def total_accurate_shots_v_points_correlation():
data = get_plot_data('midfielders', 'total_shots', 'points')
total_shots_data = data['total_shots']
points = data['points']
shot_accuracy_data = get_plot_data('midfielders', 'shot_accuracy', 'points')['shot_accuracy']
accurate_shots = [total_shots_data[i]*shot_accuracy_data[i] for i in range(len(total_shots_data))]
r = pearsonr(accurate_shots, points)
return r
def total_accurate_shots_v_price_correlation():
data = get_plot_data('forwards', 'total_shots', 'price')
total_shots_data = data['total_shots']
price = data['price']
shot_accuracy_data = get_plot_data('forwards', 'shot_accuracy', 'price')['shot_accuracy']
accurate_shots = [total_shots_data[i]*shot_accuracy_data[i] for i in range(len(total_shots_data))]
r = pearsonr(accurate_shots, price)
return r
def combined_score_v_price_correlation():
data = get_plot_data('midfielders', 'total_shots', 'price')
total_shots_data = data['total_shots']
price = data['price']
shot_accuracy_data = get_plot_data('midfielders', 'shot_accuracy', 'points')['shot_accuracy']
chances_created_data = get_plot_data('midfielders', 'chances_created', 'points')['chances_created']
combined_score = [total_shots_data[i]*shot_accuracy_data[i]+0.33*chances_created_data[i] for i in range(len(total_shots_data))]
r = pearsonr(combined_score, price)
return r
def get_forwards_sorted_by_total_score():
"""
Score is defined as total accurate shots
:return:
"""
data = get_plot_data('forwards', 'total_shots', 'name')
total_shots_data = data['total_shots']
players = data['name']
shot_accuracy_data = get_plot_data('forwards', 'shot_accuracy', 'points')['shot_accuracy']
scores = [total_shots_data[i]*shot_accuracy_data[i] for i in range(len(total_shots_data))]
player_scores = [(players[i], scores[i]) for i in range(len(players))]
player_scores = sorted(player_scores, key=lambda x: x[1], reverse=True)
print(player_scores)
def get_midfielders_sorted_by_total_score():
"""
Score is defined as total accurate shots + key passes
:return:
"""
data = get_plot_data('midfielders', 'total_shots', 'name')
total_shots_data = data['total_shots']
players = data['name']
shot_accuracy_data = get_plot_data('midfielders', 'shot_accuracy', 'points')['shot_accuracy']
chances_created_data = get_plot_data('midfielders', 'chances_created', 'points')['chances_created']
scores = [total_shots_data[i]*shot_accuracy_data[i]+0.33*chances_created_data[i] for i in range(len(total_shots_data))]
player_scores = [(players[i], scores[i]) for i in range(len(players))]
player_scores = sorted(player_scores, key=lambda x: x[1], reverse=True)
print(player_scores)
def get_forwards_sorted_by_value():
"""
Value is defined as score per price
:return:
"""
data = get_plot_data('forwards', 'total_shots', 'name')
total_shots_data = data['total_shots']
players = data['name']
data = get_plot_data('forwards', 'shot_accuracy', 'price')
shot_accuracy_data = data['shot_accuracy']
prices = data['price']
values = [(total_shots_data[i]*shot_accuracy_data[i])/prices[i] for i in range(len(total_shots_data))]
player_values = [(players[i], values[i], prices[i]) for i in range(len(players))]
player_values = sorted(player_values, key=lambda x: x[1], reverse=True)
print(player_values)
def shot_accuracy_v_points_correlation():
data = get_plot_data('forwards', 'shot_accuracy', 'points')
r = pearsonr(data['shot_accuracy'], data['points'])
return r
def attack_score_v_price_correlation():
data = get_plot_data('forwards', 'total_shots', 'price')
r = pearsonr(data['total_shots'], data['price'])
return r
__author__ = 'gj'
from bokeh.plotting import figure, output_file, show
from utils.read_data import get_plot_data
data = get_plot_data('forwards', 'total_shots', 'points')
total_shots_data = data['total_shots']
points = data['points']
shot_accuracy_data = get_plot_data('shot_accuracy', 'points')['shot_accuracy']
accurate_shots = [total_shots_data[i]*shot_accuracy_data[i] for i in range(len(total_shots_data))]
output_file("total_accurate_shots_v_points_scatter.html")
p = figure(plot_width=600, plot_height=600)
p.circle(accurate_shots, points, size=20, color="navy", alpha=0.5)
show(p)
__author__ = 'gj'
from bokeh.plotting import figure, output_file, show
from utils.read_data import get_plot_data
data = get_plot_data('forwards', 'attack_score', 'points')
output_file("attack_score_v_points_scatter.html")
p = figure(plot_width=600, plot_height=600)
p.circle(data['attack_score'], data['points'], size=20, color="navy", alpha=0.5)
show(p)
__author__ = 'gj'
from bokeh.plotting import figure, output_file, show
from utils.read_data import get_plot_data
data = get_plot_data('forwards', 'shot_accuracy', 'points')
output_file("shot_accuracy_v_points_scatter.html")
p = figure(plot_width=600, plot_height=600)
p.circle(data['shot_accuracy'], data['points'], size=20, color="navy", alpha=0.5)
show(p)
__author__ = 'gj'
from bokeh.plotting import figure, output_file, show
from utils.read_data import get_plot_data
data = get_plot_data('forwards', 'total_shots', 'points')
output_file("total_shots_v_points_scatter.html")
p = figure(plot_width=600, plot_height=600)
p.circle(data['total_shots'], data['points'], size=20, color="navy", alpha=0.5)
show(p)
def chances_created_v_points_correlation():
data = get_plot_data('midfielders', 'chances_created', 'points')
r = pearsonr(data['chances_created'], data['points'])
return r
def key_passes_v_points_correlation():
data = get_plot_data('midfielders', 'key_passes', 'points')
r = pearsonr(data['key_passes'], data['points'])
return r
def total_shots_v_points_correlation():
data = get_plot_data('forwards', 'total_shots', 'points')
r = pearsonr(data['total_shots'], data['points'])
return r
def shots_inside_box_v_price_correlation():
data = get_plot_data('forwards', 'shots_inside_box', 'price')
r = pearsonr(data['shots_inside_box'], data['price'])
return r
__author__ = 'gj'
from bokeh.plotting import figure, output_file, show
from utils.read_data import get_plot_data
data = get_plot_data('midfielders', 'total_shots', 'points')
total_shots_data = data['total_shots']
points = data['points']
shot_accuracy_data = get_plot_data('midfielders', 'shot_accuracy', 'points')['shot_accuracy']
key_passes_data = get_plot_data('midfielders', 'key_passes', 'points')['key_passes']
combined_score = [total_shots_data[i]*shot_accuracy_data[i]+0.33*key_passes_data[i] for i in range(len(total_shots_data))]
output_file("combined_score_v_points_scatter.html")
p = figure(plot_width=600, plot_height=600)
p.circle(combined_score, data['points'], size=20, color="navy", alpha=0.5)
show(p)
def attack_score_v_points_correlation():
data = get_plot_data('forwards', 'attack_score', 'points')
r = pearsonr(data['attack_score'], data['points'])
return r
def shots_inside_box_v_points_correlation():
data = get_plot_data('midfielders', 'shots_inside_box', 'points')
r = pearsonr(data['shots_inside_box'], data['points'])
return r
