IPL Fielding Analytics Dashboard: CPM Efficiency, Catch Distribution & Performance Insights

By abhishek.verma75000 · May 14, 2026

This project delivers a comprehensive analysis of IPL fielding performance using statistical modeling, efficiency metrics, and interactive visualizations.…

The analysis shows how many top-ranked fielders each team contributes to the leaderboard. A bar chart and data table have been generated to visualize the distribution across all teams.

The bar chart and data table show that RR and RCB are tied for the highest total catches with 15 each, while CSK leads all teams in average CPM at 1.600. The visualization uses color intensity to show CPM levels across teams, making it easy to spot both metrics at once.

A bar chart and data table have been generated showing the top 5 players by Catches Per Match (CPM) efficiency. The visualization displays each player's CPM value alongside their team, with hover details showing total catches and matches played.

A scatter plot was generated showing all players positioned by their match count (x-axis) versus CPM performance (y-axis), with dashed lines marking the median thresholds for both metrics. Players appearing in the upper-right quadrant of the chart are those who combine high match counts with elite CPM — these are your top performers. The chart also uses bubble size to represent catches and color-coding by team, making it easy to spot standout players at a glance.

The analysis produced two interactive charts and supporting data tables showing how catches are distributed across rank tiers (Top 1-5, Mid 6-10, Lower 11-15) and team affiliations. The first bar chart breaks down total catches by rank tier, while the second stacked bar chart shows catches per team colored by rank tier contribution.

The analysis identifies players who deliver high catches-per-match (CPM) efficiency despite playing fewer matches than their peers. Two visualizations were generated: a scatter plot showing all players with standouts highlighted in red (above avg CPM of 0.96 and below avg matches of 7.13), and a bar chart ranking those standout players by CPM. These charts make it easy to spot hidden gems — players who perform at a high level but haven't had as many opportunities to play.

The analysis produced a styled summary statistics table and a box plot visualizing the distribution of three key fielding metrics: matches, catches, and cpm. The table displays mean, median, standard deviation, min, max, and quartile values for each column, while the box plot highlights the spread and any outliers across all three metrics.

The correlation heatmap has been generated showing the relationships between matches, catches, and catches-per-match (cpm). The strongest relationship in the data is between catches and cpm (r=0.843), meaning players who take more catches also tend to have higher efficiency per match. Matches played shows a moderate negative correlation with cpm (r=-0.562), suggesting that players who play fewer matches actually tend to be more efficient per game. Meanwhile, matches and total catches have virtually no relationship (r=-0.064), indicating that simply playing more matches does not lead to more catches.

A full comparison of all 15 players' official ranks versus their CPM-based performance ranks has been produced, along with a diverging bar chart highlighting the biggest discrepancies. The comparison table and chart show that most players are ranked fairly close to where their catches-per-match efficiency would place them, but a few stand out significantly.

The IQR outlier detection analysis has been completed for the catches and cpm columns. A scatter plot highlights all players, with outliers marked in red diamonds, and a detailed table lists the flagged players. One outlier player was identified based on their catches or CPM values falling outside the acceptable IQR bounds.