Basketball dataset
Donated on 7/1/2019
It's data collected from different volunteers that are done in a basketball practice: dribbling, pass, shoot, picking the ball, and holding the ball.
Dataset Characteristics
Time-Series
Subject Area
Games
Associated Tasks
Classification
Feature Type
Integer
# Instances
10000
# Features
7
Dataset Information
Additional Information
There are different trials. For which, pass, shoot and pick up the ball have 5. and hold and dribble there are 2. First of all, we gathered the 4 users who were willing to be our test samples. Then, one by one we made them do the following 5 activities: Pass, hold the ball, shoot pick up the ball, and dribble. Each activity had a different way of gathering its corresponding data. For holding the ball, we made the volunteer stand in one place in a holding position. Once ready, we run the app. After 5 seconds we stop it and save the data with the user’s first initial, the activity and the number of the trial. For this label we did a total of 3 trials for each person. Next we started collecting the data of passing. The volunteer starts with the ball in a holding position. Next we run the app, for which after 3 seconds we tell the volunteer to pass the ball to one of us, once finish we stop the app. For this label we did a total of 5 trails for each person. Then, we collected the data of dribbling. The volunteers start with the ball in holding position. Then, 3 seconds after we run the app we tell him to dribble and after 5 he started the dribbling we said to stop. Once he stops we go and stop the app. For this label we did a total of 3 trials for each person. Continuing with the activity of shooting, we let the volunteer get ready in a holding position with the ball, and then we run the app. For which, he shoots immediately after we start the application. Once finish, we stop the app. For this label we did a total of 5 trials for each person. Finally, we gather the data of picking the ball. For this data, we just start the app, and after 3 seconds the user picks up the ball, and at the 6 seconds we stop the app gathering this range of data. For this label we did a total of 5 trails for each person. Finally we did a different way of collecting the data. For which one user in a set of time did each activity spontanously. Every time he did an activity we collected the time for which he did it in a chronometer, and in a video all for which started closely at the same time.
Has Missing Values?
No
Variable Information
we use acceloremeter measures x y z in ms2 and gyroscope measures r phi theta. for X the data is in g
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset basketball_dataset = fetch_ucirepo(id=587) # data (as pandas dataframes) X = basketball_dataset.data.features y = basketball_dataset.data.targets # metadata print(basketball_dataset.metadata) # variable information print(basketball_dataset.variables)
Basketball dataset. (2019). UCI Machine Learning Repository. https://doi.org/10.24432/C56G77.
@misc{misc_basketball_dataset_587,
title = {{Basketball dataset}},
year = {2019},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C56G77}
}DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.