PitchPulse AI

Cricket fans consume massive amounts of IPL data every season, but most statistics are scattered across multiple platforms and presented in a fragmented way. Casual viewers struggle to identify meaningful trends such as team momentum, venue impact, player consistency, and wicket distribution without manually comparing multiple sources. The goal of this project was to build a centralized IPL analytics dashboard that transforms raw match data into clear, interactive, and visually engaging insights. Process The project started with studying existing cricket analytics platforms and sports broadcast dashboards to understand how professional sports data is visualized. I analyzed common user pain points such as information overload, poor readability, and lack of comparative insights. I first created rough dashboard layouts focused on KPI cards, trend analysis, and comparative statistics. Early designs contained too many graphs and looked cluttered, so I simplified the structure by grouping related insights into sections like batting, bowling, venues, and season trends. For data handling, match-level IPL datasets were cleaned and transformed to calculate metrics such as strike rate, economy rate, average runs, wicket distribution, and net run rate. Results The final dashboard successfully consolidated season-wide IPL insights into a single analytics platform with an intuitive and visually engaging interface. It provided quick access to team standings, player performance, venue trends, and season statistics without requiring users to navigate across multiple sources. The dashboard improved data readability through structured visual hierarchy and interactive analytics sections. Key insights such as batting dominance, bowling effectiveness, and scoring trends became easier to interpret within seconds. Reflection In the next iteration, I would integrate real-time IPL APIs to provide live match analytics instead of static datasets. I would also add interactive filters, drill-down comparisons, and AI-based prediction models for player performance and match outcomes. Additionally, I would conduct formal user testing with cricket fans and fantasy league players to gather usability metrics and improve dashboard navigation based on real user behavior.