ChessLytics

Introduction: Curiosity, Strategy, and a Chessboard

Before I ever wrote a single SQL query or processed a dataset, I was just a chess player trying to understand why I kept losing the same types of games.

It started like this: I'd play five games online. Win two, lose three. I'd feel like I was doing okay—until I checked my history and noticed I had lost to the exact same opening… again. That pattern repeated itself over weeks, and I began wondering whether there was something deeper going on. Was I making predictable mistakes? Was my intuition failing me under time pressure?

This question—what am I missing?—sparked the creation of ChessLytics, a data analytics platform that helps players make sense of their chess performance by combining large-scale game processing with behavioral insights. What started as a small personal project eventually became a full data pipeline analyzing thousands of games, complete with automated dashboards, user-specific reports, and scalable cloud architecture.

While I built ChessLytics to improve at chess, I ended up learning how to build real-world data systems, work with messy and complex datasets, and think critically about user behavior. It taught me more than any classroom project. And more importantly, it taught me how to connect data with decisions.

Identifying the Problem: Beyond the Game History

Platforms like Chess.com do a great job of letting users review their games. But those reviews are usually one-off. They show what happened in a single game, but they don't help players step back and identify patterns across time. I wanted answers like:

The only way to answer these questions was to collect all my games, clean and structure the data, and start digging in. At first, I did this manually—exporting my PGN files and reviewing them in Excel. But the process was tedious and slow. I knew there had to be a better way, and that's when I decided to automate the entire pipeline.

Designing the System: From PGN to Dashboard

To make ChessLytics scalable, I had to build a system that could process thousands of games across multiple users, while keeping it efficient and secure. Here's how I approached it:

Initial System Architecture

Here's how I designed the initial ChessLytics architecture using Google Cloud Platform to solve these chess analytics challenges:

Initial GCP Architecture: Complete data pipeline from Chess.com API and user uploads through in-memory processing to BigQuery analytics and interactive dashboards.

Multi-Cloud Hybrid Architecture

Data Engineering Pipeline: Multi-cloud hybrid architecture combining Azure Databricks for distributed processing and Google Cloud Platform for analytics and visualization.

Data Processing Pipeline

1

Data Ingestion: Lichess monthly game dumps (30GB+ compressed)

2

Distributed Processing: PySpark with ZST decompression & PGN parsing

3

Data Lake: Delta Lake with partitioning by YearMonth

4

Analytics: Cross-cloud integration for comprehensive insights

Key Benefits

Scalability

Handle millions of games with distributed processing

Reliability

ACID transactions and data versioning with Delta Lake

Cost Optimization

Use best-in-class services from each cloud provider

Flexibility

Hybrid approach allows for future expansion

Initial Architecture: Google Cloud Platform Solution

When I first built ChessLytics, I designed a comprehensive Google Cloud Platform architecture that could handle chess game analysis at scale. This initial solution focused on processing Chess.com API data and user uploads efficiently.

The architecture was built around Google Cloud's powerful analytics services, providing a solid foundation for chess analytics while maintaining cost-effectiveness and scalability.

Evolution: Multi-Cloud Hybrid Architecture

As ChessLytics grew, I hit a major limitation: Chess.com's API rate limits prevented me from processing the massive scale of chess games I wanted to analyze. I needed a solution that could handle millions of games efficiently.

That's when I discovered Lichess's monthly game dumps—compressed archives containing all games played on their platform each month. This presented an opportunity to build a truly scalable, multi-cloud data engineering solution.

Databricks Notebook: PySpark notebook showing Lichess game processing pipeline with ZST decompression, PGN parsing, and Delta Lake writes.

What I Learned from the Data

With the system in place, I started to see fascinating trends—both in my own gameplay and across users.

For example, I discovered that I was frequently making mistakes in the same late-midgame positions when under time pressure. My accuracy dropped significantly after move 25, especially when I had less than two minutes on the clock. That insight alone helped me improve my time management and focus in endgames.

Other users shared similar feedback. Some found that they played too passively with the black pieces, while others were overusing risky openings without realizing how poor their results were. I even built analytics that looked at rating volatility and performance after losses, helping players understand how emotion was affecting their decision-making.

These patterns made the experience feel incredibly rewarding. I wasn't just building charts—I was helping people see themselves more clearly.

Sample Analytics Dashboard: Looker Studio dashboard showing personalized chess analytics with multiple charts and insights.

Challenges I Faced

Like any real-world project, ChessLytics came with plenty of challenges. A few stand out:

Why It Matters

ChessLytics was never just about chess—it was about learning how to build something that matters. It taught me how to work with data from end to end: ingestion, transformation, analysis, visualization, and user feedback.

But more than that, it taught me to think like a systems designer. Every feature I added had to balance performance, usability, and accuracy. Every insight I surfaced had to be statistically sound and meaningful. And every line of code I wrote had to scale.

These are the same skills I hope to bring into future work, especially in environments where data is critical to making informed, high-stakes decisions—whether it's in finance, health, or national security.

Conclusion

ChessLytics started as a tool to help me stop losing chess games. But in the end, it taught me how to think like a data engineer. It challenged me to design scalable systems, analyze complex behavior, and build tools that people actually use.

Most importantly, it taught me that data is never just numbers—it's a reflection of people, patterns, and decisions. And when used responsibly, it can be a powerful tool for understanding the world and making it better.

That's the mindset I carry into every data project I take on, and one I'm excited to bring to the next chapter of my career.