Data
Chess-Games-of-Woman-Grandmasters-(2009---2021)

Chess-Games-of-Woman-Grandmasters-(2009---2021)

active ARFF CC BY-NC-SA 4.0 Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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  • Computer Systems Machine Learning
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Chess A lot has changed in chess over the years with computer engines and AI algorithms. But how has human performance changed along with the rise in technology? WGM Becoming a Woman Grandmaster (WGM) is no easy feat. Its the highest-ranking chess title to woman and over the lifetime of FIDE, as of January, 2020 there are only 458 WGMs in the world. Certainly a very elite, strong and accomplished set of individuals. Content The dataset consists of all games played by Woman Grandmasters on chess.com from September-2009. The game moves are available in the standard PGN format along with metadata information of the game, players, results and ratings. The notebook for preparing this dataset is published here. Acknowledgements chess.com for making these games publicly available.

15 features

game_idstring303597 unique values
0 missing
game_urlstring303597 unique values
0 missing
pgnstring300124 unique values
3473 missing
time_controlstring94 unique values
0 missing
end_timestring302684 unique values
0 missing
ratednominal2 unique values
0 missing
time_classstring4 unique values
0 missing
rulesstring7 unique values
0 missing
wgm_usernamestring199 unique values
0 missing
white_usernamestring40110 unique values
0 missing
white_ratingnumeric2569 unique values
0 missing
white_resultstring14 unique values
0 missing
black_usernamestring40537 unique values
0 missing
black_ratingnumeric2576 unique values
0 missing
black_resultstring14 unique values
0 missing

19 properties

304767
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
3473
Number of missing values in the dataset.
3473
Number of instances with at least one value missing.
2
Number of numeric attributes.
1
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
13.33
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
6.67
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
6.67
Percentage of binary attributes.
1.14
Percentage of instances having missing values.
Average class difference between consecutive instances.
0.08
Percentage of missing values.

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