Opening Databases

Definition

An opening database is a structured collection of chess games—historical, contemporary, human, and engine-generated—organised so that the moves can be queried, filtered, and statistically analysed. Instead of leafing through printed encyclopedias such as ECO, modern players turn to digital repositories that instantly reveal how often a position has occurred, what moves grandmasters or engines chose, and what the results were.

How They Are Used

Preparation: Before a tournament round, a player looks up the opponent’s past games to discover preferred openings and likely novelties.
Trend Analysis: Coaches and theoreticians track new moves that are scoring well and old main lines that are falling out of favour.
Engine Training: Modern neural-network engines such as Leela or Dragon often seed their self-play with weighted opening books derived from databases to ensure variety.
Publishing & Teaching: Authors cite database statistics (“15 … h6 scores 54 % over 2 300 games”) to justify recommendations.
Refutation Hunts: Analysts mine long-forgotten sideline games searching for overlooked improvements—sometimes called archaeology in the database.

Strategic and Historical Significance

The spread of digital opening databases in the late 1980s (pioneered by ChessBase 1.0 in 1987) revolutionised pre-game preparation. Whereas Bobby Fischer carried index cards and Garry Kasparov had seconds hunched over Informant volumes, today’s grandmaster hits Ctrl + F and gets twenty thousand Sicilian Najdorf games sorted by performance rating.

This ease of access has shifted the theoretical arms race:

Lines once considered safe rapidly become razor-sharp because engines uncover tactical landmines buried deep in the database.
Surprise value is harder to achieve, so elite players sometimes choose rarity over objectivity, steering toward less-trodden paths.
The “novelty window” (the time before a new move enters every database) has shrunk from months to hours—sometimes minutes in online events.

Example Query

Suppose you load the following Najdorf position after 10 … e5:

A typical database report might show:

14 … exf4 (played 1 284×, 52 % for Black)
14 … g5 (played 463×, 49 % for Black)
14 … Bxe7 (novelty; found only in engine games)

By clicking exf4, you can browse all sub-variations, filter by player rating >2500, or jump directly to Magnus Carlsen’s win against Radjabov (Wijk aan Zee 2012).

Notable Anecdotes

During the 2018 World Championship, Fabiano Caruana’s second accidentally uploaded a YouTube video showing his preparation database window. Internet sleuths paused the video frame-by-frame to read the file names (e.g., “Anti-Marshall idea 19…Bd7!!”).
Kasparov vs. Deep Blue, 1997: IBM fed Deep Blue a curated database of Kasparov’s previous openings. In Game 2 the computer surprised Garry with 11…Be6 in the Caro-Kann, an idea lifted directly from that database.
The first handheld database device was the MonRoi Personal Chess Manager (2005), allowing players to carry tens of thousands of games in their pocket—now trivial on any phone.

Interesting Facts

The latest commercial “Mega Database” editions top 9 million games and grow by roughly 300 000 games per year.
Statisticians estimate that fewer than 5 % of all games in large databases have been manually checked for blunders; the rest are harvested from online platforms with engine screening to remove obvious nonsense.
Some professional players maintain private databases of correspondence and engine games, refusing to share them to preserve theoretical edges.

Opening Databases