Pitch Vision Analytics: IPL AI Strategy Engine

Cricket fans, analysts, and captains can access lots of raw IPL scorecards, but very few tools turn ball-by-ball history into fast, usable tactical decisions. The gap was not data availability, but decision usability: there was no single system combining live win prediction, phase-wise team analytics, clutch-performance intelligence, batter-bowler matchups, and captain-level bowling recommendations in one deployable product. We set out to build an AI-driven IPL strategy engine that converts historical data into match-ready insights. Process We treated the dataset as a cricket intelligence layer, not just a reporting source. We cleaned and standardized ball-by-ball and match data, resolved team aliases, derived innings phases, and built reusable analytical marts. We implemented toss impact, phase analysis, elite leaderboards, and surprise insights. Then we developed calibrated win-prediction models, matchup engines, partnership analysis, and a clutch-performance system. Captain AI evolved into a realistic phase-aware tactical planner with legal bowling logic. Finally, we built a clean sports-broadcast style UI and deployed the full FastAPI + React + Ollama stack on Google Cloud Cloud Run for a live public demo. Results The final product is a live IPL AI analytics platform that goes beyond static dashboards. It delivers win prediction, tactical team splits, clutch-performance leaderboards, matchup intelligence, partnership analysis, and a Captain AI planner that generates legal next-5-over bowling strategies. Its biggest USP is converting raw cricket data into actionable match decisions. We refined the engine through multiple iterations by adding team-wise filtering, realistic bowling constraints, remaining-over controls, and non-consecutive-over enforcement for smarter tactical recommendations. Reflection With more time, we would strengthen Captain AI using deeper squad awareness, live-match constraints, venue tendencies, bowling-hand matchups, and pressure-based batter intent prediction. We would also evolve Impact Lab into a player-specific optimization engine driven by historical phase performance. On the product side, we would add model calibration and recommendation-confidence dashboards, while improving user testing and tactical workflows to make Captain AI faster, clearer, and more intuitive during live decision-making.