Game Animation

Note: I've forked this repository from https://github.com/samirak93/Game-Animation. The code is not written by me. I just made some small changes to make it easier to use:

• I've cleaned up unneccesary folders like __pycache__ and .ipynb_checkpoints.
• I've added a .gitignore file to keep the repository clean.
• I've added an environment.yml file such that you can install all necessary requirements to run the notebooks.
• I've changed the calculation of the convex hull of teams to not inlcude the goalkeeper.

An easy way to visualize player tracking data with animation. Built using Bokeh Plots, this tool can be used to visualise player movements for all sport. It also includes visualising voronoi and convex hulls, player speed and total distance covered by each player, interactively.

Installation

The interactive plots are provided in jupyter notebooks. The easiest way to get started is to use Anaconda. After installing Anaconda, follow these steps.

Download the repository by clicking the download button on the top right or running the following command in the command line:

git clone https://github.com/seidlr/Game-Animation.git

Then change into the new directory

cd Game-Animation

and install all necessary dependencies into a fresh conda environment by running

conda env update

This will create a new environment callled game-animation. Activate it by running

conda activate game-animation

Now you are ready to run the jupyter notebook server.

jupyter notebook

New Features:

Visualize players being marked by opposition players and find the distance between them. Statistics such as total time player was marked, average distance between the players can also be calculated and visualised.

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Player Marking

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The code was tested on Python 3.7.1, but should work for other versions as well.

The following packages are needed in order to run the code (Bokeh plot version has to be 1.2.0 but for other packages with older versions, the tool should work) :

Package	Version
Numpy	1.16.4
Pandas	0.24.2
Bokeh	1.2.0
Scipy	1.3.0
Notebook	5.7.8

The animation plot can be viewed directly on a Jupyter Notebook.

Usage

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• 3 sample notebook examples (Soccer_example.ipynb, Basketball_example.ipynb and NFL_example.ipynb) are placed in the folder.

• The background pitch images are placed in /static/images/ folder.

Please see Functions Helper for additional information regarding usage of different functions.

Please see Notes for additional information regarding potential errors and usability of the tool.

Sample basketball animation

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#import make_plot from game_animation

from Animation.game_animation import make_plot
from bokeh.io import show, output_notebook
import pandas as pd
from functools import partial

#output_notebook shows the graph within the notebook
output_notebook()

df = pd.read_csv("sample_data/sample_basketball.csv")
image_url=["static/images/basketball.png"]

id_def=65
id_att=37
x_range=(0,94)
y_range=(0,50)

make_anim_plot = partial(make_plot, df=df, id_def = id_def, id_att = id_att,
                           headers = ["x", "y", "team_id", "player_id","time"], 
                           image_url=image_url, slider_steps=1,sport='basketball', 
                           x_range=x_range,y_range=y_range, anim_speed=50)

show(make_anim_plot)

Sample soccer animation:

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from Animation.game_animation import make_plot
from bokeh.io import show, output_notebook
import pandas as pd
from functools import partial

output_notebook()

df = pd.read_csv('sample_data/soccer_sample.csv')
image_url = 'static/images/soccer.png'

x_range=(-52.5,52.5)
y_range=(-34, 34)

id_def = 2
id_att = 1

make_anim_plot = partial(make_plot, df=df,image_url=image_url, id_def=id_def, id_att = id_att,
                           x_range=x_range, y_range=y_range, slider_steps=1,
                           headers = ["x", "y", "team_id", "player_id","time"], 
                           anim_speed=60)

show(make_anim_plot)

Sample player marking animation:

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from Animation.player_marking import player_marking
from bokeh.io import show, output_notebook
import pandas as pd
from functools import partial

output_notebook()

df = pd.read_csv('sample_data/soccer_sample.csv')
image_url = 'static/images/soccer.png'

x_range=(-52.5,52.5)
y_range=(-34, 34)

id_def = 2
id_att = 1

make_anim_plot = partial(player_marking, df=df,image_url=image_url, id_def=id_def, id_att = id_att,
                           x_range=x_range, y_range=y_range, slider_steps=1,
                           headers = ["x", "y", "team_id", "player_id","time"], 
                           anim_speed=60, attack=True,sport='football')

show(make_anim_plot)

Sample player marking stats:

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from Animation.marking_details import marking_stats
import pandas as pd

df = pd.read_csv('sample_data/soccer_sample.csv')

id_def = 2
id_att = 1

marking = marking_stats(df=df, id_def=id_def, id_att = id_att,
                           time_steps=1,
                           headers = ["x", "y", "team_id", "player_id","time"], 
                           attack=True,threshold = 10)

Sample Outputs:

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Game Animation

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Player Marking Animation

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Player Marking Stats

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All feedbacks are appreciated.

Reach out to me on Twitter and you can also find my LinkedIn page here