About

Welcome to Pawnalyze, a haven for chess enthusiasts and data lovers alike! My name is Caleb Wetherell, a Seattle-based data scientist with a penchant for chess, albeit not mastering it just yet. As a serial hobbyist, I dive into intriguing projects that blend my love for data and my array of interests, with chess taking center stage on Pawnalyze.

Pawnalyze is the culmination of my passion for data science and chess. I simulate and make predictions for chess tournaments, and recently started analyzing the complexity of chess positions. This project reflects my journey in learning and entertainment, and I am thrilled to share it with you. For the curious minds, the code that powers these simulations is available on GitHub.

Elocator: Unveiling Chess Complexity

In my continuous exploration of chess through data, I've launched a groundbreaking project named Elocator. This open-source tool is designed to predict the complexity of chess positions for humans, providing a unique lens to understand what makes a position challenging. By defining complexity as the expected change in win percentage after a move, Elocator offers a novel perspective on the game's intricate dynamics.

Elocator operates on a dataset of FENs, analyzing the loss in win percentage when a grandmaster makes a move. Underpinned by a neural network trained on over 100,000 chess moves by grandmasters, it assigns a complexity score to positions, aiming to deepen our understanding of chess strategy.

Recognition

Join The Journey

As Pawnalyze continues to evolve, so does my commitment to enhancing the chess community's understanding and appreciation of the game. Whether through the analytical insights from simulated tournaments or the groundbreaking analysis provided by Elocator, our mission remains to enrich the chess experience for players and enthusiasts around the world.

I invite you to explore the depths of chess analytics with me.