Data
European-Soccer-Dataset-by-Role

European-Soccer-Dataset-by-Role

active ARFF Database: Open Database, Contents: Database Contents Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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A modified version of the European Soccer Database by Hugo Mathien (https://www.kaggle.com/hugomathien). New players' performance indicators have been created from the original dataset. Data features: + 25,000 matches 11 European Countries with their lead championship Seasons from 2008 to 2016 25 role-based Performance Indicators: players and teams' attributes (sourced from EA Sports' FIFA video game series, including the weekly updates) have been subtracted by location (home - away) and averaged by match and role (X, Y coordinates of the team line up). Acknowledgements This dataset has been created in the academic research context and can be used without additional permissions or fees. The newly created indicators have been used to test the performance of several predictive models. For more information about how the data have been treated and modeled, as well as if you like to use these data in a publication, presentation, or other research product, please consult/use the following citation: Carpita, M., Ciavolino, E., Pasca, P. (2019). Exploring and modelling team performances of the Kaggle European Soccer database. Statistical Modelling, 19(1), 74-101.

31 features

id_matchnumeric25979 unique values
0 missing
country_idnumeric11 unique values
0 missing
country_namestring11 unique values
0 missing
seasonstring8 unique values
0 missing
date_matchstring1694 unique values
0 missing
ATT_ATT_diffnumeric3438 unique values
2185 missing
ATT_CEN_diffnumeric4524 unique values
921 missing
ATT_DEF_diffnumeric3589 unique values
989 missing
ATT_GOK_diffnumeric733 unique values
1598 missing
DEF_ATT_diffnumeric3019 unique values
2185 missing
DEF_CEN_diffnumeric4282 unique values
921 missing
DEF_DEF_diffnumeric2947 unique values
989 missing
DEF_GOK_diffnumeric515 unique values
1598 missing
GOK_GOK_diffnumeric703 unique values
1598 missing
MEN_ATT_diffnumeric3355 unique values
2185 missing
MEN_CEN_diffnumeric4606 unique values
921 missing
MEN_DEF_diffnumeric4184 unique values
989 missing
MEN_GOK_diffnumeric1306 unique values
1598 missing
MOV_ATT_diffnumeric3420 unique values
2185 missing
MOV_CEN_diffnumeric3763 unique values
921 missing
MOV_DEF_diffnumeric4348 unique values
989 missing
MOV_GOK_diffnumeric1051 unique values
1598 missing
POW_ATT_diffnumeric3015 unique values
2185 missing
POW_CEN_diffnumeric4031 unique values
921 missing
POW_DEF_diffnumeric4232 unique values
989 missing
POW_GOK_diffnumeric808 unique values
1598 missing
SKI_ATT_diffnumeric4017 unique values
2185 missing
SKI_CEN_diffnumeric4873 unique values
921 missing
SKI_DEF_diffnumeric4105 unique values
989 missing
SKI_GOK_diffnumeric858 unique values
1598 missing
outcomestring2 unique values
0 missing

19 properties

25979
Number of instances (rows) of the dataset.
31
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
35756
Number of missing values in the dataset.
2770
Number of instances with at least one value missing.
27
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
10.66
Percentage of instances having missing values.
4.44
Percentage of missing values.
Average class difference between consecutive instances.
87.1
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
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.
0
Number of binary attributes.

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