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bestTeam.py
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bestTeam.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Nov 4 21:30:11 2017
@author: admin
"""
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
path='F:\Study Material\sem 5\Big Data Project\complete-fifa-2017-player-dataset-global/'
df=pd.read_csv(path+'FullData.csv')
Names= pd.read_csv(path+'PlayerNames.csv')
df.assign(Index=np.nan)
df['Index'] = [v.split('/')[2] for v in Names['url']]
del df['Nationality']
del df['National_Position']
del df['National_Kit']
#defining weightage factors
a= 1
b= 2
c= 3
d= 4
#GoalKeeping Indices
df['gk_Shot_Stopper'] = (b*df.Reactions + b*df.Composure + a*df.Speed + a*df.Strength + c*df.Jumping + b*df.GK_Positioning + c*df.GK_Diving + d*df.GK_Reflexes + b*df.GK_Handling)/(2*a + 4*b + 2*c + 1*d)
df['gk_Sweeper'] = (b*df.Reactions + b*df.Composure + b*df.Speed + a*df.Short_Pass + a*df.Long_Pass + b*df.Jumping + b*df.GK_Positioning + b*df.GK_Diving + d*df.GK_Reflexes + b*df.GK_Handling + d*df.GK_Kicking + c*df.Vision)/(2*a + 4*b + 3*c + 2*d)
#Defending Indices
df['df_studds'] = (c*df.Marking + c*df.Sliding_Tackle + c*df.Standing_Tackle + c*df.Aggression + a*df.Reactions + b*df.Interceptions + c*df.Strength)/(5*c + b + a)
df['df_sweeper'] = (c*df.Ball_Control + b*df.Reactions + b*df.Interceptions + d*df.Vision + b*df.Composure + b*df.Short_Pass + b*df.Long_Pass)/(5*b + c + d)
df['df_aerial_studds'] = (c*df.Marking + b*df.Sliding_Tackle + b*df.Standing_Tackle + c*df.Sliding_Tackle + c*df.Reactions + c*df.Interceptions + b*df.Balance + c*df.Jumping + b*df.Agility + d*df.Heading)/(5*c + 4*b + d)
df['df_wing_back'] = (b*df.Ball_Control + a*df.Dribbling + a*df.Marking + c*df.Sliding_Tackle + b*df.Standing_Tackle + c*df.Attacking_Position + d*df.Vision + c*df.Crossing + b*df.Short_Pass + b*df.Long_Pass + d*df.Acceleration +d*df.Speed + c*df.Stamina + a*df.Finishing)/(3*a + 4*b + 4*c + 3*d)
#Midfielding Indices
df['mf_controller'] = (a*df.Weak_foot + c*df.Ball_Control + a*df.Dribbling + a*df.Marking + a*df.Reactions + d*df.Vision + c*df.Composure + d*df.Short_Pass + d*df.Long_Pass)/(2*c + 3*d + 4*a)
df['mf_beast'] = (b*df.Agility + b*df.Balance + b*df.Jumping + c*df.Strength + c*df.Stamina + b*df.Speed + a*df.Acceleration + b*df.Short_Pass + d*df.Aggression + d*df.Reactions + d*df.Marking + c*df.Standing_Tackle + c*df.Sliding_Tackle + d*df.Interceptions)/(1*a + 5*b + 4*c + 4*d)
df['mf_playmaker'] = (b*df.Ball_Control + a*df.Dribbling + a*df.Marking + b*df.Reactions + d*df.Vision + c*df.Crossing + c*df.Short_Pass + c*df.Long_Pass + a*df.Curve + a*df.Long_Shots + c*df.Freekick_Accuracy)/(4*a + 2*b + 4*c + d)
df['mf_attacker'] = (b*df.Ball_Control + c*df.Dribbling + b*df.Vision + b*df.Crossing + b*df.Short_Pass + b*df.Long_Pass + c*df.Agility + a*df.Curve + c*df.Long_Shots + b*df.Freekick_Accuracy + d*df.Finishing)/(a + 6*b + 3*c + d)
#Attacking Indices
df['at_left_wing'] = (c*df.Weak_foot + c*df.Ball_Control + c*df.Dribbling + c*df.Speed + d*df.Acceleration + b*df.Vision + c*df.Crossing + b*df.Short_Pass + b*df.Long_Pass + b*df.Aggression + b*df.Agility + a*df.Curve + c*df.Long_Shots + b*df.Freekick_Accuracy + d*df.Finishing)/(a + 6*b + 6*c + 2*d)
df['at_right_wing'] = (c*df.Weak_foot + c*df.Ball_Control + c*df.Dribbling + c*df.Speed + d*df.Acceleration + b*df.Vision + c*df.Crossing + b*df.Short_Pass + b*df.Long_Pass + b*df.Aggression + b*df.Agility + a*df.Curve + c*df.Long_Shots + b*df.Freekick_Accuracy + d*df.Finishing)/(a + 6*b + 6*c + 2*d)
df['at_striker'] = (b*df.Weak_foot + b*df.Ball_Control + a*df.Vision + b*df.Aggression + b*df.Agility + a*df.Curve + a*df.Long_Shots + d*df.Balance + d*df.Finishing + d*df.Heading + c*df.Jumping + c*df.Dribbling)/(3*a + 4*b + 2*c + 3*d)
col=['Name', 'gk_Shot_Stopper', 'gk_Sweeper', 'df_studds', 'df_sweeper', 'df_aerial_studds', 'df_wing_back', 'mf_controller', 'mf_beast', 'mf_playmaker', 'mf_attacker', 'at_left_wing', 'at_right_wing', 'at_striker']
new_df=df[col]
"""Goal Keeper Selection"""
sns.set(style="white", context="talk")
f, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 15), sharex=False)
shotStopper= new_df.sort_values('gk_Shot_Stopper', ascending=False)[:7]
x1 = np.array(list(shotStopper['Name']))
y1 = np.array(list(shotStopper['gk_Shot_Stopper']))
sweeperKeeper= new_df.sort_values('gk_Sweeper', ascending=False)[:7]
x2 = np.array(list(sweeperKeeper['Name']))
y2 = np.array(list(sweeperKeeper['gk_Sweeper']))
shotStopper= shotStopper.reset_index(drop=True)
sweeperKeeper= sweeperKeeper.reset_index(drop=True)
sns.barplot(x1,y1,palette="Greens_d",ax=ax1)
ax1.set_ylabel("Shot Stopping Score")
sns.barplot(x2, y2, palette="Greens_d", ax=ax2)
ax2.set_ylabel("Sweeping Score")
print("Our 3 best Goal Keepers are:")
print(sweeperKeeper.Name[0])
print(sweeperKeeper.Name[1])
print(shotStopper.Name[1])
"""Defenders Selection"""
sns.set(style="white", context="talk")
# Set up the matplotlib figure
f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(14, 21), sharex=False)
defender_studds = df[(df['Club_Position'] == 'CB') | (df['Club_Position'] == 'LCB') | (df['Club_Position'] == 'RCB')].sort_values('df_studds', ascending=False)[:7]
x1 = np.array(list(defender_studds['Name']))
y1 = np.array(list(defender_studds['df_studds']))
defender_aerial_studds = df[(df['Club_Position'] == 'CB') | (df['Club_Position'] == 'LCB') | (df['Club_Position'] == 'RCB')].sort_values('df_aerial_studds', ascending=False)[:7]
x2 = np.array(list(defender_aerial_studds['Name']))
y2 = np.array(list(defender_aerial_studds['df_aerial_studds']))
defender_sweeper = df[(df['Club_Position'] == 'CB') | (df['Club_Position'] == 'LCB') | (df['Club_Position'] == 'RCB')].sort_values('df_sweeper', ascending=False)[:7]
x3 = np.array(list(defender_sweeper['Name']))
y3 = np.array(list(defender_sweeper['df_sweeper']))
defender_wing_back = df[(df['Club_Position'] == 'LWB') | (df['Club_Position'] == 'LB') | (df['Club_Position'] == 'RWB') | (df['Club_Position'] == 'RB')].sort_values('df_wing_back', ascending=False)[:7]
x4 = np.array(list(defender_wing_back['Name']))
y4 = np.array(list(defender_wing_back['df_wing_back']))
sns.barplot(x1, y1, palette="Reds_d", ax=ax1)
ax1.set_ylabel("Studds Score")
sns.barplot(x2, y2, palette="Reds_d", ax=ax2)
ax2.set_ylabel("Aerial Studds Score")
sns.barplot(x3, y3, palette="Reds_d", ax=ax3)
ax3.set_ylabel("Sweeper Score")
sns.barplot(x4, y4, palette="Reds_d", ax=ax4)
ax4.set_ylabel("Wing Back Score")
sweeperKeeper= sweeperKeeper.reset_index(drop=True)
plt.tight_layout(h_pad=5)
sns.set(style="white", context="talk")
# Set up the matplotlib figure
f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(14, 21), sharex=False)
# Generate some sequential data
ss = df[(df['Club_Position'] == 'CDM') | (df['Club_Position'] == 'CM') | (df['Club_Position'] == 'LDM') | (df['Club_Position'] == 'RDM')].sort_values('mf_controller', ascending=False)[:7]
x1 = np.array(list(ss['Name']))
y1 = np.array(list(ss['mf_controller']))
ss = df[(df['Club_Position'] == 'CDM') | (df['Club_Position'] == 'CM') | (df['Club_Position'] == 'LDM') | (df['Club_Position'] == 'RDM')].sort_values('mf_beast', ascending=False)[:7]
x2 = np.array(list(ss['Name']))
y2 = np.array(list(ss['mf_beast']))
ss = df[(df['Club_Position'] == 'CM') | (df['Club_Position'] == 'LCM') | (df['Club_Position'] == 'RCM') | (df['Club_Position'] == 'LM') | (df['Club_Position'] == 'RM')].sort_values('mf_playmaker', ascending=False)[:7]
x3 = np.array(list(ss['Name']))
y3 = np.array(list(ss['mf_playmaker']))
ss = df[(df['Club_Position'] == 'CM') | (df['Club_Position'] == 'LCM') | (df['Club_Position'] == 'RCM') | (df['Club_Position'] == 'LM') | (df['Club_Position'] == 'RM')].sort_values('mf_attacker', ascending=False)[:7]
x4 = np.array(list(ss['Name']))
y4 = np.array(list(ss['mf_attacker']))
sns.barplot(x1, y1, palette=sns.color_palette("RdBu", n_colors=7), ax=ax1)
ax1.set_ylabel("Controller Score")
sns.barplot(x2, y2, palette=sns.color_palette("RdBu", n_colors=7), ax=ax2)
ax2.set_ylabel("Beast Score")
sns.barplot(x3, y3, palette=sns.color_palette("RdBu", n_colors=7), ax=ax3)
ax3.set_ylabel("PlayMaker Score")
sns.barplot(x4, y4, palette=sns.color_palette("RdBu", n_colors=7), ax=ax4)
ax4.set_ylabel("Attacker Score")
plt.tight_layout(h_pad=5)
sns.set(style="white", context="talk")
# Set up the matplotlib figure
f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14, 17), sharex=False)
# Generate some sequential data
ss = df[(df['Club_Position'] == 'LW') | (df['Club_Position'] == 'LM') | (df['Club_Position'] == 'LS')].sort_values('at_left_wing', ascending=False)[:7]
x1 = np.array(list(ss['Name']))
y1 = np.array(list(ss['at_left_wing']))
ss = df[(df['Club_Position'] == 'RW') | (df['Club_Position'] == 'RM') | (df['Club_Position'] == 'RS')].sort_values('at_right_wing', ascending=False)[:7]
x2 = np.array(list(ss['Name']))
y2 = np.array(list(ss['at_right_wing']))
ss = df[(df['Club_Position'] == 'ST') | (df['Club_Position'] == 'LS') | (df['Club_Position'] == 'RS') | (df['Club_Position'] == 'CF')].sort_values('at_striker', ascending=False)[:7]
x3 = np.array(list(ss['Name']))
y3 = np.array(list(ss['at_striker']))
sns.barplot(x1, y1, palette=sns.color_palette('RdGy', n_colors=7), ax=ax1)
ax1.set_ylabel("Left Wing")
sns.barplot(x2, y2, palette=sns.color_palette("RdGy", n_colors=7), ax=ax2)
ax2.set_ylabel("Right Wing")
sns.barplot(x3, y3, palette=sns.color_palette("RdGy", n_colors=7), ax=ax3)
ax3.set_ylabel("Striker")
plt.tight_layout(h_pad=5)