OpenML
The-Big-Five-European-soccer-leagues-data

The-Big-Five-European-soccer-leagues-data

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Onur Yildirim
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • Computer Systems Machine Learning
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Context 5 countries (Tha major five soccer leagues). 44269 games. 25 seasons. 226 teams. Content All game scores of the big five European soccer leagues (England, Germany, Spain, Italy and France) for the 1995/96 to 2019/20 seasons. Acknowledgements The construction of the dataset was made possible thanks to football.db What's football.db? A free open public domain football database scheme for use in any (programming) language e.g. uses datasets in (structured) text using the football.txt format. More [football.db Project Site ](http://openfootball.github.io) Inspiration This data set could help: + Analyse the evolution of football in the 5 major leagues over the last 25 years. + Prepare all kinds of dashboards on the games, seasons, teamsetc. + Analyze the differences between countries in terms of league level. + Identify patterns, schemes in the dataetc. Have fun!

15 features

Roundnumeric42 unique values
0 missing
Datestring4209 unique values
0 missing
Team_1string226 unique values
0 missing
FTstring66 unique values
0 missing
HTstring33 unique values
0 missing
Team_2string226 unique values
0 missing
Yearnumeric25 unique values
0 missing
Countrystring5 unique values
0 missing
FT_Team_1numeric11 unique values
0 missing
FT_Team_2numeric10 unique values
0 missing
HT_Team_1numeric8 unique values
0 missing
HT_Team_2numeric7 unique values
0 missing
GGDnumeric10 unique values
0 missing
Team_1_(pts)numeric3 unique values
0 missing
Team_2_(pts)numeric3 unique values
0 missing

19 properties

44269
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).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
9
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
60
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
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.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

0 tasks

Define a new task