Mohammad Irfan
Featured project
IPL Insights Dashboard
The objective of this analysis was to identify the key factors influencing match outcomes in IPL cricket using ball-by-ball data. The project focused on evaluating how different phases of an innings — Powerplay, Middle Overs, and Death Overs — impact winning and losing performances. The analysis also examined toss influence, top batting and bowling performers, seasonal scoring trends, and the relationship between phase-wise scoring patterns and match-winning probability. By transforming raw ball-by-ball data into match-level insights through Power BI and Excel visualizations, the project aimed Process The project began with collecting and importing IPL ball-by-ball data into Power BI and Excel for detailed analysis. The dataset was cleaned and transformed by removing duplicate match records, handling tie and no-result matches, and creating separate calculations for match-level and innings-level analysis. Overs were categorized into Powerplay, Middle Overs, and Death Overs phases to study scoring behavior across different stages of an innings. Using DAX measures and Excel formulas, key metrics such as toss win percentage, phase-wise average runs, top run scorers, top wicket takers, and seasonal scoring trends were calculated. Interactive charts and dashboards were then developed to compare winning and losing teams, analyze toss impact, evaluate scoring patterns, and identify performance Results The analysis revealed that toss advantage had minimal impact on overall match outcomes, with toss-winning teams winning only around half of the matches. Phase-wise scoring analysis showed that winning teams consistently outperformed losing teams, especially during the middle overs, indicating that overs 7–15 play a crucial role in controlling IPL matches. The project also identified a strong relationship between higher powerplay scores and increased match-winning probability, with teams scoring 60+ in the powerplay achieving significantly better win percentages. Seasonal trend analysis further Reflection The analysis could be improved further by incorporating advanced metrics such as strike rate progression, boundary percentage, bowling economy by phase, and venue-specific performance trends. Predictive models could also be introduced to estimate match outcomes based on phase-wise scoring patterns and momentum shifts. Additionally, a more optimized data model with enhanced interactivity and drill-through capabilities would make the dashboard more dynamic and suitable for deeper strategic cricket analysis.