Turning IPL Data Into an Actual Dashboard, Not Just a Chart Dump

Cricket data is a genuinely great sandbox for learning real data analytics — it's messy, it's seasonal, it has categorical and numerical fields tangled together (venues, players, scores, outcomes), and there's enough of it (IPL alone spans well over a decade) to make cleaning and structuring it a real exercise, not a toy dataset.

From raw data to something decision-ready

The IPL Dashboard project takes raw match and player data through the full analytics pipeline: cleaning inconsistent records, handling missing or malformed fields, and restructuring everything into a shape that's actually queryable for insight — team performance trends, player stats over seasons, venue-specific patterns, that kind of thing.

The goal deliberately wasn't "make a pretty chart." It was to practice the unglamorous 80% of analytics work — cleaning and structuring — that has to happen correctly before any visualization is trustworthy. A beautiful dashboard built on uncleaned data is worse than no dashboard, because it looks authoritative while being wrong.

Why TypeScript + Python together

Python handles the data wrangling and analysis side — it's the right tool for cleaning, aggregating, and reshaping tabular data. TypeScript powers the presentation layer, where type safety matters more, since a dashboard is exactly the kind of UI where a silently-wrong prop type turns into a silently-wrong chart.

What this project is really practicing

Data analytics work isn't usually about a novel algorithm — it's about the judgment calls in cleaning (what counts as an outlier vs. real data, how to handle a missing venue field, whether to interpolate or drop) that determine whether the final numbers can be trusted. This project is where I practiced making those calls deliberately instead of skipping straight to the chart.

Why two languages

Layer	Technology	Role
Data wrangling	Python	Cleaning, aggregation, reshaping
Presentation	TypeScript	Type-safe dashboard UI

A beautiful dashboard built on uncleaned data is worse than no dashboard — it looks authoritative while being wrong.

Built with TypeScript, Python, HTML, and JavaScript. Source on GitHub.