Let's Analyze Your Favourite Cricketer - The IPL Story

April 27, 2026 · Data Analysis

Its IPL season again, and everyone boasts how well their favourite cricketer scoring X runs, taking Y wickets and yada yada yada…

Let's Analyze Your Favourite Cricketer - The IPL Story

So, I created a dashboard that literally finds this info for you, how a batter batted against a team, a bowler bowled against a team, and which batter got thrashed by a bowler.

Try it yourself: IPL Cricket Dashboard

Now, how did I do it, why did I do it, what do I want to show?

Why Did I Do It?

I see commentators talking about how well player A did against player B in last IPL or similar. So, this IPL season got me thinking how I could see this analysis for myself and show it to others as well. There is a lot of data analysis that can go into it, given you ask the right questions. Questions such as, how many runs Dhoni scored in 2023-2025 IPL seasons, how many wickets Bumrah took of RCB team in 2020-2024 IPL season.

Of course there is more analysis that can be done here, more data that can be sorted and more trends that can be useful to cricket analysts. I would love for someone to come up with more such questions and I'd be happy to include that in the project.

How Did I Do It?

This kind of raw data is usually available open source for people to use. All you need to do is search for it. So, I asked Gemini to search for me what links are available online. I also gave some of the above why details to it to get a rough plan for the entire project. It was able to get me the correct links to the dataset, and draft a rough plan.

  • First, I downloaded the data from Cricsheet and viewed the json files to understand what information I have.
  • Second, I decided to use Streamlit for easy dashboard layout.
  • Third, I figured out some research questions mentioned above.
  • Fourth, I reviewed the rough plan that Gemini gave me. I made sure that the json files are converted into a smaller data format parquet file. That json files data is loaded into parquet format using a separate python file, and then the info is rendered using Streamlit. I fed this plan into Claude to whip up a simple, yet effective dashboard.

Results

What I got is an interactive Streamlit dashboard for exploring IPL match data at the delivery level. Supports career analysis, phase breakdowns, season-by-season trends, and head-to-head matchups.

Try it yourself: IPL Cricket Dashboard

You can find a guide to navigate the dashboard as well. I have just updated it.

Guide to IPL Cricket Analysis

Email or contact me if you have any questions, and I would be happy to reply!

Follow me at my Medium blog !

Quick Update:

On my first Medium blog: My First Ever Blog — An overview of publishing your portfolio for free, I got my first 5 claps from @alex , great motivation to begin with! I do plan to include a simple routine job, that checks everyday for a new match and updates the parquet file accordingly, so the Streamlit app always has updated data.