Shreya Venkatesan

IPL Analytics Dashboard

IPL discussions are often based on opinions rather than data-driven insights. Fans and analysts frequently debate whether winning the toss affects match outcomes, which match phase contributes most to victory, and which players perform consistently across seasons. However, ball-by-ball IPL datasets are complex and difficult to analyze manually. This project aimed to transform raw IPL data into an interactive Tableau dashboard that clearly visualizes toss impact, phase-wise scoring trends, and top-performing batters and bowlers. Process The project began with importing and understanding the IPL ball-by-ball dataset in Tableau. Data fields such as batting team, bowler, batter, over number, toss winner, and match winner were analyzed to identify patterns linked to winning matches. Calculated fields were created to divide overs into Powerplay, Middle Overs, and Death Overs. Additional fields classified whether the batting team was the winning or losing side. Toss win percentages, phase-wise scoring patterns, and top player statistics were visualized using interactive charts. Multiple dashboard layouts, color schemes, and chart types were tested before finalizing a clean and readable design. Results The analysis revealed that winning the toss has only a minimal impact on match outcomes, with toss winners winning only slightly more matches overall. The strongest relationship with winning was observed during death overs, where winning teams consistently scored at higher rates. The dashboard also identified the top-performing IPL batters and bowlers across multiple seasons. The final Tableau dashboard successfully converted large-scale IPL ball-by-ball data into clear, interactive, and easy-to-understand visual insights. Reflection If given more time, I would expand the dashboard with season filters, venue-based analysis, and team-wise comparisons to generate deeper insights. I would also include advanced metrics such as strike rate trends, economy rate analysis, and win probability comparisons. Adding interactive hover insights and predictive analytics using Python or machine learning models could further improve the project. In future iterations, I would also focus more on responsive dashboard design and storytelling elements to make the insights even easier for non-technical users to understand.