When Web Scraping Failed, I Built My Own Dataset by Hand 🏏💻
When I started building CricField – AI-Powered Cricket Fielding Analyzer 🏏🤖, I was hyped about the tech — machine learning, real-time predictions, mobile integration. But before I could train any models or sketch fancy UI layouts, I had to face the real beast: data collection. 😬
I assumed I’d find a solid cricket dataset somewhere online — something with shot types, field placements, ball-by-ball insights… you know, the works. But reality hit hard: there was nothing close to what I needed. No fielding labels. No contextual data. Just raw, scattered text.
So I rolled up my sleeves and decided to scrape the data myself. I tried using web scraping tools on platforms like ESPN Cricinfo — hoping I could automate the process. But let’s be real — it sucked. 😩 The commentary was inconsistent, the HTML structure tricky, and parsing human-written phrases like “nudged past square leg for a couple” turned out to be a nightmare for code.
So, I ditched automation and went full manual. I focused on two legendary T20 batsmen — Babar Azam and Jos Buttler, each with 1000+ balls faced — ideal for pattern analysis. I started manually reading ball-by-ball commentary from their matches, one delivery at a time, interpreting the play, and mapping it to meaningful labels like Shot Type and Shot Placement (Area).
It was slow. Painful. Tedious. Some days I’d spend hours labeling just one match. But in that grind, I learned something powerful — when the data doesn’t exist, you don’t give up… you create it. 💡
Eventually, I curated a dataset that was clean, labeled, and built from real-world game intelligence — not synthetic junk. This data became the backbone of my dual-output neural network, capable of predicting the top 9 field placements and top 3 shot types, all based on live match context.
It wasn’t just technical. It was emotional. I fought through fatigue, imposter syndrome, and the temptation to cut corners. But now, when CricField makes an intelligent recommendation, I know it’s built on data I truly understand — because I created it. 🧠🔥
That struggle taught me more than any tutorial could — about data integrity, perseverance, and the beauty of doing things the hard way. 🏏💻❤️