Library for visualizing soccer event stream data
Project description
matplotsoccer
This is a package to visualize soccer data
To install it simply
pip install matplotsoccer
The most important functions are
- Plotting a field with
matplotsoccer.field()
:
- Plotting a heatmap with
matplotsoccer.heatmap(matrix)
- Plotting soccer event stream data. Here is an example of five actions in the SPADL format (see https://github.com/ML-KULeuven/socceraction) leading up to Belgium's second goal against England in the third place play-off in the 2018 FIFA world cup.
game_id | period_id | seconds | team | player | start_x | start_y | end_x | end_y | actiontype | result | bodypart |
---|---|---|---|---|---|---|---|---|---|---|---|
8657 | 2 | 2179 | Belgium | Axel Witsel | 37.1 | 44.8 | 53.8 | 48.2 | pass | success | foot |
8657 | 2 | 2181 | Belgium | Kevin De Bruyne | 53.8 | 48.2 | 70.6 | 42.2 | dribble | success | foot |
8657 | 2 | 2184 | Belgium | Kevin De Bruyne | 70.6 | 42.2 | 87.4 | 49.1 | pass | success | foot |
8657 | 2 | 2185 | Belgium | Eden Hazard | 87.4 | 49.1 | 97.9 | 38.7 | dribble | success | foot |
8657 | 2 | 2187 | Belgium | Eden Hazard | 97.9 | 38.7 | 105 | 37.4 | shot | success | foot |
Here is the phase visualized using matplotsoccer.actions()
matplotsoccer.actions(
location=actions[["start_x", "start_y", "end_x", "end_y"]],
action_type=actions.type_name,
team=actions.team_name,
result= actions.result_name == "success",
label=actions[["time_seconds", "type_name", "player_name", "team_name"]],
labeltitle=["time","actiontype","player","team"],
zoom=False
)
(c) Tom Decroos 2019
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