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BestColumnPredictorEuro2020.py
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BestColumnPredictorEuro2020.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Aug 17 15:16:02 2021
@author: deanp
Program to load Euro 2020 data and plot scatter plots with best fit line on
a single figure. Includes statistical analysis for r and p values of regression.
"""
#%%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.font_manager
import matplotlib as mpl
from scipy import stats as sp
#%%
#Load data
df = pd.read_excel("C:/Users/deanp/OneDrive/Desktop/Football Analytics/Data/Uefa Euro Data/Euro 2020 Match and Goal Data.xlsx",
sheet_name=3)
#%%
# Slice df, setup into home and away columns
home_gd = df["Home GD"]
away_gd = df["Away GD"]
match_GD = home_gd.append(away_gd)
home_columns = df.iloc[:, range(6, 31)]
away_columns = df.iloc[:, range(31, len(df.columns))]
#%%
from pprint import pprint
#Statistical analysis to get r and p values
column_names = list(away_columns.columns)
r_values = {}
p_values = {}
for col in range(0, len(column_names)):
home_col = home_columns.iloc[:, col]
away_col = away_columns.iloc[:, col]
predictor = home_col.append(away_col, ignore_index=True)
result = sp.linregress(predictor, match_GD)
r_values[column_names[col]] = result.rvalue
p_values[column_names[col]] = result.pvalue
sorted_r_values = sorted(r_values.items(), key=lambda x:x[1])
for i in range(0, 25):
stat_name = sorted_r_values[i][0]
r_value = round(sorted_r_values[i][1], 3)
p_value = round(p_values.get(stat_name), 3)
pprint("|" + stat_name + "|" + str(r_value) + "|" + str(p_value) + "|")
#%%
#Plotting the scatter plot matrix
#Setup
text_color = 'white'
mpl.rcParams['font.family'] = "Arial"
fig, ax = plt.subplots(5, 5, figsize=(12, 12))
fig.set_facecolor('#313332')
row = -1
column = 0
#Loop to produce matrix of scatter plots
for col in range(0, len(column_names)):
if ((col % 5) == 0): # logic to increment row and column correctly
row += 1
column = 0
home_col = home_columns.iloc[:, col]
away_col = away_columns.iloc[:, col]
predictor = home_col.append(away_col, ignore_index=True)
m, b = np.polyfit(predictor, match_GD, 1)
ax[row, column].scatter(x=predictor, y=match_GD, color='#89CFF0', alpha=0.35)
bounds = ax[row, column].get_xbound()
x = np.arange(bounds[0], bounds[1], step=(bounds[1] - bounds[0])/8)
y = b+m*x
ax[row, column].plot(x, y, color='red')
ax[row, column].set_xlabel(column_names[col], fontsize=14, color=text_color)
if (column == 0):
ax[row, column].set_ylabel('Match GD', fontsize=14, color=text_color)
ax[row, column].patch.set_facecolor('#313332')
ax[row, column].spines["top"].set_visible(False)
ax[row, column].spines["right"].set_visible(False)
ax[row, column].spines["bottom"].set_color(text_color)
ax[row, column].spines["left"].set_color(text_color)
ax[row, column].tick_params(axis='x', colors=text_color)
ax[row, column].tick_params(axis='y', colors=text_color)
column += 1
fig.text(
x=0.05,
y=0,
s="Created by Dean Patel. Data provided by Fotmob.",
fontsize=9,
fontstyle="italic",
color=text_color,
)
fig.tight_layout(pad=3.0)
fig.savefig('C:/Users/deanp/OneDrive/Desktop/Football Analytics/Output/predictor_plots/' +
'scatterMatrix.png', dpi=300, bbox_inches='tight')