Download in PGN format

At this section, the database is divided into different time ranges in order to reduce the file size.

Monthly updates for the database can be found in a separate tab. They contain the games of the respective month. It is possible that these files will be uploaded again over time. The version number will then be adjusted and this will be indicated in the version history. The new process will mainly be necessary due to new games from the Lichess Broadcast system.

To unzip the files, Windows 7-Zip or another program that can handle the 7z format is required. On Mac, unpacking is possible with Keka, for example.

The latest change:

07/08/2026

Bugfix-Release 2026-07-08 online

07/07/2026

• TWIC 1648 to 1652
• Games of the Lichess Broadcast System from 06/04/2026 to 07/06/2026

This update introduces significant improvements that directly enhance the accuracy and structure of the game data. My main goal was to make the database even more reliable for your opening preparation and opponent analysis, featuring more precise player profiles and a much cleaner dataset.

Matching the Right Games to the Right Player

Nothing is more frustrating during opponent preparation than seeing games from players with similar names mixed up. To prevent this, I have heavily refined how player identities are handled:

• Locked Player Identities: Once a game is clearly linked to an official FIDE-ID, I now lock this connection within the system. Automated algorithms can no longer accidentally overwrite it due to name similarities. This ensures your opponent statistics remain completely reliable.
• Resolving Incomplete Names: Often, game notations miss first names (e.g., listing only “Aronian”). The system now intelligently cross-references these incomplete entries with historical performance data to assign them to the correct player profile. This means you won’t miss crucial games when preparing for your next match.

A Clean Database Without Annoying Duplicates

To keep your opening tree clean and ensure your statistics aren’t skewed by duplicate entries, I have completely overhauled the duplicate detection engine:

• Detecting Incomplete Games: Sometimes, a short, incomplete fragment of a game exists alongside the full match. The system now reliably identifies these using the first 10 moves and merges them, entirely independent of how the players’ names were spelled.
• Merging Name Variations: Different spellings (like “Fischer, Bobby” and “Fischer, R.”) are automatically standardized. This reduces duplicate entries caused by inconsistent tournament data by approximately 40%.
• Filtering out “Ghost Players”: The database now automatically cross-references player activity with rating histories. This fixes errors, removes non-existent “ghost players,” and makes the database roughly 10–15% more accurate.

Additional Practical Benefits for Your Training

• No Useless Mini-Games: All games with fewer than 11 moves have been completely removed. They hold no value for opening preparation and only unnecessarily bloat the database and your loading times.
• OTB and Online Strictly Separated: The data is now automatically and strictly categorized into “Online” and “Over-the-Board”. This allows me to provide two separate files, giving you precise control over which material you want to analyze.
• Verified Player Corrections: When players send me corrections for their own games, this data is now given the highest priority (identifiable in the database by the tag [SourceQuality "-1"]), to ensure that these games appear in the database files.
• Historical ELO Reference: To assist the system in correct player matching and to process missing rating data more precisely, I now use the ELO history file provided with the program Scid vs PC as a reliable reference.

Despite all these optimizations, I want to be transparent: achieving a 100% accurate automated process is factually impossible given the massive raw dataset of over 35 million games. However, I have implemented rigorous automated validation checks to ensure the highest possible standard of data quality is maintained for you.