I'm trying to create a shot map by scraping data from a website to find football data. I want the hometeam's shots to be on one side of the pitch, and the awayteam's shots to be on the other side. Any help? I'm trying to solve the problem by using an if-statement with if h/a = h.... and else....
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from mplsoccer.pitch import Pitch
from pandas.core.indexes.base import Index
text_color = 'w'
data = pd.read_csv('shotmaps.csv')
fig, ax = plt.subplots(figsize=(13,8.5))
fig.set_facecolor('#22312b')
ax.patch.set_facecolor('#22312b')
pitch = Pitch(half=False, positional=True, shade_middle=True, positional_color='black', line_color='black', axis=True, label=True, tick=True)
pitch.draw(ax=ax)
plt.title('Manchester United vs Newcastle', fontsize=24, c=text_color)
if data['H/A'].eq('h').any():
plt.scatter(data['x-coordinate']*120,data['y-coordinate']*80, c = '#ea6969', s=70)
if data['H/A'].eq('a').any():
plt.gca().invert_xaxis()
plt.scatter(data['x-coordinate']*120,data['y-coordinate']*80, c = 'blue', s=70)
This is the csv I'm getting my DataFrame from:
Unnamed: 0 Minute Player Situation Team \
0 0 8 Cristiano Ronaldo OpenPlay Manchester United
1 1 10 Cristiano Ronaldo OpenPlay Manchester United
2 2 18 Raphael Varane FromCorner Manchester United
3 3 22 Cristiano Ronaldo OpenPlay Manchester United
4 4 25 Bruno Fernandes OpenPlay Manchester United
5 5 28 Bruno Fernandes OpenPlay Manchester United
6 6 29 Bruno Fernandes OpenPlay Manchester United
7 7 29 Jadon Sancho OpenPlay Manchester United
8 8 31 Jadon Sancho OpenPlay Manchester United
9 9 31 Harry Maguire FromCorner Manchester United
10 10 38 Cristiano Ronaldo OpenPlay Manchester United
11 11 42 Nemanja Matic OpenPlay Manchester United
12 12 46 Mason Greenwood OpenPlay Manchester United
13 13 46 Cristiano Ronaldo OpenPlay Manchester United
14 14 53 Nemanja Matic OpenPlay Manchester United
15 15 58 Bruno Fernandes OpenPlay Manchester United
16 16 61 Cristiano Ronaldo OpenPlay Manchester United
17 17 65 Raphael Varane FromCorner Manchester United
18 18 75 Mason Greenwood FromCorner Manchester United
19 19 79 Bruno Fernandes OpenPlay Manchester United
20 20 91 Jesse Lingard OpenPlay Manchester United
21 21 6 Joelinton OpenPlay Newcastle United
22 22 14 Matt Ritchie OpenPlay Newcastle United
23 23 22 Miguel Almirón OpenPlay Newcastle United
24 24 22 Matt Ritchie OpenPlay Newcastle United
25 25 27 Joe Willock OpenPlay Newcastle United
26 26 35 Allan Saint-Maximin SetPiece Newcastle United
27 27 55 Javier Manquillo OpenPlay Newcastle United
28 28 66 Joelinton OpenPlay Newcastle United
29 29 68 Allan Saint-Maximin OpenPlay Newcastle United
30 30 78 Allan Saint-Maximin OpenPlay Newcastle United
31 31 79 Joe Willock FromCorner Newcastle United
32 32 86 Joelinton OpenPlay Newcastle United
xG Result x-coordinate y-coordinate H/A
0 0.057108 MissedShots 0.978 0.338 h
1 0.039677 MissedShots 0.972 0.720 h
2 0.219654 MissedShots 0.930 0.616 h
3 0.026905 BlockedShot 0.766 0.521 h
4 0.006393 MissedShots 0.494 0.800 h
5 0.009298 MissedShots 0.718 0.804 h
6 0.021526 MissedShots 0.744 0.452 h
7 0.369672 MissedShots 0.892 0.514 h
8 0.128696 BlockedShot 0.851 0.527 h
9 0.042829 SavedShot 0.905 0.424 h
10 0.108275 BlockedShot 0.940 0.342 h
11 0.014675 BlockedShot 0.732 0.382 h
12 0.015693 SavedShot 0.802 0.176 h
13 0.887859 Goal 0.976 0.516 h
14 0.015525 MissedShots 0.697 0.488 h
15 0.030891 BlockedShot 0.787 0.418 h
16 0.301787 Goal 0.923 0.632 h
17 0.070338 MissedShots 0.905 0.519 h
18 0.027712 BlockedShot 0.862 0.285 h
19 0.031612 Goal 0.756 0.499 h
20 0.369022 Goal 0.865 0.522 h
21 0.070895 MissedShots 0.860 0.675 a
22 0.016308 BlockedShot 0.767 0.720 a
23 0.011347 BlockedShot 0.744 0.310 a
24 0.014497 MissedShots 0.784 0.699 a
25 0.068061 MissedShots 0.927 0.330 a
26 0.015108 MissedShots 0.765 0.707 a
27 0.264944 Goal 0.933 0.328 a
28 0.060307 SavedShot 0.872 0.660 a
29 0.006815 MissedShots 0.698 0.726 a
30 0.020886 BlockedShot 0.739 0.479 a
31 0.013166 SavedShot 0.706 0.464 a
32 0.059875 MissedShots 0.912 0.593 a
CodePudding user response：
First, split your dataframe into home and away teams:
home_team = df.loc[df["H/A"] == "h"]
away_team = df.loc[df["H/A"] == "a"]
Now, let's say the home team is attacking left-right. An x-coordinate of, say, 0.9 indicates that the home team shot from a location 90% up the pitch from their goal, which would be at an x-coordinate of 0.9 * pitch_length.
On the other hand, the away team shooting at 0.9 would indicate a location 90% up the pitch from their goal, or 10% up the pitch from the home team's goal. In other words, x = (1 - 0.9) * pitch_length
So do that.
plt.scatter(home_team['x-coordinate']*120, home_team['y-coordinate']*80, c = '#ea6969', s=70)
plt.scatter((1-away_team['x-coordinate'])*120, away_team['y-coordinate']*80, c = 'blue', s=70)
