Analysiscsv.py for extracting analysis and exporting as CSV

Last update: Apr 25, 2022

Related tags

Data Analysis wcc_analysis

Overview

wcc_analysis

Lichess page documentation: https://lichess.org/page/world-championships

Each WCC has a study, studies are fetched using: https://lichess.org/api#operation/studyAllChaptersPgn

Source PGNs are found in /analysed_pgns

Analysiscsv.py for extracting analysis and exporting as CSV - sample export is found in analysis.csv

Visualize.py takes in CSV and makes some charts, examples are shown below.

Output 1: Table of games by combined ACPL

Year	Game No.	White Player	Black Player	Combined ACPL
2021	3	Nepomniachtchi, Ian	Carlsen, Magnus	6.62
1978	15	Kortschnoj, Viktor	Karpov, Anatoly	6.67
1987	18	Kasparov, Gary	Karpov, Anatoly	6.69
1972	16	Fischer, Robert James	Spassky, Boris V	6.86
2014	9	Carlsen, Magnus	Anand, Viswanathan	6.95
....	....	....	...	...
1886	11	Zukertort, Johannes Hermann	Steinitz, William	153.59
1957	9	Botvinnik, Mikhail	Smyslov, Vassily	154.90
1892	19	Chigorin, Mikhail	Steinitz, William	167.13
1889	15	Chigorin, Mikhail	Steinitz, William	211.98
1892	15	Chigorin, Mikhail	Steinitz, William	212.33

Output 2: Some graphs of average combined acpl vs. time

Output 3: All games by ACPL vs. num moves

Note some games are off the table in both x and y axes.

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Comments

Missing Anand vs Topalov (2010) Championship

Hi, nice analysis. A minor detail, you are missing the Anand vs Topalov (2010) match (and analysed_pgn folder contains 2008 match duplicate data with a rename). For completion's sake, maybe you would want to re-run with 2010 included.

opened by natrys 2
Post match update?

The charts are very nice, but for them to give a fair picture the analysis should include all games of the Carlsen-Nepo match that has now finished. Very curious to see if it is still more accurate than previous world championships!

opened by dennisjaheruddin 1
Compare total CPL or scaled CPL?

Hi, I wonder if anyone has tried plotting the total centipawn loss (ACPL × number of moves) to see if that yields anything more interesting.

ACPL is expected to be low in drawn games, because the computer can usually tell it's a draw many moves before the players agree. For those last few moves, all positions will be evaluated as very close to 0 (or perhaps exactly 0) and that brings down the ACPL. But the total CPL would not be affected by a long run of 0.0 evaluations in the endgame.

Conversely, if an overwhelmingly winning or losing position is reached in a time scramble, then CPL becomes less important: a 500 CPL might be a blunder if it took you from +4 to -1, but it hardly matters if it's a drop from +25 to +20 when you're trying to save as much clock time as possible. Multiplying each move's CPL by something like sgn(evaluation)==sgn(old_evaluation)?exp(-abs(evaluation+old_evaluation)):1 should dampen the effect of skewed-position time scrambles. (but I say that without having actually checked how often this occurs in master games)

opened by ssb22 1

Owner

GitHub

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