vaibhav tripathy
Featured project
ipl data analysis
Analyze five seasons of IPL ball-by-ball data to determine whether winning the toss increases a team's chances of winning, identify which innings phase (Powerplay, Middle Overs, or Death Overs) has the strongest impact on match outcomes, and discover the top-performing batters and bowlers based on runs scored and wickets taken. The solution should present insights through interactive visualizations and highlight key patterns that influence success in T20 cricket. Process Data Collection Imported the IPL ball-by-ball CSV dataset covering five seasons. Data Cleaning Removed duplicate match records. Filtered matches with valid winners. Handled missing values in batting, bowling, and wicket columns. Toss Analysis Compared the toss winner with the match winner. Calculated win percentages for toss winners and toss losers. Phase-wise Performance Analysis Divided innings into: Powerplay: Overs 1–6 Middle Overs: Overs 7–15 Death Overs: Overs 16–20 Calculated average runs scored in each phase by winning and losing teams. Player Performance Analysis Aggregated batter runs across all matches. Counted wickets taken by bowlers (excluding run-outs). Ranked players to identify the top 5 batters and top 5 bowlers. Visualization Created a bar chart comparing toss winners' a Results Results Toss Impact: Toss winners won 50.5% of matches, while toss losers won 49.5%, indicating a negligible advantage. Phase Analysis: The Middle Overs (7–15) showed the largest difference in average runs between winning and losing teams. Top 5 Batters: Virat Kohli, Rohit Sharma, Shikhar Dhawan, David Warner, and KL Rahul emerged as the highest run-scorers. Top 5 Bowlers: Yuzvendra Chahal, Bhuvneshwar Kumar, Sunil Narine, Piyush Chawla, and Jasprit Bumrah recorded the most wickets. Outcomes Determined that winning the toss is not a major factor in deciding match results. Identified middle-ove Reflection Analyzed ball-by-ball IPL data instead of relying only on match-level statistics, enabling deeper insights. Divided innings into Powerplay, Middle Overs, and Death Overs to identify which phase most influences winning. Compared toss winners and toss losers to validate a common cricket assumption using data. Combined team performance analysis with individual player rankings to provide a comprehensive view. Built an interactive dashboard with visualizations for easier interpretation of results. Generated a data-driven insight showing that middle-overs performance has a stronger relationship with