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serie_a_matches_2015_2016

serie_a_matches_2015_2016

active ARFF Public Domain (CC0) Visibility: public Uploaded 15-11-2019 by Matteo Caorsi
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The dataset contains the serie a matches for season 2015-2016

38 features

idnumeric379 unique values
0 missing
country_idnumeric1 unique values
0 missing
league_idnumeric1 unique values
0 missing
seasonstring1 unique values
0 missing
stagenumeric38 unique values
0 missing
datestring97 unique values
0 missing
match_api_idnumeric379 unique values
0 missing
home_team_api_idnumeric20 unique values
0 missing
away_team_api_idnumeric20 unique values
0 missing
home_team_goalnumeric7 unique values
0 missing
away_team_goalnumeric6 unique values
0 missing
home_player_1numeric41 unique values
0 missing
home_player_2numeric73 unique values
1 missing
home_player_3numeric65 unique values
0 missing
home_player_4numeric87 unique values
3 missing
home_player_5numeric84 unique values
3 missing
home_player_6numeric118 unique values
0 missing
home_player_7numeric82 unique values
1 missing
home_player_8numeric113 unique values
1 missing
home_player_9numeric108 unique values
1 missing
home_player_10numeric99 unique values
5 missing
home_player_11numeric97 unique values
8 missing
away_player_1numeric41 unique values
0 missing
away_player_2numeric77 unique values
2 missing
away_player_3numeric71 unique values
0 missing
away_player_4numeric84 unique values
1 missing
away_player_5numeric83 unique values
0 missing
away_player_6numeric105 unique values
0 missing
away_player_7numeric92 unique values
0 missing
away_player_8numeric113 unique values
0 missing
away_player_9numeric117 unique values
4 missing
away_player_10numeric108 unique values
5 missing
away_player_11numeric106 unique values
9 missing
goalstring353 unique values
0 missing
possessionstring379 unique values
0 missing
B365Hnumeric73 unique values
0 missing
B365Dnumeric25 unique values
0 missing
B365Anumeric74 unique values
0 missing

62 properties

379
Number of instances (rows) of the dataset.
38
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
44
Number of missing values in the dataset.
44
Number of instances with at least one value missing.
34
Number of numeric attributes.
0
Number of nominal attributes.
4.14
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.77
Third quartile of skewness among attributes of the numeric type.
167624.09
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.07
First quartile of kurtosis among attributes of the numeric type.
141212.98
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
12376.5
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
2.46
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
162969.21
Mean of means among attributes of the numeric type.
0.82
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
84.91
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
Average number of distinct values among the attributes of the nominal type.
0.69
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.1
Number of attributes divided by the number of instances.
1.36
Mean skewness among attributes of the numeric type.
144673.34
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Percentage of instances belonging to the most frequent class.
89802.65
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.05
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
122751.3
Second quartile (Median) of standard deviation of attributes of the numeric type.
15.21
Maximum kurtosis among attributes of the numeric type.
1.11
Minimum of means among attributes of the numeric type.
11.61
Percentage of instances having missing values.
Third quartile of entropy among attributes.
2060451.58
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.31
Percentage of missing values.
2.85
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
89.47
Percentage of numeric attributes.
173167.54
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-0.04
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.

8 tasks

0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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