Classification dataset for identifying/recognizing roles of basketball players.
The dataset is made up of 80 samples corresponding to four classes (Point Guard, Shooting Guard, Small Forward, Center) which are linked to 13 attributes (Height, Blocks, Rebounds, Assists, Points, Personal Fouls Made, Personal Fouls Received, Free Throws Percentage, 2-point Field Goals Percentage, 3-point Field Goals Percentage, Turnovers, Steals, and Global Assessment).
The dataset is perfectly balanced with 20 samples belonging to each class. Numerical values associated to each sample correspond to statistics available online at the website of the Spanish Basketball League ACB (http://www.acb.com/). For each player, we have taken statistics related to season 2017-2018.
In the repository you can find 4 files (all of them in *.arff format, i.e., the Weka format):
ACB.csv.arff (the whole dataset with header in Spanish)
ACB-EN.csv.arff (the same dataset but with header in English)
ACB.train.csv.arff (80% of samples taken from the orginal dataset)
ACB.test.csv.arff (the remaining 20% of samples from the orginal dataset)
Notice that Weka (https://www.cs.waikato.ac.nz/ml/weka/) is the Waikato Environment for Knowledge Analysis. We selected the Weka format because Weka is a very well-known open source Data Mining project, leaded by researchers affiliated to the University of Waikato (New Zeland), and with a huge community of users and developers worldwide.