{
 "metadata": {
  "nikola": {
   "category": "",
   "date": "2015-10-26 12:36:20 UTC",
   "description": "",
   "link": "",
   "slug": "epl-24-25-october-2015",
   "tags": "",
   "title": "EPL 24-25 October 2015",
   "type": "text"
  },
  "signature": "sha256:442d5b4c3a0a2aedbd1a59638b925ff670b16dad0b998631058c6b1ca4782a07"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# EPL 24-25th October\n",
      "\n",
      "Weekly look at the weekend games in the EPL:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import league_analysis\n",
      "from IPython.display import display, HTML\n",
      "epl = league_analysis.epl\n",
      "weekend_matches = league_analysis.get_matches(epl, '24/10/2015', '25/10/2015')\n",
      "league_analysis.display_given_matches(weekend_matches)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Arsenal</td><td>Everton</td></tr>\n",
        "      <tr><td>Goals</td><td>2</td><td>1</td></tr>\n",
        "      <tr><td>Shots</td><td>20</td><td>10</td></tr>\n",
        "      <tr><td>SOT</td><td>5</td><td>5</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Aston Villa</td><td>Swansea</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>2</td></tr>\n",
        "      <tr><td>Shots</td><td>9</td><td>11</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Leicester</td><td>Crystal Palace</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>12</td><td>9</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Norwich</td><td>West Brom</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>1</td></tr>\n",
        "      <tr><td>Shots</td><td>16</td><td>7</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Stoke</td><td>Watford</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>2</td></tr>\n",
        "      <tr><td>Shots</td><td>12</td><td>13</td></tr>\n",
        "      <tr><td>SOT</td><td>1</td><td>3</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>West Ham</td><td>Chelsea</td></tr>\n",
        "      <tr><td>Goals</td><td>2</td><td>1</td></tr>\n",
        "      <tr><td>Shots</td><td>12</td><td>8</td></tr>\n",
        "      <tr><td>SOT</td><td>5</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Bournemouth</td><td>Tottenham</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>5</td></tr>\n",
        "      <tr><td>Shots</td><td>11</td><td>14</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>9</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Liverpool</td><td>Southampton</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>1</td></tr>\n",
        "      <tr><td>Shots</td><td>15</td><td>8</td></tr>\n",
        "      <tr><td>SOT</td><td>2</td><td>3</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Man United</td><td>Man City</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>6</td><td>6</td></tr>\n",
        "      <tr><td>SOT</td><td>1</td><td>1</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Sunderland</td><td>Newcastle</td></tr>\n",
        "      <tr><td>Goals</td><td>3</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>9</td><td>21</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>8</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7efc49420390>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Manchester United 0-0 Manchester City\n",
      "\n",
      "A very even game, neither side giving up much. Manchester United typically have not shown much ambition to get a lot of shots, prefering to cultivate excellent chances whilst restricting their opponents. Manchester City on the other hand have tended to bombard their opponents. In this sense United will be pretty happy with how they restricted their Manchester neighbours.\n",
      "\n",
      "Having watched the game I felt City had the better of the first half and United the second. The best save of the match was Joe Hart to his left near the post and United also managed to hit the bar with a clever attempt at a lob. But overall, a game that could be easily forgotten.\n",
      "\n",
      "<!-- TEASER_END -->"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Sunderland 3-0 Newcastle\n",
      "\n",
      "Something of a bottom of the table clash. These two teams have been amoung the worst in the premier league. In terms of the score line a pretty convincing home victory will appease many Sunderland fans, and may well have Steve McClaren wondering how much longer he will be Newcastle manager. A look at the shot numbers though would result in a very different conclusion. There may be some score-line effects here but even still Newcastle demonstrate that they can produce shots/chances when behind, at least against fellow strugglers.\n",
      "\n",
      "These two teams both currently look on course for relegation, one derby result is not going to change that, what is required is a longer term change in their underlying numbers, and currently Newcastle look a bit more likely to do that."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Similar Games:\n",
      "\n",
      "There have so far been 16 (of 100) games that have resulted in one team winning by 3 or more goals:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matches_so_far = league_analysis.get_matches(epl, '01/08/2015', '25/10/2015')\n",
      "high_result_matches = [m for m in matches_so_far if abs(m.FTHG - m.FTAG) >= 3]\n",
      "league_analysis.display_given_matches(high_result_matches)\n",
      "display(HTML('There have been {0} matches in total'.format(len(matches_so_far))))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>West Brom</td><td>Man City</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>3</td></tr>\n",
        "      <tr><td>Shots</td><td>9</td><td>19</td></tr>\n",
        "      <tr><td>SOT</td><td>2</td><td>7</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Southampton</td><td>Everton</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>3</td></tr>\n",
        "      <tr><td>Shots</td><td>17</td><td>10</td></tr>\n",
        "      <tr><td>SOT</td><td>4</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Man City</td><td>Chelsea</td></tr>\n",
        "      <tr><td>Goals</td><td>3</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>18</td><td>10</td></tr>\n",
        "      <tr><td>SOT</td><td>8</td><td>3</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Liverpool</td><td>West Ham</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>3</td></tr>\n",
        "      <tr><td>Shots</td><td>13</td><td>12</td></tr>\n",
        "      <tr><td>SOT</td><td>1</td><td>5</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Southampton</td><td>Norwich</td></tr>\n",
        "      <tr><td>Goals</td><td>3</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>23</td><td>6</td></tr>\n",
        "      <tr><td>SOT</td><td>8</td><td>1</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Leicester</td><td>Arsenal</td></tr>\n",
        "      <tr><td>Goals</td><td>2</td><td>5</td></tr>\n",
        "      <tr><td>Shots</td><td>16</td><td>26</td></tr>\n",
        "      <tr><td>SOT</td><td>7</td><td>12</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Man United</td><td>Sunderland</td></tr>\n",
        "      <tr><td>Goals</td><td>3</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>12</td><td>9</td></tr>\n",
        "      <tr><td>SOT</td><td>7</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Tottenham</td><td>Man City</td></tr>\n",
        "      <tr><td>Goals</td><td>4</td><td>1</td></tr>\n",
        "      <tr><td>Shots</td><td>15</td><td>22</td></tr>\n",
        "      <tr><td>SOT</td><td>8</td><td>8</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Man City</td><td>Newcastle</td></tr>\n",
        "      <tr><td>Goals</td><td>6</td><td>1</td></tr>\n",
        "      <tr><td>Shots</td><td>23</td><td>6</td></tr>\n",
        "      <tr><td>SOT</td><td>11</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Arsenal</td><td>Man United</td></tr>\n",
        "      <tr><td>Goals</td><td>3</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>12</td><td>9</td></tr>\n",
        "      <tr><td>SOT</td><td>5</td><td>5</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Everton</td><td>Man United</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>3</td></tr>\n",
        "      <tr><td>Shots</td><td>12</td><td>10</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>7</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Man City</td><td>Bournemouth</td></tr>\n",
        "      <tr><td>Goals</td><td>5</td><td>1</td></tr>\n",
        "      <tr><td>Shots</td><td>15</td><td>5</td></tr>\n",
        "      <tr><td>SOT</td><td>11</td><td>1</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Watford</td><td>Arsenal</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>3</td></tr>\n",
        "      <tr><td>Shots</td><td>8</td><td>17</td></tr>\n",
        "      <tr><td>SOT</td><td>1</td><td>6</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Newcastle</td><td>Norwich</td></tr>\n",
        "      <tr><td>Goals</td><td>6</td><td>2</td></tr>\n",
        "      <tr><td>Shots</td><td>11</td><td>18</td></tr>\n",
        "      <tr><td>SOT</td><td>6</td><td>6</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Bournemouth</td><td>Tottenham</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>5</td></tr>\n",
        "      <tr><td>Shots</td><td>11</td><td>14</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>9</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Sunderland</td><td>Newcastle</td></tr>\n",
        "      <tr><td>Goals</td><td>3</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>9</td><td>21</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>8</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7efc2e633550>"
       ]
      },
      {
       "html": [
        "There have been 100 matches in total"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7efc2e6334e0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There have only been 6 games in which a team has won by 3 or more goals despite taking few shots:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def lower_shots_wins(match):\n",
      "    return ((match.HS > match.AS and match.FTAG > match.FTHG) or\n",
      "            (match.HS < match.AS and match.FTAG < match.FTHG))\n",
      "win_shots = [m for m in high_result_matches if lower_shots_wins(m)]\n",
      "league_analysis.display_given_matches(win_shots)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Southampton</td><td>Everton</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>3</td></tr>\n",
        "      <tr><td>Shots</td><td>17</td><td>10</td></tr>\n",
        "      <tr><td>SOT</td><td>4</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Liverpool</td><td>West Ham</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>3</td></tr>\n",
        "      <tr><td>Shots</td><td>13</td><td>12</td></tr>\n",
        "      <tr><td>SOT</td><td>1</td><td>5</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Tottenham</td><td>Man City</td></tr>\n",
        "      <tr><td>Goals</td><td>4</td><td>1</td></tr>\n",
        "      <tr><td>Shots</td><td>15</td><td>22</td></tr>\n",
        "      <tr><td>SOT</td><td>8</td><td>8</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Everton</td><td>Man United</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>3</td></tr>\n",
        "      <tr><td>Shots</td><td>12</td><td>10</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>7</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Newcastle</td><td>Norwich</td></tr>\n",
        "      <tr><td>Goals</td><td>6</td><td>2</td></tr>\n",
        "      <tr><td>Shots</td><td>11</td><td>18</td></tr>\n",
        "      <tr><td>SOT</td><td>6</td><td>6</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Sunderland</td><td>Newcastle</td></tr>\n",
        "      <tr><td>Goals</td><td>3</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>9</td><td>21</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>8</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7efc2e63b160>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Of these:\n",
      "* Sunderland were outshot by 12 shots, the next highest were outshot by 7 shots, Everton, Tottenham, and Newcastle.\n",
      "* All were equal or better via shots on target, so Sunderland are the only team to have won by 3 or more goals despite having fewer shots on target, and 3-8 is not even close.\n",
      "\n",
      "In fact, only 13 matches, including Sunderland vs Newcastle have had the side with fewer shots on target winning, of these, Only 4 had a goal difference of more than 1. The other three matches with a goal difference of more than had a SOT difference of exactly 1 or 2."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def lower_sots_wins(match):\n",
      "    return ((match.HST > match.AST and match.FTAG > match.FTHG) or\n",
      "            (match.HST < match.AST and match.FTAG < match.FTHG))\n",
      "win_shots = [m for m in matches_so_far if lower_sots_wins(m)]\n",
      "league_analysis.display_given_matches(win_shots)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Man United</td><td>Tottenham</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>9</td><td>9</td></tr>\n",
        "      <tr><td>SOT</td><td>1</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Arsenal</td><td>West Ham</td></tr>\n",
        "      <tr><td>Goals</td><td>0</td><td>2</td></tr>\n",
        "      <tr><td>Shots</td><td>22</td><td>8</td></tr>\n",
        "      <tr><td>SOT</td><td>6</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>West Brom</td><td>Chelsea</td></tr>\n",
        "      <tr><td>Goals</td><td>2</td><td>3</td></tr>\n",
        "      <tr><td>Shots</td><td>15</td><td>15</td></tr>\n",
        "      <tr><td>SOT</td><td>6</td><td>5</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Chelsea</td><td>Crystal Palace</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>2</td></tr>\n",
        "      <tr><td>Shots</td><td>26</td><td>13</td></tr>\n",
        "      <tr><td>SOT</td><td>9</td><td>6</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Man United</td><td>Liverpool</td></tr>\n",
        "      <tr><td>Goals</td><td>3</td><td>1</td></tr>\n",
        "      <tr><td>Shots</td><td>9</td><td>8</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>West Ham</td><td>Newcastle</td></tr>\n",
        "      <tr><td>Goals</td><td>2</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>17</td><td>14</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Man City</td><td>West Ham</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>2</td></tr>\n",
        "      <tr><td>Shots</td><td>27</td><td>6</td></tr>\n",
        "      <tr><td>SOT</td><td>8</td><td>3</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Newcastle</td><td>Watford</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>2</td></tr>\n",
        "      <tr><td>Shots</td><td>15</td><td>13</td></tr>\n",
        "      <tr><td>SOT</td><td>5</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Southampton</td><td>Man United</td></tr>\n",
        "      <tr><td>Goals</td><td>2</td><td>3</td></tr>\n",
        "      <tr><td>Shots</td><td>15</td><td>10</td></tr>\n",
        "      <tr><td>SOT</td><td>8</td><td>3</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Stoke</td><td>Bournemouth</td></tr>\n",
        "      <tr><td>Goals</td><td>2</td><td>1</td></tr>\n",
        "      <tr><td>Shots</td><td>8</td><td>11</td></tr>\n",
        "      <tr><td>SOT</td><td>5</td><td>6</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>West Brom</td><td>Sunderland</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>8</td><td>9</td></tr>\n",
        "      <tr><td>SOT</td><td>1</td><td>2</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Leicester</td><td>Crystal Palace</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>12</td><td>9</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>4</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Sunderland</td><td>Newcastle</td></tr>\n",
        "      <tr><td>Goals</td><td>3</td><td>0</td></tr>\n",
        "      <tr><td>Shots</td><td>9</td><td>21</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>8</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7efc2b8bb320>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Overall then, if I were a Sunderland fan I would enjoy the derby victory for what it is, rather than for what it may signal in the future."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Aston Villa 1-2 Swansea\n",
      "\n",
      "This turned out to be the last game in charge for Tim Sherwood. From the stats Swansea had the better of the play and whilst they may not have *dominated* their hosts, it can hardly be said to have been an undeserved victory.\n",
      "\n",
      "Swansea are a decent club, but to lose at home to Swansea in such a routine manner is probably the most disheartening element for Villa fans. Swansea are good enough that, on their day, they could pick up a surprising away victory at the Etihad, but it would be something of a shock, and likely involve beating the shot numbers (that is winning despite having fewer shots/shots on target). The fact that Swansea were likely favourites going in to the match, suggests Villa are in deep trouble. Whoever takes over from Tim Sherwood will have their work cut out.\n",
      "\n",
      "A small non-fact-based comment. To me, it seems unlikely that Sunderland's woes are much attributable to Dick Advocaat, so I'm skeptical that a change in manager will have much effect for Sunderland. At Villa, it seems more plausible to me that a change in manager will indeed usher in a change of fortune, but they aren't going to be hitting the top half of the table, this is about avoiding relegation."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Bournemouth 1-5 Tottenham\n",
      "\n",
      "I'll admit to a bit of a soft spot for Bournemouth. They took something of a beating here this week. However, the shot statistics show that this was probably unflattering to Bournemouth who took a very early lead, only to then give up 5 goals. But it was 5 goals from 14 shots, 9 of which were on target. Tottenham have started to pick up and we'll see below their PDO has improved of late. Only two matches this season have produced 5 or more goals from 14 or fewer shots:\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def five_goals_from_fourteen_shots(match):\n",
      "    return ((match.HS <= 14 and match.FTHG >= 5) or (match.AS <= 14 and match.FTAG >= 5))\n",
      "relevant_matches = [m for m in matches_so_far if five_goals_from_fourteen_shots(m)]\n",
      "league_analysis.display_given_matches(relevant_matches)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Newcastle</td><td>Norwich</td></tr>\n",
        "      <tr><td>Goals</td><td>6</td><td>2</td></tr>\n",
        "      <tr><td>Shots</td><td>11</td><td>18</td></tr>\n",
        "      <tr><td>SOT</td><td>6</td><td>6</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Bournemouth</td><td>Tottenham</td></tr>\n",
        "      <tr><td>Goals</td><td>1</td><td>5</td></tr>\n",
        "      <tr><td>Shots</td><td>11</td><td>14</td></tr>\n",
        "      <tr><td>SOT</td><td>3</td><td>9</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7efc2b8bbdd8>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Which is not that surprising since of course 5 goals are not scored that often no matter how many shots. However, for all league data I have (top 4 English leagues plus Scottish Premiership, since season 2010-2011), it has occured just 138 times in 12003, or about one percent of the time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "league_analysis.get_fraction_of_matches(five_goals_from_fourteen_shots)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(138, 12003)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Having said that, when teams *do* score five goals or more, it's not that rare that they do so with 14 shots or fewer. Here we see that only 336 games have involved either team scoring five or more goals. So in 138 of the 336 matches, or a bit over a third of the time, that a team scores 5 or more goals they do so with 14 or fewer shots. This suggests that a 5 goal game is quite often the result of a good conversion rate rather than a complete dominance. When a team creates a 3 goal advantage they may well have less offensive ambition and hence only score additional goals from very good chances, but that's a bit speculative."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def five_goals(match):\n",
      "    return match.FTHG >= 5 or match.FTAG >= 5\n",
      "five_goal_matches = league_analysis.get_all_matches(filter_fun=five_goals)\n",
      "league_analysis.get_fraction_of_matches(five_goals_from_fourteen_shots, five_goal_matches)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "(138, 336)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Small aside, there were 2 games in which **both** teams scored 5 or more goals, both of which were 5-5 draws and both of which had one team with fewer than 14 shots. In the above these would have been counted in the 138, but should really be counted twice in the 336, so it should really be 138 from 338."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def both_five_goals(match):\n",
      "    return match.FTHG >= 5 and match.FTAG >= 5\n",
      "league_analysis.display_given_matches(league_analysis.get_all_matches(filter_fun=both_five_goals))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>Chesterfield</td><td>Crewe</td></tr>\n",
        "      <tr><td>Goals</td><td>5</td><td>5</td></tr>\n",
        "      <tr><td>Shots</td><td>16</td><td>8</td></tr>\n",
        "      <tr><td>SOT</td><td>8</td><td>8</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>\n",
        "<div style=\"display:inline-block;\">\n",
        "    <table>\n",
        "      <tr><th></th><th>Home</th><th>Away</th></tr>\n",
        "      <tr><td>Team</td><td>West Brom</td><td>Man United</td></tr>\n",
        "      <tr><td>Goals</td><td>5</td><td>5</td></tr>\n",
        "      <tr><td>Shots</td><td>15</td><td>12</td></tr>\n",
        "      <tr><td>SOT</td><td>8</td><td>8</td></tr>\n",
        "      \n",
        "    </table>\n",
        "    </div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7efc2b8bb908>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Arsenal 2-1 Everton\n",
      "\n",
      "Arsenal like Tottenham have started to see their fruits of the labour more in recent weeks. Generally speaking Arsenal have been very good, but have lost/drawn the odd game that they have dominated, meaning that their points total and league position weren't quite reflective of how good they were. However their luck, or conversion, has begun to pick up, a bit like Tottenham, in the following chart we depict rank in PDO in the league. Remember, we take PDO has *some* measure of the amount of luck a team has received, so a higher PDO means more good luck. Of course it's not flawless but it can help show teams that are artificially high or low in the league. Both Tottenham and Arsenal start pretty low but have started to pick up of late."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "after_game_no_dicts = league_analysis.collect_after_game_dicts(epl, '01/08/2015', '26/10/2015')\n",
      "teams = ['Tottenham', 'Arsenal', 'Watford', 'Liverpool',\n",
      "         'Southampton', 'Sunderland', 'Chelsea', 'Bournemouth']\n",
      "league_analysis.plot_changing_stats(epl, after_game_no_dicts, 'pdo', rankings=True, teams=teams)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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y/fffc8YZZxAfH098fDzdu3dn3759TJgwoVjPV9kpaSD5l54Ot9wCDzwAZvDC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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7efc494208d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In contrast Sunderland, who get a big rise in PDO this week after their (arguably lucky) win against Newcastle, can't really blame their league position on bad luck. Even before this week they have been steadily mid-table or above in terms of PDO. Similarly Chelsea have been above mid-table for PDO for most of the season, albeit on a steadily decreasing path (which is likely just a statistical quirk). Finally note also Liverpool and Southampton, both have had pretty rotten 'luck' all season, so don't be surprised to see them improve, a fact which will no doubt be attributed to Klopp's arrival. Which isn't to say he won't have some positive effect outside of what is observable in PDO. Bournemouth have just now overtaken Southampton has the team with the lowest PDO."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Leicester 1-0 Crystal Palace, Norwich 0-1 West Brom\n",
      "\n",
      "Interesting matches, both quite evenly matched. Both home teams had a higher shot count, but a lower shots on target count (albeit only 3 to 4).\n",
      "\n",
      "Leicester continuing a good run and arguably deserved this, although of course were helped by an early second half red card for Crystal Palace.\n",
      "\n",
      "Norwich will feel pretty agrieved to have lost such a game at home, but West Brom are typically out-performing their shot numbers, having a points-to-shots ratio behind only Manchester United in the league. Note that this ratio is typically low for teams with few points, because they lose a lot of games in which they gain zero points, but typically have more than zero shots."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pairs = [(stats.teamname, stats.points / float(stats.shots_for))\n",
      "         for stats in epl.team_stats.values()]\n",
      "league_analysis.display_ranked_table(['Team', 'Points/Shots'], pairs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table><tr><th>Position</th> <th>Team</th> <th>Points/Shots</th></tr><tr><td>1</td> <td>Man United</td> <td>0.19047619047619047</td></tr>\n",
        "<tr><td>2</td> <td>West Brom</td> <td>0.14736842105263157</td></tr>\n",
        "<tr><td>3</td> <td>West Ham</td> <td>0.14492753623188406</td></tr>\n",
        "<tr><td>4</td> <td>Leicester</td> <td>0.12666666666666668</td></tr>\n",
        "<tr><td>5</td> <td>Crystal Palace</td> <td>0.12</td></tr>\n",
        "<tr><td>6</td> <td>Man City</td> <td>0.11891891891891893</td></tr>\n",
        "<tr><td>7</td> <td>Stoke</td> <td>0.11650485436893204</td></tr>\n",
        "<tr><td>8</td> <td>Arsenal</td> <td>0.11224489795918367</td></tr>\n",
        "<tr><td>9</td> <td>Everton</td> <td>0.1111111111111111</td></tr>\n",
        "<tr><td>10</td> <td>Tottenham</td> <td>0.1069182389937107</td></tr>\n",
        "<tr><td>11</td> <td>Watford</td> <td>0.10483870967741936</td></tr>\n",
        "<tr><td>12</td> <td>Swansea</td> <td>0.09774436090225563</td></tr>\n",
        "<tr><td>13</td> <td>Liverpool</td> <td>0.0958904109589041</td></tr>\n",
        "<tr><td>14</td> <td>Southampton</td> <td>0.09271523178807947</td></tr>\n",
        "<tr><td>15</td> <td>Chelsea</td> <td>0.07746478873239436</td></tr>\n",
        "<tr><td>16</td> <td>Bournemouth</td> <td>0.0761904761904762</td></tr>\n",
        "<tr><td>17</td> <td>Sunderland</td> <td>0.06451612903225806</td></tr>\n",
        "<tr><td>18</td> <td>Newcastle</td> <td>0.06060606060606061</td></tr>\n",
        "<tr><td>19</td> <td>Norwich</td> <td>0.06040268456375839</td></tr>\n",
        "<tr><td>20</td> <td>Aston Villa</td> <td>0.037037037037037035</td></tr></table>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7efc49420b38>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# West Ham 2-1 Chelsea\n",
      "\n",
      "Not sure what more to write about these two teams. West Ham do seem to be riding some kind of wave. Charitably we might call this a wave of efficiency, uncharitably we would call it a wave of luck. Personally think the latter, and that by the end of season West Ham will be in the bottom half of the league, but, similar to last season, they have probably amassed enough points so far to avoid much of a relegation battle, unless their efficiency seriously drops off. In addition, their underlying numbers might be better if they hadn't spent as much time in the lead of matches as they have.\n",
      "\n",
      "Chelsea are just not performing well. It seems strange to talk of West Ham riding their luck when they have beaten the champions and done so with better shot counts for both total shots and shots on target. If we didn't know that Chelsea were the champions, and employers of some of the most expensive and best talent in the league, we would not be particularly surprised that West Ham out shot Chelsea at home."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Stoke 0-2 Watford\n",
      "\n",
      "Nice and timely away victory for Watford. They deserved it too. Stoke have brought in some talented players but they are yet to reap much benefit. I'd be worried if I were a Stoke fan, here I rank the teams on the addition of their total shot ratios and their shots on target ratios, Stoke are behind even Aston Villa:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pairs = [(stats.teamname, stats.tsr + stats.sotr)\n",
      "         for stats in epl.team_stats.values()]\n",
      "league_analysis.display_ranked_table(['Team', 'TSR+SOTR'], pairs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table><tr><th>Position</th> <th>Team</th> <th>TSR+SOTR</th></tr><tr><td>1</td> <td>Man City</td> <td>1.4377323420074348</td></tr>\n",
        "<tr><td>2</td> <td>Arsenal</td> <td>1.2478861087144089</td></tr>\n",
        "<tr><td>3</td> <td>Southampton</td> <td>1.21610019472473</td></tr>\n",
        "<tr><td>4</td> <td>Tottenham</td> <td>1.2078947368421051</td></tr>\n",
        "<tr><td>5</td> <td>Liverpool</td> <td>1.1481699538808754</td></tr>\n",
        "<tr><td>6</td> <td>Man United</td> <td>1.0796615905245348</td></tr>\n",
        "<tr><td>7</td> <td>Leicester</td> <td>1.0470664087685364</td></tr>\n",
        "<tr><td>8</td> <td>Swansea</td> <td>1.0435937636094417</td></tr>\n",
        "<tr><td>9</td> <td>Norwich</td> <td>1.0090895025914752</td></tr>\n",
        "<tr><td>10</td> <td>West Ham</td> <td>0.9728781856441431</td></tr>\n",
        "<tr><td>11</td> <td>Chelsea</td> <td>0.9501465201465202</td></tr>\n",
        "<tr><td>12</td> <td>Bournemouth</td> <td>0.9429577464788732</td></tr>\n",
        "<tr><td>13</td> <td>Watford</td> <td>0.909973939824686</td></tr>\n",
        "<tr><td>14</td> <td>Crystal Palace</td> <td>0.9005376344086022</td></tr>\n",
        "<tr><td>15</td> <td>Everton</td> <td>0.899438202247191</td></tr>\n",
        "<tr><td>16</td> <td>Aston Villa</td> <td>0.8294219904389396</td></tr>\n",
        "<tr><td>17</td> <td>Stoke</td> <td>0.8110290093048713</td></tr>\n",
        "<tr><td>18</td> <td>West Brom</td> <td>0.7935439046550158</td></tr>\n",
        "<tr><td>19</td> <td>Newcastle</td> <td>0.757066230456119</td></tr>\n",
        "<tr><td>20</td> <td>Sunderland</td> <td>0.6717939854821984</td></tr></table>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7efc49420dd8>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Liverpool 1-1 Southampton\n",
      "\n",
      "That same table shows why Liverpool fans needn't be quite as upset as they probably are. Southampton are also doing perfectly fine. Both could do with a bit of an uptick in luck/efficiency.\n",
      "\n",
      "That's all for this week. Thanks for reading this far."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}