For analysis, we want to transform this string into a one-hot encoding of the board. FEN is a descriptive string listing the location of each piece on the board. Transform the game format to one that can be processed numerically.Ĭhess.py provides out-of-the-box support for steps 1–3 with export to Forsyth-Edwards-Notation (FEN).Iterate through each move, exporting the board position.I downloaded fourteen months of chess tournament data from This Week in Chess. As my work expanded, I decided to use chess.py because it provides PGN parsing as well as support for exporting board positions. If you’d like to know the details of how parsing chess notations work, check out my article on the topic. When first starting my explorations on chess positions, I wrote my own parser for portable game notation (PGN) files. With a goal in hand, the next step is “get a lot of data.” Parsing Large Numbers of Chess Games As an initial goal, I wanted to find the probability that each of these 768 positions comes up in a random board position selected from a database of chess games. Multiplying these together, we get a total of 768 possible piece positions. In chess, there are 6 pieces, 2 colors, 8 ranks, and 8 files. Intuitively, a king having wandered all the way across the board is less likely than it staying safely away on its own starting rank. If I were to take a chess game at random, and stop it at a random move, then the pieces are much more likely to be in some places than others. Photo by Artur Shamsutdinov on Unsplash An Initial Goal for AnalysisĪ game of chess is a sequence of board positions defined by player moves.
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