Data
UFC-Fights-(2010---2020)-with-Betting-Odds

UFC-Fights-(2010---2020)-with-Betting-Odds

active ARFF Attribution 4.0 International (CC BY 4.0) Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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  • Computer Systems Machine Learning
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Context There are some great UFC datasets out there, but I could not find one that included gambling odds. So I went and made one myself. This dataset focuses very generally on the fights and hopes to be able to draw very broad conclusions. More a more in depth statistical fight analysis I would recommend Rajeev Warrier's excellent datasetwhich was the inspiration for my work. Content This dataset consists of 11 columns of data with basic information about every match that took place between March 21, 2010 and March 14, 2020. Column Definitions: R_fighter and B_fighter: The names of the fighter in the red corner and the fighter in the blue corner R_odds and B_odds: The American odds of the fighter winning. date: The date of the fight location: The location of the fight country: The country the fight occurred in Winner: The winner of the fight ('Red' or 'Blue') title_bout: Was this fight a title bout? ('True' or 'False') weight_class: What weight class did this fight occur at? gender: Male or Female Acknowledgements I was inspired by the work of Rajeev Warrier Want More? My work, including a scraper to help gather data for upcoming events, can be found on my GitHub. I promise I'll add more documentation soon.

11 features

R_fighterstring1217 unique values
1288 missing
B_fighterstring1400 unique values
1288 missing
R_oddsnumeric289 unique values
1288 missing
B_oddsnumeric295 unique values
1288 missing
datestring362 unique values
1288 missing
locationstring144 unique values
1288 missing
countrystring27 unique values
1288 missing
Winnerstring3 unique values
1288 missing
title_boutnominal2 unique values
1288 missing
weight_classstring13 unique values
1288 missing
genderstring2 unique values
1288 missing

19 properties

5528
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
14168
Number of missing values in the dataset.
1288
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.
18.18
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
9.09
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.
9.09
Percentage of binary attributes.
23.3
Percentage of instances having missing values.
Average class difference between consecutive instances.
23.3
Percentage of missing values.

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