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15PointsMovingAverage.py
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15PointsMovingAverage.py
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"""
Created on Fri Mar 12 05:53:35 2021
@author: davsu428
"""
import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dflist=[]
seasonst=[]
for year in range(5,21,1):
if year<9:
yeartext='0'+str(year)+'0'+str(year+1)
yeartext2='0'+str(year)+'-0'+str(year+1)
elif year==9:
yeartext='0910'
yeartext2='09-10'
else:
yeartext=str(year)+str(year+1)
yeartext2=str(year)+'-'+str(year+1)
seasonst=seasonst+[yeartext2]
#For England
performance_year = pd.read_csv("https://www.football-data.co.uk/mmz4281/"+yeartext+"/E0.csv",delimiter=',')
#For Spain
#performance_year = pd.read_csv("https://www.football-data.co.uk/mmz4281/"+yeartext+"/SP1.csv",delimiter=',')
dflist=dflist+[performance_year]
performance= pd.concat(dflist)
teams=list(set(performance['AwayTeam']))
ma=38
teamdf=dict()
for team in teams:
game=0
rows=[]
matches=performance[((performance['AwayTeam']==team) | (performance['HomeTeam']==team))]
for i,match in matches.iterrows():
game=game+1
#matchdate = datetime.datetime.strptime(match['Date'],"%d/%m/%y")
if match['AwayTeam']==team:
goalsfor=match['FTAG']
goalsagainst=match['FTHG']
oddsfor=match['PSA']
if match['FTR']=='A':
points=3
profit=oddsfor-1
elif match['FTR']=='D':
points=1
profit=-1
else:
points=0
profit=-1
if match['HomeTeam']==team:
goalsfor=match['FTHG']
goalsagainst=match['FTAG']
oddsfor=match['PSH']
if match['FTR']=='H':
points=3
profit=oddsfor-1
elif match['FTR']=='D':
points=1
profit=-1
else:
points=0
profit=-1
goaldiff=goalsfor-goalsagainst
rows.append([matchdate,goalsfor,goalsagainst,goaldiff,points,profit,season,game])
ma_goaldiff=np.convolve(goaldiff, np.ones(10)/10, mode='valid')
df = pd.DataFrame(rows, columns=["Date", "For","Against","Difference","Points","Profit","Season","Game"])
#df = df.sort_values('Date', ascending=True)
df['PointsRA'] = df['Points'].rolling(window=ma, win_type='triang').mean()
teamdf[team]=df
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
fig,ax=plt.subplots(num=1)
comparison1='Man City'
comp_color1='lightblue'
comparison3='Liverpool'
comp_color3='red'
comparison2='Man United'
comp_color2='darkred'
#
#comparison1='Real Madrid'
#comp_color1='blue'
#comparison2='Barcelona'
#comp_color2='darkred'
#comparison3='Ath Madrid'
#comp_color3='red'
ax.plot(teamdf[comparison1]['Game'], teamdf[comparison1]['PointsRA'], linewidth=2, linestyle='-',color=comp_color1)
ax.plot(teamdf[comparison2]['Game'], teamdf[comparison2]['PointsRA'], linewidth=2 , linestyle='-',color=comp_color2)
ax.plot(teamdf[comparison3]['Game'], teamdf[comparison3]['PointsRA'], linewidth=2 , linestyle='-',color=comp_color3)
ax.set_title(str(ma) + ' game moving average')
plt.gcf().autofmt_xdate()
ax.legend([comparison1,comparison2,comparison3])
ax.set_ylim(1,3.2)
ax.set_xticks(np.arange(0,max(teamdf[comparison2]['Game']),38))
ax.set_xticklabels(seasonst)
ax.set_xlim(0,max(teamdf[comparison2]['Game'])+40)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
fig.show()
ax.set_ylabel('Rolling Average Points Per Game')
ax.set_xlabel('Season')
fig.savefig('dip.png', dpi=None, bbox_inches="tight")