{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LFC Data Analysis: The Transfers" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "See Terry's blog [Inspiring Transfers](http://terrydolan.blogspot.co.uk/2015/09/inspiring-transfers.html) for a discussion of the data analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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.\n", "\n", "The analysis uses [IPython Notebook](http://ipython.org/notebook.html), [python](https://www.python.org/), [pandas](http://pandas.pydata.org/) and [matplotlib](http://matplotlib.org/) to explore the data. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set-up" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the modules needed for the analysis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import sys\n", "import collections\n", "from datetime import datetime\n", "from __future__ import division\n", "\n", "# enable inline plotting\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print version numbers." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "python version: 2.7.8 |Anaconda 2.3.0 (64-bit)| (default, Jul 2 2014, 15:12:11) [MSC v.1500 64 bit (AMD64)]\n", "pandas version: 0.16.2\n", "matplotlib version: 1.4.3\n", "numpy version: 1.9.2\n" ] } ], "source": [ "print 'python version: {}'.format(sys.version)\n", "print 'pandas version: {}'.format(pd.__version__)\n", "print 'matplotlib version: {}'.format(mpl.__version__)\n", "print 'numpy version: {}'.format(np.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the data into a dataframes and munge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create dataframe of LFC transfers\n", "Data source: lfchistory.net\n", "\n", "Note that all transfers are shown for FSG's ownership, from Oct 2010 to September 2015. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(90, 6)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LFC_TRANSFERS_CSV_FILE = 'data\\lfc_transfers_2010-2011_2015-2016.csv'\n", "dflfc_transfers = pd.read_csv(LFC_TRANSFERS_CSV_FILE, parse_dates=['Date'])\n", "\n", "# convert Fee to millions of pounds\n", "dflfc_transfers.Fee = dflfc_transfers.Fee/1000000\n", "\n", "# show shape\n", "dflfc_transfers.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDate
02010-2011InLuis SuarezAjax22.82011-01-31
12010-2011InAndy CarrollNewcastle United35.02011-01-31
22010-2011OutCharles ItandjeAtromitos0.02011-01-01
32010-2011OutRyan BabelHoffenheim5.82011-01-25
42010-2011OutFernando TorresChelsea50.02011-01-31
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee Date\n", "0 2010-2011 In Luis Suarez Ajax 22.8 2011-01-31\n", "1 2010-2011 In Andy Carroll Newcastle United 35.0 2011-01-31\n", "2 2010-2011 Out Charles Itandje Atromitos 0.0 2011-01-01\n", "3 2010-2011 Out Ryan Babel Hoffenheim 5.8 2011-01-25\n", "4 2010-2011 Out Fernando Torres Chelsea 50.0 2011-01-31" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDate
852015-2016OutSebastian CoatesSunderland4.02015-07-01
862015-2016OutIago AspasSevilla4.42015-06-12
872015-2016OutRaheem SterlingManchester City49.02015-07-14
882015-2016OutRickie LambertWest Bromwich Albion3.02015-07-31
892015-2016OutFabio BoriniSunderland10.02015-08-31
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee \\\n", "85 2015-2016 Out Sebastian Coates Sunderland 4.0 \n", "86 2015-2016 Out Iago Aspas Sevilla 4.4 \n", "87 2015-2016 Out Raheem Sterling Manchester City 49.0 \n", "88 2015-2016 Out Rickie Lambert West Bromwich Albion 3.0 \n", "89 2015-2016 Out Fabio Borini Sunderland 10.0 \n", "\n", " Date \n", "85 2015-07-01 \n", "86 2015-06-12 \n", "87 2015-07-14 \n", "88 2015-07-31 \n", "89 2015-08-31 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers.tail()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Season object\n", "Direction object\n", "Player object\n", "Club object\n", "Fee float64\n", "Date datetime64[ns]\n", "dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check Date (of transfer) column is datetime data type\n", "dflfc_transfers.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create dataframe of LFC players giving birthdate of each player\n", "Data source: lfchistory.net" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(756, 3)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LFC_PLAYERS_CSV_FILE = 'data\\lfc_players_september2015_upd.csv' \n", "dflfc_players = pd.read_csv(LFC_PLAYERS_CSV_FILE, parse_dates=['birthdate'])\n", "dflfc_players.rename(columns={'player': 'Player', 'birthdate': 'Birthdate', 'country': 'Country'}, inplace=True)\n", "dflfc_players.shape" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Player object\n", "Birthdate datetime64[ns]\n", "Country object\n", "dtype: object" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_players.dtypes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PlayerBirthdateCountry
0Gary Ablett1965-11-19England
1Alan A'Court1934-09-30England
2Charlie Adam1985-12-10Scotland
3Daniel Agger1984-12-12Denmark
4Andrew Aitken1909-08-25England
5Luis Alberto1992-09-28Spain
6John Aldridge1958-09-18England
7George Allan1875-08-23 00:00:00Scotland
8Joe Allen1990-03-14Wales
9Messina Allman1883-01-01 00:00:00England
\n", "
" ], "text/plain": [ " Player Birthdate Country\n", "0 Gary Ablett 1965-11-19 England\n", "1 Alan A'Court 1934-09-30 England\n", "2 Charlie Adam 1985-12-10 Scotland\n", "3 Daniel Agger 1984-12-12 Denmark\n", "4 Andrew Aitken 1909-08-25 England\n", "5 Luis Alberto 1992-09-28 Spain\n", "6 John Aldridge 1958-09-18 England\n", "7 George Allan 1875-08-23 00:00:00 Scotland\n", "8 Joe Allen 1990-03-14 Wales\n", "9 Messina Allman 1883-01-01 00:00:00 England" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_players.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create dataframe of premier league transfers over last 5 seasons\n", "Data source: transferleague.co.uk" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(20, 6)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "PREM_TRANSFERS_CSV_FILE = 'data\\prem_transfers_2011-2012_2015-2016.csv'\n", "dfprem_transfers = pd.read_csv(PREM_TRANSFERS_CSV_FILE, skiprows=2, header=True)\n", "\n", "# convert money to millions of pounds\n", "dfprem_transfers.Purchased = np.round(dfprem_transfers.Purchased/1000000, 1)\n", "dfprem_transfers.Sold = np.round(dfprem_transfers.Sold/1000000, 1)\n", "dfprem_transfers.NetSpend = np.round(dfprem_transfers.NetSpend/1000000, 1)\n", "dfprem_transfers.PerSeasonSpend = np.round(dfprem_transfers.PerSeasonSpend/1000000, 1)\n", "\n", "# show shape\n", "dfprem_transfers.shape" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PositionTeamPurchasedSoldNetSpendPerSeasonSpend
01Manchester City472.7150.3322.464.5
12Manchester United405.2139.5265.753.1
24Chelsea461.8246.4215.443.1
33Liverpool349.0186.4162.632.5
45Arsenal253.6154.699.019.8
\n", "
" ], "text/plain": [ " Position Team Purchased Sold NetSpend PerSeasonSpend\n", "0 1 Manchester City 472.7 150.3 322.4 64.5\n", "1 2 Manchester United 405.2 139.5 265.7 53.1\n", "2 4 Chelsea 461.8 246.4 215.4 43.1\n", "3 3 Liverpool 349.0 186.4 162.6 32.5\n", "4 5 Arsenal 253.6 154.6 99.0 19.8" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfprem_transfers.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PositionTeamPurchasedSoldNetSpendPerSeasonSpend
1516Southampton163.6142.820.84.2
1617Swansea79.966.213.72.7
1718Everton91.880.811.02.2
1819Watford12.015.3-3.2-0.6
1920Tottenham Hotspur254.6291.4-36.7-7.3
\n", "
" ], "text/plain": [ " Position Team Purchased Sold NetSpend PerSeasonSpend\n", "15 16 Southampton 163.6 142.8 20.8 4.2\n", "16 17 Swansea 79.9 66.2 13.7 2.7\n", "17 18 Everton 91.8 80.8 11.0 2.2\n", "18 19 Watford 12.0 15.3 -3.2 -0.6\n", "19 20 Tottenham Hotspur 254.6 291.4 -36.7 -7.3" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfprem_transfers.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create dataframe of premier league wage bill over last 5 seasons\n", "Data source: guardian.co.uk etc" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(20, 5)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "PREM_WAGES_CSV_FILE = 'data\\prem_wages_2012-2013_2014-2015.csv'\n", "dfprem_wages = pd.read_csv(PREM_WAGES_CSV_FILE, skiprows=4, header=True)\n", "\n", "# show shape\n", "dfprem_wages.shape" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RankTeam2012-20132013-20142014-2015
01Manchester United181187.0215.8
12Manchester City233216.0205.0
23Chelsea176190.0192.7
34Arsenal154166.4166.4
45Liverpool132140.0144.0
56Tottenham Hotspur96112.0100.4
67Newcastle United6268.078.3
712Queens Park Rangers78NaN75.3
810Sunderland5855.069.5
99Everton6366.069.3
108Aston Villa7274.069.3
1115West Bromwich Albion5449.065.4
1214West Ham United5660.063.9
1316Stoke City6255.060.6
1411Southampton4747.055.2
1513Swansea City4955.048.1
1617Hull CityNaNNaN43.3
1718Crystal PalaceNaNNaN45.7
1819Leicester CityNaNNaN36.6
1920BurnleyNaNNaN21.5
\n", "
" ], "text/plain": [ " Rank Team 2012-2013 2013-2014 2014-2015\n", "0 1 Manchester United 181 187.0 215.8\n", "1 2 Manchester City 233 216.0 205.0\n", "2 3 Chelsea 176 190.0 192.7\n", "3 4 Arsenal 154 166.4 166.4\n", "4 5 Liverpool 132 140.0 144.0\n", "5 6 Tottenham Hotspur 96 112.0 100.4\n", "6 7 Newcastle United 62 68.0 78.3\n", "7 12 Queens Park Rangers 78 NaN 75.3\n", "8 10 Sunderland 58 55.0 69.5\n", "9 9 Everton 63 66.0 69.3\n", "10 8 Aston Villa 72 74.0 69.3\n", "11 15 West Bromwich Albion 54 49.0 65.4\n", "12 14 West Ham United 56 60.0 63.9\n", "13 16 Stoke City 62 55.0 60.6\n", "14 11 Southampton 47 47.0 55.2\n", "15 13 Swansea City 49 55.0 48.1\n", "16 17 Hull City NaN NaN 43.3\n", "17 18 Crystal Palace NaN NaN 45.7\n", "18 19 Leicester City NaN NaN 36.6\n", "19 20 Burnley NaN NaN 21.5" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfprem_wages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create dataframe of league table for 2014-2015\n", "Data source: http://www.sportsmole.co.uk/football/premier-league/2014-15/table.html" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(20, 20)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "PREM_TABLE_CSV_FILE = 'data\\prem_table_2014-2015.csv'\n", "dfprem_table_2014_2015 = pd.read_csv(PREM_TABLE_CSV_FILE, skiprows=2, header=True)\n", "\n", "# show shape\n", "dfprem_table_2014_2015.shape" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RankTeamPHWHDHLHFHAAWADALAFAATWTDTLGFGAGDPTS
01Chelsea38154036911533723269373324187
12Manchester City381432441410453924247783384579
23Arsenal381252411410453022229771363575
34Manchester United381423411568521222010862372570
45Tottenham Hotspur38103631249462729197125853564
56Liverpool38105430208382228188125248462
\n", "
" ], "text/plain": [ " Rank Team P HW HD HL HF HA AW AD AL AF AA TW \\\n", "0 1 Chelsea 38 15 4 0 36 9 11 5 3 37 23 26 \n", "1 2 Manchester City 38 14 3 2 44 14 10 4 5 39 24 24 \n", "2 3 Arsenal 38 12 5 2 41 14 10 4 5 30 22 22 \n", "3 4 Manchester United 38 14 2 3 41 15 6 8 5 21 22 20 \n", "4 5 Tottenham Hotspur 38 10 3 6 31 24 9 4 6 27 29 19 \n", "5 6 Liverpool 38 10 5 4 30 20 8 3 8 22 28 18 \n", "\n", " TD TL GF GA GD PTS \n", "0 9 3 73 32 41 87 \n", "1 7 7 83 38 45 79 \n", "2 9 7 71 36 35 75 \n", "3 10 8 62 37 25 70 \n", "4 7 12 58 53 5 64 \n", "5 8 12 52 48 4 62 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfprem_table_2014_2015.head(6)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RankTeamPHWHDHLHFHAAWADALAFAATWTDTLGFGAGDPTS
1516Sunderland3848716273971526717143153-2238
1617Aston Villa38568182552121332108203157-2638
1718Hull City38559192436101427811193351-1835
1819Burnley38478142135111432712192853-2533
1920Queens Park Rangers3865823242116194986244273-3130
\n", "
" ], "text/plain": [ " Rank Team P HW HD HL HF HA AW AD AL AF AA TW \\\n", "15 16 Sunderland 38 4 8 7 16 27 3 9 7 15 26 7 \n", "16 17 Aston Villa 38 5 6 8 18 25 5 2 12 13 32 10 \n", "17 18 Hull City 38 5 5 9 19 24 3 6 10 14 27 8 \n", "18 19 Burnley 38 4 7 8 14 21 3 5 11 14 32 7 \n", "19 20 Queens Park Rangers 38 6 5 8 23 24 2 1 16 19 49 8 \n", "\n", " TD TL GF GA GD PTS \n", "15 17 14 31 53 -22 38 \n", "16 8 20 31 57 -26 38 \n", "17 11 19 33 51 -18 35 \n", "18 12 19 28 53 -25 33 \n", "19 6 24 42 73 -31 30 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfprem_table_2014_2015.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create dataframe of Deloiite Football Money League\n", "Data source: [Deloitte Football Money League](http://www2.deloitte.com/uk/en/pages/sports-business-group/articles/deloitte-football-money-league.html)\n", "\n", "The revenue (in pounds) is in the detailed report." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# note that the revenue figures are for 2014\n", "deloitte_review_2015 = {'Team': ['Manchester United', 'Manchester City', 'Chelsea', 'Arsenal', 'Liverpool', 'Tottenham Hotspur'],\n", " 'Revenue': [433.2, 346.5, 324.4, 300.5, 255.8, 180.5]}\n", "df_revenue = pd.DataFrame(data=deloitte_review_2015, columns=['Team', 'Revenue'])\n", "df_revenue = df_revenue.set_index('Team')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Revenue
Team
Manchester United433.2
Manchester City346.5
Chelsea324.4
Arsenal300.5
Liverpool255.8
Tottenham Hotspur180.5
\n", "
" ], "text/plain": [ " Revenue\n", "Team \n", "Manchester United 433.2\n", "Manchester City 346.5\n", "Chelsea 324.4\n", "Arsenal 300.5\n", "Liverpool 255.8\n", "Tottenham Hotspur 180.5" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_revenue" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse the data\n", "\n", "Ask a question and find the answer!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show ins and outs for 2015-2016" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDate
782015-2016InAdam BogdanFree Transfer0.02015-07-01
792015-2016InDanny IngsTribunal (estimate)6.02015-07-01
802015-2016InJames MilnerFree Transfer0.02015-07-01
812015-2016InJoe GomezCharlton Athletic6.02015-06-20
822015-2016InNathaniel ClyneSouthampton12.52015-07-01
832015-2016InRoberto FirminoHoffenheim29.02015-07-06
842015-2016InChristian BentekeAston Villa32.52015-07-22
852015-2016OutSebastian CoatesSunderland4.02015-07-01
862015-2016OutIago AspasSevilla4.42015-06-12
872015-2016OutRaheem SterlingManchester City49.02015-07-14
882015-2016OutRickie LambertWest Bromwich Albion3.02015-07-31
892015-2016OutFabio BoriniSunderland10.02015-08-31
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee \\\n", "78 2015-2016 In Adam Bogdan Free Transfer 0.0 \n", "79 2015-2016 In Danny Ings Tribunal (estimate) 6.0 \n", "80 2015-2016 In James Milner Free Transfer 0.0 \n", "81 2015-2016 In Joe Gomez Charlton Athletic 6.0 \n", "82 2015-2016 In Nathaniel Clyne Southampton 12.5 \n", "83 2015-2016 In Roberto Firmino Hoffenheim 29.0 \n", "84 2015-2016 In Christian Benteke Aston Villa 32.5 \n", "85 2015-2016 Out Sebastian Coates Sunderland 4.0 \n", "86 2015-2016 Out Iago Aspas Sevilla 4.4 \n", "87 2015-2016 Out Raheem Sterling Manchester City 49.0 \n", "88 2015-2016 Out Rickie Lambert West Bromwich Albion 3.0 \n", "89 2015-2016 Out Fabio Borini Sunderland 10.0 \n", "\n", " Date \n", "78 2015-07-01 \n", "79 2015-07-01 \n", "80 2015-07-01 \n", "81 2015-06-20 \n", "82 2015-07-01 \n", "83 2015-07-06 \n", "84 2015-07-22 \n", "85 2015-07-01 \n", "86 2015-06-12 \n", "87 2015-07-14 \n", "88 2015-07-31 \n", "89 2015-08-31 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers[dflfc_transfers.Season == '2015-2016']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What was biggest fee paid for a Liverpool player over the data period?" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDate
12010-2011InAndy CarrollNewcastle United352011-01-31
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee Date\n", "1 2010-2011 In Andy Carroll Newcastle United 35 2011-01-31" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_in = dflfc_transfers[dflfc_transfers.Direction == 'In']\n", "dflfc_transfers_in[dflfc_transfers_in.Fee == dflfc_transfers_in.Fee.max()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What was biggest fee received for a player?" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDate
772014-2015OutLuis SuarezBarcelona752014-07-16
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee Date\n", "77 2014-2015 Out Luis Suarez Barcelona 75 2014-07-16" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_out = dflfc_transfers[dflfc_transfers.Direction == 'Out']\n", "dflfc_transfers_out[dflfc_transfers_out.Fee == dflfc_transfers_out.Fee.max()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summarise Fees In and Out" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Fee
SeasonDirection
2010-2011In57.80
Out55.80
2011-2012In53.48
Out24.30
2012-2013In49.90
Out15.50
2013-2014In44.80
Out28.70
2014-2015In116.85
Out88.62
2015-2016In86.00
Out70.40
\n", "
" ], "text/plain": [ " Fee\n", "Season Direction \n", "2010-2011 In 57.80\n", " Out 55.80\n", "2011-2012 In 53.48\n", " Out 24.30\n", "2012-2013 In 49.90\n", " Out 15.50\n", "2013-2014 In 44.80\n", " Out 28.70\n", "2014-2015 In 116.85\n", " Out 88.62\n", "2015-2016 In 86.00\n", " Out 70.40" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers.groupby(['Season', 'Direction']).sum()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Show as 'unstacked' dataframe" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
InOut
Season
2010-201157.8055.80
2011-201253.4824.30
2012-201349.9015.50
2013-201444.8028.70
2014-2015116.8588.62
2015-201686.0070.40
\n", "
" ], "text/plain": [ " In Out\n", "Season \n", "2010-2011 57.80 55.80\n", "2011-2012 53.48 24.30\n", "2012-2013 49.90 15.50\n", "2013-2014 44.80 28.70\n", "2014-2015 116.85 88.62\n", "2015-2016 86.00 70.40" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_fsg = dflfc_transfers.groupby(['Season', 'Direction']).sum().unstack()\n", "df_fsg.columns = df_fsg.columns.droplevel()\n", "del df_fsg.columns.name\n", "df_fsg" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Calculate the net spend for the data period, Oct 2011 to Aug 2015" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
InOutNetSpend
Season
2010-201157.8055.802.00
2011-201253.4824.3029.18
2012-201349.9015.5034.40
2013-201444.8028.7016.10
2014-2015116.8588.6228.23
2015-201686.0070.4015.60
\n", "
" ], "text/plain": [ " In Out NetSpend\n", "Season \n", "2010-2011 57.80 55.80 2.00\n", "2011-2012 53.48 24.30 29.18\n", "2012-2013 49.90 15.50 34.40\n", "2013-2014 44.80 28.70 16.10\n", "2014-2015 116.85 88.62 28.23\n", "2015-2016 86.00 70.40 15.60" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_fsg['NetSpend'] = df_fsg.In - df_fsg.Out\n", "df_fsg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate total Fees and NetSpend, over the 6 seasons" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "In 408.83\n", "Out 283.32\n", "NetSpend 125.51\n", "dtype: float64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_fsg.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate average (mean) Fees and NetSpend, over the 6 seasons" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "In 68.138333\n", "Out 47.220000\n", "NetSpend 20.918333\n", "dtype: float64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_fsg.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the the average Net Spend over last 5 seasons?" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "24.701999999999995" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_fsg['2011-2012':'2015-2016'].NetSpend.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where do most players come from?" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Southampton 4\n", "Free Transfer 3\n", "Roma 2\n", "Newcastle United 2\n", "Sunderland 2\n", "dtype: int64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_in.Club.value_counts().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where do most players go to?" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Free Transfer 5\n", "West Ham United 3\n", "Bolton Wanderers 2\n", "Chelsea 2\n", "Blackpool 2\n", "dtype: int64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_out.Club.value_counts().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which players were bought for more than £15M?" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDate
02010-2011InLuis SuarezAjax22.82011-01-31
12010-2011InAndy CarrollNewcastle United35.02011-01-31
122011-2012InJordan HendersonSunderland16.02011-06-09
132011-2012InStewart DowningAston Villa18.52011-07-15
322012-2013InJoe AllenSwansea City15.02012-08-10
522013-2014InMamadou SakhoParis St Germain15.02013-09-02
652014-2015InMario BalotelliAC Milan16.02014-08-25
662014-2015InLazar MarkovicBenfica19.82014-07-15
672014-2015InDejan LovrenSouthampton20.02014-07-27
682014-2015InAdam LallanaSouthampton25.02014-07-01
832015-2016InRoberto FirminoHoffenheim29.02015-07-06
842015-2016InChristian BentekeAston Villa32.52015-07-22
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee Date\n", "0 2010-2011 In Luis Suarez Ajax 22.8 2011-01-31\n", "1 2010-2011 In Andy Carroll Newcastle United 35.0 2011-01-31\n", "12 2011-2012 In Jordan Henderson Sunderland 16.0 2011-06-09\n", "13 2011-2012 In Stewart Downing Aston Villa 18.5 2011-07-15\n", "32 2012-2013 In Joe Allen Swansea City 15.0 2012-08-10\n", "52 2013-2014 In Mamadou Sakho Paris St Germain 15.0 2013-09-02\n", "65 2014-2015 In Mario Balotelli AC Milan 16.0 2014-08-25\n", "66 2014-2015 In Lazar Markovic Benfica 19.8 2014-07-15\n", "67 2014-2015 In Dejan Lovren Southampton 20.0 2014-07-27\n", "68 2014-2015 In Adam Lallana Southampton 25.0 2014-07-01\n", "83 2015-2016 In Roberto Firmino Hoffenheim 29.0 2015-07-06\n", "84 2015-2016 In Christian Benteke Aston Villa 32.5 2015-07-22" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_in[dflfc_transfers_in.Fee >= 15]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which players were sold for more than £15M?" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDate
42010-2011OutFernando TorresChelsea502011-01-31
592013-2014OutAndy CarrollWest Ham United152013-06-19
772014-2015OutLuis SuarezBarcelona752014-07-16
872015-2016OutRaheem SterlingManchester City492015-07-14
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee Date\n", "4 2010-2011 Out Fernando Torres Chelsea 50 2011-01-31\n", "59 2013-2014 Out Andy Carroll West Ham United 15 2013-06-19\n", "77 2014-2015 Out Luis Suarez Barcelona 75 2014-07-16\n", "87 2015-2016 Out Raheem Sterling Manchester City 49 2015-07-14" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_out[dflfc_transfers_out.Fee >= 15]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot FSG's LFC Transfers per Season for last 5 seasons (2011-12 to 2015-16)\n", "\n", "Note that the analysis is carried out in September 2015. Therefore the 2015-2016 NetSpend does not include the January 2016 tranfer window." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
InOutNetSpend
Season
2011-201253.4824.3029.18
2012-201349.9015.5034.40
2013-201444.8028.7016.10
2014-2015116.8588.6228.23
2015-201686.0070.4015.60
\n", "
" ], "text/plain": [ " In Out NetSpend\n", "Season \n", "2011-2012 53.48 24.30 29.18\n", "2012-2013 49.90 15.50 34.40\n", "2013-2014 44.80 28.70 16.10\n", "2014-2015 116.85 88.62 28.23\n", "2015-2016 86.00 70.40 15.60" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_fsg.ix['2011-2012':]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average Transfer Net Spend per Season is £24.7M\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAJqCAYAAAACBnePAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_netspend = df_fsg.ix['2011-2012':].NetSpend.mean()\n", "print 'Average Transfer Net Spend per Season is £{}M'.format(round(avg_netspend, 1))\n", "ax = df_fsg.ix['2011-2012':].plot(kind='bar', figsize=(12, 9), color=('r', 'y', 'b'), legend=False)\n", "plt.axhline(avg_netspend, color='b', linestyle='--')\n", "ax.set_ylabel('Transfers in Pounds (Millions)')\n", "ax.set_title(\"FSG's LFC Transfers per Season for the Last 5 Seasons\\n(2011-2012 to 2015-2016)\")\n", "ax.text(-.4, 97, 'prepared by: @lfcsorted', fontsize=9)\n", "\n", "# create legend\n", "l1 = plt.Line2D([], [], linewidth=7, color='r')\n", "l2 = plt.Line2D([], [], linewidth=7, color='y')\n", "l3 = plt.Line2D([], [], linewidth=7, color='b')\n", "l4 = plt.Line2D([], [], linewidth=1, color='b', linestyle='--')\n", "labels = ['Transfers In', 'Transfers Out', 'Transfer Net Spend', 'Average Transfer Net Spend per Season']\n", "ax.legend([l1, l2, l3, l4], labels, fancybox=True, shadow=True, framealpha=0.8, loc='upper left')\n", "\n", "# plot and save current figure\n", "fig = plt.gcf() \n", "plt.show()\n", "fig.savefig('FSGTransfersPerSeason2011-2015.png', bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's compare Liverpool's transfers to the other top 6 teams." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is net spend per season for top 5 teams over last 5 seasons?" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PositionTeamPurchasedSoldNetSpendPerSeasonSpend
01Manchester City472.7150.3322.464.5
12Manchester United405.2139.5265.753.1
24Chelsea461.8246.4215.443.1
33Liverpool349.0186.4162.632.5
45Arsenal253.6154.699.019.8
56West Ham106.916.590.418.1
67Newcastle136.067.668.413.7
78Sunderland118.459.658.911.8
89West Bromwich Albion74.822.552.210.4
914Crystal Palace74.623.950.710.1
1010Leicester55.18.646.49.3
1111Stoke City70.224.146.29.2
1215Norwich City63.619.643.98.8
1312Aston Villa121.890.231.66.3
1415Bournemouth30.63.227.45.5
1516Southampton163.6142.820.84.2
1617Swansea79.966.213.72.7
1718Everton91.880.811.02.2
1819Watford12.015.3-3.2-0.6
1920Tottenham Hotspur254.6291.4-36.7-7.3
\n", "
" ], "text/plain": [ " Position Team Purchased Sold NetSpend PerSeasonSpend\n", "0 1 Manchester City 472.7 150.3 322.4 64.5\n", "1 2 Manchester United 405.2 139.5 265.7 53.1\n", "2 4 Chelsea 461.8 246.4 215.4 43.1\n", "3 3 Liverpool 349.0 186.4 162.6 32.5\n", "4 5 Arsenal 253.6 154.6 99.0 19.8\n", "5 6 West Ham 106.9 16.5 90.4 18.1\n", "6 7 Newcastle 136.0 67.6 68.4 13.7\n", "7 8 Sunderland 118.4 59.6 58.9 11.8\n", "8 9 West Bromwich Albion 74.8 22.5 52.2 10.4\n", "9 14 Crystal Palace 74.6 23.9 50.7 10.1\n", "10 10 Leicester 55.1 8.6 46.4 9.3\n", "11 11 Stoke City 70.2 24.1 46.2 9.2\n", "12 15 Norwich City 63.6 19.6 43.9 8.8\n", "13 12 Aston Villa 121.8 90.2 31.6 6.3\n", "14 15 Bournemouth 30.6 3.2 27.4 5.5\n", "15 16 Southampton 163.6 142.8 20.8 4.2\n", "16 17 Swansea 79.9 66.2 13.7 2.7\n", "17 18 Everton 91.8 80.8 11.0 2.2\n", "18 19 Watford 12.0 15.3 -3.2 -0.6\n", "19 20 Tottenham Hotspur 254.6 291.4 -36.7 -7.3" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfprem_transfers" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TeamPerSeasonSpend
0Manchester City64.5
1Manchester United53.1
2Chelsea43.1
3Liverpool32.5
4Arsenal19.8
19Tottenham Hotspur-7.3
\n", "
" ], "text/plain": [ " Team PerSeasonSpend\n", "0 Manchester City 64.5\n", "1 Manchester United 53.1\n", "2 Chelsea 43.1\n", "3 Liverpool 32.5\n", "4 Arsenal 19.8\n", "19 Tottenham Hotspur -7.3" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top6_2014_15 = [ 'Chelsea', 'Manchester City', 'Manchester United', 'Arsenal', 'Tottenham Hotspur', 'Liverpool']\n", "df_prem_transfers_top6 = dfprem_transfers[['Team', 'PerSeasonSpend']][dfprem_transfers.Team.isin(top6_2014_15)]\n", "df_prem_transfers_top6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot Top 6 Premier League Teams Average Transfer Net Spend per Season for Last 5 Seasons" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAKUCAYAAAApa9/bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df_prem_transfers_top6.plot(x='Team', kind='bar', figsize=(12, 9), color=('b', 'b', 'b', 'r', 'b', 'b'), legend=False)\n", "ax.set_ylabel('Net Spend per Season in Pounds (Millions)')\n", "ax.set_title(\"Top 6 Premier League Teams Average Transfer Net Spend per Season for Last 5 Seasons\\n(2011-2012 to 2015-2016)\")\n", "plt.axhline(0, color='grey') # x axis at net spend = 0\n", "ax.text(-0.45, -9, 'prepared by: @lfcsorted', fontsize=9)\n", "\n", "# plot and save current figure\n", "fig = plt.gcf() \n", "plt.show()\n", "fig.savefig('PremTransferSpendPerSeason2011-2015.png', bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the revenue of top 6 premier league clubs?" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Revenue
Team
Manchester United433.2
Manchester City346.5
Chelsea324.4
Arsenal300.5
Liverpool255.8
Tottenham Hotspur180.5
\n", "
" ], "text/plain": [ " Revenue\n", "Team \n", "Manchester United 433.2\n", "Manchester City 346.5\n", "Chelsea 324.4\n", "Arsenal 300.5\n", "Liverpool 255.8\n", "Tottenham Hotspur 180.5" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_revenue.head(6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot Top 6 English Premier League Teams by Revenue" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAKFCAYAAADlFN7YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df_revenue.plot(kind='bar', figsize=(12, 9), color=('b', 'b', 'b', 'b', 'r', 'b'), legend=False)\n", "ax.set_ylabel('Revenue in Pounds (Millions)')\n", "ax.set_title('Top 6 English Premier League Teams by Revenue (2014)')\n", "ax.text(4.45, 440, 'prepared by: @lfcsorted', fontsize=9)\n", "\n", "# plot and save current figure\n", "fig = plt.gcf() \n", "plt.show()\n", "fig.savefig('PremRevenue2014.png', bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now examine the age of the LIverpool transfers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First create new transfer dataframe with birthdate of player" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDate
02010-2011InLuis SuarezAjax22.82011-01-31
12010-2011InAndy CarrollNewcastle United35.02011-01-31
22010-2011OutCharles ItandjeAtromitos0.02011-01-01
32010-2011OutRyan BabelHoffenheim5.82011-01-25
42010-2011OutFernando TorresChelsea50.02011-01-31
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee Date\n", "0 2010-2011 In Luis Suarez Ajax 22.8 2011-01-31\n", "1 2010-2011 In Andy Carroll Newcastle United 35.0 2011-01-31\n", "2 2010-2011 Out Charles Itandje Atromitos 0.0 2011-01-01\n", "3 2010-2011 Out Ryan Babel Hoffenheim 5.8 2011-01-25\n", "4 2010-2011 Out Fernando Torres Chelsea 50.0 2011-01-31" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers.head()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(90, 6)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers.shape" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PlayerBirthdateCountry
0Gary Ablett1965-11-19England
1Alan A'Court1934-09-30England
2Charlie Adam1985-12-10Scotland
3Daniel Agger1984-12-12Denmark
4Andrew Aitken1909-08-25England
\n", "
" ], "text/plain": [ " Player Birthdate Country\n", "0 Gary Ablett 1965-11-19 England\n", "1 Alan A'Court 1934-09-30 England\n", "2 Charlie Adam 1985-12-10 Scotland\n", "3 Daniel Agger 1984-12-12 Denmark\n", "4 Andrew Aitken 1909-08-25 England" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_players.head()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(90, 8)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob = pd.DataFrame.merge(dflfc_transfers, dflfc_players, how='left')\n", "dflfc_transfers_with_dob.shape" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDateBirthdateCountry
02010-2011InLuis SuarezAjax22.82011-01-311987-01-24Uruguay
12010-2011InAndy CarrollNewcastle United35.02011-01-311989-01-06England
22010-2011OutCharles ItandjeAtromitos0.02011-01-011982-11-02France
32010-2011OutRyan BabelHoffenheim5.82011-01-251986-12-19Netherlands
42010-2011OutFernando TorresChelsea50.02011-01-311984-03-20Spain
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee Date \\\n", "0 2010-2011 In Luis Suarez Ajax 22.8 2011-01-31 \n", "1 2010-2011 In Andy Carroll Newcastle United 35.0 2011-01-31 \n", "2 2010-2011 Out Charles Itandje Atromitos 0.0 2011-01-01 \n", "3 2010-2011 Out Ryan Babel Hoffenheim 5.8 2011-01-25 \n", "4 2010-2011 Out Fernando Torres Chelsea 50.0 2011-01-31 \n", "\n", " Birthdate Country \n", "0 1987-01-24 Uruguay \n", "1 1989-01-06 England \n", "2 1982-11-02 France \n", "3 1986-12-19 Netherlands \n", "4 1984-03-20 Spain " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob.head()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Season object\n", "Direction object\n", "Player object\n", "Club object\n", "Fee float64\n", "Date datetime64[ns]\n", "Birthdate datetime64[ns]\n", "Country object\n", "dtype: object" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob.dtypes" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check to see if any Birthdates are missing\n", "dflfc_transfers_with_dob.Birthdate.isnull().any()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDateBirthdateCountry
202011-2012OutChris MavingaRennes12011-07-19NaTNaN
692014-2015OutKristoffer PetersonUtrecht02014-08-27NaTNaN
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee Date \\\n", "20 2011-2012 Out Chris Mavinga Rennes 1 2011-07-19 \n", "69 2014-2015 Out Kristoffer Peterson Utrecht 0 2014-08-27 \n", "\n", " Birthdate Country \n", "20 NaT NaN \n", "69 NaT NaN " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show missing entries (these have been reported to lfchistory.net)\n", "dflfc_transfers_with_dob[dflfc_transfers_with_dob.Birthdate.isnull()]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDateBirthdateCountry
202011-2012OutChris MavingaRennes12011-07-191991-05-26France
692014-2015OutKristoffer PetersonUtrecht02014-08-271994-11-28Sweden
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee Date \\\n", "20 2011-2012 Out Chris Mavinga Rennes 1 2011-07-19 \n", "69 2014-2015 Out Kristoffer Peterson Utrecht 0 2014-08-27 \n", "\n", " Birthdate Country \n", "20 1991-05-26 France \n", "69 1994-11-28 Sweden " ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# fill in mising data\n", "dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Chris Mavinga', 'Country'] = 'France'\n", "dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Chris Mavinga', 'Birthdate'] = pd.Timestamp('19910526')\n", "dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Kristoffer Peterson', 'Country'] = 'Sweden'\n", "dflfc_transfers_with_dob.loc[dflfc_transfers_with_dob.Player == 'Kristoffer Peterson', 'Birthdate'] = pd.Timestamp('19941128')\n", "dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Player == 'Chris Mavinga') | \n", " (dflfc_transfers_with_dob.Player == 'Kristoffer Peterson')]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add age at transfer date to the dataframe" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def age_at(dob, this_date):\n", " \"\"\"Return age in years at this_date for given date of birth.\n", " \n", " Note that both dob and this_date are of type datetime.\"\"\"\n", " \n", " return round((this_date - dob).days/365.0, 1)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonDirectionPlayerClubFeeDateBirthdateCountryAgeAtTransfer
852015-2016OutSebastian CoatesSunderland4.02015-07-011990-10-07Uruguay24.7
862015-2016OutIago AspasSevilla4.42015-06-121987-08-01Spain27.9
872015-2016OutRaheem SterlingManchester City49.02015-07-141994-12-08Jamaica20.6
882015-2016OutRickie LambertWest Bromwich Albion3.02015-07-311982-02-16England33.5
892015-2016OutFabio BoriniSunderland10.02015-08-311991-03-29Italy24.4
\n", "
" ], "text/plain": [ " Season Direction Player Club Fee \\\n", "85 2015-2016 Out Sebastian Coates Sunderland 4.0 \n", "86 2015-2016 Out Iago Aspas Sevilla 4.4 \n", "87 2015-2016 Out Raheem Sterling Manchester City 49.0 \n", "88 2015-2016 Out Rickie Lambert West Bromwich Albion 3.0 \n", "89 2015-2016 Out Fabio Borini Sunderland 10.0 \n", "\n", " Date Birthdate Country AgeAtTransfer \n", "85 2015-07-01 1990-10-07 Uruguay 24.7 \n", "86 2015-06-12 1987-08-01 Spain 27.9 \n", "87 2015-07-14 1994-12-08 Jamaica 20.6 \n", "88 2015-07-31 1982-02-16 England 33.5 \n", "89 2015-08-31 1991-03-29 Italy 24.4 " ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob['AgeAtTransfer'] = dflfc_transfers_with_dob.apply(lambda row: age_at(row.Birthdate, row.Date), axis=1)\n", "dflfc_transfers_with_dob.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is average age of incoming players by season?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Average age of all transfers in, including free" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "23.749999999999996" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Direction == 'In') & \n", " (dflfc_transfers_with_dob.Fee >= 0)]\\\n", " ['AgeAtTransfer'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Average age of all transfers in, excluding free" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "22.757575757575758" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Direction == 'In') & \n", " (dflfc_transfers_with_dob.Fee > 0)]\\\n", " ['AgeAtTransfer'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Average age of all transfers in, excluding free, by season" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AgeAtTransfer
Season
2010-201123.050000
2011-201222.157143
2012-201321.666667
2013-201423.200000
2014-201523.750000
2015-201622.760000
\n", "
" ], "text/plain": [ " AgeAtTransfer\n", "Season \n", "2010-2011 23.050000\n", "2011-2012 22.157143\n", "2012-2013 21.666667\n", "2013-2014 23.200000\n", "2014-2015 23.750000\n", "2015-2016 22.760000" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob[(dflfc_transfers_with_dob.Direction == 'In') & \n", " (dflfc_transfers_with_dob.Fee > 0)]\\\n", " [['Season', 'AgeAtTransfer']].groupby('Season').mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Average age of all transfers in, including free, by season (rounded)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AgeAtTransfer
Season
2010-201123.1
2011-201224.3
2012-201322.0
2013-201424.6
2014-201523.4
2015-201624.4
\n", "
" ], "text/plain": [ " AgeAtTransfer\n", "Season \n", "2010-2011 23.1\n", "2011-2012 24.3\n", "2012-2013 22.0\n", "2013-2014 24.6\n", "2014-2015 23.4\n", "2015-2016 24.4" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob[dflfc_transfers_with_dob.Direction == 'In']\\\n", "[['Season', 'AgeAtTransfer']].groupby('Season').agg(lambda x: round(x.mean(), 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot age at transfer in" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAFECAYAAAADYATiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZkk4sM3Mkvh/V0USgkLOj2sAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dflfc_transfers_with_dob[dflfc_transfers_with_dob.Direction == 'In']\\\n", "[['Season', 'AgeAtTransfer']].groupby('Season').agg(lambda x: round(x.mean(), 1)).plot(kind='bar', legend=False, \n", " title='Age at Transfer In')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is average age of incoming and outgoing players by season" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAFECAYAAAADYATiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dflfc_transfers_with_dob[['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction'])\\\n", " .agg(lambda x: round(x.mean(), 1)).unstack()\\\n", " .plot(kind='bar', title='Age at Transfer', ylim=(0,40), yticks=range(0,35,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Excluding free" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAFECAYAAAADYATiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIhIR/x9iC4VDCxAA3QAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dflfc_transfers_with_dob[dflfc_transfers_with_dob.Fee > 0][['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction'])\\\n", " .agg(lambda x: round(x.mean(), 1)).unstack()\\\n", " .plot(kind='bar', title='Age at Transfer (non-zero)', ylim=(0,40), yticks=range(0,35,5))" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AgeAtTransfer
SeasonDirection
2010-2011In23.1
Out26.4
2011-2012In24.3
Out26.0
2012-2013In22.0
Out28.3
2013-2014In24.6
Out23.6
2014-2015In23.4
Out24.8
2015-2016In24.4
Out26.2
\n", "
" ], "text/plain": [ " AgeAtTransfer\n", "Season Direction \n", "2010-2011 In 23.1\n", " Out 26.4\n", "2011-2012 In 24.3\n", " Out 26.0\n", "2012-2013 In 22.0\n", " Out 28.3\n", "2013-2014 In 24.6\n", " Out 23.6\n", "2014-2015 In 23.4\n", " Out 24.8\n", "2015-2016 In 24.4\n", " Out 26.2" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob[['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction']).agg(lambda x: round(x.mean(), 1))" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AgeAtTransfer
SeasonDirection
2010-2011In23.1
Out25.5
2011-2012In22.2
Out24.5
2012-2013In21.7
Out28.8
2013-2014In23.2
Out24.9
2014-2015In23.7
Out25.4
2015-2016In22.8
Out26.2
\n", "
" ], "text/plain": [ " AgeAtTransfer\n", "Season Direction \n", "2010-2011 In 23.1\n", " Out 25.5\n", "2011-2012 In 22.2\n", " Out 24.5\n", "2012-2013 In 21.7\n", " Out 28.8\n", "2013-2014 In 23.2\n", " Out 24.9\n", "2014-2015 In 23.7\n", " Out 25.4\n", "2015-2016 In 22.8\n", " Out 26.2" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob[dflfc_transfers_with_dob.Fee > 0][['Season', 'Direction', 'AgeAtTransfer']].groupby(['Season', 'Direction']).agg(lambda x: round(x.mean(), 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Note that Outs do not include players leaving at end of contract e.g. Gerrard or those retiring e.g. Carragher" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analyse age of starting line-ups over last 3 seasons" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(66, 5)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LFC_GAME1_CSV_FILE = 'data\\lfc_pl_opening_games_aug2015.csv'\n", "dflfc_openers = pd.read_csv(LFC_GAME1_CSV_FILE, parse_dates=['Date'])\n", "\n", "# show shape\n", "dflfc_openers.shape" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonPlayerOppositionVenueDate
612010-2011Joe ColeArsenalHome2010-08-15
622010-2011Javier MascheranoArsenalHome2010-08-15
632010-2011Milan JovanovicArsenalHome2010-08-15
642010-2011Dirk KuytArsenalHome2010-08-15
652010-2011David N'GogArsenalHome2010-08-15
\n", "
" ], "text/plain": [ " Season Player Opposition Venue Date\n", "61 2010-2011 Joe Cole Arsenal Home 2010-08-15\n", "62 2010-2011 Javier Mascherano Arsenal Home 2010-08-15\n", "63 2010-2011 Milan Jovanovic Arsenal Home 2010-08-15\n", "64 2010-2011 Dirk Kuyt Arsenal Home 2010-08-15\n", "65 2010-2011 David N'Gog Arsenal Home 2010-08-15" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_openers.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add age at opening game" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(66, 6)" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_openers = pd.DataFrame.merge(dflfc_openers, dflfc_players[['Player', 'Birthdate']], how='left')\n", "dflfc_openers.shape" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonPlayerOppositionVenueDateBirthdate
02015-2016Simon MignoletStokeAway2015-08-091988-03-06
12015-2016Nathaniel ClyneStokeAway2015-08-091991-04-05
22015-2016Dejan LovrenStokeAway2015-08-091989-07-05
32015-2016Joe GomezStokeAway2015-08-091997-05-23
42015-2016Martin SkrtelStokeAway2015-08-091984-12-15
\n", "
" ], "text/plain": [ " Season Player Opposition Venue Date Birthdate\n", "0 2015-2016 Simon Mignolet Stoke Away 2015-08-09 1988-03-06\n", "1 2015-2016 Nathaniel Clyne Stoke Away 2015-08-09 1991-04-05\n", "2 2015-2016 Dejan Lovren Stoke Away 2015-08-09 1989-07-05\n", "3 2015-2016 Joe Gomez Stoke Away 2015-08-09 1997-05-23\n", "4 2015-2016 Martin Skrtel Stoke Away 2015-08-09 1984-12-15" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_openers.head()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonPlayerOppositionVenueDateBirthdateAgeAtOpener
612010-2011Joe ColeArsenalHome2010-08-151981-11-0828.8
622010-2011Javier MascheranoArsenalHome2010-08-151984-06-0826.2
632010-2011Milan JovanovicArsenalHome2010-08-151981-04-1829.3
642010-2011Dirk KuytArsenalHome2010-08-151980-07-2230.1
652010-2011David N'GogArsenalHome2010-08-151989-04-0121.4
\n", "
" ], "text/plain": [ " Season Player Opposition Venue Date Birthdate \\\n", "61 2010-2011 Joe Cole Arsenal Home 2010-08-15 1981-11-08 \n", "62 2010-2011 Javier Mascherano Arsenal Home 2010-08-15 1984-06-08 \n", "63 2010-2011 Milan Jovanovic Arsenal Home 2010-08-15 1981-04-18 \n", "64 2010-2011 Dirk Kuyt Arsenal Home 2010-08-15 1980-07-22 \n", "65 2010-2011 David N'Gog Arsenal Home 2010-08-15 1989-04-01 \n", "\n", " AgeAtOpener \n", "61 28.8 \n", "62 26.2 \n", "63 29.3 \n", "64 30.1 \n", "65 21.4 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_openers['AgeAtOpener'] = dflfc_openers.apply(lambda row: age_at(row.Birthdate, row.Date), axis=1)\n", "dflfc_openers.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate average age of team" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AgeAtOpener
Season
2010-201127.6
2011-201225.4
2012-201326.5
2013-201427.0
2014-201525.9
2015-201625.1
\n", "
" ], "text/plain": [ " AgeAtOpener\n", "Season \n", "2010-2011 27.6\n", "2011-2012 25.4\n", "2012-2013 26.5\n", "2013-2014 27.0\n", "2014-2015 25.9\n", "2015-2016 25.1" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_openers[['Season', 'AgeAtOpener']].groupby('Season').agg(lambda x: round(x.mean(), 1))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAE6CAYAAAACxHlyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njrqvVAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dflfc_openers[['Season', 'AgeAtOpener']].groupby('Season').agg(lambda x: round(x.mean(), 1)).plot(kind='bar', ylim=(24,28))" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonPlayerFeeAgeAtTransfer
02010-2011Luis Suarez22.8024.0
12010-2011Andy Carroll35.0022.1
52011-2012Alexander Doni0.0031.8
62011-2012Craig Bellamy0.0032.2
72011-2012Jordon Ibe0.5016.0
82011-2012Joao Carlos Teixeira0.8319.0
92011-2012Sebastian Coates4.9020.9
102011-2012Jose Enrique6.0025.6
112011-2012Charlie Adam6.7525.6
122011-2012Jordan Henderson16.0021.0
132011-2012Stewart Downing18.5027.0
262012-2013Nuri Sahin0.0024.0
272012-2013Samed Yesil1.0018.3
282012-2013Oussama Assaidi3.0024.0
292012-2013Philippe Coutinho8.5020.6
302012-2013Fabio Borini10.4021.3
312012-2013Daniel Sturridge12.0023.4
322012-2013Joe Allen15.0022.4
452013-2014Kolo Toure0.0032.3
462013-2014Aly Cissokho0.0025.9
472013-2014Victor Moses0.0022.7
482013-2014Luis Alberto6.8020.7
492013-2014Iago Aspas7.0025.9
502013-2014Tiago Ilori7.0020.5
512013-2014Simon Mignolet9.0025.3
522013-2014Mamadou Sakho15.0023.6
602014-2015Javier Manquillo0.0020.3
612014-2015Rickie Lambert4.5032.3
622014-2015Emre Can9.7520.5
632014-2015Divick Origi9.8019.3
642014-2015Alberto Moreno12.0022.1
652014-2015Mario Balotelli16.0024.1
662014-2015Lazar Markovic19.8020.4
672014-2015Dejan Lovren20.0025.1
682014-2015Adam Lallana25.0026.2
782015-2016Adam Bogdan0.0027.8
792015-2016Danny Ings6.0023.0
802015-2016James Milner0.0029.5
812015-2016Joe Gomez6.0018.1
822015-2016Nathaniel Clyne12.5024.3
832015-2016Roberto Firmino29.0023.8
842015-2016Christian Benteke32.5024.6
\n", "
" ], "text/plain": [ " Season Player Fee AgeAtTransfer\n", "0 2010-2011 Luis Suarez 22.80 24.0\n", "1 2010-2011 Andy Carroll 35.00 22.1\n", "5 2011-2012 Alexander Doni 0.00 31.8\n", "6 2011-2012 Craig Bellamy 0.00 32.2\n", "7 2011-2012 Jordon Ibe 0.50 16.0\n", "8 2011-2012 Joao Carlos Teixeira 0.83 19.0\n", "9 2011-2012 Sebastian Coates 4.90 20.9\n", "10 2011-2012 Jose Enrique 6.00 25.6\n", "11 2011-2012 Charlie Adam 6.75 25.6\n", "12 2011-2012 Jordan Henderson 16.00 21.0\n", "13 2011-2012 Stewart Downing 18.50 27.0\n", "26 2012-2013 Nuri Sahin 0.00 24.0\n", "27 2012-2013 Samed Yesil 1.00 18.3\n", "28 2012-2013 Oussama Assaidi 3.00 24.0\n", "29 2012-2013 Philippe Coutinho 8.50 20.6\n", "30 2012-2013 Fabio Borini 10.40 21.3\n", "31 2012-2013 Daniel Sturridge 12.00 23.4\n", "32 2012-2013 Joe Allen 15.00 22.4\n", "45 2013-2014 Kolo Toure 0.00 32.3\n", "46 2013-2014 Aly Cissokho 0.00 25.9\n", "47 2013-2014 Victor Moses 0.00 22.7\n", "48 2013-2014 Luis Alberto 6.80 20.7\n", "49 2013-2014 Iago Aspas 7.00 25.9\n", "50 2013-2014 Tiago Ilori 7.00 20.5\n", "51 2013-2014 Simon Mignolet 9.00 25.3\n", "52 2013-2014 Mamadou Sakho 15.00 23.6\n", "60 2014-2015 Javier Manquillo 0.00 20.3\n", "61 2014-2015 Rickie Lambert 4.50 32.3\n", "62 2014-2015 Emre Can 9.75 20.5\n", "63 2014-2015 Divick Origi 9.80 19.3\n", "64 2014-2015 Alberto Moreno 12.00 22.1\n", "65 2014-2015 Mario Balotelli 16.00 24.1\n", "66 2014-2015 Lazar Markovic 19.80 20.4\n", "67 2014-2015 Dejan Lovren 20.00 25.1\n", "68 2014-2015 Adam Lallana 25.00 26.2\n", "78 2015-2016 Adam Bogdan 0.00 27.8\n", "79 2015-2016 Danny Ings 6.00 23.0\n", "80 2015-2016 James Milner 0.00 29.5\n", "81 2015-2016 Joe Gomez 6.00 18.1\n", "82 2015-2016 Nathaniel Clyne 12.50 24.3\n", "83 2015-2016 Roberto Firmino 29.00 23.8\n", "84 2015-2016 Christian Benteke 32.50 24.6" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_transfers_with_dob[dflfc_transfers_with_dob.Direction == 'In'][['Season', 'Player', 'Fee', 'AgeAtTransfer']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is average age of team that *finished* againt Arsenal?" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(11, 5)" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LFC_ARSENAL_CSV_FILE = 'data\\lfc_pl_vs_Arsenal_aug2015.csv'\n", "dflfc_arsenal = pd.read_csv(LFC_ARSENAL_CSV_FILE, parse_dates=['Date'], skiprows=2)\n", "\n", "# show shape\n", "dflfc_arsenal.shape" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonPlayerOppositionVenueDateBirthdate
02015-2016Simon MignoletArsenalAway2015-08-251988-03-06
12015-2016Nathaniel ClyneArsenalAway2015-08-251991-04-05
22015-2016Dejan LovrenArsenalAway2015-08-251989-07-05
32015-2016Joe GomezArsenalAway2015-08-251997-05-23
42015-2016Martin SkrtelArsenalAway2015-08-251984-12-15
52015-2016James MilnerArsenalAway2015-08-251986-01-04
62015-2016Alberto MorenoArsenalAway2015-08-251992-07-05
72015-2016Jordan HendersonArsenalAway2015-08-251990-06-17
82015-2016Jordan RossiterArsenalAway2015-08-251997-03-24
92015-2016Emre CanArsenalAway2015-08-251994-01-12
102015-2016Christian BentekeArsenalAway2015-08-251990-12-03
\n", "
" ], "text/plain": [ " Season Player Opposition Venue Date Birthdate\n", "0 2015-2016 Simon Mignolet Arsenal Away 2015-08-25 1988-03-06\n", "1 2015-2016 Nathaniel Clyne Arsenal Away 2015-08-25 1991-04-05\n", "2 2015-2016 Dejan Lovren Arsenal Away 2015-08-25 1989-07-05\n", "3 2015-2016 Joe Gomez Arsenal Away 2015-08-25 1997-05-23\n", "4 2015-2016 Martin Skrtel Arsenal Away 2015-08-25 1984-12-15\n", "5 2015-2016 James Milner Arsenal Away 2015-08-25 1986-01-04\n", "6 2015-2016 Alberto Moreno Arsenal Away 2015-08-25 1992-07-05\n", "7 2015-2016 Jordan Henderson Arsenal Away 2015-08-25 1990-06-17\n", "8 2015-2016 Jordan Rossiter Arsenal Away 2015-08-25 1997-03-24\n", "9 2015-2016 Emre Can Arsenal Away 2015-08-25 1994-01-12\n", "10 2015-2016 Christian Benteke Arsenal Away 2015-08-25 1990-12-03" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_arsenal = pd.merge(dflfc_arsenal, dflfc_players[['Player', 'Birthdate']])\n", "dflfc_arsenal" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SeasonPlayerOppositionVenueDateBirthdateAgeAtGame
02015-2016Simon MignoletArsenalAway2015-08-251988-03-0627.5
12015-2016Nathaniel ClyneArsenalAway2015-08-251991-04-0524.4
22015-2016Dejan LovrenArsenalAway2015-08-251989-07-0526.2
32015-2016Joe GomezArsenalAway2015-08-251997-05-2318.3
42015-2016Martin SkrtelArsenalAway2015-08-251984-12-1530.7
52015-2016James MilnerArsenalAway2015-08-251986-01-0429.7
62015-2016Alberto MorenoArsenalAway2015-08-251992-07-0523.2
72015-2016Jordan HendersonArsenalAway2015-08-251990-06-1725.2
82015-2016Jordan RossiterArsenalAway2015-08-251997-03-2418.4
92015-2016Emre CanArsenalAway2015-08-251994-01-1221.6
102015-2016Christian BentekeArsenalAway2015-08-251990-12-0324.7
\n", "
" ], "text/plain": [ " Season Player Opposition Venue Date Birthdate \\\n", "0 2015-2016 Simon Mignolet Arsenal Away 2015-08-25 1988-03-06 \n", "1 2015-2016 Nathaniel Clyne Arsenal Away 2015-08-25 1991-04-05 \n", "2 2015-2016 Dejan Lovren Arsenal Away 2015-08-25 1989-07-05 \n", "3 2015-2016 Joe Gomez Arsenal Away 2015-08-25 1997-05-23 \n", "4 2015-2016 Martin Skrtel Arsenal Away 2015-08-25 1984-12-15 \n", "5 2015-2016 James Milner Arsenal Away 2015-08-25 1986-01-04 \n", "6 2015-2016 Alberto Moreno Arsenal Away 2015-08-25 1992-07-05 \n", "7 2015-2016 Jordan Henderson Arsenal Away 2015-08-25 1990-06-17 \n", "8 2015-2016 Jordan Rossiter Arsenal Away 2015-08-25 1997-03-24 \n", "9 2015-2016 Emre Can Arsenal Away 2015-08-25 1994-01-12 \n", "10 2015-2016 Christian Benteke Arsenal Away 2015-08-25 1990-12-03 \n", "\n", " AgeAtGame \n", "0 27.5 \n", "1 24.4 \n", "2 26.2 \n", "3 18.3 \n", "4 30.7 \n", "5 29.7 \n", "6 23.2 \n", "7 25.2 \n", "8 18.4 \n", "9 21.6 \n", "10 24.7 " ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_arsenal['AgeAtGame'] = dflfc_arsenal.apply(lambda row: age_at(row.Birthdate, row.Date), axis=1)\n", "dflfc_arsenal" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "AgeAtGame 24.536364\n", "dtype: float64" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflfc_arsenal.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare team wage bills for 2014-15" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Team2014-2015
0Manchester United215.8
1Manchester City205.0
2Chelsea192.7
3Arsenal166.4
4Liverpool144.0
\n", "
" ], "text/plain": [ " Team 2014-2015\n", "0 Manchester United 215.8\n", "1 Manchester City 205.0\n", "2 Chelsea 192.7\n", "3 Arsenal 166.4\n", "4 Liverpool 144.0" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfprem_wages[['Team', '2014-2015']].head()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TeamPTS
0Chelsea87
1Manchester City79
2Arsenal75
3Manchester United70
4Tottenham Hotspur64
\n", "
" ], "text/plain": [ " Team PTS\n", "0 Chelsea 87\n", "1 Manchester City 79\n", "2 Arsenal 75\n", "3 Manchester United 70\n", "4 Tottenham Hotspur 64" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ " dfprem_table_2014_2015[['Team', 'PTS']].head()" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dfprem_table_2014_2015_wages = pd.merge(dfprem_table_2014_2015[['Team', 'PTS']], dfprem_wages[['Team', '2014-2015']])" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dfprem_table_2014_2015_wages\n", "dfprem_table_2014_2015_wages.rename(columns={'2014-2015': 'WageBill','PTS': 'Points'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TeamPointsWageBill
0Chelsea87192.7
1Manchester City79205.0
2Arsenal75166.4
3Manchester United70215.8
4Tottenham Hotspur64100.4
\n", "
" ], "text/plain": [ " Team Points WageBill\n", "0 Chelsea 87 192.7\n", "1 Manchester City 79 205.0\n", "2 Arsenal 75 166.4\n", "3 Manchester United 70 215.8\n", "4 Tottenham Hotspur 64 100.4" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfprem_table_2014_2015_wages.head()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfprem_table_2014_2015.Rank[dfprem_table_2014_2015.Team == 'Chelsea'].values[0]" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEPCAYAAABCyrPIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOox7AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dfprem_table_2014_2015_wages.plot(kind='scatter', x='WageBill', y='Points')" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = dfprem_table_2014_2015_wages.set_index('Team')" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "62.0 144.0\n" ] } ], "source": [ "(a,b) = df.ix['Liverpool']\n", "print a, b" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Ref: http://stackoverflow.com/questions/739241/date-ordinal-output\n", "def n_plus_suffix(n):\n", " \"\"\"Return n plus the suffix e.g. 1 becomes 1st, 2 becomes 2nd.\"\"\"\n", " assert isinstance(n, (int, long)), '{} is not an integer'.format(n)\n", " \n", " if 10 <= n % 100 < 20:\n", " return str(n) + 'th'\n", " else:\n", " return str(n) + {1 : 'st', 2 : 'nd', 3 : 'rd'}.get(n % 10, \"th\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot Wage Bill vs Points, with Top 6 highlighted (2014-2015)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAIwCAYAAABEJOcdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6f8DMmGuIs/itE0AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# calculate points total for 6th place\n", "sixth_place_points = df.iloc[5].Points\n", "\n", "# plot top 6 as blue circles\n", "ax = df[df.Points >= sixth_place_points].plot(kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='b')\n", "\n", "# plot others as black circles\n", "df[df.Points < sixth_place_points].plot(ax=ax, kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='k')\n", "\n", "# calculate Liverpool's points and plot as red circles\n", "lfcpoints, lfcwage = df.ix['Liverpool']\n", "ax.plot(lfcwage, lfcpoints, 'ro')\n", "\n", "# add x and y labels etc\n", "ax.set_xlabel('Wage Bill in Pounds (millions)')\n", "ax.set_ylabel('Points')\n", "ax.set_title('Wage Bill vs Points, with Top 6 highlighted (2014-2015)')\n", "ax.text(2, 21, 'prepared by: @lfcsorted', fontsize=9)\n", "\n", "# add text showing team and position for top 6\n", "for team, (points, wagebill) in df.iterrows():\n", " pos = int(dfprem_table_2014_2015.Rank[dfprem_table_2014_2015.Team == team].values[0])\n", " team_pos = '{} ({})'.format(team, n_plus_suffix(pos))\n", " if points >= sixth_place_points:\n", " ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill-len(team)-3, points+1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prettify the plot - label all teams and highlight the top 4 and top 6 areas. " ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['Chelsea', 'Manchester City', 'Arsenal', 'Manchester United',\n", " 'Tottenham Hotspur', 'Liverpool', 'Southampton', 'Swansea City',\n", " 'Stoke City', 'Crystal Palace', 'Everton', 'West Ham United',\n", " 'West Bromwich Albion', 'Leicester City', 'Newcastle United',\n", " 'Sunderland', 'Aston Villa', 'Hull City', 'Burnley',\n", " 'Queens Park Rangers'], dtype=object)" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# list all of the teams\n", "df.index.values" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAIwCAYAAABEJOcdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkmkRERERES/9P227pvkNDGeeAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# set position of text, default is centred on top of the circle\n", "# note that posotionis decided by trial and error, to give clearest plot\n", "TEAM_CL = ['Sunderland', 'West Ham United']\n", "TEAM_CR = ['Stoke City', 'Aston Villa', 'Everton']\n", "TEAM_CT = []\n", "for team in df.index.values:\n", " if team not in TEAM_CL + TEAM_CR:\n", " TEAM_CT.append(team)\n", "\n", " \n", "# calculate points total for 6th place\n", "sixth_place_points = df.iloc[5].Points\n", "\n", "# plot top 6 as blue circles\n", "ax = df[df.Points >= sixth_place_points].plot(kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='b')\n", "\n", "# plot others as black circles\n", "df[df.Points < sixth_place_points].plot(ax=ax, kind='scatter', x='WageBill', y='Points', figsize=(12,9), color='k')\n", "\n", "# calculate Liverpool's points and plot as red circles\n", "lfcpoints, lfcwage = df.ix['Liverpool']\n", "ax.plot(lfcwage, lfcpoints, 'ro')\n", "\n", "# add x and y labels etc\n", "ax.set_xlabel('Wage Bill in Pounds (millions)')\n", "ax.set_ylabel('Points')\n", "ax.set_title('Wage Bill vs Points, with Top 6 highlighted (2014-2015)')\n", "ax.text(2, 21, 'prepared by: @lfcsorted', fontsize=9)\n", "\n", "# add text showing team and position\n", "for team, (points, wagebill) in df.iterrows():\n", " pos = int(dfprem_table_2014_2015.Rank[dfprem_table_2014_2015.Team == team].values[0])\n", " team_pos = '{} ({})'.format(team, n_plus_suffix(pos))\n", " if team in TEAM_CT:\n", " ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill-len(team)-3, points+1))\n", " elif team in TEAM_CR:\n", " #print 'team cr: {}'.format(team)\n", " ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill+2, points-0.5))\n", " elif team in TEAM_CL:\n", " #print 'team cl: {}'.format(team)\n", " ax.annotate(s=team_pos, xy=(wagebill,points), xytext=(wagebill-3*len(team)-6, points-0.5))\n", " else:\n", " raise('unexpected error')\n", "\n", "# add key areas to the graph and label\n", "from matplotlib.patches import Rectangle\n", "XTEXT_OFFSET = 1\n", "YTEXT_OFFSET = -2\n", "\n", "# add top 4 area, with annotation in top left hand corner of rectangle\n", "top4rect_bl_x = df[0:4].WageBill.min() # bottom left x co-ord of rectangle\n", "top4rect_bl_y = df[0:4].Points.min() # bottom left y co-ord of rectangle\n", "top4rect_width = df[0:4].WageBill.max() - top4rect_bl_x # width of rectangle\n", "top4rect_height = df[0:4].Points.max() - top4rect_bl_y # height of rectangle\n", "top4rect_tl_x = df[0:4].WageBill.min() # top left x co-ord for annotation\n", "top4rect_tl_y = df[0:4].Points.max() # top left y co-ord for annotation\n", "top4rect_xtext = top4rect_tl_x + XTEXT_OFFSET # text x co-ord for annotation\n", "top4rect_ytext = top4rect_tl_y + YTEXT_OFFSET # text y co-ord for annotation\n", "ax.add_patch(Rectangle((top4rect_bl_x, top4rect_bl_y), top4rect_width, top4rect_height, facecolor=\"blue\", alpha=0.2))\n", "ax.annotate(s='Top 4 area', xy=(top4rect_tl_x, top4rect_tl_y), xytext=(top4rect_xtext, top4rect_ytext), color='blue')\n", "\n", "# add top 6 area, with annotation in top left hand corner of rectangle\n", "top6rect_bl_x = df[0:6].WageBill.min() # bottom left x co-ord of rectangle\n", "top6rect_bl_y = df[0:6].Points.min() # bottom left y co-ord of rectangle\n", "top6rect_width = df[0:6].WageBill.max() - top6rect_bl_x # width of rectangle\n", "top6rect_height = df[0:6].Points.max() - top6rect_bl_y # height of rectangle\n", "top6rect_tl_x = df[0:6].WageBill.min() # top left x co-ord for annotation\n", "top6rect_tl_y = df[0:6].Points.max() # top left y co-ord for annotation\n", "top6rect_xtext = top6rect_tl_x + XTEXT_OFFSET # text x co-ord for annotation\n", "top6rect_ytext = top6rect_tl_y + YTEXT_OFFSET # text y co-ord for annotation\n", "ax.add_patch(Rectangle((top6rect_bl_x, top6rect_bl_y), top6rect_width, top6rect_height, facecolor=\"lightblue\", alpha=0.2))\n", "ax.annotate(s='Top 6 area', xy=(top6rect_tl_x, top6rect_tl_y), xytext=(top6rect_xtext, top6rect_ytext), color='blue', alpha=0.7)\n", "\n", "# plot and save current figure\n", "fig = plt.gcf() \n", "plt.show()\n", "fig.savefig('PremWageBillvsPoints2014-2015.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }