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scouting_app.py
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scouting_app.py
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import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import numpy as np
import math
import seaborn as sns
from scipy import stats
import requests
import json
from urllib.parse import unquote
from urllib.request import urlopen
from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import streamlit_shadcn_ui as ui
from streamlit_extras.colored_header import colored_header
from st_keyup import st_keyup
from streamlit_extras.add_vertical_space import add_vertical_space
import streamlit_antd_components as sac
from streamlit_bokeh_events import streamlit_bokeh_events
from streamlit_vertical_slider import vertical_slider
from streamlit_lottie import st_lottie
from mplsoccer.pitch import Pitch, VerticalPitch
import matplotlib as mpl
import matplotlib.font_manager as fm
from highlight_text import fig_text
import plotly.graph_objects as go
from mplsoccer import PyPizza, add_image, FontManager
st.set_page_config(page_title='BotolaScout',
page_icon='⚽',
layout='wide',
initial_sidebar_state="expanded",)
def example():
st.write("## Notice how the output doesn't update until you hit enter")
out = st.text_input("Normal text input")
st.write(out)
st.write("## Notice how the output updates with every key you press")
out2 = st_keyup("Keyup input")
st.write(out2)
@st.cache_data
def load_data(path: str):
data = pd.read_csv(path)
return data
def convert_market_value(value):
# Ensure value is treated as string
if isinstance(value, float):
return value
if value == '-' or value == '':
return None
value = value.replace('€', '')
if 'k' in value:
return float(value.replace('k', '')) * 1e3
if 'm' in value:
return float(value.replace('m', '')) * 1e6
return float(value)
position_mapping = {
'GK': ['Goalkeeper'],
'DF': ['Defender', 'Right-Back', 'Centre-Back', 'Left-Back'],
'MID': ['Right Midfield', 'Left Midfield', 'Midfielder', 'Central Midfield', 'Defensive Midfield', 'Attacking Midfield'],
'FWD': ['Striker', 'Second Striker', 'Forward', 'Left Winger', 'Right Winger', 'Centre-Forward']
}
stats_mapping = {
'GK': ['saves', 'aerialDuelsWonPercentage', 'accurateLongBallsPercentage', 'cleanSheet', 'goalsConceded'],
'DF': ['accurateCrossesPercentage','accurateFinalThirdPasses',
'accuratePassesPercentage','accurateLongBallsPercentage','totalDuelsWonPercentage',
'ballRecovery', 'interceptions', 'aerialDuelsWonPercentage', 'clearances','blockedShots',
'errorLeadToShot', 'dribbledPast', ],
'MID': ['goalsAssistsSum','keyPasses','goalConversionPercentage','accurateCrossesPercentage',
'accuratePassesPercentage', 'accurateFinalThirdPasses', 'successfulDribblesPercentage', 'accurateLongBallsPercentage',
'ballRecovery', 'aerialDuelsWonPercentage', 'totalDuelsWonPercentage',
'dribbledPast'],
'FWD':['goalsAssistsSum','shotsOnTarget','goalConversionPercentage','keyPasses', 'accurateCrossesPercentage',
'successfulDribblesPercentage', 'accurateFinalThirdPasses','accuratePassesPercentage', 'wasFouled',
'aerialDuelsWonPercentage',
'offsides', 'scoringFrequency'],
}
spaces = ' '
pos = {'GK':'Goalkeepers', 'DF':'Defenders', 'MID':'Midfielders', 'FWD':'Forwards'}
def get_position_group(position):
for group, positions in position_mapping.items():
if position in positions:
return group
return None
def filter_positions(user_choices):
selected_positions = []
for choice in user_choices:
selected_positions.extend(position_mapping.get(choice, []))
return selected_positions
exclude_columns = ['Player',
'Nationality',
'Team_x',
'Position',
'Age',
'Height',
'Preferred Foot',
'Shirt Number',
'Market Value',
'Player Image',
'Team Logo',] # Columns to exclude from conversion
@st.cache_data
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
def convert_to_readable(name):
words = []
current_word = name[0]
for char in name[1:]:
if char.isupper() or char.isdigit():
words.append(current_word)
current_word = char
else:
current_word += char
words.append(current_word)
readable_name = ' '.join(word.capitalize() for word in words)
readable_name = readable_name.replace("Percentage", "%")
if readable_name == 'Accurate Final Third Passes':
readable_name = 'Accurate Final\nThird Passes'
if readable_name == 'Accurate Long Balls %':
readable_name = 'Accurate Long\nBalls %'
return readable_name
def get_color_category(value, df, stat, reverse_stats):
q1, q2, q3 = df[stat].quantile([0.25, 0.5, 0.75])
reverse = stat in reverse_stats
if not reverse:
if value < q1:
return '#fe003e' # Bottom 25%
elif value < q2:
return '#ffa500' # 25-50%
elif value < q3:
return '#ffef01' # 50-75%
else:
return '#00ff1d' # Top 25%
else:
if value > q3:
return '#fe003e' # Top 25%
elif value > q2:
return '#ffa500' # 50-75%
elif value > q1:
return '#ffef01' # 25-50%
else:
return '#00ff1d' # Bottom 25%
@st.cache_data
def display_stat(label, value, df, stat):
reverse_stats = ['goalsConceded', 'errorLeadToShot', 'dribbledPast', 'offsides', 'scoringFrequency']
color = get_color_category(value, df, stat, reverse_stats)
min_value = df[stat].min()
max_value = df[stat].max()
percentage = ((value - min_value) / (max_value - min_value)) * 100
if percentage == 0:
percentage = 2
if stat in reverse_stats:
color = '#00ff1d'
else:
color = '#fe003e'
if isinstance(value, float):
value = f"{value:.2f}"
else:
value = str(value)
if value == '0' or value == 'nan':
percentage = 0
if value == 'nan':
value = '--- '
st.markdown(f"""
<div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 5px;">
<span style="color: white;">{label}</span>
<span style="color: white;">{value + " min" if label == 'Scoring Frequency' else value}</span>
</div>
<div style="background-color: #1E2A38; height: 5px; width: 100%; margin-bottom: 15px;">
<div style="background-color: {color}; height: 100%; width: {percentage}%;"></div>
</div>
""", unsafe_allow_html=True)
def get_image_output(URL):
img = Image.open(urlopen(URL))
# Create a mask
mask = Image.new('L', img.size, 0)
draw = ImageDraw.Draw(mask)
draw.ellipse((0, 0) + img.size, fill=255)
# Apply the mask to the image
output = Image.new('RGBA', img.size, (0, 0, 0, 0))
output.paste(img, (0, 0), mask)
return output
@st.cache_data
def pizza_plot(player_data, params_1, values, output):
position_group = get_position_group(player_data['Position'])
# Color palette
background_color = "#0c1421" # Dark blue background
main_color = "#00CED1" # Bright turquoise for the pizza slices
accent_color = "#FF6B6B" # Coral red for emphasis
text_color = "#E0E0E0" # Light gray for most text
highlight_color = "#FFD700" # Gold for important text/numbers
# Slice colors (adjust as needed)
colors_mapping = {'DF':[2,3,5], 'MID':[4,4,3], 'FWD':[5,4,1]}
if position_group == 'GK':
slice_colors = ["#11C9AF"] * 4
else:
num_colors = colors_mapping[position_group]
slice_colors = ["#11C9AF"] * num_colors[0] + ["#FFA500"] * num_colors[1] + ["#D70232"] * num_colors[2]
# Instantiate PyPizza class
baker = PyPizza(
params=params_1,
straight_line_color="#2A3F5F", # Slightly lighter than background for subtle lines
straight_line_lw=1,
last_circle_color="#4A6491", # Highlighted outer circle
last_circle_lw=2,
other_circle_color="#2A3F5F", # Same as straight lines
other_circle_lw=1,
other_circle_ls="-.",
inner_circle_size=20
)
# Plot pizza
fig, ax = baker.make_pizza(
values,
figsize=(8, 8),
param_location=110,
slice_colors=slice_colors, # Color for individual slices
#value_colors=text_colors, # Color for the value-text
#value_bck_colors=slice_colors, # Color for the blank spaces
blank_alpha=0.4, # Alpha for blank-space colors
kwargs_slices=dict(
edgecolor="#FFFFFF", zorder=2, linewidth=1, alpha=0.8
),
kwargs_params=dict(
color=text_color, fontsize=11,
va="center", fontweight='bold'
),
kwargs_values=dict(
color=highlight_color, fontsize=11,
zorder=3, fontweight='bold',
bbox=dict(
edgecolor=accent_color, facecolor=background_color,
boxstyle="round,pad=0.2", lw=1, alpha=0.8
)
)
)
# Set background color
fig.patch.set_facecolor(background_color)
ax.set_facecolor(background_color)
# Add title
fig.text(
0.515, 0.98, f"{player_data['Player']} - {player_data['Team_x']}", size=18,
ha="center", color=highlight_color, fontweight='bold'
)
# Add subtitle
fig.text(
0.515, 0.94,
f" ┆ Percentile Rank vs Botola Pro {pos[position_group]} | Season 2023-24 ┆",
size=14,
ha="center", color=text_color
)
# Add image
ax_image = add_image(
output, fig, left=0.4478, bottom=0.4315, width=0.13, height=0.127
)
# Add rectangles for slice categories
if position_group != 'GK':
fig.text(
0.99, 0.005, f"Attacking\nPossession\nDefending", size=14,
color=text_color,
ha="center"
)
fig.patches.extend([
plt.Rectangle(
(0.89, 0.06), 0.025, 0.021, fill=True, color="#11C9AF",
transform=fig.transFigure, figure=fig
),
plt.Rectangle(
(0.89, 0.03), 0.025, 0.021, fill=True, color="#FFA500",
transform=fig.transFigure, figure=fig
),
plt.Rectangle(
(0.89, 0.00), 0.025, 0.021, fill=True, color="#d70232",
transform=fig.transFigure, figure=fig
),
])
return fig
@st.cache_data
def pizza_plot_comparison(params_1, values, values_2, player_data, player_name_2, output, output2):
position_group = get_position_group(player_data['Position'])
# Color palette
background_color = "#0c1421" # Dark blue background
text_color = "#E0E0E0" # Light gray for most text
highlight_color = "#FFD700" # Gold for important text/numbers
# Player colors
player1_color = "#11C9AF" # Turquoise for player 1
player2_color = "#FFA500" # Orange for player 2
params_offset = []
for v1, v2 in zip(values, values_2):
distance = abs(v1 - v2)
params_offset.append(distance < 10)
# instantiate PyPizza class
baker = PyPizza(
params=params_1,
straight_line_color="#2A3F5F", # Slightly lighter than background for subtle lines
straight_line_lw=1,
last_circle_color="#4A6491", # Highlighted outer circle
last_circle_lw=2,
other_circle_color="#2A3F5F", # Same as straight lines
other_circle_lw=1,
other_circle_ls="-.",
inner_circle_size=20
)
# plot pizza
fig, ax = baker.make_pizza(
values, # list of values
compare_values=values_2, # comparison values
figsize=(8, 8), # adjust figsize according to your need
blank_alpha=0.4,
kwargs_slices=dict(
facecolor=player1_color, edgecolor="#FFFFFF", zorder=2, linewidth=1, alpha=1
),
kwargs_compare=dict(
facecolor=player2_color, edgecolor="#FFFFFF", zorder=2, linewidth=1, alpha=1
),
kwargs_params=dict(
color=text_color, fontsize=11,
va="center", fontweight='bold'
),
kwargs_values=dict(
color='#000000', fontsize=11,
zorder=3, fontweight='bold',
bbox=dict(
edgecolor='#000000', facecolor=player1_color,
boxstyle="round,pad=0.2", lw=1, alpha=0.8
)
),
kwargs_compare_values=dict(
color='#000000', fontsize=11, zorder=3, fontweight='bold',
bbox=dict(
edgecolor='#000000', facecolor=player2_color,
boxstyle="round,pad=0.2", lw=1, alpha=0.8
)
)
)
# Set background color
fig.patch.set_facecolor(background_color)
ax.set_facecolor(background_color)
# adjust text for comparison-values-text
baker.adjust_texts(params_offset, offset=-0.17, adj_comp_values=True)
# add title
fig_text(
0.515, 1.01, f"<{player_data['Player']}> vs <{player_name_2}>", size=17, fig=fig,
highlight_textprops=[{"color": player1_color}, {"color": player2_color}],
ha="center", color=text_color, fontweight='bold'
)
# Add subtitle
fig.text(
0.515, 0.94,
f" ┆ Percentile Rank vs Botola Pro {pos[position_group]} | Season 2023-24 ┆",
size=14,
ha="center", color=text_color
)
# Add player images and names
# Player 1
add_image(
output, fig, left=-0.03, bottom=0.9, width=0.13, height=0.13
)
# Player 2
add_image(
output2, fig, left=0.95, bottom=0.9, width=0.13, height=0.13
)
return fig
@st.cache_data
def display_player_card(player):
st.markdown("""
<style>
body {
color: white;
background-color: #0E1117;
}
.player-card {
background-color: #1E2A38;
border-radius: 15px;
padding: 25px;
color: white;
display: flex;
flex-direction: column;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
border: 2px solid #FFA500;
}
.player-header {
display: flex;
align-items: center;
margin-bottom: 25px;
}
.player-image {
width: 120px;
height: 120px;
border-radius: 50%;
margin-right: 25px;
border: 3px solid #11C9AF;
}
.player-info {
flex-grow: 1;
}
.player-name {
font-family:cursive;
font-size: 30px;
font-weight: bold;
margin: 0;
color: white;
}
.player-team {
font-size: 18px;
margin: 5px 0;
color: white;
}
.team-logo {
width: 35px;
height: 35px;
margin-right: 10px;
vertical-align: middle;
}
.stats {
display: flex;
justify-content: space-between;
margin-bottom: 25px;
background-color: #263238;
border-radius: 10px;
padding: 15px;
border: 2px solid #11C9AF;
}
.stat {
text-align: center;
}
.stat-value {
font-size: 24px;
font-weight: bold;
color: white;
}
.stat-label {
font-size: 16px;
color: #BDD0DA;
}
.player-bio {
display: flex;
flex-wrap: wrap;
background-color: #263238;
border-radius: 10px;
padding: 15px;
border: 2px solid #11C9AF;
}
.bio-item {
width: 50%;
margin-bottom: 12px;
}
.bio-label {
font-weight: bold;
color: #BDD0DA;
}
.bio-value {
font-size: 18px;
font-weight: bold;
color: white;
}
h3 {
color: #FFA500;
margin-top: 20px;
}
""", unsafe_allow_html=True)
html = f"""
<div class="player-card">
<div class="player-header">
<img src="{player['Player Image']}" class="player-image">
<div class="player-info">
<h2 class="player-name">{player['Player']}</h2>
<p class="player-team"><img src="{player['Team Logo']}" class="team-logo">{player['Team_x']}</p>
</div>
</div>
<div class="stats">
<div class="stat">
<div class="stat-value">{player['appearances']}</div>
<div class="stat-label">Appearances</div>
</div>
<div class="stat">
<div class="stat-value">{player['minutesPlayed']}</div>
<div class="stat-label">Minutes Played</div>
</div>
<div class="stat">
<div class="stat-value">{player['rating']:.2f}</div>
<div class="stat-label">Rating</div>
</div>
</div>
<h3>Player Bio</h3>
<div class="player-bio">
<div class="bio-item"><span class="bio-label">Nationality:</span><span class="bio-value">{spaces*2} {player['Nationality']}</span></div>
<div class="bio-item"><span class="bio-label">Position:</span><span class="bio-value">{spaces*2} {player['Position']}</span></div>
<div class="bio-item"><span class="bio-label">Age:</span><span class="bio-value">{spaces*2} {player['Age']}</span></div>
<div class="bio-item"><span class="bio-label">Height:</span><span class="bio-value">{spaces*2} {player['Height']}</span></div>
<div class="bio-item"><span class="bio-label">Market Value:</span><span class="bio-value">{spaces*2} {player['Market Value']}</span></div>
<div class="bio-item"><span class="bio-label">Preferred Foot:</span><span class="bio-value">{spaces*2} {player['Preferred Foot']}</span></div>
</div>
</div>
"""
st.markdown(html, unsafe_allow_html=True)
@st.cache_data
def heatmap(df, player_name):
player_df = df[df['Player'] == player_name]
# Set up the pitch
pitch = Pitch(pitch_type='opta',
pitch_color='#0f0c2b',#22312b
line_color='#c7d5cc',#c7d5cc
corner_arcs=True,
line_zorder=2,
)
fig, ax = pitch.draw(figsize=(8, 6), constrained_layout=True, tight_layout=False)
fig.set_facecolor('#0f0c2b')#22312b
fig.patch.set_edgecolor('#FFA500')
fig.patch.set_linewidth(2)
pitch.kdeplot(player_df['x'], player_df['y'], ax=ax,
cmap='magma',#magma
fill=True,
n_levels=10,
zorder=1,
shade_lowest=True,
bw_adjust=0.3,
)
plt.tight_layout(pad=0)
return fig
@st.cache_data
def shotmap(df, player):
df_goal = df[(df["shotType"] == "goal") & (df["player_name"] == player)].copy()
df_miss = df[(df["shotType"] == "miss") & (df["player_name"] == player)].copy()
df_save = df[(df["shotType"] == "save") & (df["player_name"] == player)].copy()
df_block = df[(df["shotType"] == "block") & (df["player_name"] == player)].copy()
df_post = df[(df["shotType"] == "post") & (df["player_name"] == player)].copy()
# Set up the pitch
pitch = VerticalPitch(pitch_type='opta',
pitch_color='#0c1421',#22312b
line_color='#c7d5cc',#c7d5cc
corner_arcs=True,
#line_zorder=5,
half=True
)
fig, ax = pitch.draw(figsize=(8, 6))
fig.set_facecolor('#22312b')#22312b
fig.patch.set_edgecolor('#FFA500')
fig.patch.set_linewidth(2)
# goals:
sc_g = pitch.scatter(df_goal["new_x"],
df_goal["new_y"],
marker='football',
#s=50,
s=df_goal["xg"]*500+100,
ax=ax,
label="Goals",
zorder=4)
# missed
sc_m = pitch.scatter(df_miss["new_x"],
df_miss["new_y"],
c='red',
#s=100,
s=df_miss["xg"]*500+100,
ax=ax,
label="Missed",
zorder=1)
# saved
sc_s = pitch.scatter(df_save["new_x"],
df_save["new_y"],
c='yellow',
#s=100,
s=df_save["xg"]*500+100,
ax=ax,
label="Saved",
zorder=3)
# blocked
sc_b = pitch.scatter(df_block["new_x"],
df_block["new_y"],
c='#2463AD',
#s=100,
s=df_block["xg"]*500+100,
ax=ax,
label="Blocked",
zorder=2)
# post
sc_p = pitch.scatter(df_post["new_x"],
df_post["new_y"],
c='#1C8714',
#s=100,
s=df_post["xg"]*500+100,
ax=ax,
label="Post",
zorder=2)
ax.legend(loc='lower left', bbox_to_anchor=(0.05, 0.05), ncol=2, fontsize=10.2, frameon=True, edgecolor='black', markerscale=0.8)
plt.tight_layout(pad=0)
return fig
@st.cache_data
def beeswarmplot(players_df, player_data, stat):
defend_positions = [
'Defender', 'Right-Back', 'Centre-Back', 'Left-Back'
]
midfield_positions = [
'Right Midfield', 'Left Midfield', 'Midfielder',
'Central Midfield', 'Defensive Midfield', 'Attacking Midfield'
]
forward_positions = [
'Striker', 'Second Striker', 'Forward',
'Left Winger', 'Right Winger', 'Centre-Forward'
]
goalkeepers = players_df[players_df['Position'] == 'Goalkeeper']
defenders = players_df[players_df['Position'].isin(defend_positions)]
midfielders = players_df[players_df['Position'].isin(midfield_positions)]
forwards = players_df[players_df['Position'].isin(forward_positions)]
position = player_data['Position']
#set default colors
text_color = '#FFFFFF'
background = '#0c1421'
if position == 'Goalkeeper':
df = goalkeepers.copy()
pos = 'Goalkeepers'
elif position in defend_positions:
df = defenders.copy()
pos = 'Defenders'
elif position in midfield_positions:
df = midfielders.copy()
pos = 'Midfielders'
elif position in forward_positions:
df = forwards.copy()
pos = 'Forwards'
# Remove players with played less than 900 min
df['minutesPlayed'] = df['minutesPlayed'].replace('-', np.nan)
df['90s'] = (df['minutesPlayed'] / 90).round(1)
df = df[df['90s'] >= 10]
# Remove players with 0 value for the stat
df = df[df[stat] > 0]
# Calculate quartiles
q1, q2, q3 = df[stat].quantile([0.25, 0.5, 0.75])
# Create color categories
df['color_category'] = pd.cut(df[stat],
bins=[-np.inf, q1, q2, q3, np.inf],
labels=['Bottom 25%', '25-50%', '50-75%', 'Top 25%'])
fig, ax = plt.subplots(figsize=(6,4))
fig.set_facecolor(background)
ax.patch.set_facecolor(background)
#fig.patch.set_edgecolor('#FFA500')
#fig.patch.set_linewidth(2)
#set up our base layer
mpl.rcParams['xtick.color'] = text_color
mpl.rcParams['ytick.color'] = text_color
spines = ['top','bottom','left','right']
for x in spines:
if x in spines:
ax.spines[x].set_visible(False)
reverse_stats = ['goalsConceded', 'errorLeadToShot', 'dribbledPast', 'offsides', 'scoringFrequency']
if stat in reverse_stats:
custom_palette={'Bottom 25%': '#00ff1d', '25-50%': '#ffef01', '50-75%': '#ffa500', 'Top 25%': '#fe003e'}
else:
custom_palette={'Bottom 25%': '#fe003e', '25-50%': '#ffa500', '50-75%': '#ffef01', 'Top 25%': '#00ff1d'}
sns.swarmplot(x=stat, data=df,
hue='color_category',
palette=custom_palette,
size=15, zorder=1,
legend=False)
# Remove the legend if you don't need it
#plt.legend([],[], frameon=False)
# Set x-axis ticks based on data range
x_min = df[stat].min()
x_max = df[stat].max()
x_range = x_max - x_min
if x_range <= 30:
step = 2
elif x_range <= 50:
step = 5
elif x_range <= 100:
step = 10
else:
step = 20
x_ticks = np.arange(np.floor(x_min), np.ceil(x_max) + step, step)
plt.xticks(x_ticks)
#plot player
if player_data['Player'] in df['Player'].values:
value = df.loc[df['Player'] == player_data['Player'], stat].values[0]
plt.scatter(x=value,y=0,c=text_color,s=250,zorder=2)
title_text = f"Distribution of {stat} Among Botola Pro {pos}"
# Get a cursive font
cursive_fonts = [f for f in fm.fontManager.ttflist if 'cursive' in f.name.lower()]
if cursive_fonts:
cursive_font = cursive_fonts[0].fname
else:
cursive_font = 'Arial' # Fallback to Arial if no cursive font is found
ax.text(0.5, 0.93, title_text,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=12,
fontfamily='cursive',
fontweight='bold',
color='white',
wrap=True)
plt.xlabel(stat,c=text_color)
#plt.tight_layout(pad=-0.5)
return fig
@st.cache_data
def player_radar_chart(player_name, metrics, values, avg_values):
# Ensure values are integers
numeric_values = [int(v) for v in values if str(v).isdigit()]
numeric_avg_values = [int(v) for v in avg_values if str(v).isdigit()]
if not numeric_values or not numeric_avg_values:
print("Error: No valid integer values provided.")
return
# Ensure the first value is repeated at the end to close the polygon
values_plot = numeric_values + [numeric_values[0]]
avg_values_plot = numeric_avg_values + [numeric_avg_values[0]]
metrics_plot = metrics + [metrics[0]]
# Create the trace for the average values
avg_trace = go.Scatterpolar(
r=avg_values_plot,
theta=metrics_plot,
name='GK Average',
line=dict(color='#FFA500', width=2),
hoverinfo='text',
text=[f'GK Average {metric}: {value}' for metric, value in zip(metrics, numeric_avg_values)] + [f'GK Average {metrics[0]}: {numeric_avg_values[0]}']
)
# Create the trace for the player
player_trace = go.Scatterpolar(
r=values_plot,
theta=metrics_plot,
fill='toself',
name=player_name,
fillcolor='rgba(17, 201, 175, 0.5)', # Semi-transparent fill
line=dict(color='#11C9AF', width=2),
hoverinfo='text',
text=[f'{player_name} {metric}: {value}' for metric, value in zip(metrics, numeric_values)] + [f'{player_name} {metrics[0]}: {numeric_values[0]}']
)
# Create the layout
grid_color = '#BDD0DA'
layout = go.Layout(
polar=dict(
radialaxis=dict(
visible=True,
range=[0, 70],
color='white',
gridcolor=grid_color,
tickfont=dict(size=13), # Increased tick font size
tickmode='array',
tickvals=[0, 10, 20, 30, 40, 50, 60, 70],
),
angularaxis=dict(
gridcolor=grid_color,
linecolor='white',
tickfont=dict(color='white', size=14) # Increased angular tick font size
),
bgcolor='#0c1421'
),
showlegend=True,
legend=dict(font=dict(color='white', size=14), bgcolor='rgba(12, 20, 33, 0.8)'), # Increased legend font size
width=600,
height=500,
paper_bgcolor='#0c1421',
plot_bgcolor='#0c1421',
font=dict(color='white', size=14) # Increased global font size
)
# Create the figure and display it
fig = go.Figure(data=[player_trace, avg_trace], layout=layout)
# Update the radial axis to ensure consistent scaling
fig.update_layout(
polar=dict(
radialaxis=dict(
range=[0, 70],
tickmode='array',
tickvals=[0, 10, 20, 30, 40, 50, 60, 70],
ticktext=['0', '10', '20', '30', '40', '50', '60', '70']
)
)
)
st.plotly_chart(fig, use_container_width=True)
def player_details(df, player_data):
heatmap_df = pd.read_csv('https://raw.githubusercontent.com/MS3B09/Botola-Scout/main/Datasets/Botola%20Players%20HeatMaps.csv')
shotmap_df = pd.read_csv('https://raw.githubusercontent.com/MS3B09/Botola-Scout/main/Datasets/Botola%20Players%20ShotMaps.csv')
spaces = ' '
st.set_option('deprecation.showPyplotGlobalUse', False)
st.markdown("""
<style>
.colored-line {
height: 3px;
width: 1370px;
background: linear-gradient(to right, #11C9AF, #FFA500, #9b59b6);
margin: -10px 0;
}
.title {
font-family:cursive;
font-size: 40px;
font-weight: bold;
text-align: center;
margin-bottom: 30px;
background-color: #082630;
padding: 20px;
border-radius: 10px;
border: 2px solid #11C9AF;
}
</style>
""", unsafe_allow_html=True)
col1, col2 = st.columns((1,8))
with col1:
lottie_animation = load_lottieurl("https://lottie.host/a79be04b-d18c-4f2b-8341-9bd28d0ab5d5/ke10ceU8Pg.json")
st_lottie(lottie_animation, loop=False, height=150, width=150, speed=1)
with col2:
add_vertical_space(1)
#st.title('Pape Badji Stats for Moghreb Tetouan 2023/24')
st.markdown(f"<p class='title'>{player_data['Player']} Stats for {player_data['Team_x']} 2023/24</p>", unsafe_allow_html=True)
st.markdown('<div class="colored-line"></div>', unsafe_allow_html=True)
col1, col2 = st.columns(2)
with col1:
st.markdown("<h1><span style='color:#FFA500; font-size:40px; font-family:cursive'>Player Profile </span></h1>", unsafe_allow_html=True)
display_player_card(player_data)
with col2:
add_vertical_space(5)
st.markdown(f"<h1>{spaces*3}<span style='color:#FFA500; font-size:40px; font-family:cursive'>Season HeatMap </span></h1>", unsafe_allow_html=True)
fig = heatmap(heatmap_df, player_data['Player'])
st.pyplot(fig, bbox_inches='tight', pad_inches=0.00, use_container_width=True)
col1, col2 = st.columns(2)
with col1:
add_vertical_space(4)
st.markdown(f"<h1>{spaces}<span style='color:#FFA500; font-size:40px; font-family:cursive'>Key Stats </span></h1>", unsafe_allow_html=True)
add_vertical_space(4)
position_group = get_position_group(player_data['Position'])
if position_group:
# Get the stats for the position group
key_stats = stats_mapping[position_group]
stats_df = df[df['Position'].isin(position_mapping[position_group])]
# Remove players with played less than 900 min
stats_df['minutesPlayed'] = stats_df['minutesPlayed'].replace('-', np.nan)
stats_df['90s'] = (stats_df['minutesPlayed'] / 90).round(1)
if player_data['minutesPlayed'] >= 900:
stats_df = stats_df[stats_df['90s'] >= 10]
midpoint = math.ceil(len(key_stats) / 2)
col01,col02 = st.columns(2)
# Display stats in two columns
for i, stat in enumerate(key_stats):
stats_df[stat] = stats_df[stat].replace('-', np.nan)
pos_stats_df = stats_df[stats_df[stat] > 0]
# Choose the column based on the index
with col01 if i < midpoint else col02:
stat_name = convert_to_readable(stat)
display_stat(stat_name, player_data[stat], pos_stats_df, stat)
with col2:
add_vertical_space(1)
st.markdown(f"<h1>{spaces}<span style='color:#FFA500; font-size:40px; font-family:cursive'>Key Stats Distribution</span></h1>", unsafe_allow_html=True)
stat = st.selectbox(
label=' ╰┈➤ Select a Stat : ❗(For Players who Played >= 900 mins)',
options=key_stats,
index=0,
format_func=convert_to_readable,
#label_visibility="collapsed",
)
fig = beeswarmplot(df, player_data, stat)
st.pyplot(fig, bbox_inches='tight', pad_inches=0)
#col1, col2, col3 = st.columns((1,2,1))
col1, col2 = st.columns((1,1.2))
with col1:
add_vertical_space(2)
if position_group == 'GK':
st.markdown(f"<h1>{spaces}<span style='color:#FFA500; font-size:40px; font-family:cursive'>Attribute Overview </span></h1>", unsafe_allow_html=True)
gk_df = pd.read_csv('https://raw.githubusercontent.com/MS3B09/Botola-Scout/main/Datasets/GK%20Attributes.csv')
params = list(gk_df.columns)
params = params[2:]
if player_data['Player'] in gk_df['Player'].values:
player = gk_df.loc[gk_df['Player']==player_data['Player']].reset_index()
player = list(player.loc[0])
values = player[3:]
else:
values = [0,0,0,0,0]
avg_values = [53, 64, 47, 63, 61]
player_radar_chart(player_data['Player'], params, values, avg_values)
else:
st.markdown(f"<h1>{spaces}<span style='color:#FFA500; font-size:40px; font-family:cursive'>Season ShotMap </span></h1>", unsafe_allow_html=True)
add_vertical_space(1)
fig = shotmap(shotmap_df, player_data['Player'])
st.pyplot(fig, bbox_inches='tight', pad_inches=0)
with col2:
add_vertical_space(2)
st.markdown(f"<h1>{spaces}<span style='color:#FFA500; font-size:30px; font-family:cursive'>╰┈➤ Compare with other {pos[position_group]}: </span></h1>", unsafe_allow_html=True)
col01,col02 = st.columns(2)
with col01:
add_vertical_space(1)
clubs = df['Team_x'].unique()
clubs = np.insert(clubs, 0, '- - -')
team_name_2 = st.selectbox(
label=f'Team :',
options=clubs,
index=0,
#label_visibility="collapsed",
)
with col02:
add_vertical_space(1)
players_pos_df = df[
(df['Position'].isin(position_mapping[position_group])) &
(df['Team_x'] == team_name_2) &
(df['rating'] != '-')]
players_pos = ['- - -'] + players_pos_df['Player'].tolist()
player_name_2 = st.selectbox(
label='Player :',
options=players_pos,
index=0,
#label_visibility="collapsed",
)
pizza_df = df[df['Position'].isin(position_mapping[position_group])]
params = stats_mapping[position_group]
reverse_stats = ['goalsConceded', 'errorLeadToShot', 'dribbledPast', 'offsides', 'scoringFrequency']
# Create a new list to store the filtered params
filtered_params = []
params_1 = []
for param in params:
pizza_df[param] = pizza_df[param].replace('-', 0.0)
pizza_df[param] = pizza_df[param].replace(np.nan, 0.0)
if param not in reverse_stats:
filtered_params.append(param)
params_1.append(convert_to_readable(param))
player = [player_data[param] for param in filtered_params]
values = []
for x in range(len(filtered_params)):
values.append(math.floor(stats.percentileofscore(pizza_df[filtered_params[x]],player[x])))
output = get_image_output(player_data['Player Image'])
if player_name_2 == '- - -':
fig = pizza_plot(player_data, params_1, values, output)
st.pyplot(fig, bbox_inches='tight', pad_inches=0)
else:
player2_data = df[df['Player'] == player_name_2].iloc[0]