the dataset.

This week data dive analyzes groupby and probability along with trends of total sixes in recent seasons and across venues. It explores IPL team participation across seasons, identifies missing team–season combinations, with a heatmap.

IPL Data Dive Week1 Summary Statistics