Data from: Luck, skill, and depth of competition in games and social hierarchies
Data files
Oct 16, 2024 version files 204.14 KB
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data_CC0.zip
200.33 KB
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README.md
3.80 KB
Abstract
Patterns of wins and losses in pairwise contests, such as occur in sports and games, consumer research and paired comparison studies, and human and animal social hierarchies, are commonly analyzed using probabilistic models that allow one to quantify the strength of competitors or predict the outcome of future contests. Here we generalize this approach to incorporate two additional features: an element of randomness or luck that leads to upset wins, and a "depth of competition" variable that measures the complexity of a game or hierarchy. Fitting the resulting model we estimate depth and luck in a range of games, sports, and social situations. In general, we find that social competition tends to be "deep," meaning it has a pronounced hierarchy with many distinct levels, but also that there is often a nonzero chance of an upset victory. Competition in sports and games, by contrast, tends to be shallow and in most cases there is little evidence of upset wins.
https://doi.org/10.5061/dryad.kh18932fc
Description of the data and file structure
A collection of data sets from various sports leagues, human and animal hierarchies, compiled for a comparative study of the depth and luck involved in the various competitions.
Files and variables
File: data_CC0.zip
Description: .zip file containing all files, contains the following when unzipped:
Folder: adj_matrices
Description: Folder containing .txt files with space-separated adjacency matrices representing the data sets. For each file, the entry in row i and column j contains the number of times player i beats player j in the data set. Contains the following data sets with the appropriate citations:
- chess.txt: Online chess games on lichess.com in 2016
- soccer.txt: Men’s international association football matches 2010–2019.
Folder: gml_files
Description: Folder containing .gml files, representations of the directed networks in Graph Modeling Language (GML) format.
Folder: matches
Description: Folder containing .txt files with space-separated edgelists representing the data sets. For each file, each row consists of the winner followed by the loser of a given match.
File: pairwise-ranking.zip (Zenodo)
Description: .zip file archiving the python package hosted at https://github.com/maxjerdee/pairwise-ranking to fit the model presented in the paper. Contains README.md file explaining usage. Also includes further data sets in the same format under the data/ directory within the CC BY-NC-SA 4.0 license.
Code/software
No software is required in order to view the data. To perform the analysis described in the paper we have created a python package hosted at: https://github.com/maxjerdee/pairwise-ranking and archived in pairwise-ranking.zip
Access information
Other publicly accessible locations of the data:
Data was derived from the following sources:
- baboons.txt: M. Franz, E. McLean, J. Tung, J. Altmann, S. C. Alberts, Proceedings of the Royal Society B 282, 20151512 (2015).
- business_depts.txt: A. Clauset, S. Arbesman, D. B. Larremore, Science Advances 1, e1400005 (2015).
- chess.txt: https://www.kaggle.com/datasets/arevel/chess-games. Accessed: 2023-10-07.
- cs_depts.txt: A. Clauset, S. Arbesman, D. B. Larremore, Science Advances 1, e1400005 (2015).
- dogs.txt: M. J. Silk, M. A. Cant, S. Cafazzo, E. Natoli, R. A. McDonald, Proceedings of the Royal Society B 286, 20190536 (2019).
- history_depts.txt: A. Clauset, S. Arbesman, D. B. Larremore, Science Advances 1, e1400005 (2015).
- hyenas.txt: E. D. Strauss, K. E. Holekamp, Proceedings of the National Academy of Sciences 116, 8919 (2019).
- mice.txt: C. M. Williamson, B. Franks, J. P. Curley, Frontiers in Behavioral Neuroscience 10, 152 (2016).
- monkeys.txt: C. Vilette, T. Bonnell, P. Henzi, L. Barrett, Behavioral Ecology 31, 1379 (2020).
- soccer.txt: M. J ̈urisoo, International men’s football results from 1872 to 2023, https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017. Accessed: 2023-10-07.
- sparrows.txt: D. J. Watt, Animal Behaviour 34, 16 (1986).
- tennis.txt: J. Sackmann, ATP tennis data, https://github.com/JeffSackmann/tennis_atp. Accessed: 2023-10-07.
This data set of pairwise comparisons has been aggregated across a variety of sources given in our paper. These directed interactions are given in adjaency matrix, graph modeling language, and edge list formats.