LFC Data Analysis: The Transfers¶
See Terry's blog Inspiring Transfers for a discussion of the data analysis.
This notebook analyses Liverpool FC's transfers over the last 5 seasons, from 2011-2012 to 2014-15. It also compares Liverpool's average net transfer spend, revenue and wage bill to the other teams in the top 6.
The analysis uses IPython Notebook, python, pandas and matplotlib to explore the data.
Set-up¶
Import the modules needed for the analysis.
In [1]:
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sys
import collections
from datetime import datetime
from __future__ import division
# enable inline plotting
%matplotlib inline
Print version numbers.
In [2]:
print 'python version: {}'.format(sys.version)
print 'pandas version: {}'.format(pd.__version__)
print 'matplotlib version: {}'.format(mpl.__version__)
print 'numpy version: {}'.format(np.__version__)
Load the data into a dataframes and munge¶
Create dataframe of LFC transfers¶
Data source: lfchistory.net
Note that all transfers are shown for FSG's ownership, from Oct 2010 to September 2015.
In [3]:
LFC_TRANSFERS_CSV_FILE = 'data\lfc_transfers_2010-2011_2015-2016.csv'
dflfc_transfers = pd.read_csv(LFC_TRANSFERS_CSV_FILE, parse_dates=['Date'])
# convert Fee to millions of pounds
dflfc_transfers.Fee = dflfc_transfers.Fee/1000000
# show shape
dflfc_transfers.shape
Out[3]:
In [4]:
dflfc_transfers.head()
Out[4]:
In [5]:
dflfc_transfers.tail()
Out[5]:
In [6]:
# check Date (of transfer) column is datetime data type
dflfc_transfers.dtypes
Out[6]:
Create dataframe of LFC players giving birthdate of each player¶
Data source: lfchistory.net
In [7]:
LFC_PLAYERS_CSV_FILE = 'data\lfc_players_september2015_upd.csv'
dflfc_players = pd.read_csv(LFC_PLAYERS_CSV_FILE, parse_dates=['birthdate'])
dflfc_players.rename(columns={'player': 'Player', 'birthdate': 'Birthdate', 'country': 'Country'}, inplace=True)
dflfc_players.shape
Out[7]:
In [8]:
dflfc_players.dtypes
Out[8]:
In [9]:
dflfc_players.head(10)
Out[9]:
Create dataframe of premier league transfers over last 5 seasons¶
Data source: transferleague.co.uk
In [10]:
PREM_TRANSFERS_CSV_FILE = 'data\prem_transfers_2011-2012_2015-2016.csv'
dfprem_transfers = pd.read_csv(PREM_TRANSFERS_CSV_FILE, skiprows=2, header=True)
# convert money to millions of pounds
dfprem_transfers.Purchased = np.round(dfprem_transfers.Purchased/1000000, 1)
dfprem_transfers.Sold = np.round(dfprem_transfers.Sold/1000000, 1)
dfprem_transfers.NetSpend = np.round(dfprem_transfers.NetSpend/1000000, 1)
dfprem_transfers.PerSeasonSpend = np.round(dfprem_transfers.PerSeasonSpend/1000000, 1)
# show shape
dfprem_transfers.shape
Out[10]:
In [12]:
dfprem_transfers.head()
Out[12]:
In [13]:
dfprem_transfers.tail()
Out[13]:
Create dataframe of premier league wage bill over last 5 seasons¶
Data source: guardian.co.uk etc
In [14]:
PREM_WAGES_CSV_FILE = 'data\prem_wages_2012-2013_2014-2015.csv'
dfprem_wages = pd.read_csv(PREM_WAGES_CSV_FILE, skiprows=4, header=True)
# show shape
dfprem_wages.shape
Out[14]:
In [15]:
dfprem_wages
Out[15]:
Create dataframe of league table for 2014-2015¶
Data source: http://www.sportsmole.co.uk/football/premier-league/2014-15/table.html
In [16]:
PREM_TABLE_CSV_FILE = 'data\prem_table_2014-2015.csv'
dfprem_table_2014_2015 = pd.read_csv(PREM_TABLE_CSV_FILE, skiprows=2, header=True)
# show shape
dfprem_table_2014_2015.shape
Out[16]:
In [17]:
dfprem_table_2014_2015.head(6)
Out[17]:
In [18]:
dfprem_table_2014_2015.tail()
Out[18]:
Create dataframe of Deloiite Football Money League¶
Data source: Deloitte Football Money League
The revenue (in pounds) is in the detailed report.
In [19]:
# note that the revenue figures are for 2014
deloitte_review_2015 = {'Team': ['Manchester United', 'Manchester City', 'Chelsea', 'Arsenal', 'Liverpool', 'Tottenham Hotspur'],
'Revenue': [433.2, 346.5, 324.4, 300.5, 255.8, 180.5]}
df_revenue = pd.DataFrame(data=deloitte_review_2015, columns=['Team', 'Revenue'])
df_revenue = df_revenue.set_index('Team')
In [20]:
df_revenue
Out[20]:
Analyse the data¶
Ask a question and find the answer!
Show ins and outs for 2015-2016
In [21]:
dflfc_transfers[dflfc_transfers.Season == '2015-2016']
Out[21]:
What was biggest fee paid for a Liverpool player over the data period?
In [22]:
dflfc_transfers_in = dflfc_transfers[dflfc_transfers.Direction == 'In']
dflfc_transfers_in[dflfc_transfers_in.Fee == dflfc_transfers_in.Fee.max()]
Out[22]:
What was biggest fee received for a player?
In [23]:
dflfc_transfers_out = dflfc_transfers[dflfc_transfers.Direction == 'Out']
dflfc_transfers_out[dflfc_transfers_out.Fee == dflfc_transfers_out.Fee.max()]
Out[23]:
Summarise Fees In and Out
In [24]:
dflfc_transfers.groupby(['Season', 'Direction']).sum()
Out[24]:
Show as 'unstacked' dataframe
In [25]:
df_fsg = dflfc_transfers.groupby(['Season', 'Direction']).sum().unstack()
df_fsg.columns = df_fsg.columns.droplevel()
del df_fsg.columns.name
df_fsg
Out[25]:
In [27]:
df_fsg['NetSpend'] = df_fsg.In - df_fsg.Out
df_fsg
Out[27]:
Calculate total Fees and NetSpend, over the 6 seasons
In [28]:
df_fsg.sum()
Out[28]:
Calculate average (mean) Fees and NetSpend, over the 6 seasons
In [29]:
df_fsg.mean()
Out[29]:
What is the the average Net Spend over last 5 seasons?
In [30]:
df_fsg['2011-2012':'2015-2016'].NetSpend.mean()
Out[30]:
Where do most players come from?
In [31]:
dflfc_transfers_in.Club.value_counts().head()
Out[31]:
Where do most players go to?
In [32]:
dflfc_transfers_out.Club.value_counts().head()
Out[32]:
Which players were bought for more than £15M?
In [33]:
dflfc_transfers_in[dflfc_transfers_in.Fee >= 15]
Out[33]:
Which players were sold for more than £15M?
In [34]:
dflfc_transfers_out[dflfc_transfers_out.Fee >= 15]
Out[34]:
Plot FSG's LFC Transfers per Season for last 5 seasons (2011-12 to 2015-16)
Note that the analysis is carried out in September 2015. Therefore the 2015-2016 NetSpend does not include the January 2016 tranfer window.
In [35]:
df_fsg.ix['2011-2012':]
Out[35]:
In [36]:
avg_netspend = df_fsg.ix['2011-2012':].NetSpend.mean()
print 'Average Transfer Net Spend per Season is £{}M'.format(round(avg_netspend, 1))
ax = df_fsg.ix['2011-2012':].plot(kind='bar', figsize=(12, 9), color=('r', 'y', 'b'), legend=False)
plt.axhline(avg_netspend, color='b', linestyle='--')
ax.set_ylabel('Transfers in Pounds (Millions)')
ax.set_title("FSG's LFC Transfers per Season for the Last 5 Seasons\n(2011-2012 to 2015-2016)")
ax.text(-.4, 97, 'prepared by: @lfcsorted', fontsize=9)
# create legend
l1 = plt.Line2D([], [], linewidth=7, color='r')
l2 = plt.Line2D([], [], linewidth=7, color='y')
l3 = plt.Line2D([], [], linewidth=7, color='b')
l4 = plt.Line2D([], [], linewidth=1, color='b', linestyle='--')
labels = ['Transfers In', 'Transfers Out', 'Transfer Net Spend', 'Average Transfer Net Spend per Season']
ax.legend([l1, l2, l3, l4], labels, fancybox=True, shadow=True, framealpha=0.8, loc='upper left')
# plot and save current figure
fig = plt.gcf()
plt.show()
fig.savefig('FSGTransfersPerSeason2011-2015.png', bbox_inches='tight')
Now let's compare Liverpool's transfers to the other top 6 teams.
What is net spend per season for top 5 teams over last 5 seasons?
In [37]:
dfprem_transfers
Out[37]:
In [39]:
top6_2014_15 = [ 'Chelsea', 'Manchester City', 'Manchester United', 'Arsenal', 'Tottenham Hotspur', 'Liverpool']
df_prem_transfers_top6 = dfprem_transfers[['Team', 'PerSeasonSpend']][dfprem_transfers.Team.isin(top6_2014_15)]
df_prem_transfers_top6
Out[39]:
Plot Top 6 Premier League Teams Average Transfer Net Spend per Season for Last 5 Seasons
In [40]:
ax = df_prem_transfers_top6.plot(x='Team', kind='bar', figsize=(12, 9), color=('b', 'b', 'b', 'r', 'b', 'b'), legend=False)
ax.set_ylabel('Net Spend per Season in Pounds (Millions)')
ax.set_title("Top 6 Premier League Teams Average Transfer Net Spend per Season for Last 5 Seasons\n(2011-2012 to 2015-2016)")
plt.axhline(0, color='grey') # x axis at net spend = 0
ax.text(-0.45, -9, 'prepared by: @lfcsorted', fontsize=9)
# plot and save current figure
fig = plt.gcf()
plt.show()
fig.savefig('PremTransferSpendPerSeason2011-2015.png', bbox_inches='tight')
What is the revenue of top 6 premier league clubs?
In [42]:
df_revenue.head(6)
Out[42]:
Plot Top 6 English Premier League Teams by Revenue
In [43]:
ax = df_revenue.plot(kind='bar', figsize=(12, 9), color=('b', 'b', 'b', 'b', 'r', 'b'), legend=False)
ax.set_ylabel('Revenue in Pounds (Millions)')
ax.set_title('Top 6 English Premier League Teams by Revenue (2014)')
ax.text(4.45, 440, 'prepared by: @lfcsorted', fontsize=9)
# plot and save current figure
fig = plt.gcf()
plt.show()
fig.savefig('PremRevenue2014.png', bbox_inches='tight')
Let's now examine the age of the LIverpool transfers
First create new transfer dataframe with birthdate of player
In [44]:
dflfc_transfers.head()
Out[44]:
In [45]:
dflfc_transfers.shape
Out[45]:
In [46]:
dflfc_players.head()
Out[46]:
In [47]:
dflfc_transfers_with_dob = pd.DataFrame.merge(dflfc_transfers, dflfc_players, how='left')
dflfc_transfers_with_dob.shape
Out[47]:
In [48]:
dflfc_transfers_with_dob.head()
Out[48]:
In [49]:
dflfc_transfers_with_dob.dtypes
Out[49]:
In [50]:
# check to see if any Birthdates are missing
dflfc_transfers_with_dob.Birthdate.isnull().any()
Out[50]:
In [51]:
# show missing entries (these have been reported to lfchistory.net)
dflfc_transfers_with_dob[dflfc_transfers_with_dob.Birthdate.isnull()]
Out[51]:
In [52]:
# fill in mising data
dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Chris Mavinga', 'Country'] = 'France'
dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Chris Mavinga', 'Birthdate'] = pd.Timestamp('19910526')
dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Kristoffer Peterson', 'Country'] = 'Sweden'
dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Kristoffer Peterson', 'Birthdate'] = pd.Timestamp('19941128')
dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Player == 'Chris Mavinga') |
(dflfc_transfers_with_dob.Player == 'Kristoffer Peterson')]
Out[52]:
Add age at transfer date to the dataframe
In [53]:
def age_at(dob, this_date):
"""Return age in years at this_date for given date of birth.
Note that both dob and this_date are of type datetime."""
return round((this_date - dob).days/365.0, 1)
In [54]:
dflfc_transfers_with_dob['AgeAtTransfer'] = dflfc_transfers_with_dob.apply(lambda row: age_at(row.Birthdate, row.Date), axis=1)
dflfc_transfers_with_dob.tail()
Out[54]:
What is average age of incoming players by season?
Average age of all transfers in, including free
In [55]:
dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Direction == 'In') &
(dflfc_transfers_with_dob.Fee >= 0)]\
['AgeAtTransfer'].mean()
Out[55]:
Average age of all transfers in, excluding free
In [56]:
dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Direction == 'In') &
(dflfc_transfers_with_dob.Fee > 0)]\
['AgeAtTransfer'].mean()
Out[56]:
Average age of all transfers in, excluding free, by season
In [57]:
dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Direction == 'In') &
(dflfc_transfers_with_dob.Fee > 0)]\
[['Season', 'AgeAtTransfer']].groupby('Season').mean()
Out[57]:
Average age of all transfers in, including free, by season (rounded)
In [58]:
dflfc_transfers_with_dob[dflfc_transfers_with_dob.Direction == 'In']\
[['Season', 'AgeAtTransfer']].groupby('Season').agg(lambda x: round(x.mean(), 1))
Out[58]:
Plot age at transfer in
In [59]:
dflfc_transfers_with_dob[dflfc_transfers_with_dob.Direction == 'In']\
[['Season', 'AgeAtTransfer']].groupby('Season').agg(lambda x: round(x.mean(), 1)).plot(kind='bar', legend=False,
title='Age at Transfer In')
Out[59]:
What is average age of incoming and outgoing players by season
In [60]:
dflfc_transfers_with_dob[['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction'])\
.agg(lambda x: round(x.mean(), 1)).unstack()\
.plot(kind='bar', title='Age at Transfer', ylim=(0,40), yticks=range(0,35,5))
Out[60]:
Excluding free
In [61]:
dflfc_transfers_with_dob[dflfc_transfers_with_dob.Fee > 0][['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction'])\
.agg(lambda x: round(x.mean(), 1)).unstack()\
.plot(kind='bar', title='Age at Transfer (non-zero)', ylim=(0,40), yticks=range(0,35,5))
Out[61]:
In [62]:
dflfc_transfers_with_dob[['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction']).agg(lambda x: round(x.mean(), 1))
Out[62]:
In [63]:
dflfc_transfers_with_dob[dflfc_transfers_with_dob.Fee > 0][['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction']).agg(lambda x: round(x.mean(), 1))
Out[63]:
Note that Outs do not include players leaving at end of contract e.g. Gerrard or those retiring e.g. Carragher¶
Analyse age of starting line-ups over last 3 seasons
In [64]:
LFC_GAME1_CSV_FILE = 'data\lfc_pl_opening_games_aug2015.csv'
dflfc_openers = pd.read_csv(LFC_GAME1_CSV_FILE, parse_dates=['Date'])
# show shape
dflfc_openers.shape
Out[64]:
In [65]:
dflfc_openers.tail()
Out[65]:
Add age at opening game
In [66]:
dflfc_openers = pd.DataFrame.merge(dflfc_openers, dflfc_players[['Player', 'Birthdate']], how='left')
dflfc_openers.shape
Out[66]:
In [67]:
dflfc_openers.head()
Out[67]:
In [68]:
dflfc_openers['AgeAtOpener'] = dflfc_openers.apply(lambda row: age_at(row.Birthdate, row.Date), axis=1)
dflfc_openers.tail()
Out[68]:
Calculate average age of team
In [69]:
dflfc_openers[['Season', 'AgeAtOpener']].groupby('Season').agg(lambda x: round(x.mean(), 1))
Out[69]:
In [70]:
dflfc_openers[['Season', 'AgeAtOpener']].groupby('Season').agg(lambda x: round(x.mean(), 1)).plot(kind='bar', ylim=(24,28))
Out[70]:
In [71]:
dflfc_transfers_with_dob[dflfc_transfers_with_dob.Direction == 'In'][['Season', 'Player', 'Fee', 'AgeAtTransfer']]
Out[71]:
What is average age of team that finished againt Arsenal?
In [71]:
LFC_ARSENAL_CSV_FILE = 'data\lfc_pl_vs_Arsenal_aug2015.csv'
dflfc_arsenal = pd.read_csv(LFC_ARSENAL_CSV_FILE, parse_dates=['Date'], skiprows=2)
# show shape
dflfc_arsenal.shape
Out[71]:
In [72]:
dflfc_arsenal = pd.merge(dflfc_arsenal, dflfc_players[['Player', 'Birthdate']])
dflfc_arsenal
Out[72]:
In [73]:
dflfc_arsenal['AgeAtGame'] = dflfc_arsenal.apply(lambda row: age_at(row.Birthdate, row.Date), axis=1)
dflfc_arsenal
Out[73]:
In [74]:
dflfc_arsenal.mean()
Out[74]:
Compare team wage bills for 2014-15
In [75]:
dfprem_wages[['Team', '2014-2015']].head()
Out[75]:
In [76]:
dfprem_table_2014_2015[['Team', 'PTS']].head()
Out[76]:
In [77]:
dfprem_table_2014_2015_wages = pd.merge(dfprem_table_2014_2015[['Team', 'PTS']], dfprem_wages[['Team', '2014-2015']])
In [78]:
dfprem_table_2014_2015_wages
dfprem_table_2014_2015_wages.rename(columns={'2014-2015': 'WageBill','PTS': 'Points'}, inplace=True)
In [79]:
dfprem_table_2014_2015_wages.head()
Out[79]:
In [80]:
dfprem_table_2014_2015.Rank[dfprem_table_2014_2015.Team == 'Chelsea'].values[0]
Out[80]:
In [81]:
dfprem_table_2014_2015_wages.plot(kind='scatter', x='WageBill', y='Points')
Out[81]:
In [82]:
df = dfprem_table_2014_2015_wages.set_index('Team')
In [83]:
(a,b) = df.ix['Liverpool']
print a, b
In [84]:
# Ref: http://stackoverflow.com/questions/739241/date-ordinal-output
def n_plus_suffix(n):
"""Return n plus the suffix e.g. 1 becomes 1st, 2 becomes 2nd."""
assert isinstance(n, (int, long)), '{} is not an integer'.format(n)
if 10 <= n % 100 < 20:
return str(n) + 'th'
else:
return str(n) + {1 : 'st', 2 : 'nd', 3 : 'rd'}.get(n % 10, "th")
Plot Wage Bill vs Points, with Top 6 highlighted (2014-2015)
In [85]:
# calculate points total for 6th place
sixth_place_points = df.iloc[5].Points
# plot top 6 as blue circles
ax = df[df.Points >= sixth_place_points].plot(kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='b')
# plot others as black circles
df[df.Points < sixth_place_points].plot(ax=ax, kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='k')
# calculate Liverpool's points and plot as red circles
lfcpoints, lfcwage = df.ix['Liverpool']
ax.plot(lfcwage, lfcpoints, 'ro')
# add x and y labels etc
ax.set_xlabel('Wage Bill in Pounds (millions)')
ax.set_ylabel('Points')
ax.set_title('Wage Bill vs Points, with Top 6 highlighted (2014-2015)')
ax.text(2, 21, 'prepared by: @lfcsorted', fontsize=9)
# add text showing team and position for top 6
for team, (points, wagebill) in df.iterrows():
pos = int(dfprem_table_2014_2015.Rank[dfprem_table_2014_2015.Team == team].values[0])
team_pos = '{} ({})'.format(team, n_plus_suffix(pos))
if points >= sixth_place_points:
ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill-len(team)-3, points+1))
Prettify the plot - label all teams and highlight the top 4 and top 6 areas.
In [86]:
# list all of the teams
df.index.values
Out[86]:
In [87]:
# set position of text, default is centred on top of the circle
# note that posotionis decided by trial and error, to give clearest plot
TEAM_CL = ['Sunderland', 'West Ham United']
TEAM_CR = ['Stoke City', 'Aston Villa', 'Everton']
TEAM_CT = []
for team in df.index.values:
if team not in TEAM_CL + TEAM_CR:
TEAM_CT.append(team)
# calculate points total for 6th place
sixth_place_points = df.iloc[5].Points
# plot top 6 as blue circles
ax = df[df.Points >= sixth_place_points].plot(kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='b')
# plot others as black circles
df[df.Points < sixth_place_points].plot(ax=ax, kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='k')
# calculate Liverpool's points and plot as red circles
lfcpoints, lfcwage = df.ix['Liverpool']
ax.plot(lfcwage, lfcpoints, 'ro')
# add x and y labels etc
ax.set_xlabel('Wage Bill in Pounds (millions)')
ax.set_ylabel('Points')
ax.set_title('Wage Bill vs Points, with Top 6 highlighted (2014-2015)')
ax.text(2, 21, 'prepared by: @lfcsorted', fontsize=9)
# add text showing team and position
for team, (points, wagebill) in df.iterrows():
pos = int(dfprem_table_2014_2015.Rank[dfprem_table_2014_2015.Team == team].values[0])
team_pos = '{} ({})'.format(team, n_plus_suffix(pos))
if team in TEAM_CT:
ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill-len(team)-3, points+1))
elif team in TEAM_CR:
#print 'team cr: {}'.format(team)
ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill+2, points-0.5))
elif team in TEAM_CL:
#print 'team cl: {}'.format(team)
ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill-3*len(team)-6, points-0.5))
else:
raise('unexpected error')
# add key areas to the graph and label
from matplotlib.patches import Rectangle
XTEXT_OFFSET = 1
YTEXT_OFFSET = -2
# add top 4 area, with annotation in top left hand corner of rectangle
top4rect_bl_x = df[0:4].WageBill.min() # bottom left x co-ord of rectangle
top4rect_bl_y = df[0:4].Points.min() # bottom left y co-ord of rectangle
top4rect_width = df[0:4].WageBill.max() - top4rect_bl_x # width of rectangle
top4rect_height = df[0:4].Points.max() - top4rect_bl_y # height of rectangle
top4rect_tl_x = df[0:4].WageBill.min() # top left x co-ord for annotation
top4rect_tl_y = df[0:4].Points.max() # top left y co-ord for annotation
top4rect_xtext = top4rect_tl_x + XTEXT_OFFSET # text x co-ord for annotation
top4rect_ytext = top4rect_tl_y + YTEXT_OFFSET # text y co-ord for annotation
ax.add_patch(Rectangle((top4rect_bl_x, top4rect_bl_y), top4rect_width, top4rect_height, facecolor="blue", alpha=0.2))
ax.annotate(s='Top 4 area', xy=(top4rect_tl_x, top4rect_tl_y), xytext=(top4rect_xtext, top4rect_ytext), color='blue')
# add top 6 area, with annotation in top left hand corner of rectangle
top6rect_bl_x = df[0:6].WageBill.min() # bottom left x co-ord of rectangle
top6rect_bl_y = df[0:6].Points.min() # bottom left y co-ord of rectangle
top6rect_width = df[0:6].WageBill.max() - top6rect_bl_x # width of rectangle
top6rect_height = df[0:6].Points.max() - top6rect_bl_y # height of rectangle
top6rect_tl_x = df[0:6].WageBill.min() # top left x co-ord for annotation
top6rect_tl_y = df[0:6].Points.max() # top left y co-ord for annotation
top6rect_xtext = top6rect_tl_x + XTEXT_OFFSET # text x co-ord for annotation
top6rect_ytext = top6rect_tl_y + YTEXT_OFFSET # text y co-ord for annotation
ax.add_patch(Rectangle((top6rect_bl_x, top6rect_bl_y), top6rect_width, top6rect_height, facecolor="lightblue", alpha=0.2))
ax.annotate(s='Top 6 area', xy=(top6rect_tl_x, top6rect_tl_y), xytext=(top6rect_xtext, top6rect_ytext), color='blue', alpha=0.7)
# plot and save current figure
fig = plt.gcf()
plt.show()
fig.savefig('PremWageBillvsPoints2014-2015.png', bbox_inches='tight')
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