Independent CS2 team and player ratings, match predictions, historical rankings, event brackets and match statistics.
Listed in
Python, SQLite, Alembic, HTML/CSS/JavaScript, Gunicorn, Nginx, and Cloudflare.
CS ELO Analyzer combines historical CS2 team ratings, player performance analytics, match predictions, event data, and bracket structures in one place.
Its main focus is not just showing the current standings, but tracking how teams and players evolve over time. Users can explore historical rankings, rating changes, match histories, player statistics, event results, and prediction history.
The site uses its own independent Elo-based team rating system and player rating methodology rather than simply reproducing rankings from another platform.
CS ELO Analyzer is designed for people who want to understand performance, not just see match results.
It provides historical team ratings, rating changes over time, detailed player statistics, independent player ratings, match predictions with recorded prediction history, and structured tournament data.
The emphasis is on making long-term trends easy to explore: how strong a team was at a particular point in time, how its rating changed after matches, how players have performed across different periods, and how predictions compare with actual results.
The primary audience is Counter-Strike 2 fans who enjoy looking beyond scores and tournament results.
This includes esports followers, statistics enthusiasts, analysts, and data-driven fans who want to compare teams and players, explore historical form, follow rating changes, study tournaments, and evaluate upcoming matches.
CS ELO Analyzer started with a simple idea: create an independent rating system that could show how strong Counter-Strike teams really were over time.
What began as a team Elo ranking gradually grew into a much broader CS2 analytics project. Historical rankings were followed by match predictions, detailed match and event pages, player statistics, an independent player rating, player rankings, and tournament structures.
The project continues to evolve around the same principle: turn Counter-Strike match data into useful, understandable information while preserving the historical context behind the numbers.
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