Project Overview
This dashboard converts raw Lichess game data into an interactive Power BI report designed for performance analysis. The report gives a clear view of rating trends, win/loss patterns, opening performance, monthly activity, and accuracy metrics.
Live Power BI Dashboard
Explore the live dashboard below. The report is built from over 5,000 Lichess games processed through a Python ETL pipeline and modeled in Power BI to analyze rating trends, opening performance, win rate, accuracy, and game volume.
Data Pipeline
Lichess API → Python extraction and parsing → structured dataset → Power BI model → DAX measures → interactive dashboard.
Dashboard Features
- KPI cards for total games, win rate, average accuracy, and average user rating
- Rating trend over time
- Monthly game volume
- Win/loss/draw breakdown
- Win rate by opening
- Opening variation matrix with games, wins, losses, and win rate
- Embedded Power BI report for interactive viewing
Technical Challenges
- Extracting usable fields from raw Lichess game data
- Normalizing game results into win/loss/draw categories
- Grouping opening names and variations consistently
- Building DAX measures for win rate and summary KPIs
- Designing a dashboard that supports quick performance analysis rather than just displaying charts
Reporting Value
Raw game histories are difficult to analyze directly. Individual games contain useful details, but trends only become visible after the data is extracted, cleaned, grouped, and modeled. The finished report provides a reusable performance review tool that summarizes long-term trends and highlights which openings, time periods, and result categories deserve closer review.