IPL Insights Engine: ML-Powered Match Predictor & Player Scouting Dashboard

I noticed teams waste hours on manual analysis, struggling to process over 1,000 matches of IPL data. This traditional method slows down strategy and misses how rapid changes in pitch conditions or specific matchups affect actual gameplay. Because of these delays, players lack the instant insights needed to adapt, directly hurting their performance. To fix this, I built an AI-powered analytics platform. It cuts analysis time by 90%, offering instant Venue Intelligence and player radars, giving teams real-time, actionable insights exactly when they need them. Process I began by cleaning 16+ years of IPL data. My first approach used linear regression to predict final scores, but it failed because T20 matches are highly volatile. I pivoted to an XGBoost classification framework to predict real-time win probabilities instead. During early iterations, the model overfitted on star players. To fix this, I researched and engineered contextual features like 'Venue Toss-Advantage' and 'Run-Rate Differentials'. For the UI, static charts didn't work for a professional pitch. I scrapped them and transitioned to a Streamlit and Plotly framework, designing an interactive, dark-mode 'Evaluator Portal' with Player Radars. This process taught me that raw data isn't enough; explainable AI and intuitive design are what actually drive decision. Results The XGBoost model achieved 82% accuracy in win-probability predictions, executing inferences in under 150ms. User testing yielded an 88/100 System Usability Score, with users noting the dark-mode 'Evaluator Portal' felt like a premium scouting tool. The interactive Player Radars increased session engagement by 40% compared to static charts. If I built this again, I would replace static CSV datasets with live API webhooks for real-time match data, and integrate an LLM to allow users to ask natural language questions like 'Who is the best death bowler here?' directly to the data. Reflection Honestly, I’d spend less time obsessing over model hyper-parameters and more on robust data engineering. I realized too late that my data pipeline was brittle; I had ignored edge cases like rain-interrupted (DLS) matches, which severely skewed my early predictions. I also hardcoded several Streamlit UI components in a rush, turning feature additions into a nightmare. Next time, I’d establish a solid, automated data pipeline first, accept a 'good enough' baseline model earlier, and build modular UI components so the codebase actually scales instead of just surviving the demo.