[721]篮球数据集（Basketball dataset）

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0、数据编号：721
1、数据名称：篮球数据集（Basketball dataset）
2、数据来源：4 students from Universidad de los andes, for which they prefer to be Anonymous.
3、时间跨度：截至2019-06-30
4、区域范围：
5、数据大小：1.86MB
6、数据格式：csv
7、数据简介：这是从不同志愿者那里收集的数据，这些数据是在篮球练习中完成的：运球、传球、投篮、捡球和持球。
属性：有不同的试验。为此，传球、射门和捡球有 5 个。并保持和运球有 2。
首先，我们收集了愿意成为我们测试样本的 4 位用户。
然后，我们一一让他们做以下5个活动：传球、持球、投篮、运球。
每个活动都有不同的方式来收集其相应的数据。

为了拿球，我们让志愿者站在一个地方保持持球。
准备就绪后，我们运行该应用程序。5 秒后，我们停止它并保存用户™的第一个首字母、
活动和试用编号的数据。对于这个标签，我们总共对每个人进行了3次试验。

接下来，我们开始收集传球数据。志愿者从持球开始。
接下来我们运行应用程序，3 秒后我们告诉志愿者将球传给我们中的一个人，完成后
我们停止应用程序。对于这个标签，我们总共为每个人做了 5 条小径。

然后，我们收集了运球的数据。志愿者从持球开始。然后，在我们运行应用程序 3 秒后，我们告诉他运球，5 秒
后他开始运球，我们说停止。
一旦他停下来，我们就去停止应用程序。对于这个标签，我们总共对每个人进行了3次试验。
继续投篮活动，我们让志愿者准备好持球姿势，
然后我们运行应用程序。为此，他在我们启动应用程序后立即开枪。完成后，我们停止应用程序。 对于这个标签，我们总共对每个人进行了5次试验。
最后，我们收集采摘球的数据。对于这些数据，我们只需启动应用程序，
3 秒后用户捡起球，在 6 秒后我们停止应用程序收集此范围的数据。
对于这个标签，我们总共为每个人做了 5 条小径。
最后，我们做了一种不同的方法来收集数据。对于哪个用户在一组时间内自发地执行每个活动。每次他做一项活动时，我们都会
在计时器上收集他做活动的时间，并在视频中收集所有时间，所有这些都是在同一时间紧密开始的。
属性信息：
我们使用MS2中的加速度计测量x y z和陀螺仪测量R Phi Theta。对于 X，数据以 g 为单位
英文原文：
It’s data collected from different volunteers that are done in a basketball practice: dribbling, pass, shoot, picking the ball, and holding the ball. 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.

Attribute Information:

we use acceloremeter measures x y z in ms2 and gyroscope measures r phi theta.

参考文献：

[1]杨铁黎, 张建华, 王必琪. NBA职业篮球市场的成功经验及启示[J]. 北京体育大学学报, 2000, 23(3):3.