Sahil Sharma

IPL Crunch ’26: Breaking Down Data to Insights

Cricket fans often believe that toss advantage, death-over hitting, and star players are the biggest factors behind IPL success. However, these assumptions are usually based on perception rather than data. This project aimed to analyze IPL match data using Microsoft Excel to identify whether common cricket narratives actually hold true. The goal was to uncover hidden patterns related to toss impact, match phases, batting consistency, and bowling specialization through data cleaning, pivot-based analysis, and visual storytelling. Process I started by cleaning and organizing the IPL ball-by-ball dataset in Excel to make it analysis-ready. New calculated columns such as match outcome indicators, batting-phase classification, and batting-first results were created to simplify deeper analysis. Using Pivot Tables and Pivot Charts, I explored multiple dimensions of IPL gameplay including toss impact, scoring patterns across phases, top batters, top wicket-takers, and bowling specialization. Initially, I expected death overs and toss wins to dominate match outcomes, but exploratory analysis revealed that middle-over control created the largest scoring gap between winning and losing teams. I then redesigned the dashboard and insights around these surprising findings to create a more narrative-driven executive summary instead of Results The analysis revealed that toss advantage was significantly weaker than popular IPL perception, with toss-winning teams winning only around 50.5% of matches. The surprising finding was that middle overs created the highest scoring gap between winning and losing teams, suggesting that sustained control during overs 7–15 influences match outcomes more than explosive death-over hitting. The final dashboard transformed raw IPL data into actionable cricket insights using only Microsoft Excel tools such as Pivot Tables, Pivot Charts, and exploratory data analysis techniques. Reflection If given more time, I would expand the project by adding advanced performance metrics such as strike rates, economy rates, venue-wise trends, and season comparisons. I would also improve interactivity by building slicers and dynamic dashboards for better user exploration. Additionally, integrating Power BI based analytics could help uncover deeper and more interactive insights beyond.