If you came here to know about me, the person who built this website, please go to the Contact page instead.

About this website

As I collected feedback on my work, connected with people involved in Cricket, and with astute observers of the game, I looked for common themes that I could help address. I was also researching quantitative work that had been done in other sports. I realized that unbiased evaluation of player performance (not to be confused with skill) was a foundational area that addresses several critical questions and opens up paths to more nuanced ones. At the most basic level, assessing how a player performed in a given appearance with a consideration of the game situation that they faced helps evaluate the performance fairly and reasonably. Aggregating player performances and comparing players based on the the strength and volume of the available evidence allows for judicious player evaluation that can then be plugged into roster building, risk assessment and other strategic decision making scenarios.

In mid-2020, I developed a method to use ball by ball event data to produce a prediction of outcomes for an average player in a given T20 game context and evaluated evidence weighted contributions for batters and bowlers relative to the expected performance of an average player playing in their place. I also developed a win probability model to evaluate player impact on game outcomes.

This website exposes the results of these models in the form of aggregated player level advanced stats for different splits that should provide a well rounded evaluation of player performance weighted by evidence and relative to the average player. For more details on making sense of the metrics, please refer to the Glossary.

I obviously have put a lot more work in the automation and the backend number crunching than I have in the aesthetics of the site. This is mostly $ & time relateed prioritization that I had to do.
To use the website, go to the home page and navigate to the player page using the player's last name. Once you are on the player page, scroll vertically to move through the different splits and horizontally if needed to view different stats for a given split. You can jump across splits using the index on the right of the player page.

How is this data produced?

Since late 2020, I have automated the ETL, model building and static website generation process to run at a regular cadence on the AWS cloud. While this makes it a hands-off process that can scale securely as needed, it does use considerable storage and compute resources and racks up a monthly bill that I personally pay for.
If you use this website and the data that it exposes regularly, consider buying me a coffee or two to help pay for it.

Disclaimers

Acknowledgements