notebooks/exploring_team_valuation_nba.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/noahgift/.socialpowernba/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEAM</th>\n",
       "      <th>GMS</th>\n",
       "      <th>PCT</th>\n",
       "      <th>TOTAL_MILLIONS</th>\n",
       "      <th>AVG_MILLIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>41</td>\n",
       "      <td>104</td>\n",
       "      <td>0.888882</td>\n",
       "      <td>0.021680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>41</td>\n",
       "      <td>103</td>\n",
       "      <td>0.811366</td>\n",
       "      <td>0.019789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>41</td>\n",
       "      <td>101</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.017608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>0.805400</td>\n",
       "      <td>0.019643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>0.813050</td>\n",
       "      <td>0.019830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               TEAM  GMS  PCT  TOTAL_MILLIONS  AVG_MILLIONS\n",
       "0     Chicago Bulls   41  104        0.888882      0.021680\n",
       "1  Dallas Mavericks   41  103        0.811366      0.019789\n",
       "2  Sacramento Kings   41  101        0.721928      0.017608\n",
       "3        Miami Heat   41  100        0.805400      0.019643\n",
       "4   Toronto Raptors   41  100        0.813050      0.019830"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_df = pd.read_csv(\"../data/nba_2017_attendance.csv\");attendance_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>SALARY_MILLIONS</th>\n",
       "      <th>ENDORSEMENT_MILLIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>31</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>27</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>12</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                NAME                   TEAM  SALARY_MILLIONS  \\\n",
       "0       LeBron James    Cleveland Cavaliers               31   \n",
       "1       Kevin Durant  Golden State Warriors               27   \n",
       "2      Stephen Curry  Golden State Warriors               12   \n",
       "3       James Harden        Houston Rockets               27   \n",
       "4  Russell Westbrook  Oklahoma City Thunder               27   \n",
       "\n",
       "   ENDORSEMENT_MILLIONS  \n",
       "0                    55  \n",
       "1                    36  \n",
       "2                    35  \n",
       "3                    20  \n",
       "4                    15  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endorsement_df = pd.read_csv(\"../data/nba_2017_endorsements.csv\");endorsement_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEAM</th>\n",
       "      <th>VALUE_MILLIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>3300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>2200.0</td>\n",
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      "text/plain": [
       "                    TEAM  VALUE_MILLIONS\n",
       "0        New York Knicks          3300.0\n",
       "1     Los Angeles Lakers          3000.0\n",
       "2  Golden State Warriors          2600.0\n",
       "3          Chicago Bulls          2500.0\n",
       "4         Boston Celtics          2200.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valuations_df = pd.read_csv(\"../data/nba_2017_team_valuations.csv\");valuations_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>NAME</th>\n",
       "      <th>POSITION</th>\n",
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       "      <th>SALARY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>SF</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>30963450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike Conley</td>\n",
       "      <td>PG</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>26540100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>C</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>26540100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dirk Nowitzki</td>\n",
       "      <td>PF</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>25000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carmelo Anthony</td>\n",
       "      <td>SF</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>24559380.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "              NAME POSITION                 TEAM      SALARY\n",
       "0     LeBron James       SF  Cleveland Cavaliers  30963450.0\n",
       "1      Mike Conley       PG    Memphis Grizzlies  26540100.0\n",
       "2       Al Horford        C       Boston Celtics  26540100.0\n",
       "3    Dirk Nowitzki       PF     Dallas Mavericks  25000000.0\n",
       "4  Carmelo Anthony       SF      New York Knicks  24559380.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_df = pd.read_csv(\"../data/nba_2017_salary.csv\");salary_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>GP</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>MIN</th>\n",
       "      <th>OFFRTG</th>\n",
       "      <th>DEFRTG</th>\n",
       "      <th>NETRTG</th>\n",
       "      <th>...</th>\n",
       "      <th>AST RATIO</th>\n",
       "      <th>OREB%</th>\n",
       "      <th>DREB%</th>\n",
       "      <th>REB%</th>\n",
       "      <th>TO RATIO</th>\n",
       "      <th>EFG%</th>\n",
       "      <th>TS%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>PACE</th>\n",
       "      <th>PIE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>OKC</td>\n",
       "      <td>28</td>\n",
       "      <td>81</td>\n",
       "      <td>46</td>\n",
       "      <td>35</td>\n",
       "      <td>34.6</td>\n",
       "      <td>107.9</td>\n",
       "      <td>104.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>...</td>\n",
       "      <td>23.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>27.9</td>\n",
       "      <td>16.7</td>\n",
       "      <td>12.2</td>\n",
       "      <td>47.6</td>\n",
       "      <td>55.4</td>\n",
       "      <td>40.8</td>\n",
       "      <td>102.31</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boban Marjanovic</td>\n",
       "      <td>DET</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>8.4</td>\n",
       "      <td>104.3</td>\n",
       "      <td>102.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>...</td>\n",
       "      <td>5.1</td>\n",
       "      <td>16.6</td>\n",
       "      <td>31.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>54.5</td>\n",
       "      <td>60.6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>97.20</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Demetrius Jackson</td>\n",
       "      <td>BOS</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>124.2</td>\n",
       "      <td>117.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>...</td>\n",
       "      <td>31.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>75.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>87.46</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anthony Davis</td>\n",
       "      <td>NOP</td>\n",
       "      <td>24</td>\n",
       "      <td>75</td>\n",
       "      <td>31</td>\n",
       "      <td>44</td>\n",
       "      <td>36.1</td>\n",
       "      <td>104.2</td>\n",
       "      <td>102.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>...</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>26.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>51.8</td>\n",
       "      <td>58.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>100.19</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>HOU</td>\n",
       "      <td>27</td>\n",
       "      <td>81</td>\n",
       "      <td>54</td>\n",
       "      <td>27</td>\n",
       "      <td>36.4</td>\n",
       "      <td>113.6</td>\n",
       "      <td>107.3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>...</td>\n",
       "      <td>27.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>21.2</td>\n",
       "      <td>12.3</td>\n",
       "      <td>14.1</td>\n",
       "      <td>52.5</td>\n",
       "      <td>61.3</td>\n",
       "      <td>34.1</td>\n",
       "      <td>102.98</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              PLAYER TEAM  AGE  GP   W   L   MIN  OFFRTG  DEFRTG  NETRTG  \\\n",
       "0  Russell Westbrook  OKC   28  81  46  35  34.6   107.9   104.6     3.3   \n",
       "1   Boban Marjanovic  DET   28  35  16  19   8.4   104.3   102.4     1.9   \n",
       "2  Demetrius Jackson  BOS   22   5   1   4   3.4   124.2   117.8     6.3   \n",
       "3      Anthony Davis  NOP   24  75  31  44  36.1   104.2   102.5     1.7   \n",
       "4       James Harden  HOU   27  81  54  27  36.4   113.6   107.3     6.3   \n",
       "\n",
       "   ...   AST RATIO  OREB%  DREB%  REB%  TO RATIO  EFG%   TS%  USG%    PACE  \\\n",
       "0  ...        23.4    5.3   27.9  16.7      12.2  47.6  55.4  40.8  102.31   \n",
       "1  ...         5.1   16.6   31.3  23.9       5.7  54.5  60.6  24.8   97.20   \n",
       "2  ...        31.1    9.1   11.8  10.3       0.0  87.5  75.3  17.2   87.46   \n",
       "3  ...         7.3    6.7   26.9  17.0       8.4  51.8  58.0  32.6  100.19   \n",
       "4  ...        27.6    3.5   21.2  12.3      14.1  52.5  61.3  34.1  102.98   \n",
       "\n",
       "    PIE  \n",
       "0  23.0  \n",
       "1  19.6  \n",
       "2  19.4  \n",
       "3  19.2  \n",
       "4  19.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pie_df = pd.read_csv(\"../data/nba_2017_pie.csv\");pie_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>GP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>ORPM</th>\n",
       "      <th>DRPM</th>\n",
       "      <th>RPM</th>\n",
       "      <th>WINS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LeBron James, SF</td>\n",
       "      <td>CLE</td>\n",
       "      <td>74</td>\n",
       "      <td>37.8</td>\n",
       "      <td>6.49</td>\n",
       "      <td>1.93</td>\n",
       "      <td>8.42</td>\n",
       "      <td>20.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stephen Curry, PG</td>\n",
       "      <td>GS</td>\n",
       "      <td>79</td>\n",
       "      <td>33.4</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0.14</td>\n",
       "      <td>7.41</td>\n",
       "      <td>18.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jimmy Butler, SG</td>\n",
       "      <td>CHI</td>\n",
       "      <td>76</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.82</td>\n",
       "      <td>1.80</td>\n",
       "      <td>6.62</td>\n",
       "      <td>17.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russell Westbrook, PG</td>\n",
       "      <td>OKC</td>\n",
       "      <td>81</td>\n",
       "      <td>34.6</td>\n",
       "      <td>6.74</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>6.27</td>\n",
       "      <td>17.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Draymond Green, PF</td>\n",
       "      <td>GS</td>\n",
       "      <td>76</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1.55</td>\n",
       "      <td>5.59</td>\n",
       "      <td>7.14</td>\n",
       "      <td>16.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    NAME TEAM  GP   MPG  ORPM  DRPM   RPM   WINS\n",
       "0       LeBron James, SF  CLE  74  37.8  6.49  1.93  8.42  20.43\n",
       "1      Stephen Curry, PG   GS  79  33.4  7.27  0.14  7.41  18.80\n",
       "2       Jimmy Butler, SG  CHI  76  37.0  4.82  1.80  6.62  17.35\n",
       "3  Russell Westbrook, PG  OKC  81  34.6  6.74 -0.47  6.27  17.34\n",
       "4     Draymond Green, PF   GS  76  32.5  1.55  5.59  7.14  16.84"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plus_minus_df = pd.read_csv(\"../data/nba_2017_real_plus_minus.csv\");plus_minus_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PS/G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>OKC</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>34.6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.845</td>\n",
       "      <td>1.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>10.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>James Harden</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>HOU</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>36.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>18.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.847</td>\n",
       "      <td>1.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Isaiah Thomas</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>BOS</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>33.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Anthony Davis</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>NOP</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>36.1</td>\n",
       "      <td>10.3</td>\n",
       "      <td>20.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.802</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>DeMar DeRozan</td>\n",
       "      <td>SG</td>\n",
       "      <td>27</td>\n",
       "      <td>TOR</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>35.4</td>\n",
       "      <td>9.7</td>\n",
       "      <td>20.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>27.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rk             Player Pos  Age   Tm   G  GS    MP    FG   FGA  ...     FT%  \\\n",
       "0   1  Russell Westbrook  PG   28  OKC  81  81  34.6  10.2  24.0  ...   0.845   \n",
       "1   2       James Harden  PG   27  HOU  81  81  36.4   8.3  18.9  ...   0.847   \n",
       "2   3      Isaiah Thomas  PG   27  BOS  76  76  33.8   9.0  19.4  ...   0.909   \n",
       "3   4      Anthony Davis   C   23  NOP  75  75  36.1  10.3  20.3  ...   0.802   \n",
       "4   5      DeMar DeRozan  SG   27  TOR  74  74  35.4   9.7  20.9  ...   0.842   \n",
       "\n",
       "   ORB  DRB   TRB   AST  STL  BLK  TOV   PF  PS/G  \n",
       "0  1.7  9.0  10.7  10.4  1.6  0.4  5.4  2.3  31.6  \n",
       "1  1.2  7.0   8.1  11.2  1.5  0.5  5.7  2.7  29.1  \n",
       "2  0.6  2.1   2.7   5.9  0.9  0.2  2.8  2.2  28.9  \n",
       "3  2.3  9.5  11.8   2.1  1.3  2.2  2.4  2.2  28.0  \n",
       "4  0.9  4.3   5.2   3.9  1.1  0.2  2.4  1.8  27.3  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "br_stats_df = pd.read_csv(\"../data/nba_2017_br.csv\");br_stats_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ELO</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>CONF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1770</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1661</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1636</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1617</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1602</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ELO                   TEAM  CONF\n",
       "0  1770  Golden State Warriors  West\n",
       "1  1661      San Antonio Spurs  West\n",
       "2  1636   Los Angeles Clippers  West\n",
       "3  1617              Utah Jazz  West\n",
       "4  1602        Houston Rockets  West"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elo_df = pd.read_csv(\"../data/nba_2017_elo.csv\");elo_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attendance_valuation_df = attendance_df.merge(valuations_df, how=\"inner\", on=\"TEAM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEAM</th>\n",
       "      <th>GMS</th>\n",
       "      <th>PCT</th>\n",
       "      <th>TOTAL_MILLIONS</th>\n",
       "      <th>AVG_MILLIONS</th>\n",
       "      <th>VALUE_MILLIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>41</td>\n",
       "      <td>104</td>\n",
       "      <td>0.888882</td>\n",
       "      <td>0.021680</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>41</td>\n",
       "      <td>103</td>\n",
       "      <td>0.811366</td>\n",
       "      <td>0.019789</td>\n",
       "      <td>1450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>41</td>\n",
       "      <td>101</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.017608</td>\n",
       "      <td>1075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>0.805400</td>\n",
       "      <td>0.019643</td>\n",
       "      <td>1350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>0.813050</td>\n",
       "      <td>0.019830</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TEAM  GMS  PCT  TOTAL_MILLIONS  AVG_MILLIONS  VALUE_MILLIONS\n",
       "0     Chicago Bulls   41  104        0.888882      0.021680          2500.0\n",
       "1  Dallas Mavericks   41  103        0.811366      0.019789          1450.0\n",
       "2  Sacramento Kings   41  101        0.721928      0.017608          1075.0\n",
       "3        Miami Heat   41  100        0.805400      0.019643          1350.0\n",
       "4   Toronto Raptors   41  100        0.813050      0.019830          1125.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_valuation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x10890b320>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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rqraqMq+ttqpyUL8611RNzGvbczje6zHy6F+daysn52+v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QkXAGfrPT5Xs66+eADTau5PBWDDGHNo4b4AAMbY44yxGMZYzKVLl9ql4IR0JKqzpLuhOku6G6qz\npLuhOkt6MsZY8GOPPVZ3mv7y5cudIiIi+rdH3hUVFWzAgAF+x44dU9eue+mll5wefPBBj5bmkZiY\nqNJqtY1H9F8zzTvvvNPH399fV1hYKGtunwMHDljNmzfPrbntJpMJNjY2QS0tZ09ws050uQTASMbY\nCQAjAeQCMLcmA875Ws55COc8pE+fPh1RRkLaFdVZ0t1QnSXdDdVZ0t1QnSU9mVKp5Lt377Y7f/58\nu18R0tLSkr/77rtnFyxY4C5JEjIzMxVffPFFnw8++OBcS/Y3mUytPuYHH3zgsH79+j779+9Pd3Bw\naPa36+jRo8s/++yzs81tvxV1RVAiF0D9yJBrzbo6nPM8zvk0zvlgAMtq1l1uyb7k+rjEwSurwHnN\nrcS7ukiEkHqojRJCSMeifpY0h+pG0yQu2RtN5QGcS8FGU3mAxCX7G81TJpPxuXPnXoqKinJqvC0v\nL08+fvx4L39/f52/v7/up59+sgIAjUajLygokEmShN69ewetXr3aAQCmTp3q+d///rdX/Tzuu+++\nK05OTqaPPvrI4cknn3R7/vnn8/r06WM2m82YP3++q4+Pj59Go9F//vnndgCwfft2m9DQUN9Ro0Z5\n+/r6+tXPKzExUaXT6fSHDh2ybOq5/Pvf/7ZfvXp1v/3796c7OTmZASA4ONh34cKFLgEBATpPT0//\nffv2WdUeZ8yYMV4AUFxcLEybNs1To9HoNRqNfuPGjb0bvw6BgYHabdu29crMzFQEBwf7arVavY+P\nj19tfj1Bu0elWuAvAD6MsQGoDig8AODB+gkYY44AijjnEoAXAKyv2bQXQFS9yS3H1WwnLcQlDpRV\nwLTpe0hnzkEY6ArFQ/eAW1uCCayri0fILY/aKCGEdCzqZ0lzqG40TeKSfbmxyGPLn0uFrII4eDoG\nKWcNifKwUtlDYELRjeT93HPP5QcEBPi98sorF+qvf+KJJ9wiIiIujh8/viwjI0M5fvx4nzNnziSF\nhISU/fzzz9ZeXl5GV1dX46FDh6yfeuqpwuPHj1t/+eWXOY3zX7NmzdkhQ4boPD09K5988skiAFi/\nfr1denq6OiUlJSkvL08eGhqqHzduXCkAJCYmWsbHxyf5+PhUJSYmqgDg+PHjFrNnzx745ZdfZoaG\nhhoaH+PcuXOqF1980S0mJibZxcVFrL+Nc46EhISUr776yjYyMrL/2LFjM+pv/+c//9nf0dFRTE9P\nT5YkCfVP+8jOzlZMnjzZ+4033jg3ZcqU0mXLlvWbMGFCyRtvvHFBFEWUl5ffrGc9tFqnPxHOuQjg\nKVQHGFIAfMM5T2KMRTLGptQkuwtAGmMsHYATgDdq9i0C8BqqAxt/AYisWUdaqspU3dGeygEkCdKp\nHJg2fQ9UtX6IEiGkA1AbJYSQjkX9LGkO1Y0mmUSDy5Y/lwpnLsVA4iLOXIrBlj+XCibR0OTcfq1h\nb28vzZgxo/Ctt97qW3/94cOHez3zzDPuWq1Wf88993iXlZXJSkpKhBEjRpT99ttv1vv377eZP39+\nfkpKijozM1PRq1cvc69evaTG+Xt6eprCwsKuPPbYY3WTshw6dMj6/vvvL5LL5XB3dxdvv/320kOH\nDlkBQFBQUJmPj09VbdrCwkL59OnTvTZv3nymqYAEADg4OJj69u1b9dVXX111VcgZM2ZcBoCwsLCK\nc+fOKRtv//3333tFRETkA4AgCOjTp48ZAERRZOHh4ZoVK1acnTJlSikADBkypHzTpk2Oixcvdv7r\nr7/Utra2Vz3f7qpLoiuc892ccw3n3ItzXhtwWM4531lzfxvn3KcmzXzOubHevus55941y+ddUf5u\nTaWAdKbhqVTSmXOAStFFBSKENEBtlBBCOhb1s6Q5VDeapJSrlVkFcQ3WZRXEQSlXX/Ujuy1eeOGF\ni5s3b3as/88/5xzHjx9PSU1NTU5NTU3Oz88/aWtrK40dO7b06NGjNocPH7YeN25cqYODg7hp0ya7\noUOHljaXvyAIkMmanXeyAUtLywY/9G1sbMz9+vUz/fbbb82eKqFWq6W9e/dmrF27tu+6desaBCYs\nLCwkoPpUFbPZ3OLhNnK5nOv1+ooff/yx7pSUKVOmlP76669pzs7OpkceeWTAxx9/fMOn0NwsesyQ\nD9JCRhOEga4NVgkDXQHjrR0BJuSmQW2UEEI6FvWzpDlUN5pUJRqqPB0bXgzC0zEIVaKhqpldWsXJ\nycl8zz33FG/evNmxdt3w4cOvvPnmm3WjJ44cOaIGAG9vb1NxcbE8MzPTQq/XVw0bNqzso48+6jdy\n5Miylh5vxIgRZd9++62d2WzG2bNn5TExMdbDhw8vbyqtUqnke/bsOfXll1/2+fTTT68aCVHLzc1N\n3LVrV8by5ctdt2/fbtPSsowcOfLKqlWr+gKAJEm4dOlSXfTkP//5T2ZCQoL65ZdfdgKA9PR0pZub\nm2nJkiUFs2fPLjhx4kST81t0RxSUuNUoFVA8dA8Eb3dAECB4u0Px0D2A8taOABNy06A2SgghHYv6\nWdIcqhtNUsjVubOGREkD+4RAYHIM7BOCWUOiJIVc3W4XHFi2bNmFy5cv1813uHbt2rPHjx+30mg0\nei8vL7/Vq1fXXX4mKCiofMCAAZUAcNddd5Xm5+crxowZ0+xIicYeffTRYh8fn0qtVusXHh6uiYqK\nOtd4Loj6bG1tpT179mS89957/bZu3WrbXDp/f3/jt99+e/rxxx8f8Pvvv7coYPD222/n5efnK3x8\nfPx0Op3+p59+sq7dplAo8MMPP5zZt2+f7YoVKxx3795to9Pp/HQ6nX7nzp12S5YsyW/pc77ZMc57\n/oyyISEhPCYmpquLcdPgEq8+N06lqI78KhW39OQ9TejyF4Pq7K2tDW2U6izpbqjOki5F/SxpTg/6\nnnzNQsfHx2cFBgYWtDQziUv2JtHgopSrlVWioUohV+fe6CSX5NYVHx/vGBgY6Fn7uCuuvkG6GBMY\nYFFzCphFu5wKRghpR9RGCSGkY1E/S5pDdaNpAhOKVAqrIgBQKXrMlSjJTYJO3yCEEEIIIYQQQkiX\noKAEIYQQQgghhBBCugQFJQghhBBCCCGEENIlKChBCCGEEEIIIYSQLkFBCUIIIYQQQgghhHQJCkoQ\nQgghhBBCCOlUMpksWKvV6n19ffV6vV63b9++Nl3W4/nnn+/XnuVavXq1g4+Pj59Go9HrdDr98uXL\nna6VPiIion9tmujoaIesrCxF7baZM2d6xMbGWrRn+XoiCkoQQgghhBBCCOlUKpVKSk1NTU5LS0t+\n7bXXcpcuXeralnyio6Od26tM33zzTa81a9b03bdvX3p6enry8ePHU2xtbc0t3X/Tpk2OOTk5dUGJ\nr7/+Ojs4OLiyvcrXU1FQghBCCCGEEEJIsyQu2VeYygM4l4IrTOUBEpfs2zP/kpISma2trQgAkiTh\niSeecK0drbBu3To7AMjOzlaEhIT4arVavY+Pj9+ePXusFy5c6GI0GgWtVqufMmXKAAB45ZVXnHx8\nfPx8fHz8IiMj+wJAWlqacuDAgX4PPPCAh7e3t98dd9zhU1ZWxhqX45133nF+6623znl6epoAQK1W\n88WLFxcAQFJSkmrEiBE+fn5+uuDgYN8TJ040GAHx+eef2yUmJlrOnTt3oFar1ZeVlbHQ0FDf33//\n3RIAtm3b1kuv1+t8fX31w4YN0wDArl27rLVarV6r1ep1Op2+uLj4lvx9Lu/qAhBCCCGEEEIIuTlJ\nXLIvMRZ5rIpdKqQUxUFnH6SMCI7ysFXZQ2BCUVvzrQ0mGI1GVlBQoNi9e3c6AGzYsKF3QkKCOiUl\nJen8+fPy0NBQ3bhx48rWr19vHx4eXvL2229fEEURpaWlwoQJE8q++OKLvqmpqckAcPDgQcvNmzc7\nxMbGpnDOERwcrAsPDy91dHQ05+TkWGzatOlMWFhY9qRJkwZu2LDBbuHChQ3Kn5GRob7jjjsqmirv\n/PnzPdauXZsdEBBgPHDggNWCBQvcjx49ml67/dFHHy3++OOP+65YseLsnXfe2SCPvLw8+VNPPeX5\n66+/pmq12qqLFy/KAGDlypX9oqOjs8eNG1deUlIiWFpaSm19PbuzLonEMMYmMMbSGGOnGGPPN7Hd\nnTH2C2PsBGPsJGNsUs16T8aYgTEWV7P8u/NLTwghhBBCCCG3hkrR4LIqdqmQWBgDMxeRWBiDVbFL\nhUrR4HIj+daevpGZmZn03//+N+PRRx8dIEkSDh48aHP//fcXyeVyuLm5iUOGDCk7dOiQ5dChQ8u3\nbNniGBER0f/YsWNqOzu7q37A//rrr9aTJk263KtXL8nW1la6++67i3/55RcbAHBxcTGGhYUZAGDw\n4MEVWVlZqpaWtaSkRDhx4oT1jBkzvLRarX7hwoUe+fn5iuvvWVcuq9DQ0FKtVlsFAE5OTmYAGDp0\naNmSJUvcXn/99b4FBQUyhaLFWfYonR6UYIzJAHwEYCIAPYBZjDF9o2QvAviGcz4YwAMA1tTbdppz\nHlSz/L1TCk0IIYQQQgghtyC1XK1MKYprsC6lKA5quVrZXscYM2ZMeXFxsfz8+fPNjuSfOHFi2e+/\n/57m4uJS9be//W3A6tWrHVpzDKVSyWvvy2QyLoriVadveHt7Gw4fPmzZeL3ZbIaNjY2YmpqaXLuc\nOXMmqTXHb0pUVNSFTz/9NNtgMAgjRozQNj4l5FbRFSMlQgGc4pyf4ZxXAdgK4P8apeEAetXctwWQ\n14nlI4QQQgghhBACwCAaqnT2QQ3W6eyDYBANVe11jBMnTlhIkgQnJyfxzjvvLN22bZu9KIrIy8uT\nHzt2zHrEiBHl6enpSldXV9PixYsL5s6de+n48eOWACCXy7nRaGQAMGrUqLLdu3f3Li0tFa5cuSLs\n3r3bbtSoUaUtLcc///nPCy+88IJrTk6OHAAqKyvZqlWrHO3t7SVXV9eq9evX2wHV81788ccf6sb7\nW1tbm0tKSmSN1991113lx44ds0lNTVUCQO3pG0lJSarQ0FDDG2+8cWHQoEHliYmJt2RQoivmlHAB\ncLbe43MAhjRK8wqAnxhjTwOwAjCm3rYBjLETAK4AeJFzfrADy0oIIYQQQgghtywLuTo3Ijiq/pwS\niAiOkizk6twbybd2TgkA4Jzj448/zpLL5ZgzZ87lI0eOWOt0Oj/GGH/11VfPubu7ix9++KFDdHR0\nP7lczi0tLc1fffVVJgDMnj37kk6n0/v7+1fs3Lkz88EHHyy87bbbdAAwZ86cS3fccYchLS2tRaM6\nZs6cWXLhwgV5eHi4L+ccjDHMnj27AAC2bNly5rHHHvN4++23nUVRZFOnTi0aNmyYof7+c+fOLXj6\n6ac9nnvuOSkmJialdn3//v3F6OjorKlTp3pLkgQHBwfTkSNHMt55552+R44c6cUY476+vob77ruv\n5EZe0+6Kcc6vn6o9D8jYfQAmcM7n1zyeA2AI5/ypemkiasq2kjE2DMBnAPwBKABYc84LGWPBALYD\n8OOcX2niOI8DeBwA3N3dg7Ozszv6qXUbnEsQRQPkcnXdLWO35ESvzblqKFenHJTqbI/Sye2M6izp\nbqjOklbr4u8vt2Sdpe+M3do162x8fHxWYGBgQUszk7hkXykaXNRytdIgGqos5OrcG5nkktza4uPj\nHQMDAz1rH3dFr5ILwK3eY9eadfXNA/ANAHDO/wBgAcCRc27knBfWrI8FcBqApqmDcM7Xcs5DOOch\nffr0aeen0H1xLsFoKMbRfYuw44uhOLpvEYyGYnB+S070elOhOttz3CrtjOos6W6oznZft0q/2lhX\n1tlb9TUnTROYUGSpsEpgTIi1VFglUECCtKdWByUYY5aMMUW9x76MsUWMsWktzOIvAD6MsQGMMSWq\nJ7Lc2ShNDoDwmvx1qA5KXGKM9amZKBOMsYEAfACcae1zuJWJogF//foCCi7EgHMRBRdi8NevL0AU\nDdffmRDSItTOCCGkfVG/2vnoNSeEdJa2jJTYA8ATABhj3gD+ADAQwJOMsTevtzmpWMMAACAASURB\nVDPnXATwFIC9AFJQfZWNJMZYJGNsSk2yxQAeY4zFA9gC4BFefZ7JnQBOMsbiAGwD8HfOOUXpWkEu\nV6PwYsPZcwsvxkEuv2qeFkJIG1E7I4SQ9kX9auej15wQ0lnaMtGlHec8o+b+wwC2cM6frhn1EAvg\nhetlwDnfDWB3o3XL691PBnBHE/v9B8B/2lBmUkMUDXBwCkLBhZi6dQ5OQRBFAxQKqy4sGSE9B7Uz\nQghpX9Svdj56zQkhnaUtIyXqz4w5GsA+AKi5vCedZHaTk8vVuP2uN+HYLwSMyeHYLwS33/UmRb0J\naUfUzgghpH1Rv9r56DUnhHSWtoyUOMkYW4HqySm9AfwEAIyx3u1ZMNIxGBOgUtth6Nj3aCZlQjoI\ntTNCCGlf1K92PnrNCSGdpS29ymMAClA9r8Q4znlFzXo9gBXtVC7SgRgToFBYNbglhLQvameEENK+\nqF/tfPSak45kaWk5uL3zjIyM7KtSqW4rLCyUtXfeABAdHe0wd+5c9xvNpzXPPSIiov/y5cudbvSY\nN7NW9yyccwPn/C3O+TOc8/h6649wzje2b/EIIYQQQgghhJDr27Ztm72/v3/5pk2baBQ/AJPJ1NVF\naJG2XBL0F8bYgWaW/R1RSEIIIYQQQgghXUPikn25yRAgcR5cfSvZd8Rx0tLSlEOHDtVoNBr9sGHD\nNBkZGUoAWL9+vZ2Pj4+fr6+vPiQkxLepfZOSklQVFRWyyMjI3G+++aaufNHR0Q7jxo3zGjFihI+H\nh4f/3//+d9fabe+9956jp6enf0BAgO6BBx7wqB0FkZeXJx8/fryXv7+/zt/fX/fTTz9dNbtrc2l2\n7dplrdVq9VqtVq/T6fTFxcUt+s29efNm20GDBml1Op0+LCxMc/bs2aumWli5cqXjnXfe6VNWVsaS\nkpJUI0aM8PHz89MFBwf7njhxwgIApk+f7vnggw+6Dxo0SLtgwQLXtpanM7VlToklTawbCuCfAPJv\nrDiEEEIIIYQQQm4WEpfsi4xXPJYdXyPEFaUjyF6jfOO2hR72ql4QmFDUnsdasGCB++zZswuffvrp\nwvfff99hwYIFbj///PPpt956y/mnn35KHzBggKmgoKDJUzM2bNhgN3Xq1KIJEyaUPfbYYxZnz56V\nu7m5iQCQnJxsGR8fn6xWqyVvb2//JUuWXJTL5VixYoXz8ePHk3v37i2FhYVp/Pz8DADwxBNPuEVE\nRFwcP358WUZGhnL8+PE+Z86cSap/vObSrFy5sl90dHT2uHHjyktKSgRLS8sWXQxi7NixZQ888ECq\nIAhYtWqVY2RkZL9169adq90eFRXVZ//+/b327t17Sq1W8/nz53usXbs2OyAgwHjgwAGrBQsWuB89\nejQdAM6fP688fvx4qlwux+jRo73bUp7O1OqgBOc8tvY+Y2wkgJcAWAD4O+f8x3YsGyGEEEIIIYSQ\nLmQQjS7Ljq8RYgpTAAAxhSlYdnyNsOr2RS5WCnW7BiVOnDhh9eOPP54GgAULFhS9+uqrrgAQEhJS\nNnv2bM/p06cXz549u7ipfb/77juH77777pRMJsOkSZOKN27caLd06dJLADB8+PArDg4OZgDw9vau\nPH36tCo/P18+ZMiQUicnJzMATJ06tTg9Pd0CAA4fPtwrIyOj7lIzZWVlspKSkgYjDJpLM3To0LIl\nS5a43X///UWzZs0q9vLyalEQIDMzU3nvvfe6Xrp0SVFVVSW4ubkZa7dt3brVoX///lV79+49rVKp\neElJiXDixAnrGTNmeNWmqaqqYrX3p02bViyXV//Ub2t5OlNbRkqAMTYewIsAjADe4Jz/0q6lIoQQ\nQgghhBDS5dRyC2VcUXqDdXFF6VDLLZSdVYbNmzfnHDhwwGrnzp22wcHB+tjY2OR+/fqZa7cfO3ZM\nnZ2drZowYYIGAEwmE3N1da2qDUoolUpem1Ymk3GTycSuPsr/cM5x/PjxFEtLS97aNFFRURfuvffe\nkh07dtiOGDFCu2vXrozBgwdXXu85PvXUU+7PPPPMhdmzZ5f88MMPNpGRkf1rt2m1WkNycrJlZmam\nQqvVVpnNZtjY2IipqanJTeVlbW1dF3hoa3k6U1vmlPgLwCcAtqL6lI0SxthttUt7F5C0P84lmKvK\nG9wSQtoXtTNCCGke9ZE3J3pfSFMMYmVVkL2mwbogew0MYmVVex9r8ODB5Z9++qkdAHzyySf2ISEh\nZUD1fBGjR48uf//99/Ps7OzEM2fONAiIbNiwwX7x4sV5ubm5Cbm5uQn5+fknL168qEhPT282cDJ8\n+PDyP//80+bSpUsyk8mEHTt22NXbduXNN9/sW/v4yJEj6ib2bzJNUlKSKjQ01PDGG29cGDRoUHli\nYqJFS557aWmpzN3d3QQAX3zxhUP9bUFBQRUfffRR9pQpU7yzsrIU9vb2kqura9X69evtAECSJPzx\nxx9XlfFGytOZ2jJSohxAGYD7apb6OIDRN1oo0nE4lyAay2A0lsBS3h8GQxFUki3kKmu6zBMh7aQt\n7YxLElBVBahUgNEIKJVgArVJQkjPY5ZEmEQDlApLlBZnojTjAPr6T4Pc0o6+i3QhziWIFcU4vXcp\nys7Hwdo5CF7jo1r8vtDnWMeTuFTdduRqVIkGKORqCJ3QZtRyVe4bty2sP6cE3rhtoaSWq3JvJN/K\nykrByclpUO3jBQsWXPz3v/+dM3fuXM8PPvign4ODg7hhw4YsAFi0aJFrVlaWinPOhg8ffmXo0KGG\n+nlt377d/vvvv8+ov27ixInFX375pb2Tk1OTl6AYMGCAadGiRedDQkJ0tra2ore3d6Wtra0ZANau\nXXt2/vz57hqNRm82m9mQIUNKw8LCcurv31yad955p++RI0d6Mca4r6+v4b777itpyXNftmxZ3qxZ\ns7xsbW3F4cOHl+bk5Kjq7zN+/PiyN99889zEiRN9Dhw4kL5ly5Yzjz32mMfbb7/tLIoimzp1atGw\nYcMMjY/VkvJ0NcZ5syNSeoyQkBAeExPT1cW4KYgmA8qNxfg69hVkFcTB0zEIM4NfgZXKDnJFk8G1\nW9E1h3N1Bqqz3Vtr2xmXJKC8DKavPoOUeQrCAG8oZs8DrKxb+oWO6izpbqjO3qIkyYxyYzG2HFv6\nv/7xtuUoS/4R/QY/CJnyqgnubxY9vs6aq8qR8UMESnP/dwwblxD4TF513felHT7HyHVIXKpuO3++\nUNd2Zg15E1Yqu+YCE9ess/Hx8VmBgYEFrTi+vUE0uqjlFkqDWFmllqty23uSy65QUlIi2NraSiaT\nCePHj/d+5JFHCubOnXu5q8vV08XHxzsGBgZ61j5uUy/BGOvLGHuVMbatZnmVMdb3+nuSriZCwl85\nu3B30DK8Pu0o7g5ahr9ydkEEDc8j5EZInKPcVAWJc1RCwF85u3DmUgwkLuLMpRh8HftK8+2sqqr6\ni9zpdECSIJ1Oh+mrz6r/cSKEkB5C4hwVZhFWFva4O2gZ/F3Dq/vH45Gw8RkNgf4c6TD1P6NqbxsT\nFGqUnY9rsK7sfFzL3hf6HOtwJtGALX++0OC7xZY/X4BJvOqP8Q4hMKHISqFOEBiLrb7t/gEJAHju\nuef6a7VavUaj8XN3dzc+9NBDFJDoAq0+fYMxdgeAzQC+ALChZnUwgGOMsdmc88PtVzzS3pQyNfw8\npiPy+GHEFf6EIId+WH7bdChl9EWAkLaSOEex0YAX/9qPuMILde0qv/QMTp7dCwDIKoiDSt5MO1Op\nIGWeaphn5qnqIbCEENIDNN1PPgcASDy3H9Z2npBMhpt5pES31dRr//rt4bBTqSGw//2ZLpkMsHYO\najBSwto5qNn3hXMJ3GQAU6jBBTPQy7bhcelzrF0p5WpkFTQMGmUVxEHZ3HcL0iJr1649d/1UpKO1\nZaTESgD3cs5f5pzvrFleBnAvgFUtyYAxNoExlsYYO8UYe76J7e6MsV8YYycYYycZY5PqbXuhZr+0\nmquAkFaoMJsQefwwYgvOw8w5YgvOI/L4YVSYmzzVihDSAgbRhBf/2n9Vuxqp/XtdGk/HIBib+zfD\naIQwwLvBKmGAd/U5uYQQ0gNcq5/0dAxClamCRkp0kKZe+xf/2g+D2PC7n6BQw2t8FGxcQsAEOWxc\nQuA1PqrJ94VzCeaKYpz/LgJnVg7D+e8iIEyaBCEo+H/59aDPMc4lmEzlDW47W5VogKdjUIN1no5B\nqOqkkRKEdKS2BCV6cc5PNF7JOY8DYHO9nRljMgAfAZgIQA9gFmNM3yjZiwC+4ZwPBvAAgDU1++pr\nHvsBmABgTU1+pIUs5UrEFV5osC6u8AIs5Z12RR9Cehy1XNFku3Lu5QaByTGwTwhmhUZB0Vx3pVRC\nMXseBC8NIAgQvDTV5+IqqV0SQro/zqVr9pOzQqOgkKtpkssO0txrr5YrGqxjTIDc0g4+k1cheOEf\n8Jm8qtlJLrnJgIvfL0VlTgwgiajMicHFPS9DNmFyj/sc41xCZWUx9h9YhA2bhmL/gUWorCzu9MCE\nQq7GrCFvYmCfkP99txjyJhQ0UoL0AG25+gZjjNlxzosbrbRHy4IcoQBOcc7P1Oy3FcD/Aah/jVUO\noFfNfVsAeTX3/w/AVs65EUAmY+xUTX5/tOF53JIMYhWCHPohtuB83bogh34wiFWwUtAQO0LawiCa\nmmxX5aZKvD71DxRfycTp9P/CT/8gmgpLMEEAt7KG4pEFNGs5IaTHEUUDrlSWNtlPVoim6on6BPqP\nqaM09xllEE2wUjQMGjAm1J2qca1TaZhCjcpzDU8lqDwXB8G+L1Rvre5Rn2OiaMBvv7+ACxerT2u5\ncDEGv/3+AsJHvweFovNONxKYACuVHR4Oe6/Tr75BSEdrSy1+D8BPjLGRjDGbmuUuAD/WbLseFwBn\n6z0+V7OuvlcAPMQYOwdgN4CnW7EvuQa1TIFXBt+BYEdnyBhDsKMzXhl8B9QyxfV3JoQ0SS1X4PXb\nw69qV7FHX8OGTbfj+533I/7kOsiv8W8GEwQwCwswxqpve8AXOUIIAQC5XI3EuNVXff94/fZwqMEp\nINHBmvqMev328KtGSrQGNxlg4drwVAIL16DqOSZ62OeYXK7GxfyGAZiL+XHX/EzvKAIToFJYgdXc\nUkCC9BStrsmc87UAXgXwGoCsmiUSwOuc80/aqVyzAHzBOXcFMAnARtbKMX2MsccZYzGMsZhLly61\nU7G6P7PZgLzT/8GrAYNweMo8vBowCHmn/wOzmc5H62pUZ7svgTHYqdRYMXQ8Dv/ffKwYOg55p/+D\nrKy9dWmc+gZB7GHnfVKdJd0N1dmuIYoGGCrykXL83brvH68H3Q4bARDo9NFrao86e/Vn1PirJrls\ndbkUajjdEwUL9xBAkMPCPQRO90SB9cB5QUTRAKe+DQMwPfEzvSvIZLJgrVar9/Hx8Zs4ceLA0tLS\nVv3ee/755/vVf2xpaTm4PcqVlpam9PHx8au/7tixY2qtVqvXarV6W1vbIBcXlwCtVqsPCwvTtCbv\n4cOH+xQXFwsmkwk2NjZBjbebTKa610Wr1er9/Px0+/fvtwKAxMRElVarbTztQY/QpvAa5/wHzvmd\nnHOHmuVOzvn3Ldw9F4BbvceuNevqmwfgm5pj/QHAAoBjC/etLeNaznkI5zykT58+LSxazyeXq6HT\nzoRQMzJCkCmg087skmgvaYjqbHfHAfa/S6zptDPRzykEjMnRzykEI++M6nHtjOos6W6oznYOiUso\nF43Vl58UjZDJLDDyzjdRaSjEj9/PwE/7/g61XA65XEXzSFxHe9VZgTFYKZQNboGr3yuphfMkMCZA\nZmkH52mrMHDxH3CetgqyZuaf6O7kcjVG3vlmo8/0N3vcZ3pXUKlUUmpqanJGRkaSQqHgK1eubFEl\nlyQJZrMZ0dHRzh1dxlqhoaGG1NTU5NTU1OQxY8Zcfv3118+lpqYmHzlyJL1+OpPp2hcPOHToUIad\nnd01G5qlpaW59lgvvfRS3osvvtgpZwaIotgZh2lSWy4J+iGq53xoEuf8H9fJ4i8APoyxAagOKDwA\n4MFGaXIAhAP4gjGmQ3VQ4hKAnQA2M8ZWAegPwAfAsdY+h1sZB1DOGZad3I24wmwEOXggKngmlADa\nHi8n5NYmcQnFxnIsjf26QbsKD4+GXKaEKBogp0ncCCG3gOb6QztVb4SPfg9yuZr6xJtE8+9Vy04L\nYEwAq5l3gvXgS7kyJsDCwu6Wr78Sl+wNoslFLVcqDWJVlVquyBWYUNRe+Q8fPrzs5MmTagB45ZVX\nnL766itHAJgzZ86l5cuX56elpSnHjx+vGTx4cFlCQoJVUFBQudFoFLRarV6j0Rh27tyZWZtXSUmJ\nMGHCBO+SkhKZKIps+fLleQ899NDltLQ05cSJE31CQ0PLYmJirJ2cnKr27t17ytramh88eNBy/vz5\nngBw1113XWlN2bdv324TFRXV38rKypydna06c+ZM0ujRo70vXryoMBqNwsKFCy9GREQUAICTk9Og\npKSkJFtbW3NL8i4pKZHZ2tpeFS1ISkpSzZ0717OiokImCAL/8MMPc0aPHl3+9NNPu+zbt88WAIqK\niuSjR48u2bp1a/aHH37osHbt2r4mk4mFhISUffnllzmSJMHe3j5oxowZhYcOHbJZs2ZN9rfffmv3\n888/28pkMh4eHl7y8ccfNzkAoL21ZaLLmGtsazZYUZeAc5Ex9hSAvQBkANZzzpMYY5EAYjjnOwEs\nBrCOMbaoJs9HOOccQBJj7BtUT4opAniSc96iN5RUM5hNWBr7NWILqtttbEEmlsZ+jVVD5sBKThNd\nEtIW12pXCiZ06kRYhBDSla75PaOmL6Q+8eZA3wlbjtX7LL8V66/EJftiY7nH0tivhZoAljIqeKZH\nTQDrhgMTJpMJe/fu7TVu3LgrBw8etNy8ebNDbGxsCuccwcHBuvDw8FJHR0dzTk6O6rPPPssMDw/P\nAgBLS0u71NTU5Mb5WVpaSrt27Tplb28vnT9/Xj5kyBDtgw8+eBkAcnJyLDZt2nQmLCwse9KkSQM3\nbNhgt3DhwqJ58+Z5fvDBBzkTJ04se+KJJ1xb+xwSExMt4+Pjk3x8fKoAYMuWLZlOTk7m0tJSISgo\nSDdnzpziPn36tOh3a0VFhUyr1eqNRiMrKChQ7NmzJ61xGnd3d9PBgwfTLS0t+YkTJywefvhhz5Mn\nT6Z++OGHuQByL126JAsLC9P+4x//yP/rr78sduzY0fv48eMpCoUCs2bN8li3bp39vHnzisrKymQj\nR44sXb9+/dmzZ8/K9+/fb5uRkZEkCAIKCgo6bcKfVgclOOdfNreNMbaihXnsRvUElvXXLa93PxnA\nHc3s+waAN1pUWHIVtUyJuMLsBuviCrOhltE5nYS0FbUrQgipRv1h90HvFWkpg2hyWRr7tdAogCWs\nCp3jYqVQtTkoUTvSAQCGDBlS+swzzxS8++67fSZNmnS5V69eEgDcfffdxb/88ovNjBkzLjs7O1eF\nh4eXXy9fSZLYs88+63r06FFrQRCQn5+vPHfunBwAXFxcjGFhYQYAGDx4cEVWVpaqoKBAVlpaKps4\ncWIZAPztb38rPHDggG1rnktQUFBZbUACAKKiopz27NnTGwAuXryoTElJUfXp06eiJXnVnr4BAHv3\n7rV+9NFHB6SnpzcIvlRWVrJ58+Z5pKSkWMpkMn727Nm6SKLZbMb06dMHPvvss+fDwsIMkZGRfU+e\nPGkVEBCgr9lXcHV1rQIAhULB58yZcxkA+vbtaxYEgc+aNcvj7rvvLpk5c2ZJa16DG9GWkRLXcj+A\nJe2cJ2lHBnMVghw86qLiABDk4AGDuYqi4oS0UbPtSqyEpVwFs8kAmeLWG+pJCLk1cC7V9XMG0Ujf\nM7qJ7vydsH6do8/YjqeWK5VNBrDkyhuKYNXOKdHS9JaWli2a9OSTTz6xLywslCckJKSoVCru4uIS\nYDAYBABQKpV1I/tlMhmvXX+j6pdt+/btNkeOHLGJjY1Nsba25sHBwb5tPc748ePL8vPzlRcvXmww\nauG1115zcnV1rdq+fXtmVVUVs7GxqZvk89lnn3Xx9PQ0Pvnkk0UAwDnHrFmzCj744IO8+nmYTCao\nVCpJqLlSjkql4vHx8Snbt2/v9e2339p98sknfQ4fPpzRlnK3Vnu3XpqW4CanFuR4I3gmgh0HQMYE\nBDsOwBvBM6EW2js+RcitQy1TIKqJdnXh5Fc48OkwxO+NQJWhGLyFE4gRQkh3wbmEKkMx4vdG4MCn\nw3Dh5FdXfc+ICp5Jlx6/CTX12dUd3qvGdY4+YzueQayqCnLwaLCu+s+XqqpmdmmzUaNGle3evbt3\naWmpcOXKFWH37t12o0aNKm0qrVwu50aj8arfnyUlJTJHR0eTSqXi33//vU1eXt41gyeOjo5mGxsb\n8969e60B4IsvvrC/kedw+fJlWe/evUVra2seExNjkZCQ0OZzfmJiYiwYY7zxqR8lJSUyZ2dnkyAI\n+OijjxyqZzoANm7c2PvIkSPW69atO1ubduLEiaU7duywP3/+vBwALly4IMvIyLjqNSkuLhaKi4tl\ns2bNKvn444/PJicnW7a13K3Vlokum3uTGCgocdOTxEqY8o5hxe2zYKWwRLmpAmXnjkJyHQqhB09S\nREhHEpiA3kpLrAx9CJZyFSpEI0rPHkZmzMcAgOK8GCTuX4rA8asgp3ZGCOkhJC7BIFZCrbaD5/B/\nITf2U2TGfIwBAFaFzoZabgGDuQpqmaJFEyeSziUwAXYqK6waMgdqmbLbvFdmkwGJ+5eiOK96mrvu\n9hkrcQkGsxFqmQUM5kqoZaqb/jVXyxW5UcEz688pgajgmZJarmj3SRCHDx9e8eCDDxbedtttOqB6\noss77rjDkJaWdtWP6NmzZ1/S6XR6f3//ivoTXc6fP79o4sSJ3hqNRj9o0KCKAQMGVF7vuJ999lnW\n/PnzPRljrZ7osrH777+/5NNPP+3j5eXlN3DgwMpBgwZd95ST+mrnlKh9/Mknn2TWjmaoFRERkT9j\nxgyvTZs2OY4dO7akdgTI+++/73ThwgVlYGCgDgAmT55cvGLFivPPP/983qhRozSSJEGhUPA1a9Zk\ne3p6NggqFRUVye69917vqqoqxjnH66+/fhadhNVGVVq8A2OZqJ58sskABOd8QDuUq12FhITwmJhr\nzc9565AkMwoqLuGlk+sRV5SOIHsNXhv0Nzha9oEgdNpcJje7Lg+uUZ3tXqpnMC/F0uMfNWhXF45G\nI//0XgAAE+QYPf+PjhpeSnWWdDdUZ7u5a/V7lzL3d2R/11Wozt4kOJdw4NNh4NL/LkjQwZ+x7aap\ndhN125OwU9l0RGDimnU2Pj4+KzAwsKClmXX01TfIrSU+Pt4xMDDQs/Zxq2s/53wA53xgzW2DBcDw\n9iwsaX8GsxEvnVyPmMIUiNyMmMIUvHRyPQxmY1cXjZBuy2A2Yunxj65qVy7B8+vS9O4XBLPJ0IWl\nJISQ9nOtfo/6O9KRzCYDevcLarCuu9S5ptrN0uMfdYvv4QITiqwUqgSBsdjqWwpIkPbT3iG5P9o5\nP9LO1HI14orSG6yLK0qHWq7uohIR0v2pZRZNtiu73p5gghx2/UPgHx4FmYLaGSGkZ7hWv0f9HelI\nMoUa/uFRsOsf0u0+Y5trN2qZRReViJCbQ3vPbtjlQ9vItRnMlQiy1yCmMKVuXZC9BgZzJawoMEFI\nmzTbrsRKjJ7/B80MTgjpca7V71mq7ai/Ix2GMQFKtR0Cx6/qdlffoO/hhDStvVtv6yaoIJ1OLVMh\n6rYnEeKgg5zJEOKgQ9RtT0Itu7kv/UTIzazZdiW3AGMC5EqrbvFliRBCWup6/R4hHan+Z2t3+oyl\n7+GENK0tV9/4EE0HHxiA3jdcItKhqmdatsGq0EXdatZfQm5m1K4IIbca6vcIaT1qN4Q0rS2nb1xr\n2l+aErgbEJhQN0SMhooR0j6oXRFCbjXU7xHSetRuCLlaW66+8eW1lo4oJGlf1dcVLwevuZW41NVF\nIqTHoXZGCOkJqC8j3RXV3Zvfv/71r37e3t5+Go1Gr9Vq9QcOHLBqr7wjIyP7qlSq2woLC2UtSf/8\n88/3u5HjPfvss/23b99u09L0+/fvtxo0aJBWq9XqBw4c6BcREdH/Ro7f3bXl9I3vcY25IzjnU26o\nRKRDSVxCibEYq2JfQEpRHHT2QYgIfhO2KjsaOkZIO6F2RgjpCagvI90V1d2b388//2y1d+/e3gkJ\nCclqtZqfP39ebjQa2+2iCdu2bbP39/cv37RpU+9nnnmm8Hrpo6Ojnd96660LbT3e+++/n9ea9PPm\nzRuwZcuW08OGDTOIooj4+Ph2uQSLKIqQy9v7WhYdry2tcgWAlddYyE3MaDZgX9Z3mBewDF9PPop5\nAcuwL+s7GM03/7WdCbmZSZyj3FQFiXNUiFXYl/UdEgtjYOYiEgtjsCr2BWpnhJBuobY/AxgqxCr0\nVjlQX3aLqf+ZVnvbnRjNBqyKfYE+h9uRxCX7clNVgMR5cPWtZH8j+eXm5irs7e1FtVrNAcDZ2Vn0\n9PQ0AcCSJUuc/f39dT4+Pn6zZs3ykKTqUS6hoaG+CxYscAkICNB5enr679mzx7qpvJOSklQVFRWy\nyMjI3G+++aaunNHR0Q7jxo3zGjFihI+Hh4f/3//+d1cAWLhwoYvRaBS0Wq1+ypQpAwDglVdecfLx\n8fHz8fHxi4yM7AsAaWlpyoEDB/o98MADHt7e3n533HGHT1lZGQOA6dOne37++ed2ALBjxw4bnU6n\n12g0+hkzZngaDIargi1FRUVyd3d3EwDI5XIEBwdXAkBERET/e++9d0BQUJDWw8PDf+XKlY4A8MMP\nP9iMGjXKu3b/uXPnukdHRzsAgIuLS8CCBQtc9Hq9bv369Xavv/56Xy8vLz+NRqOfPHnywBt5nzpL\nW07f+O1aS0cUkrQflaDGcLdpeOvkn7hj5wd46+SfGO42DSqBzmkjpK0kAX56jQAAIABJREFUzlFs\nrMDiP7cjbOf7WPLnTgx3m4bh/cfXpUkpioOFjNoZIeTm1rg/ey3uIGbpFtf1Z9SX9XyN68DiP7ej\n2FjRrQITFjI1UoriGqyjutt2Epfsi40Gj8V/blfW1AllsdHgcSOBiXvvvfdKXl6e0tPT0/+hhx5y\n37VrV12A4bnnnstPTExMycjISDIYDMLWrVtta7eJosgSEhJS3n777bORkZFNnvKwYcMGu6lTpxZN\nmDChLDMz0+Ls2bN1QweSk5Mtt2/ffiYlJSVp586ddqdOnVKsWbMmV6VSSampqck7d+7MPHjwoOXm\nzZsdYmNjU2JiYlI2bNjQ5/Dhw2oAyMnJsfjHP/6Rf+rUqSRbW1vzhg0b7Oofu6Kigj3xxBMDvv76\n69Pp6enJoiji3Xff7dO4jI8//vhFnU7nP3bsWK93333XsaKioi5wkZKSoj506FDa0aNHU999993+\nWVlZiuu9ng4ODmJycnLK448/XhwdHd0vMTExOT09PfmLL77Ivt6+N4NWByUYYyevtbQwjwmMsTTG\n2CnG2PNNbH+PMRZXs6Qzxi7X22aut21na8t/q6swVyHyxH7EFpyFmUuILTiLyBP7UWGu6uqiEdJt\nGUQTlsXsuqpdTdf8vS6Nzj4IlfQPDSHkJtd0f3agrj+jvqzna6oOLIvZBYNo6uqitVil2QCdfVCD\ndVR3284gii7LYnYJjeqEYBBFl7bmaWtrKyUmJiavXr06u0+fPuLDDz/sVfvP/48//mgzaNAgrUaj\n0R85csQmMTGxLpo0Y8aMYgAICwsrP3funLKpvL/77juHuXPnFslkMkyaNKl448aNdYGD4cOHX3Fw\ncDBbWlpyb2/vytOnT191PdZff/3VetKkSZd79eol2draSnfffXfxL7/8YgMALi4uxrCwMAMADB48\nuCIrK6vB/vHx8Raurq7GQYMGGQHgkUceKTx06NBVc02sWLHi/B9//JEyZsyYK998843DXXfdpand\nNnHixMvW1tbc2dlZHDZs2JWDBw9ed66NuXPnFtfe9/X1NUydOnXAmjVr7BUKRbeIJrblhBMJ1XNK\nbAbwPYBWtW7GmAzARwDGAjgH4C/G2E7OeXJtGs75onrpnwYwuF4WBs55w16GtJilXIW4wtwG6+IK\nc2Epp+sjE9JWarmiyXblbuMGGZPXncuqon9oCCE3ueb6MzcbN/g7hFBfdgtorg6o5df9s/amoZKp\nERH85lVzSlDdbRu1XKFspk40GRRoKblcjsmTJ5dOnjy5dNCgQYaNGzc6zJ8/v2jx4sUef/75Z7K3\nt7cpIiKif2VlZd0f6RYWFrx2X7PZfNVpEceOHVNnZ2erJkyYoAEAk8nEXF1dq5YuXXoJAJRKZd2P\ndJlMxk0mU6vmsWi8v8FgaPMkJX5+fkY/P79LERERlxwcHIIuXLggAwDGGhaJMQaFQsFrT2MBgMbz\nb9jY2NRt/OWXXzJ+/PFHmx07dtiuWLHCOS0tLUmhuLnbb1tO3wgCMAuANaoDE28A8AOQyzlvyfCQ\nUACnOOdnOOdVALYC+L9rpJ8FYEtry0maZhBNCHJoGNQMcnDpVtFvQm42zbWrCrEK304+ihdCV8FW\n2Zsm1yKE3JQ4l1Blqr1KgbHJ/qxSFLF0yHs0UWAPUv99r70FesZ3RYEJsFXZYemQ9/Dt5KNUd2+Q\nQTRVNVMn2jzUOj4+XpWQkFD3r+iJEyfUrq6uVRUVFQIA9OvXTywpKRG+//57u+ZzudqGDRvsFy9e\nnJebm5uQm5ubkJ+ff/LixYuK9PT0awZQ5HI5r/2hP2rUqLLdu3f3Li0tFa5cuSLs3r3bbtSoUaUt\nOX5gYGBlbm6uMjExUVVTHocRI0Zcte/WrVtta4MMCQkJFjKZjDs6OpoB4Mcff+xdUVHBLly4IDt6\n9KjN8OHDy728vIynTp1SGwwGVlBQIDt06FCvpo5vNptx+vRp5T333FP60Ucf5ZaVlclKSkpadAWS\nrtSmqTk556kAXgbwMmNsJoANAN4G8G4LdncBcLbe43MAhjSVkDHmAWAAgAP1VlswxmIAiADe4pxv\nb2bfxwE8DgDu7u4tKNatQS2T4//Zu/Pwpqr0D+Dfc3Oz0gLdS1toS/e0JUBLZREVUCyKjIqMCmPR\nEfgBorKIigsqKMoojoI46DAgyCIjbogI4qgIIgNlqXSn0FL2rpQuaZKbe35/pGVa6E66pH0/z5Mn\nzc25975J3pyTnnvuua/H3oWXEnfgWOE59Hfzxeuxd0GrcLxZWjsbylnHpRWVeCP2bryY+F2t79Xx\n9E9xMOWf8PHoj7uGvgmdxgWsE/0gopwljoZy9nqcy6ioLMaO/QtwPv8Y4iKn4vXYh2v9Tngj9m5o\nRSUEdkMHRUkLtFbOXvu512yn6mrTqnPAkQhMgFa0jXqvvictoxXFc2/E3u3/YuJ3Qo2ckLWieK7x\ntet25coVxVNPPdXnypUrCoVCwQMCAkzr1q077e7ubp00aVJ+REREpIeHh2QwGMqbs92vv/7a9dtv\nvz1Rc9mYMWOK161b5+rl5VVvz1rVPvVRUVEV27Zty544cWLhwIEDIwDgkUceyR82bJgxIyOj0UpQ\np9PxVatW5UyYMCHIarXCYDBUPPPMM/nXltuwYYPb888/31uj0ciiKPLVq1dnV181IyIiomLo0KFh\nxcXF4jPPPHOhegLQe+65pzg8PDzSz8/PFBkZWVHX/iVJYhMnTgwsLS1VcM7ZlClT8qo7Ozoyxlsw\naQ1jzBfAQwDuA1AM4N8AvuKclzVh3QcAxHPOp1Q9fgTATZzzWXWUfQ6AH+f8yZr75pyfY4z1ha2z\nYhTn/GRD+4yNjeWJiYlNf4GdmNlSjsPpmxDYZzQ8u/dG3pUzyM79ATHhE6FSUoVdxW6XI2opylnH\nI3MOo2SBVlTCKJnwR/qnOJD8j6vP+3nG4k+3/L21vmeUs8TRUM52EGZLOb75dQ7O5v3vvRgcNQP9\nwh+BVlRfrdcE1u4fWXtr9zfAnjlb1+des52q3aZRDjioBj+wpKSkHIPBUNDUjclcdjVKkq9WVKqM\nksWsFcVzAhOKbjxMUtPcuXN9nJycrIsWLbrU3rG0pqSkJHeDwRBQ/bjZh8cZY3sAOMPWEfEYgOrr\nvqoYY66c88aS8xyA3jUe+1Utq8tDAJ6ouYBzfq7q/hRj7BfY5ptosFOC/I9S1OJgyj9r/bMkMBGD\nox5vx6gIcXwCY+hWdWqlTlThYMo/az1/Pv8YlCKdy0oI6ViUohbn82tfpeBgyj8xOOpxsBr1Gulc\n6vrca7ZTNds0ygECAAITiropVUUA5QSxv5aMI/YH4ALg/wDsApBYdTtcdd+YQwBCGGOBjDEVbB0P\n111FgzEWXrWf32ssc2GMqav+dgcwDEDqteuS+lkkI3w8as8T6uPRHxaJZiMmxF7oe0YIcRRUX3VN\n9LkT0jG9++675zv7KIm6tGSiywDOeWDVrW+NWyDnvG91OcZYZD3rSwBmwdahkQbg35zzFMbYIsbY\nuBpFHwLwGa99fkkEgETGWBKAn2GbU4I6JZpBKWpx19A34ecZC4GJ8POMxV1D36QjuITYEX3PCCGO\nguqrrok+d0JIR9Kasxt+CmBgXU9wzncA2HHNsoXXPH61jvX2A4i2X4hdD2MCdBoX/OmWv0MpamGR\njFCK2k41+R4h7Y2+Z4QQR0H1VddEnzshpCNpzU4Jmg2ng2JMuDrZHk1uSUjroO8ZIcRRUH3VNdHn\nTgjpKFqzO7T5l/UgbULmHOUWS617Qroq+j4QQroCquu6Nvr8CSEdGY3R6mJkzlFsqsT83/fg5q83\nY/7ve1BsqqTGiXRJ9H0ghHQFVNd1bfT5k44oIyNDFRISUmsOwrlz5/osXLjQCwCWL1/ulpOTo2xs\nO3FxcWG//vqrrrn7Ih2LXTslGGM+NR6a7bltYh9GScLLB3/D4YJLsHKOwwWX8PLB32CUpPYOjZA2\nR98HQkhXQHVd10afP3FEGzZscM/NzW20U4J0DvYeKXGg+g/O+WA7b5vYgVYUcawwr9ayY4V50Iqt\nOb0IIR1TW34fuCyDm8rBedW9LNt9H4QQUlN1vUNtf9fWlM+f2ijSGJlz13KLOVrmPKbq3rW19rV2\n7VqX5ORkXUJCQt/w8HB9WVkZe+aZZ3pFRUVFhISERD788MP+co0c3bx5s0t0dHREQEBA1M6dO50a\n2nZGRoYqJiYmTK/XR+j1+ojdu3d3A4DZs2f7hIeH68PDw/Wenp79HnjggYC//e1vHtXLfH19o2+6\n6abQ1nrNXZ29OyVocssOzihJ6O/mWWtZfzdP6i0nXVJbfR+4LIOXF6Py07koXzgElZ/OBS8vph99\nhJBWU7PeqSg4Q21/F9ZYW0dtFGmMzLlrscno/8yBXaph36zGMwd2qYpNRv/W6ph47LHHiqOioirW\nr19/Kj09PdXJyYnPnz8/Lzk5Oe3EiRMpRqNR+Oyzz3pUl5ckiR0/fjxt6dKlZxYtWuTT0LZ9fHyk\nvXv3ZqampqZt2bLl1Jw5c/oAwHvvvXc+PT09dd++fRk9e/aUnn766bxnn302Pz09PTUpKSnN29vb\n/PTTT19qjddL7N8pQSendXBabsXigYMQ4+4FBWOIcffC4oGDoOXW9g6NkDanFUUsjhtW+/sQN8z+\nRw8tRpi2vABrdiIgS7BmJ8K05QXAYrTvfgghpFqNekf4aRUWD4hp/bqOdEiNtnXURpFGGCWL70uH\n/iMcLrhQdQrQBbx06D+CUbL4tnSbjNV9LLu+5d9//71zv379wkNDQ/X79+93Tk5O1lY/N2HChGIA\nGDp0aPnZs2dVDe3XbDaziRMnBoSGhuonTJgQdPLkSU31c7IsY8KECYFPPPHEpeHDh1dUL3/88cd7\n33LLLaUTJ04sad6rJE3V7NaIMbYCdXc+MAA9bzgi0qqYUgXt12/gbyOmQuc2EhWFZyHseAdswmvt\nHRohbU5gDC5qDd4eciu0ogijJEErihDqaRBbTKWF9fSxWousp48BKm09KxBCyA2qUe/If+yCFsDf\nRk6Hzn1U69V1pENqtK2jNoo0QisqVccKL9ZadqzwIrSissEOgIZ4eXlJJSUliprLioqKFIGBgaZr\ny1ZUVLB58+b5//e//00NDg62zJ0716eysvLqwXWNRsMBQBRFWK3WBiu2N954w8vT09PyxRdfZMuy\nDK1WG1P93Lx583x69eplfvrppwurly1fvtzt7NmzqnXr1uW29LWSxrVkpEQigMN13BIBPGm/0Eir\nMBvBruQB792HipcHAe/dZ3tspt5w0jUJjKGbUlnr3u7MRij8+9dapPDvT987Qkjruabekf/YBcU3\nb4CZK1qvriMdVoNtHbVRpBFGyWLu7+Zda1l/N28YJUuLL2zQo0cP2dPT07Jt2zZnALh06ZLil19+\n6TFy5MgyAHBycrJWd1pUVFQIAODt7S2VlJQI3377rUtL91tSUqLo1auXRaFQ4MMPP3SzWm2jxTdt\n2tRjz5493desWXOmuuzevXt1K1as8P7888+zFQpFvdskN67ZIyU45+vqWs4Y0wC454YjIq1LqYXq\nL+9BghIqtQJmkxUiLIBS3d6RkS6Ecw6LBVAqcfW+vuF6zSVzDrMEqEXAJAEqEe3/41uphfrBJbbh\nsaePQeHfH+oHlwBKOgpFCLEPWZZhsTCoVIDZDCip3umSZM4hWTkgs6a3sZQrpBFaUXnu9UGj/F86\n9B/hWOFF9HfzxuuDRslaUXnuRra7bt267JkzZ/Z59tlnewPAc889dz4yMtIEAAkJCQVPPvmk//z5\n8+XExMS0SZMm5UdERER6eHhIBoOhvDn7sVgsTKVSyQAwe/bsvPHjxwd99tlnbiNHjizRarUyALz/\n/vtely5dUvbv3z8CAOLj4y+fPXtWVVJSohg+fHgYABgMhvItW7acvpHXTOrG+A1co5gxpgBwJ4CH\nAYwGsJdz/oCdYrOb2NhYnpiY2N5hdAiyLMNoBHbvMuHieRnePgLuuFMNrRYQBHtPMeKw2v3wUWfO\nWc45jBUcP9TIwdF3qqHVsRvumJA5R5mRY93PJpy6KKOvt4DJI9Rw0rJ6OyZkzlEpydCIwtX71ujE\n4LJsOz9XpbUdfVJqwez3naOcJY6GctaOrLKMynradtZ69U5X0+FzVuYclSYOixn4+cfmtbFclsEl\nM4xMhFZUwChZoRUV7d+pT25Egx9eUlJSjsFgKGjqxmTOXY2SxVcrKlVGyWLWispzAmNFNx5m69uw\nYUPPTZs2ue7YseNUe8dCbJKSktwNBkNA9eMWtUyMsVsZYx8ByAHwOIA7AAR2xA4JUpvFwrB7lwnn\nz8qQZeD8WRm7d5lgsVCjQ9qGxQL8cE0O/rDLBIvlxrdtloB1P5uQdUGGzIGsCzLW/WyCuZ4J5mXO\nUWyyYP6+NNyy9QDm70tDsckC+QY6a+vDBAFM3Q2MVd3TPwaEEDuQOYfUQNtO9U7XYZYAo8nWIdHc\nNpYzhmJZwLP70nHL1gN4dl96q7WHxDEJjBV1U6qOC4wdrrp3iA6J2bNn+yxevNjnxRdfvNDesZD6\nNbt1YoydBfAmgH0A9Jzz8QCMnPOKhtckHYFKBVw8X/sSTxfPy1C1eJoaQppHqaw7B5XKG9+2WgRO\nXay97VMXZajrOVGtUpKx8PdMHMm/AivnOJJ/BQt/z0SlRJdBI4Q4hkpJpradALC1gS7OrEVtLLWH\npLN67733zmdkZKQOGzaMJknpwFrSZb4VgA+ABwHcwxjrhmZeCpQxFs8Yy2CMZTHGnq/j+b8zxo5V\n3TIZY5drPDeZMXai6ja5BfF3aWYz4O1T+2P39hFgbvE0NYQ0j8VSdw7aY6SESQL6etfedl9vAaZ6\nRkpoRAFJBaW1liUVlEIj0tFEQohj0IgCjCZObTuBSQKKS+vOhcbaWGoPCSHtqdk1Ded8NoBAAMsA\n3AYgA4AHY+zPjDGnxtavmodiJYAxAPQAHmaM6a/ZxxzOeX/OeX8AKwB8WbWuK4BXANwEIA7AK4yx\nFs++2hUxhYw77lTDx0+AIAA+frbzTpmCesJJ21AqgdHX5ODoO9V2GSmhEoHJI9QI7iVAYEBwL9uc\nEqoGRkoY3J1rLTO4O9ORIUKIw6iUZPw3v7jOtl2ppKH3XYlKBLRqYMTtzW9jqT0khLSnZl99AwC4\nbXbMnwH8zBhT4n+TXX4IwL2R1eMAZHHOTwEAY+wzAH8CkFpP+Ydh64hA1X52c86LqtbdDSAewOaW\nvI6uSFQI4Corxo1Rg2kYeCWHRZQh0mVuSCvjMgdMHNAwaAXg7rFqiEpm16tvCIzBSQtMHa1p0tU3\nNKKARUNCsfD3TCQVlMLg7oxFQ0LpyBAhpMOSZRkw4WobrlYx9PNwxsH8Yoy+ywVaNYPZDIhKThNY\ndzECY9CoAVHkuGuspsGrb9Rsk1HJoVGzNmsPbRNMW6ERFVfvaUJNQrq2ZndKMMY+4Zw/Wv2Yc24B\nsB3AdsZYU64d5AvgTI3HZ2Eb+VDXvvxhG5XxUwPr+jY5eAJwDmUFg3lVIeQTZgghKqimuwLOHKAG\ngbQSLnOgVIZpVdHVvFNPdwVEBpXKvnknMAZN1REhTRNGXygF4PnYvvBx0uJ8mRFK+g1PCOmgZFkG\nSjnMNepS1XRX9HQScZN3T2hEBqMkQ6MSIDCqzLoigTGoxP+1q3XNK1Jfm+ziLOLtmyNa9WpUtgmm\nzXj5wB9IKrgMg3tPLB7cDy5qFXVMENKFtaTF6lffE5xze08g8hCArZxza3NXZIxNY4wlMsYS8/Pz\n7RyWAzPB9mMm3QxYATndDPOqIsDU3oGRTp2zJm778VMj70yrimxHadpRpWTF8/uTMGHnPgzbuhsT\ndu7D8/uTUCk1u8rpkjp1zpJOyeFztp42nJkBndJ2tLn6nnQOrZKz9bTJzNT6eVQpWfHygT9wJL+4\nakLNYrx84A9qd7uokydPKkeNGhXk7+8f1bt376jHHnusd2VlZZ2Jl5GRoQoJCYm0x363b9/uPGLE\niODmlGeMxbz77rtXzwjYv3+/ljEWs3DhQq+WxJCTk6OMj4/v25J1O6OWdEroGGMDGGMD67o1Yf1z\nAHrXeOxXtawuD6H2qRlNXpdz/jHnPJZzHuvh4dGEsLoGpmGQT9Se+Uo+YQbT0A+Y9tapc7aevEM7\n551GVCCp4HKtZUkFl6ER6XSmpujUOUs6JUfPWWrDu55Wydl2bJOp3SXVZFnGvffeGzxu3LjLp0+f\nTs7Ozk4uLy8Xnn766etGwVvsMRv6DQoJCTF+8cUXV+cy/PTTT13DwsJafEA+ICDAsnPnzlP2ic7x\ntaRTwhe2SS7rur3ThPUPAQhhjAUyxlSwdTxsu7YQYywcgAuA32ss3gVgNGPMpWqCy9FVy0gT8UoO\nIaT2WD4hRAVeSZNhkVZUT96hnfOuUrLC4N6z1jKDe086YkMI6ZCoDSd20Y5tMrW7jkvm3LXcYomW\nOY+pune9ke19++23zmq1Wn766acLAUAURaxaterMli1b3EtLS4Xly5e7jRw5Mnjw4MGhQ4cODau5\nbkZGhiomJiZMr9dH6PX6iN27d3cDbCMa4uLiwuLj4/sGBgZGjhs3LlCWbZO1bt26tXtgYGCkXq+P\n2Lp169UkvHTpkuL2228PCg0N1RsMhvD//ve/dU5H4OvrazaZTMKZM2dEWZbx008/9Rg1alRJ9fPL\nli1zj4qKiggLC9PfeeedQaWlpQIApKSkqA0GQ3hoaKj+qaee8tHpdAOqX0P1yA9JkjBt2jS/kJCQ\nyNDQUP0bb7zheSPvrSNqSadEFud8JOd8RB23kY2tzDmXAMyCrTMhDcC/OecpjLFFjLFxNYo+BOCz\nqkk1q9ctArAYto6NQwAWVU96SZpIDaimu0IIVwEKQAivmlNC3d6BkU5NzaC+Ju/U010BdesflZE5\nR4XFWuu+mkZUYPHgfhjo4QIFYxjo4YLFg/vRERtCSIdwbf3FVdSGk+apsw1sxzaZ2l3HJHPuWmyq\n9J//+x7VzV9vxvzf96iKTZX+N9Ixcfz4ca3BYKiouczV1VXu1auXOTU1VQ0AKSkpum+++ebkoUOH\nMmqW8/Hxkfbu3ZuZmpqatmXLllNz5szpU/1cWlqaduXKlWeysrJScnNz1bt373aqqKhgs2bNCti2\nbVtWcnJyWl5e3tVZx5599lkfg8FQkZmZmbp48eJzkydPDqwv5nvvvbf4008/dfnxxx+7RUdHV6jV\n6qs/KidNmlScnJyclpGRkRoWFmZcvny5OwDMmjWr98yZM/MyMzNT/fz86hzysWzZMo/c3FxVampq\nSmZmZuqUKVMKm/t+OroWXX3jRnHOdwDYcc2yhdc8frWeddcAWNNqwXV2jKFcLcP8fz3g0l2J4isW\nWNQc3WhCLNKKmMDAnQWon3K7OtM31AxMaN0fQDLnKK604pXfzyIpvwIGDx1eG+IHF43tXFmBMbio\nVXhn2ACaBZwQ0qHUV3/1dBKgesrt6tU3oAZdZYPUqaE2kLVDmwyA2l0HZZQk35cP/iYcLrgEADhc\ncAkvH/xNeHvIrb7dlMpWO0A8fPjwK15eXtcNozGbzezxxx/3T01N1QqCgNOnT1/tmo2Oji4PCgqy\nAEBkZGTFyZMnVc7OzlY/Pz9TdHS0CQAmTZpUuHr1ag8AOHjwoPMXX3yRBQDjxo0rnTZtmlhUVCS4\nurpedz3chISEovHjxwelp6drJ06cWLRv3z6n6ucOHz6sXbhwoW9paamivLxcceutt5YAwNGjR51+\n+OGHLACYMmVK4auvvup37XZ/+umn7tOnT89XVl27t67X3Nm1pBV7DgAYYxrGWFTVTWPnuEgrqZRk\nLNh/FvfszMLN/07DPTuzsGD/WboONWl1TGBgWgGMVd23wY+fSknGK7+fxZG8Clg5cCSvAq/8Xjvf\nbRN6ibXuCSGkvdVXf5lkDqGqLhW0AnVIkHo11Aa2R5tcjdpdx6MVRdWxwrxay44V5kErinVc36Vp\noqKijElJSbqay4qKioQLFy6o9Hq9CQB0Ol2d/6C88cYbXp6enpa0tLTU48ePp1oslqsVYc3RCwqF\nApIk2S3B+vTpIymVSv7rr792Hzdu3JWaz02bNi3wgw8+yM3MzEx97rnnzptMJqqcm6Elb9bPjLG/\nwXY5znUA1gM4wxj7G2OsCRfgI+1JIwpIyq81UgpJ+RWtch1qQtob5TshxFFR/UVuFOUQsRejJJn7\nu9We5qC/myeMkmSuZ5VGjRs3rrSyslL44IMP3ADbvAozZ87sPWHChAJnZ+cGj5aWlJQoevXqZVEo\nFPjwww/drNaGBxb079+/8ty5c6qUlBQ1AHz22WdXTzu56aabSteuXesG2OakcHFxkeoaJVHttdde\nO7d48eKzolj7hIOKigqhT58+FpPJxGpuv3///mWffPKJCwCsWbOmztNdRo0adeWjjz5yr57Q89Kl\nS13ufKaW1Ep/A+AKIJBzHsM5HwggCEBPNG2iS9KOKiUZj0V6YEN8EPb+OQIb4oPwWKQHjZQgnYbt\nvFkZMucwWjgei6w9W7nBQ0f5TgjpsKrrMADYfFcI7ujT/epzVH+RxlAbSFqDVhTPLY4bJse4e0HB\nGGLcvbA4bpisFcX6rqDYKEEQ8PXXX2d9+eWXLv7+/lGBgYFRarVaXr58eaPbnD17dt7mzZvdwsLC\n9Onp6RqtVttgUut0Or5ixYrTY8eODdbr9RHu7u5S9XNLly49f/ToUV1oaKj+xRdf9P3kk0+yG9rW\nHXfcUf7II49cvnb5888/fz4uLi4iNjY2PCQkpLJ6+YoVK86sWLHCKzQ0VJ+VlaVxcnK6rgdlzpw5\n+X5+fubw8PDIsLAw/b/+9a8bmkTUEbEa80g2bQXGTgAI5desyBhTAEjnnIfYMT67iI2N5YmJie0d\nRodglWVcNgGv7ivFH3kS+nmKePVmZ/RUAwoaAlqt3ccRUs62jO0GLkayAAAgAElEQVT8WX5dfn9z\nshBrU/Kvm1OiE2n3F0M5S5qJcrYOddVhLwzpho/+uIiCSqmz1l+Oot3f9MZytgu3gaRuDX7ISUlJ\nOQaDoaCpG5M5dzVKkq9WFFVGSTJrRfGcwBhdcKARpaWlQrdu3WRBEPDxxx+7bNmyxfU///nPyfaO\nq70lJSW5GwyGgOrHLZnokl/bIVG10MoYo2tSdXAmq61D4uglWwfh0UsSXt1XiqW3OUNHfRLEwVVK\nvM78futWNzwa6Y5KSYZGFOjHGCGkQ6qrDlvyezneus0HDJzqL9IgagNJaxIYK6qe1LKbks7Yb6rf\nfvtN9/TTT/fhnKN79+7WTz75JKe9Y+qIWtIpkcoYS+Ccr6+5kDH2FwDp9gmLtBaNyPBHnlRr2R95\nEjQiNVDE8dWX31olq5pQq8udokcIcSD11mEig0BXySKNoDaQkI4nPj6+LCMjI7W94+joWtLCPQng\nCcbYL4yxZVW3PQCeAjDDvuERe6uUOPp51u6L6ucpolKiQS7E8VF+E0IcGdVh5EZQ/hBCHFVLOiW+\n4ZzfBGAxgJyq2yLOeRznvMWTnZC2oREZXr3ZGQO8RCgYMMDLdr4hjZQgnQHlNyHEkVEdRm4E5Q8h\nxFG15PQNBgCc8/8A+I99wyGtTWAMPdUcb97qDJ2SocLCoRVB5xeSTkFgDC4aYOltth9hlRKHRmQN\n5rfMOYySBK0oXr2n7wMhpLXVV/c0tw4jBPhfPvVUK67+xqP8IYQ4ipZ0SngwxubW9yTn/N0biIe0\nMplzXDab8fLBQzhWUIj+7m5YHDcILmo1NVqkU7CdN2vL5er7+sico9hkou8DIaRNNVb3NLUOIwSg\ntowQ4vhacvqGAoATAOd6bqQDM0oSXj54CIfzC2DlHIfzC/DywUMwSlLjKxPSydD3gRDSHqjuIfZE\n+UQcFWMsZurUqX7VjxcuXOg1d+5cH3vuIzExUTN48ODQgICAKH9//6j58+f3kmW5zrLbt293HjFi\nRLA9998Uy5cvd3NxcTGEh4frg4KCIpctW+beUPmacW7cuLHHCy+84N02kbaeloyUuMA5X2T3SEib\n0IoijhUU1lp2rKAQWrElqUCIY6PvAyGkPVDdQ+yJ8ok4KpVKxXfs2OFy4cKFi7169bJ7L1pZWRm7\n7777gt9///3c+++//0ppaalw9913By1dutRjwYIF+TXLWiwWe+++We65557i9evX5547d06MioqK\n/POf/3y5d+/ejb4nkyZNKgFQ0gYhtqqWjJSgcWAOzChJWDJoEPaNuRsH7r8X+8bcjSWDBlFvugPh\nMgevtIDzqnvZ8WbV5pxDMvNa9+3BKEno7+5Wa1l/dzf6PhBC7E6WZchGW92NSltbXBPVPaSlGmvL\nauaebLSgvqPEhDRE5ty13GKJljmPqbp3vdFtKhQKnpCQkL9kyRKva587f/68eOeddwZFRUVFREVF\nRfzwww/dACA0NFRfUFCgkGUZPXv27P/BBx+4AcB9990X8NVXX3WvuY1//vOfbrGxsWX333//FQBw\ndnaW//GPf+S+//77vQBg7ty5Pvfee2/gwIEDw++///7AmuteuXJFmDBhQkB0dHRERESEfsOGDT0B\nICMjQxUTExOm1+sj9Hp9xO7du7sBttELcXFxYfHx8X0DAwMjx40bF1j9XZs5c6ZvUFBQZGhoqH7a\ntGl+aICvr6/Up08fU1ZWlqq+GGpavny5W0JCQh8AOHPmjHjHHXcEhYWF6cPCwvTVsd1+++1BkZGR\nEcHBwZHvvPPO1VEYOp1uwJNPPukbFhamNxgM4WfOnBEBYM2aNS4hISGRYWFh+tjY2LAGP0Q7aUmn\nxKgb3SljLJ4xlsEYy2KMPV9PmT8zxlIZYymMsU01llsZY8eqbttuNJauRisocEsPV8j/+gWVczZC\n/tcvuKWHK7QCXbvaEXCZA2WVMP/zZ1TO2QjzP38GyiodqmOCcw5TOcfBrZX47u0KHNxaCVN5+3RM\naEURi+MGIcbDHQrGEOPhjsVxg+joEiHErmRZBspMsKy21d2W1T/jlh6uWBoXR3UPuWENtWV15R7K\nTNQxQZpF5ty12GTyn//7AdXNX32D+b8fUBWbTP726JiYP39+3pdffulaWFhY65+R//u//+s9d+7c\nS8nJyWlfffXVyenTpwcAQGxsbNmPP/7odPjwYY2fn59p3759TgBw5MgRp1GjRpXV3EZKSopm4MCB\nFTWXRUZGmioqKoSioiIBAE6cOKH59ddfM7799tvsmuVeeOGFXiNGjLhy/PjxtL1792a89NJLfleu\nXBF8fHykvXv3ZqampqZt2bLl1Jw5c/pUr5OWlqZduXLlmaysrJTc3Fz17t27nS5evKjYsWOHy4kT\nJ1IyMzNTlyxZcqGh9yM1NVV15swZtV6vN9UXQ33rTp8+vc/w4cNLMzIyUlNSUlIHDhxYCQAbN27M\nSUlJSTt27FjqRx995HXx4kUFABiNRmHIkCFlGRkZqUOGDClbsWKFBwC89dZbvX744YfMjIyM1J07\nd2Y1FK+9NLv145wX3cgOGWMKACsB3AHgLIBDjLFtnPPUGmVCACwAMIxzXswY86yxCSPnvP+NxNCl\nma2wrNsL+cQlAIB84hIs6/ZCOWUEoG1JHxVpU2YJ5ms+P/O6vVBNHQFolO0cXNNYLUDuMQsMo9TQ\neTBU5HPkHrOgb5wKoqptYxEYg4tajbeHDKarbxBCWgWXOZjJel3dbVm3F7dMGYF99/2J6h5yQ2q1\nZQoFuAkQ1AzcBIDT7z5y44yS5PvywUPC4fwCAKiet0R4e8hg325K5Q39b+jq6ipPmDCh8K233vLU\narVXe8t+++237idOnNBWPy4rK1OUlJQIw4cPL9uzZ49TTk6OasqUKXlr1671yM7OVnbv3t3avXv3\nZve2xcfHX3ZycrruyNgvv/zSfdeuXT2XL1/uDQAmk4llZWWp/P39LY8//rh/amqqVhAEnD59Wl29\nTnR0dHlQUJAFACIjIytOnjypGjlyZJlarZYffPDBgLFjx15+8MEH6zzV4ttvv3UJDw93UqlU8nvv\nvXfay8vLWl8M9b2W/fv3O2/dujUbAERRhJubmxUAli5d6vXdd9/1BICLFy8qU1JSNN7e3uVKpZI/\n9NBDJQAQExNT/uOPP3YHbB0/kyZNChg/fnzxpEmTipv7nrZEe3TJxwHI4pyfAgDG2GcA/gQgtUaZ\nqQBWcs6LAYBzntfmUXZSTCNCPln77ZRP5oFp6OiMQ1DX/flB7TifnyByBEQoUfaJCWVZMpTBAgIe\nVUMQOdrj7DCBMXRT2jp0qu8JIcQeuMwhl3IIzvW3vaxGHURISwmMQacQIZdylKw2wVLVvrrM1dDv\nPnLDtKKoqmfeErscTlqwYMGlgQMH6h966KGC6mWccxw5ciRNp9PV6jC44447Sj/++GPPs2fPmpYu\nXXpu27ZtLhs2bHAZPHhw6bXb1ev1lXv37nWquSw1NVWl0+lkV1dXGQC6detWZ0cG5xxbt27NMhgM\npprL586d6+Pp6Wn54osvsmVZhlarjal+Tq1WX41VoVBAkiSmVCpx7NixtG3btnXfunWryz/+8Q/P\nAwcOZF67v+o5JZoSw/nz55vcaGzfvt15z549zomJienOzs5yXFxcmNFoFABAFEUuCLbOSVEUIUkS\nA4BNmzbl/vTTT922bdvWIyYmRn/48OFUb29va1P32RLt0UXqC+BMjcdnq5bVFAoglDH2G2PsAGMs\nvsZzGsZYYtXye1s72E6nUoIQ5FlrkRDkCVTSeawOwVTP52dyoM/PxFD2iQmWTBmQAUumjLJPTICJ\njhASQjoXbgZKVptgvWips+7m1PYSO6rOt5rtq5xPuUdunFGSzPXMW2K2x/a9vLys99xzT/GmTZuu\nzndw8803X3nzzTevJu/+/fu1ABAcHGwpLi4Ws7OzNXq93jxkyJCylStXet96661l12532rRphYcO\nHXL++uuvnQHbxJdPPPFEnyeffPJiYzGNGDHiyrJly7yqT3X67bfftABQUlKi6NWrl0WhUODDDz90\ns1ob/l+9pKREKCoqUjz44IMlq1atOpOenq5r2rtSfwz1GTZsWOnbb7/tAQCSJKGwsFBx+fJlRY8e\nPazOzs7y0aNHNUlJSd0a229KSop65MiR5e+99955FxcX6dSpU60+lrmjjtsSAYQAuA3AwwD+yRir\nntjDn3MeC2AigPcYY0F1bYAxNq2q8yIxPz+/riJdElcroHz4ZgghXoDAIIR4QfnwzeBqmlOivTUp\nZ1UiVJOH1/r8VJOHAyrHOeLBNIAlq3antCVLBtO0U0CkxaieJY6mrXOWqW31W/kO+fq2d/JwgNpe\n0ojm5Gx1vtVU9q183e8Gyj3SXFpRPLc4bpB8zbwlslYUz9lrHy+++OLFy5cvX/1B+/HHH585cuRI\nt9DQUH1QUFDkBx984FH9XP/+/csDAwMrAeC2224rzcvLU95+++3XjZRwcnLiX375ZdaSJUt8AgIC\novR6feTAgQPLFyxY0Ogo/Lfeeuu8JEksPDxcHxwcHPnSSy/5AsDs2bPzNm/e7BYWFqZPT0/X1Dzl\npC6XL19WxMfHh4SGhuqHDBkStnjx4jMNlW9KDPX5xz/+kbtnzx7n0NBQfVRUlP7o0aOa8ePHl0iS\nxPr27Rs5f/58X4PBUN7YfufMmeMXGhqqDwkJiRw0aFDZ4MGDjU2NuaVYW08uxxgbAuBVzvmdVY8X\nAADn/M0aZVYB+C/nfG3V4/8AeJ5zfuiabX0CYDvnfGtD+4yNjeWJiYl2fR2OSq7kqPjRDM1ABoW3\nEtaLFlQe4dDdroKgoSPVVdr9jWgoZ7nMAbNkO2XDJAEqEUxo95CbTK7kuLyy0nYkp4oyVEDPJzSU\ngy3X7m8c1bOkmbpEztas7zSDFOh2lwCFtxIwSeBqBaqHzRKH0OFztt72dZYakK1gGtE2QoJyr6to\nMGeTkpJyDAZDQUNlapI5dzVKkq9WFFVGSTJrRfGcwNgNzSdBuq6kpCR3g8EQUP24PWqkQwBCGGOB\njDEVgIcAXHsVja9hGyUBxpg7bKdznGKMuTDG1DWWD0PtuShII5gK0A5X4spmK/KeqMCVzVZohyvB\n2niCQdJyTGBgGiUYq7p3oA4JwJaDPaaooQwVAMH2g6nHFDXlICGk06lZ31UetuLKZivkUg6oRfqn\nkNhdve2rkkHQ2n43CFol5R5pEYGxom5K5XGBscNV99QhQeymzcd8c84lxtgsALsAKACs4ZynMMYW\nAUjknG+rem40YywVgBXAfM55IWNsKICPGGMybB0qb9W8agdpHBMYBGeg5xMaMDXATbZGzNH+sSWO\ni3KQENJVUH1H2hLlGyHEUbXLieic8x0AdlyzbGGNvzmAuVW3mmX2A4huixg7M9uR9qq/6Tx+0g4o\nBwkhXQXVd6QtUb4RQhyR48yOR+yGyxyyBRBUgGwGBCX1opO2RTlIOoqKx5o3R5dubYNzTLW5uK8+\nblb5g/dNa6VIujaq0wixD/ouEdI1UadEF8NlDqmc49QWE8pOy3DyF9D3QTXEblTpk7ZBOUgI6Uyo\nTiPEPui7REjXRTPddDGyBbbKPtt2DeuybBmntpggW9o7MtJVUA4SQjoTqtMIsQ/6LhHSdVGnRBcj\nqICy09dcw/q0DIGufEDaCOUgIaQzoTqNEPug71LXo9PpBtR8vHz5creEhIQ+9tzH888/722vbc2d\nO9fH09OzX3h4uD4oKCjyo48+cm3JdrZv3+48YsSI4KaW379/v3bLli09WrIvR0GdEl2MbAac/Gt/\n7E7+AmRzOwVEuhzKQUJIZ0J1GiH2Qd8l0hqWL1/ey57bmz59+qX09PTUr7/+OmvevHn+JpOp1c8t\nSkxM1H333XedulOC5pToYgQl0PdB9XXn6wnK9o6MdBWUg6Qj+WRAz2aVn9lKcVSL/ebFZpW/3fp9\nM/dAE13aG9VphNgHfZc6NplzV6Nk9dWKCpVRspq1ouKcwFhRa+0vIyNDNXny5ICioiLRzc1NWr9+\nfU5ISIh5/PjxAWPHji157LHHigHbaIuKioqjp0+fVo4fP75vWVmZwmq1shUrVpzetm1bD5PJJISH\nh+tDQ0ON27Zty3711Ve9Nm7c6A4AjzzySP7ChQvzMjIyVGPGjAmJi4srS0xMdPLy8jLv2rUry8nJ\nidcXX3R0tEmj0cgFBQUKX19faf/+/doZM2b4G41Gwd/f37Rp06YcDw8Pa3JysnratGn+hYWFokKh\n4J9//vmpmtvZs2ePbvr06QFbt2492bt3b8vjjz/eJz09XStJEnvxxRfPP/DAAyVvvvmmT2VlpRAe\nHu40b968Cz4+PpZ58+b1AQDGGPbv35/u4uIi1x2pY6BOiS6GCQxiNyD4EQ3NbEzaBeUgIaQzoTqN\nEPug71LHJXPuWmwy+7984A8hqeAyDO49VYsH9/N3UatwIx0T1R0G1Y9LSkoUd9xxRwkAzJgxo8+k\nSZMKn3zyycL33nvPbcaMGb1//PHHk/Vta82aNa6jRo0qWbp06UVJklBaWirEx8eXffLJJ57p6emp\nALB3717dpk2b3A4fPpzGOUdMTEzEqFGjSt3d3a25ubmaDRs2nBo6dOjpu+66q+/69etdZs6cWe9r\n27dvn87f37/S19dXAoBHH3008O9//3vu3XffXTZ79myf5557zmfNmjVnJk6cGPjMM89cTEhIuFxR\nUcGsVivLzs5WAcDu3bu7zZ49u8+2bduyQkJCzLNmzfIdMWLElc8//zynoKBAERsbGzFu3LgrCxYs\nOJ+YmNht/fr1uQAwcuTI4OXLl58ePXp0eUlJiaDT6Ry6QwKg0ze6JCYwKNQMjFXdU2VP2hjlICGk\nM6E6jRD7oO9Sx2SUrL4vH/hDOJJfDCvnOJJfjJcP/CEYJesNXSdbrVbL6enpqdW3BQsWnK9+7ujR\no92mTZtWBAAzZswoOnz4sFND2xo8eHD55s2b3efOnetz8OBBbV0jB3755Renu+6663L37t3lHj16\nyHfffXfxzz//7AwAvr6+pqFDhxoBYMCAARU5OTnquvazatUqr+Dg4MjbbrstfMGCBRcAoLCwUFFa\nWqq4++67ywBg6tSphQcOHHAqLi4WLl26pEpISLgMADqdjjs7O8sAkJWVpZk5c2bAd999lxUSEmKu\niq/73//+917h4eH6m2++OcxkMrGsrKzrZlUZPHhw2TPPPNP79ddf9ywoKFAolY4/nIg6JQghhBBC\nCCGE1EkrKlRJBZdrLUsquAytqGjzaUhFUeRWqxUAYLVaYbFYGACMGTOm7Ndff83w9fU1//Wvfw38\n4IMP3JqzXZVKdfVUDYVCwSVJqrNHbPr06ZeysrJS1q1bd3LmzJkBFRUVLeo58/T0tKjVavnAgQO6\n6mWcc2zdujWrupPmwoULxwcOHFh57bpLliy5uHr16tNGo1EYPnx4+NGjRzUtiaEjoU4JQgghhBBC\nCCF1MkpWs8G99hxMBveeMErWVpuGdMCAAeWrV692AYCPPvrINTY2tgwA/P39zYcPH9YBwKZNm3pW\ndx5kZmaq/Pz8LPPmzStISEjIP3LkiA6wdWJUT0Y5YsSIsh07dvQsLS0Vrly5IuzYscNlxIgRpS2J\nb9KkSSXR0dHlK1eudHNzc7N2797dunPnTicA+Ne//uU2ZMiQMhcXF9nb29v86aef9gQAo9HISktL\nBQDo3r279fvvvz+xcOFC3+3btztXxXdl2bJlXrJsG+Tx22+/aavLlpWVXf2/PSUlRR0XF2d84403\nLvbr1688OTnZ4TslaE4JQgghbcL4xM7mraDIb1bxO+Luat72AcTomteO3/TlxmaV/+/9k5pVngu5\nzSo//gINbSaEENK6tKLi3OLB/WrOKYHFg/vJWlFxrrX2uWrVqtyEhISA999/37t6oksAePLJJ/PH\njh0bHBYWph85cmSJVquVAWDXrl3Oy5cv9xZFket0OuvGjRuzAWDSpEn5ERER+qioqIpt27ZlT5w4\nsXDgwIERgG2iy2HDhhkzMjJaNOLj1VdfvfDII4/0nTt3bsHatWuzZ8yY4f/UU08Jffr0MW3evDkH\nADZs2JA9depU/8WLF/solUr++eefX50Xo3fv3tJ3332XNWbMmBCdTpfz1ltvnZ82bVqf8PBwvSzL\nrHfv3qaff/45a8yYMaXvvPNOr/DwcP28efMu7Nu3z2n//v3dGWM8LCzM+MADD5Tc0JvdATDO651U\ntNOIjY3liYmJ7R1Gh8FlDpitgFoBmKyASkHn7NXW7m9GW+Us5UKn0e4fWlNytkN2SlQ2r1PigOvX\nzSrf3E6JmG8faVb5VTmpzSo/6MnDzSrfijp8zlL9SK7R7h8+5SxppgY//KSkpByDwVDQ1I219dU3\nSOeWlJTkbjAYAqof00iJLobLHCgzw7wmCfLJYghBLlD91QDupKKGq4uhXCCEkLrJVhmszALzWqof\niWNoqE0HAJhlQC0AJhlQCZTHpNkExoq6KcUiAOimpH8hiX21y5wSjLF4xlgGYyyLMfZ8PWX+zBhL\nZYylMMY21Vg+mTF2ouo2ue2i7iTMVluDdaIIkDnkE0Uwr0my9ayTroVygRBCrsNlDmay2jokqH4k\njqKhNr1UgmllFowzj8C0MgsolWydGIQQ0kG0eacEY0wBYCWAMQD0AB5mjOmvKRMCYAGAYZzzSACz\nq5a7AngFwE0A4gC8whhzacPwHZ9aAflkca1F8sli21A/0rVQLhBCyPXMMqARqX4kjqWBNt20+hTk\njDLACsgZZTCtPmXLc0II6SDaY+xNHIAszvkpAGCMfQbgTwBqnhg7FcBKznkxAHDO86qW3wlgN+e8\nqGrd3QDiAWxuo9gdn8kKIcjF1pNeRQhysZ17qKGhWF0K5QJpY9zi0azypdLwZpWXxdPNKg8AB7tf\nbrxQDQ+ZljWr/KCvLzar/JNXUppV3tVIQ7DtTi2AXzBS/UgcSwNtupxVVquonFVmO5WDEEI6iPao\nkXwBnKnx+GzVsppCAYQyxn5jjB1gjMU3Y13SAM4YlI9EQwhxBQQGIcQVykeiwRn9sO1qKBcIIaQO\nJhnSkWIo/1K7flQ9ZgBUNFKCdFAqBVR/NdTO2b8awBmDEOxUq6gQ7GSbW4IQQjqIjtrdLwIIAXAb\nAD8AvzLGopuzAcbYNADTAKBPnz72js9hMaUArlZCOTEazF0DXlAJqJVgSuoxb29tnbOUC+RGUT1L\nHE2TclYlQDncE5a9eVD+OQrMWwtUSuBqBQSaHJC0sabWs0xg4E4qqKYPrH31DQDqKX1tp3BklUEI\ndoJ6Sl9ARW09IaTjaI8a6RyA3jUe+1Utq+ksgG2ccwvnPBtAJmydFE1ZFwDAOf+Ycx7LOY/18Gje\nkOHOjAkMTKcA664CGMC6q2yP6YdWu2vrnKVcIDeK6lniaJqSs0xggLMI5e3eYL20V+eYEBT0Txxp\ne82pZ5nAwDQiGKu6F9jVfFY/EQzthwOhfiIYcBaprScdgkKhiAkPD9eHhIREjhkzpm9paamQkZGh\nCgkJiWzN/ebk5Cjj4+P7NrV8aWmpMG7cuMDQ0FB9SEhIZExMTFhJSQk1CnbUHm/mIQAhjLFAxpgK\nwEMAtl1T5mvYRkmAMeYO2+kcpwDsAjCaMeZSNcHl6KplpBlsjZaiqtGif0K7MsoFQgi5HtWNpDOh\nfCYdlVqtltPT01NPnDiRolQq+bJly9rkCEdAQIBl586dp5pafsmSJZ6enp6WzMzM1BMnTqSsWbMm\nR6VS0SVs7KjNT9/gnEuMsVmwdSYoAKzhnKcwxhYBSOScb8P/Oh9SAVgBzOecFwIAY2wxbB0bALCo\netJLQgghHVuaR3izyp93bt72rcqM5q0AgMndmlV+anLzfsz/etPGZpX3a+bElb75cc0q/9Wagc0q\nPyb5oWaV17z7bLPKE0IIcQwy565GyeqrFRUqo2Q1a0XFOYExu/0fdvPNN5f98ccfWgCwWq146KGH\n/BMTE528vLzMu3btynJycuL79+/Xzpgxw99oNAr+/v6mTZs25Xh4eFhTUlLU06dP71NUVCRqNBp5\n9erVpwcMGFA5fvz4AGdnZ2tSUlK3/Px85eLFi88+9thjxRkZGaqxY8eGnDhxIuW1117zTE5O1n3+\n+ec5Bw8e1E6aNKnvkSNH0pydna9OvHLhwgWlv7+/ufqxwWAwAUDN7QDAwoULvcrKyhTvvvvu+bi4\nuLCYmJiyffv2dS8tLVWsWrUqJz4+viwxMVHz2GOPBVosFibLMr744ouT0dHRJnu9j46qXYadcM53\ncM5DOedBnPM3qpYtrOqQALeZyznXc86jOeef1Vh3Dec8uOq2tj3iJ4QQQgghhJCuQObctbjS4v/s\nvnTVLVsP4Nl96ariSou/zLmrPbZvsViwa9eu7tHR0UYAyM3N1Tz11FN5WVlZKT169LCuX7/eBQAe\nffTRwCVLlpzNzMxMjYyMND733HM+ADBlyhT/Dz/8MDclJSXt7bffPjtjxoyrE7BcunRJmZiYmP7N\nN9+ceOWVV667QMJLL72Ul52drV6/fn3Pv/71rwErV67MqdkhAQDTpk0rWLFihXf//v3Dn3rqKZ/j\nx4+rm/K6JElix48fT1u6dOmZRYsW+QDAihUrPGbOnHkpPT099Y8//kgLDAw0N7adroDOhSGEEEII\nIYQQUiejZPVdeCBTOJJ/BVbOcST/ChYeyBSMkvWGroJoMpmE8PBwfXR0tN7Pz8/89NNPFwCAr6+v\naejQoUYAGDBgQEVOTo66sLBQUVpaqrj77rvLAGDq1KmFBw4ccCopKRGOHj3qNGHChKDw8HD9zJkz\n/fPy8pTV+xg3btxlhUKBmJiYysLCQuW1MSgUCqxfvz57+vTpgUOGDCkdPXp0+bVlhg4daszOzj4+\nZ86ci0VFReLQoUMjjhw5omns9U2YMKG4av3ys2fPqgBgyM+SVKUAACAASURBVJAh5cuWLev14osv\nep84cULl5OREp4Gg4159gxBCCCGEEEJIO9OKClVSQWmtZUkFpdCKCtWNbLd6Tolrl9ecr0GhUHCj\n0VjvgXSr1QpnZ2epru0AgEajubotzuv+/z8tLU2j0+nkixcvXtdpUa1Hjx7y5MmTL0+ePPlyQkIC\nvvnmmx4JCQlFsvy/QRWVlZW14qzetyiKsFqtDACmT59eNHz48PKvvvqqx9ixY0NWrFhxety4cbXf\n3C6IRkoQQgghhBBCCKmTUbKaDe61J3oyuDvDKFnb7NQDNzc3a/fu3a07d+50AoB//etfbkOGDClz\ndXWV/fz8zGvWrHEBAFmW8fvvv2ubut3CwkLFvHnz+vz000/pRUVF4tq1a12uLfPDDz90y8/PVwBA\nZWUly8zM1AQEBJj9/PykoqIi8eLFiwqj0ch27drVo7H9paamqiIiIkwvvfRS3p133nn52LFjTY61\nM+sSIyUOHz5cwBg7XcdT7gAK2jqeDqQrv/6GXvtOznl8WwZzrRo52xE/o44YE9C14+pIOVuXjvrZ\n1KXBWJs592azfd30olVxHmm1WGyONq/435+ra2ld72lHz9nW1pG+ExRL3a6NpbPmbEd6z1vCkeNv\n7djtlrNaUXFu0eBQ/4UHMoWkglIY3J2xaHCorBUV5+yx/aZau3Zt9owZM/yfeuopoU+fPqbNmzfn\nAMDmzZtPTZ061X/p0qW9JEli9913X9GQIUOMTdnm9OnTe0+ZMiWvX79+pnXr1uWMHDkybPTo0aW+\nvr5SdZnMzEzNrFmz/AFAlmV2++23l0yePLlYEATMmzfvwqBBgyK8vLwswcHBlY3tb8OGDa7//ve/\n3URR5B4eHpbFixdfaNm70bmw+oaxdAWMsUTOeWx7x9FeuvLrd5TX3hHj7IgxARRXR+ZI74GjxOoo\ncQKOFWtb6UjvCcVSt44US2ty9NfpyPG3d+xJSUk5BoOhyZ0irX31DdK1JCUluRsMhoDqx11ipAQh\nhBBCCCGEkJYRGCvqphSLAKCbkv6FJPZFc0oQQgghhBBCCCGkXXT1TomP2zuAdtaVX7+jvPaOGGdH\njAmguDoyR3oPHCVWR4kTcKxY20pHek8olrp1pFhak6O/TkeO35FjJ8SuuvScEoQQQgghhBDS1TR3\nTglC7OnaOSW6+kgJQgghhBBCCCGEtBPqlCCEEEIIIYQQQki7oE4JQgghhBBCCCFtSqfTDWhO+e3b\ntzuPGDEiGAA2btzY44UXXvBunchs4uLiwgICAqLCwsL0AwcODE9KSlK35v66MrqeCyGEEEIIIYQQ\nhzFp0qQSACWtvZ/169efuuWWWyreeecd9zlz5vT+6aefsuy9D1mWwTmHQqGw96YdBo2UIIQQQggh\nhBBSL5lz13KLNVrmPKbq3tVe296+fbtzXFxcWHx8fN/AwMDIcePGBcqyDADYunVr98DAwEi9Xh+x\ndevWntXrLF++3C0hIaEPAGzatKlHv379wiMiIvRDhw4NPXPmjAgAc+fO9ZkwYUJAXFxcmJ+fX/Tr\nr7/uWb3+7bffHhQZGRkRHBwc+c4777g3FuOoUaPKTp8+rQaAZ555pldUVFRESEhI5MMPP+xfHWty\ncrJ66NChoWFhYXq9Xh+RkpKiBoCXX37ZKyoqKiI0NFQ/Z84cHwDIyMhQBQQERN13330BoaGhkSdP\nnlSNHz8+ICQkJDI0NFT/2muvedYbTCdEnRKEEEIIIYQQQuokc+5aXGn1f27vGdWt/07Dc3vPqIor\nrf727JhIS0vTrly58kxWVlZKbm6uevfu3U4VFRVs1qxZAdu2bctKTk5Oy8vLU9a17h133FF27Nix\n9LS0tNQHHnigaNGiRVdP68jKytLs2bMn89ChQ2nvvPOOj8lkYgCwcePGnJSUlLRjx46lfvTRR14X\nL15scJjCl19+2SM8PNwIAPPnz89LTk5OO3HiRIrRaBQ+++yzHgAwceLEwOnTp+dlZGSkJiYmpvfp\n08fy5Zdfds/KytL88ccfaWlpaanHjh3Tff/9904AkJubq541a1Z+VlZWyqVLl8QLFy4oT5w4kZKZ\nmZn6xBNPFNrrvXUEdPoGIYQQQgghhJA6GSXZ95XfzwpH8ioAAEfyKvDK72eFpcN7+3ZTKorssY/o\n6OjyoKAgCwBERkZWnDx5UuXs7Gz18/MzRUdHmwBg0qRJhatXr/a4dt3s7GzVvffe65efn680m81C\n7969TdXPjR49+rJWq+VarVZydXW1nD17VgwKCrIsXbrU67vvvusJABcvXlSmpKRovL29y6/ddkJC\nQl+NRiP7+fmZVq1alQsA33//vfO7777rXVlZKVy+fFnU6/XG4uLi0kuXLqkSEhIuA4BOp+MA+M6d\nO7v/+uuv3fV6vR4AKioqhPT0dE3fvn3NvXr1Mo8aNaocAMLDw01nzpxRT548ufc999xTct99912x\nx/vqKGikBCGEEEIIIYSQOmlFQZWUX1FrWVJ+BbSioLLXPtRqNa/+W6FQQJIk1tR1Z82a1WfmzJl5\nmZmZqR988MFpk8l09X/cura7fft25z179jgnJiamZ2RkpEZERBiNRmOd/xevX7/+VHp6euqPP/54\nMjg42FJRUcHmzZvn/+WXX57MzMxM/ctf/lJQWVlZ7//UnHPMnj37Qnp6emp6enpqbm5u8pw5cwoA\nQKfTydXlPDw8rMnJyakjRowoXbVqlcdDDz0U0NTX3xlQpwQhhBBCCCGEkDoZJdls8NDVWmbw0MEo\nyebW3G///v0rz507p6qem+Gzzz6r83SR0tJSRZ8+fSwA8Mknn7g1tt3Lly8revToYXV2dpaPHj2q\nSUpK6tbUmCoqKgQA8Pb2lkpKSoRvv/3WBQBcXFxkb29v86efftoTAIxGIystLRXGjBlz5dNPP3Uv\nKSkRACA7O1t57ty5685WuHDhgmi1WvHoo49efvPNN88dP35cd22ZzoxO3yCEEEIIIYQQUietKJx7\nbYif/yu/nxWS8itg8NDhtSF+slYUzrXmfnU6HV+xYsXpsWPHBmu1Wvmmm24qKysru27uhxdffPH8\nww8/HNSjRw/p5ptvLs3NzW3w0p3jx48v+fjjjz369u0b2bdv30qDwXDdaRv1cXd3t06aNCk/IiIi\n0sPDQ6q57oYNG7KnTp3qv3jxYh+lUsk///zzk/fff/+VlJQUzaBBg8KrXpO8cePGbFEUec3t5uTk\nKB9//PEAWZYZACxatOhsU2PqDBjnvPFShBBCCCGEEEI6haSkpByDwVDQ1PIy565GSfbVioLKKMlm\nrSicExizy3wSpOtJSkpyNxgMAdWPaaQEIYQQQgghhJB6CYwVVU9q2U3Z4IUqCGk2mlOCEEIIIYQQ\nQggh7YI6JQghhBBCCCGEENIuukSnRHx8PAdAN7o19dbuKGfp1sxbu6OcpVszb+2OcpZuzby1O8pZ\nujXzRojD6BKdEgUFTZ7DhZAOgXKWOBrKWeJoKGeJo6GcJYR0Vl2iU4IQQgghhBBCCCEdD3VKEEII\nIYQQQghpU4yxmD/96U+B1Y8tFgtcXFwMI0aMCG7N/c6dO9dn4cKFXnU9N2DAgPCmbsdisWDWrFm+\n/v7+UeHh4frw8HD9c889511f+VtvvTW4oKCgWZcuiYuLC/v11191LV3fUdAlQQkhhBBCCCGEtCmt\nVitnZGRoy8rK2P+zd+dxUVb7H8A/55l9ZEA2QUABhWFmAEFBEBOT3EstM39uaYvVra73luZtMTNT\ns7rXrLzVtU3LNct2l0gLtxYVEpRVcQcERZB1mO05vz9gCHRAQRTR7/v14jUz53meM2eGM2dmvnPO\n93FycuLffPONs5eXl6U923TgwIHsK933qaee8i0qKpJlZWVlqNVqXlpaKixcuPCSoIQoiuCcY+fO\nnblX07arPf5GRjMlCCGEEEIIIYQ0SeTcrcoihoucR9VdurVFvUOGDCn78ssvOwPA+vXr3caNG1di\n31ZeXi6MHz8+IDw8XK/X6w1r1qzpDADLli1zHzJkSM/+/fsH+/r6hi9evNhz/vz5Xnq93hAREaEr\nKiqSALWzDB566KFuOp3OEBwcHJqUlKS2152VlaWKiYkJ8fPzC1+0aFEXe7lare4NACdPnpRFR0eH\n2I/98ccfnRq2u6KiQli3bp3nxx9/fEqtVnMAcHV1FZcuXVoAADk5OfKAgICwsWPHBmi12tCjR4/K\nfX19w8+cOSP997//7WmfWeHr6xseGxurXbt2rYu9LCAgIMzX1zf84ufKfjwAvP/++27h4eF6nU5n\nmDx5sr/VaoXVasW4ceMCgoODQ7VareGVV17pcnEdNyoKShBCCCGEEEIIcUjk3K20hvs/v6NCnrCu\nBM/vqJCX1nD/tghMTJ06tWTDhg2u1dXVLCsrSx0XF1dl3zZnzpyuCQkJ5YcOHcravXt3zty5c/3K\ny8sFADh8+LBq8+bNR/fv35/12muv+arVajErKyszOjq66oMPPnC312E0GoXs7OzMZcuWnXzsscfq\nl4rk5uYqd+7ceXj//v1ZS5Ys8TGZTKxhu1asWOE2ePDgsuzs7MysrKyM2NjY6obbMzMzFV27djW7\nurqKTT22U6dOKWbMmHEuNzc3Q6vVmu3lzz777Lns7OzMtLS0LG9vb/NTTz1VNGXKlLLs7OzM7Ozs\nTIPBUD1jxozCpur9888/lRs3bnRLTk7Ozs7OzhQEgS9fvtz9999/V585c0Z25MiRjMOHD2f+/e9/\nP3+l/4f2RkEJQgghhBBCCCEOGa3cd/6eCuFAkRU2DhwosmL+ngrBaOW+V1t3bGysMS8vT/HRRx+5\nDRkypKzhth07dji/9dZbXXU6nWHAgAEhJpOJ5ebmygGgf//+Fa6urqKPj4/VycnJNn78+AsAEB4e\nXn3ixAmFvY7JkyeXAMDIkSMrKysrBXtOhmHDhl1QqVS8a9euVjc3N0teXl6jtAb9+vWrWr9+v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MH3v5xo0bnQMDA0MNBoN+48aNne3l5eXlwvjx4wPCw8P1er3esGbNms4X1zlr1iyfe+65JzAy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k1Nb5M3LlQo14rmToYi2xHOP3TygP75k4e86pxsMlJp53VHZGidbKxb76TScbmME8pZaVS2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\niIiIiIiIiNSFKiVEREREREREpC7+PxhclyMyJzb6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116415358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"));sns.pairplot(attendance_valuation_df, hue=\"TEAM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116dae6d8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ERC2liy8iImopc/FFREQtpYsvIiJqKV18ERFRS8tqnqKSoCIiBlS901MSVETEwMo9qIiI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\nERFRS/8fzH/6Gd0LjpwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116450dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = attendance_valuation_df.corr()\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GMS</th>\n",
       "      <th>PCT</th>\n",
       "      <th>TOTAL_MILLIONS</th>\n",
       "      <th>AVG_MILLIONS</th>\n",
       "      <th>VALUE_MILLIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GMS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850839</td>\n",
       "      <td>0.85085</td>\n",
       "      <td>0.476644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_MILLIONS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.850839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.530697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVG_MILLIONS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.850850</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.530730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALUE_MILLIONS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.476644</td>\n",
       "      <td>0.530697</td>\n",
       "      <td>0.53073</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GMS       PCT  TOTAL_MILLIONS  AVG_MILLIONS  VALUE_MILLIONS\n",
       "GMS             NaN       NaN             NaN           NaN             NaN\n",
       "PCT             NaN  1.000000        0.850839       0.85085        0.476644\n",
       "TOTAL_MILLIONS  NaN  0.850839        1.000000       1.00000        0.530697\n",
       "AVG_MILLIONS    NaN  0.850850        1.000000       1.00000        0.530730\n",
       "VALUE_MILLIONS  NaN  0.476644        0.530697       0.53073        1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_valuation_df.to_csv(\"../data/nba_2017_att_val.csv\");corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valuations = attendance_valuation_df.pivot(\"TEAM\", \"TOTAL_MILLIONS\", \"VALUE_MILLIONS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11aadf588>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i4mtJm0TEu8C7knYEtgQ+zHO8mZmZ2UrXrMc2tDpoPxb9bzxd7rkJgJnX/5t2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\ngL71A8nMPkBttinLxCppMbRjr234z0cjGD9+AgCPPNSXLbfqwUYbr8/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pH35ZGvc/IL8q\n6R3gv5IGAk8Xce9JwLERkSdpENANyN9/6fWImAcg6RNgG2AL4K2I+CltfxLYPu3fCdhZWrXWsnaa\nsMlUVJ93gDskPQY8HRFTS/jsTYABkhoC1YBvMq6dBnxHknBans6zL/BkxvwbZfR/MiJWpudrjEdS\nD6AHwAMPPECPHj1KGLKZmZlZ+VO7RnXa7LA14yZNZ/6ipaxYmUeVypWYOXc+9esm/+SrX7cm38/5\nmQab1mLFyjwWLF5G3U02pv6mNfl+zvxVY82cO5/6mxb8Z6KZmeVKeJu9dVKhl9cVJSLOIVk2txXw\noaTNM69L2g1oAbwqaTJJ1dNJGV2WZpyvZM3Ju0okVUet0qNxRBSsmy60T0TcDJwJbAy8k7/srQTu\nBu6JiN2As4HqGdcmAE1JElP5c8/NmLtVRGRWYi3MPylJPBHRLyJaR0RrJ5zMzMysIvpp/iJ+XpQU\nfC9Ztpz3P51Msy03p/UOW/HaR58D8Ox7H9O+ZXMADmq5Hc++9zEAr330OW122BpJHNSyOS+P+oxl\ny1cw7Ye5TJk1h12bNszNQ5mZmWVZRa90epckYfQ/4GSSpXJI2i4iRgIjJR1Oknz6MeO+k4DeEXFT\nfoOkbyRtU8xco4B/SdqUZHnd8STJHYBXgAuAW9OxWkXE2AL3F9onjXUCMEFSG2BH4LMSPHsdYFp6\nfnqBa2OA+4Chkg6NiOnp83WLiCeVlDu1jIhxBe5jHeIxMzMzqzB+mLeAq/u/SF5eHnkBnffagQNb\nbkezhpvz14eepe/Qt9lhq/ocu99uABy7X0uu/M/zdLn6QWrXqM4tZ3YBoHmjLThkrx047tqHqVy5\nElf8rhOVK22Qfxc2M7MKqCIlnWpIylzqdQdJEuc/knoBs4E/pNduldQCEPA6v+y9lO93wBEF2p5J\n22dSiHR/qBuBD4CfSBIx89LLFwJ9JY0nec+HA+cUGKKoPn+R1AHIAz4GXizhs/cmWS43h2R/qW0L\nxPu2pJ7A85IOIUnK3SfpKqAqyX5Wv0o6lTAeMzMzswpt+yb1GXBlwb/rQZN6dXnsilN/1b5R1Src\n1qNroWOddcQ+nHXEPlmP0czM1l1eaM2drEiKiFzHUGFIqhkRCyRVIUlSPRwRz+Q6rhzzD5iZmZlV\nCIvffKjU59y4w5mlPqeZ2Vqq0FmZL3Y6rMz/Trv9py+V2e+Ba3ezq7ekscBEko27B+c4HjMzMzMz\nMzOznKhIy+tyLiJ65joGMzMzMzMzM8uO8PK6deJKJzMzMzMzMzMzyzonnczMzMzMzMzMLOu8vM7M\nzMzMzMzMrBCR5+V168KVTmZmZmZmZmZmlnVOOpmZmZmZmZmZWdZ5eZ2ZmZmZmZmZWSEich1B+eak\nk5mZmZlZCWzc4cxch2BmZlauOOlkZlaGVa++danPuWTJlFKf08zMzMzMKh4nnczMzMzMzMzMCuFP\nr1s33kjczMzMzMzMzMyyzkknMzMzMzMzMzPLOiedzMzMzMzMzMws67ynk5mZmZmZmZlZIfLCezqt\nC1c6mZmZmZmZmZlZ1jnpZGZmZmZmZmZmWeekk5lZOdSiRTNGjnxx1TFr1secf/6f2G23nRg27BlG\nj36FQYMeplatmqvu6dXrPD7+eDjjx79Jp04H5jB6MzMzM7PyIUJl/ijLKnzSSVJIejTjdRVJsyU9\nt57n7S2pZxHX3l2LcapIulHSl5LGpseVxfR/QVLdtYx1mKTWv/V+Myt9X345ib33Ppy99z6cffY5\nkkWLFjN06Evcd98/ufrqm2ndujNDh77EJZecDcCOO7agW7cu7LFHJ44++jT69LmBSpUq/P8FmJmZ\nmZlZDm0Iv3EsBHaVtHH6+hBgWg7jISL2XYvu1wONgN0iohVwAFC1YCclKkXEERExdx1iW6f7zaz0\ndey4H998M4UpU6bRosW2jBgxEoDXXx/BMcccAUCXLp158slnWbZsGZMnf8fXX0+mTZtWuQzbzMzM\nzMwquA0h6QTwAnBken4S8Hj+BUmbSHpY0geSxkjqmrafIWmwpFclTZZ0vqRL0j7vS9os7TdM0l1p\nBdJESW0z5t05vT5J0oUZcy5IvzaUNDzj3gMyg5ZUAzgLuCAilgBExPyI6J1ebyrpc0mPABOBrdJY\nt5B0TkZl1DeS3pR0dEbb55K+KfhG5d+fnp+Svi9jJT0gqXJ6/DeNd4Kki9fpO2Nm66xbt6MZMGAI\nAJ988gVdunQG4LjjjqRJk4YANGrUgKlTp6+6Z9q0GTRqtGXpB2tmZmZmVo5ElP2jLNtQkk5PAL+T\nVB1oCYzMuHYl8EZEtAU6ALdK2iS9titwHNAGuAFYFBF7AO8Bp2WMUSOtQjoXeDijfUfgUKAt8HdJ\nBSuUfg+8nN67OzC2wPXmwJSImF/Ms7UA7o2IXSLi2/zGiLg/HbcNMBW4IyKGRkSrtH0ccFtRg0ra\nCTgR2C/tvxI4GWgFNI6IXSNiN+A/xcRmZutZ1apVOfLIQ3j66ecBOPvsXpx99mm8++7z1KpVk2XL\nluc4QjMzMzMz21BtEEmniBgPNCWpcnqhwOXOwOWSxgLDgOrA1um1N9PKotnAPODZtH1COl6+x9N5\nhgO1M/ZEej4ilkbED8AsoEGBuUcBf5DUm2T5XHHJJST9Ia06+k7SVmnztxHxfjG33UWSVMuPHUmX\nAYsjom8x9x0M7AWMSt+bg4FmwCSgmaS7JR0G/FxInD0kjZY0ul+/fsU9kpmto0MPbc/YsROZNesH\nAL744muOOuoU9t33SAYMGMKkSUkuevr0mTRp0mjVfY0bN2T69O9zErOZmZmZmW0YquQ6gFI0lKSy\npz2weUa7gOMj4vPMzpL2BpZmNOVlvM5j9feuYEFb/uvM+1cWuIeIGC7pQJKlf/+VdEdEPJLR5Stg\na0m10uTXf4D/SJoIVE77LCzieZF0BrANcH5GWyegG7Cmj64S0D8irihk3N1JKrjOAboDfyzwXP2A\n/GxTGS/2MyvfunfvysCBQ1a9rldvc2bP/hFJXHHFhTz0UPI5Cs899yr9+/fhrrsepFGjBjRvvi2j\nRhUsrjQzMzMzs0x5ZfzT4cq6IiudJI0v4pggaXxpBpklDwPXRsSEAu0vAxdIEoCkPX7D2Cem9+4P\nzIuIeSW5SdI2wMyIeBB4CNgz83pELAL+DdyTLg1EUmWgWgnG3gvoCZwSEXkZ8/UFukXE4jUM8Tpw\ngqT66b2bSdom3e+pUkQMAq4qGLOZlZ4aNTbm4IMPYPDgl1a1de/elQkThjF+/JtMnz6T/v0HAvDp\np18waNBzjB37OkOHPsJFF11FXl5erkI3MzMzM7MNQHGVTnkkVSr/R7KsbE1JijItIqYCfQq5dB3w\nL2C8pErAN8BRazn8EkljSD5V7o9r6pyhPdBL0nJgAavvE5XvyjTGiZLmk3wf+gPTST7VrijnA5sB\nb6b5tNHAdyRVXoPTtukRcURhN0fEJ5KuAl5J35flwHnp/P9J2wB+VQllZqVj0aLFNG68+2ptffs+\nTN++Dxfa/5Zb7uGWW+4pjdDMzMzMzMxQFLPVuaQdSfZB6gJ8QpKAeiUiVpROeGWfpGFAz4gYnetY\nyigvrzNbB9Wrb73mTlm2ZMmUUp/TzMzMzMqtCr3+bMzWXcv877R7TBlSZr8HxW4kHhGfRcTfI2JP\nkmqnR4CLSyUyMzMzMzMzMzMrt4rdSFxSY+B3wLHAHJKE0zOlEFe5ERHtcx2DmZmZmZmZmVlZU2TS\nSdJbQC1gIPAH4Mf0UjVJm0XET6UQn5mZmZmZmZmZlUPFVTptQ7Ifz9lAj4x2pe3N1mNcZmZmZmZm\nZmY5Vcw22FYCRSadIqJpKcZhZmZmZmZmZmYVSLEbiRckaTtJV0v6eH0FZGZmZmZmZmZm5V+xG4kD\nSGoEnAj8HtgNuIlkc3EzMzMzMzMzsworL5TrEMq1IiudJPWQ9CYwDNgc+BMwIyKujYgJpRSfmZmZ\nmZmZmZmVQ8VVOt0DvAf8PiJGA0jyFlpmZqVoyZIpuQ7BzMzMzMzsNyku6dQQ6AbcLmlLYCBQtVSi\nMjMzMzMzKyWnbHNcqc/56LdPl/qcZrb2wsvr1kmRy+si4seIuD8iDgIOBuYCMyV9KunGUovQzMzM\nzMzMzMzKnRJ9el1ETI2I2yOiNdAVWLJ+wzIzMzMzMzMzs/KsuI3EL8s475Z/HhFfANXXc1xmZmZm\nZmZmZjmVFyrzR1lWXKXT7zLOryhw7bD1EIuZmZmZmZmZmWWJpK0kvSnpE0kfS7oobd9M0quSvky/\nbpq2S1IfSV9JGi9pz4yxTk/7fynp9JLMX1zSSUWcF/bazMzMzMzMzMzKlhXApRGxM9AOOE/SzsDl\nwOsR0QJ4PX0NcDjQIj16APdBkqQC/g7sDbQF/p6fqCpOcUmnKOK8sNdmZmZmZmZmZhVKlIOj2Pgj\nZkTER+n5fOBToDHJft390279gWPS867AI5F4H6grqSFwKPBqRPwUEXOAVynBKrgqxVxrJelnkqqm\njdNz0tfe08nMzMzMzMzMLMck9SCpSsrXLyL6FdKvKbAHMBJoEBEz0kvfAw3S88bAdxm3TU3bimov\nVnFJp3ERsceaBjAzMzMzM6sozrr1PFp1bM3PP87jis5/AeCES09iz0PaEHnBzz/O44FL72burDns\n1G4XLn7wcmZ/NwuAUS+9z+A+TwLQ8qA9OPXvf6RS5UoMe+I1nr3vmZw9k5lVbGmC6VdJpkySagKD\ngL9ExM/SL7smRURIWi8r2kq6vM7WgaSQ9GjG6yqSZkt6Ln19tKTLix5hreZ6KF2fWbB9mKTWGa+b\nSpr4G+eoK+ncdYnTzMzMzKwsGv7km9x6+nWrtT3/wGD+dtglXHnEpYx5fTTHXtR91bVHrh/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9tLGibpKUmfSXpMSraOl7SXpLckfSjpZUkN0/YLJX0iabykJ9K2zSQNTtvel9QyrfY5B7hY0lhJ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\nzMzMzMzMzMys6f4/Q06UAmO6l38AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aadfcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20,15))\n",
    "ax = plt.axes()\n",
    "ax.set_title(\"NBA Team AVG Attendance vs Valuation in Millions:  2016-2017 Season\")\n",
    "sns.heatmap(valuations,linewidths=.5, annot=True, fmt='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = smf.ols('VALUE_MILLIONS ~TOTAL_MILLIONS', data=attendance_valuation_df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         VALUE_MILLIONS   R-squared:                       0.282\n",
      "Model:                            OLS   Adj. R-squared:                  0.256\n",
      "Method:                 Least Squares   F-statistic:                     10.98\n",
      "Date:                Fri, 09 Feb 2018   Prob (F-statistic):            0.00255\n",
      "Time:                        15:28:41   Log-Likelihood:                -234.04\n",
      "No. Observations:                  30   AIC:                             472.1\n",
      "Df Residuals:                      28   BIC:                             474.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept      -2408.0191   1141.332     -2.110      0.044   -4745.931     -70.107\n",
      "TOTAL_MILLIONS  5132.3400   1549.033      3.313      0.003    1959.290    8305.390\n",
      "==============================================================================\n",
      "Omnibus:                        7.692   Durbin-Watson:                   1.930\n",
      "Prob(Omnibus):                  0.021   Jarque-Bera (JB):                6.355\n",
      "Skew:                           1.100   Prob(JB):                       0.0417\n",
      "Kurtosis:                       3.495   Cond. No.                         21.3\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11aeadfd0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+nyzl/Yykf5Q0TdIpBce8lCwV/qikbxe875XJe00pKPuopKvGGdcWSf8+rOxR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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aebea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(y=\"VALUE_MILLIONS\", x=\"TOTAL_MILLIONS\", data=attendance_valuation_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attendance_valuation_predictions_df = attendance_valuation_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attendance_valuation_predictions_df[\"predicted\"] = results.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "591.33219017442696"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels\n",
    "rmse = statsmodels.tools.eval_measures.rmse(attendance_valuation_predictions_df[\"predicted\"], attendance_valuation_predictions_df[\"VALUE_MILLIONS\"])\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11be9c9b0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xkdmqety9+gGgd/OfTcCDIvKvOCfmFgI7cHrhC0VkAU7yXQ3cMI64TZ5IJJTT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\n+rHoKZFpcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be9c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"predicted\", y=\"VALUE_MILLIONS\", data=attendance_valuation_predictions_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attendance_valuation_elo_df = attendance_valuation_df.merge(elo_df, how=\"inner\", on=\"TEAM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEAM</th>\n",
       "      <th>GMS</th>\n",
       "      <th>PCT</th>\n",
       "      <th>TOTAL_MILLIONS</th>\n",
       "      <th>AVG_MILLIONS</th>\n",
       "      <th>VALUE_MILLIONS</th>\n",
       "      <th>ELO</th>\n",
       "      <th>CONF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>41</td>\n",
       "      <td>104</td>\n",
       "      <td>0.888882</td>\n",
       "      <td>0.021680</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>1519</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>41</td>\n",
       "      <td>103</td>\n",
       "      <td>0.811366</td>\n",
       "      <td>0.019789</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>1420</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>41</td>\n",
       "      <td>101</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.017608</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>1393</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>0.805400</td>\n",
       "      <td>0.019643</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1569</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>0.813050</td>\n",
       "      <td>0.019830</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1600</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TEAM  GMS  PCT  TOTAL_MILLIONS  AVG_MILLIONS  VALUE_MILLIONS  \\\n",
       "0     Chicago Bulls   41  104        0.888882      0.021680          2500.0   \n",
       "1  Dallas Mavericks   41  103        0.811366      0.019789          1450.0   \n",
       "2  Sacramento Kings   41  101        0.721928      0.017608          1075.0   \n",
       "3        Miami Heat   41  100        0.805400      0.019643          1350.0   \n",
       "4   Toronto Raptors   41  100        0.813050      0.019830          1125.0   \n",
       "\n",
       "    ELO  CONF  \n",
       "0  1519  East  \n",
       "1  1420  West  \n",
       "2  1393  West  \n",
       "3  1569  East  \n",
       "4  1600  East  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_valuation_elo_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attendance_valuation_elo_df.to_csv(\"../data/  \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117b9f358>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a0/IwzyFb3bCqjsxQrXC9JKcm+fxOBQUAAACstq0MyvigJPdOcliG6SPv3d2S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\nAABgbhIKAAAAwNwkFAAAAIC5/X+R0VU6kc6SjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bec3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_elo = attendance_valuation_elo_df.corr()\n",
    "plt.subplots(figsize=(20,15))\n",
    "ax = plt.axes()\n",
    "ax.set_title(\"NBA Team Correlation Heatmap:  2016-2017 Season (ELO, AVG Attendance, VALUATION IN MILLIONS)\")\n",
    "sns.heatmap(corr_elo, \n",
    "            xticklabels=corr_elo.columns.values,\n",
    "            yticklabels=corr_elo.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GMS</th>\n",
       "      <th>PCT</th>\n",
       "      <th>TOTAL_MILLIONS</th>\n",
       "      <th>AVG_MILLIONS</th>\n",
       "      <th>VALUE_MILLIONS</th>\n",
       "      <th>ELO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GMS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850839</td>\n",
       "      <td>0.85085</td>\n",
       "      <td>0.476644</td>\n",
       "      <td>0.251144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_MILLIONS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.850839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.530697</td>\n",
       "      <td>0.285907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVG_MILLIONS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.850850</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.530730</td>\n",
       "      <td>0.285930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALUE_MILLIONS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.476644</td>\n",
       "      <td>0.530697</td>\n",
       "      <td>0.53073</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELO</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.251144</td>\n",
       "      <td>0.285907</td>\n",
       "      <td>0.28593</td>\n",
       "      <td>0.060575</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GMS       PCT  TOTAL_MILLIONS  AVG_MILLIONS  VALUE_MILLIONS  \\\n",
       "GMS             NaN       NaN             NaN           NaN             NaN   \n",
       "PCT             NaN  1.000000        0.850839       0.85085        0.476644   \n",
       "TOTAL_MILLIONS  NaN  0.850839        1.000000       1.00000        0.530697   \n",
       "AVG_MILLIONS    NaN  0.850850        1.000000       1.00000        0.530730   \n",
       "VALUE_MILLIONS  NaN  0.476644        0.530697       0.53073        1.000000   \n",
       "ELO             NaN  0.251144        0.285907       0.28593        0.060575   \n",
       "\n",
       "                     ELO  \n",
       "GMS                  NaN  \n",
       "PCT             0.251144  \n",
       "TOTAL_MILLIONS  0.285907  \n",
       "AVG_MILLIONS    0.285930  \n",
       "VALUE_MILLIONS  0.060575  \n",
       "ELO             1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_elo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11c1a1ac8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jZvZcS45x/nVJ+33ycZKNtfxUus3L3b1x9E6eNA56TNL/Su/zDUoe+39Ib2vpfU8cS70q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\nD8gCNvWN67lJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c1a1278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lmplot(x=\"ELO\", y=\"TOTAL_MILLIONS\", data=attendance_valuation_elo_df, hue=\"CONF\", size=12)\n",
    "ax.set(xlabel='ELO Score', ylabel='TOTAL ATTENDANCE IN MILLIONS', title=\"NBA Team AVG Attendance vs ELO Ranking:  2016-2017 Season\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CONF\n",
       "East    1502\n",
       "West    1543\n",
       "Name: ELO, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_valuation_elo_df.groupby(\"CONF\")[\"ELO\"].median()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CONF\n",
       "East    0.710643\n",
       "West    0.746323\n",
       "Name: TOTAL_MILLIONS, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_valuation_elo_df.groupby(\"CONF\")[\"TOTAL_MILLIONS\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = smf.ols('TOTAL_MILLIONS ~ELO', data=attendance_valuation_elo_df).fit()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         TOTAL_MILLIONS   R-squared:                       0.082\n",
      "Model:                            OLS   Adj. R-squared:                  0.049\n",
      "Method:                 Least Squares   F-statistic:                     2.493\n",
      "Date:                Fri, 09 Feb 2018   Prob (F-statistic):              0.126\n",
      "Time:                        15:28:43   Log-Likelihood:                 37.584\n",
      "No. Observations:                  30   AIC:                            -71.17\n",
      "Df Residuals:                      28   BIC:                            -68.37\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.4378      0.188      2.334      0.027       0.053       0.822\n",
      "ELO            0.0002      0.000      1.579      0.126   -5.84e-05       0.000\n",
      "==============================================================================\n",
      "Omnibus:                        0.340   Durbin-Watson:                   0.652\n",
      "Prob(Omnibus):                  0.844   Jarque-Bera (JB):                0.507\n",
      "Skew:                           0.087   Prob(JB):                        0.776\n",
      "Kurtosis:                       2.387   Cond. No.                     2.17e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.17e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(results.summary())\n",
    "      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEAM</th>\n",
       "      <th>GMS</th>\n",
       "      <th>PCT_ATTENDANCE</th>\n",
       "      <th>WINNING_SEASON</th>\n",
       "      <th>TOTAL_ATTENDANCE_MILLIONS</th>\n",
       "      <th>VALUE_MILLIONS</th>\n",
       "      <th>ELO</th>\n",
       "      <th>CONF</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>MEDIAN_HOME_PRICE_COUNTY_MILLIONS</th>\n",
       "      <th>COUNTY_POPULATION_MILLIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>41</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>0.888882</td>\n",
       "      <td>2500</td>\n",
       "      <td>1519</td>\n",
       "      <td>East</td>\n",
       "      <td>Cook</td>\n",
       "      <td>269900.0</td>\n",
       "      <td>5.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>41</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0.811366</td>\n",
       "      <td>1450</td>\n",
       "      <td>1420</td>\n",
       "      <td>West</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>314990.0</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>41</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>1075</td>\n",
       "      <td>1393</td>\n",
       "      <td>West</td>\n",
       "      <td>Sacremento</td>\n",
       "      <td>343950.0</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.805400</td>\n",
       "      <td>1350</td>\n",
       "      <td>1569</td>\n",
       "      <td>East</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>389000.0</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.813050</td>\n",
       "      <td>1125</td>\n",
       "      <td>1600</td>\n",
       "      <td>East</td>\n",
       "      <td>York-County</td>\n",
       "      <td>390000.0</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TEAM  GMS  PCT_ATTENDANCE  WINNING_SEASON  \\\n",
       "0     Chicago Bulls   41             104               1   \n",
       "1  Dallas Mavericks   41             103               0   \n",
       "2  Sacramento Kings   41             101               0   \n",
       "3        Miami Heat   41             100               1   \n",
       "4   Toronto Raptors   41             100               1   \n",
       "\n",
       "   TOTAL_ATTENDANCE_MILLIONS  VALUE_MILLIONS   ELO  CONF       COUNTY  \\\n",
       "0                   0.888882            2500  1519  East         Cook   \n",
       "1                   0.811366            1450  1420  West       Dallas   \n",
       "2                   0.721928            1075  1393  West   Sacremento   \n",
       "3                   0.805400            1350  1569  East   Miami-Dade   \n",
       "4                   0.813050            1125  1600  East  York-County   \n",
       "\n",
       "   MEDIAN_HOME_PRICE_COUNTY_MILLIONS  COUNTY_POPULATION_MILLIONS  \n",
       "0                           269900.0                        5.20  \n",
       "1                           314990.0                        2.57  \n",
       "2                           343950.0                        1.51  \n",
       "3                           389000.0                        2.71  \n",
       "4                           390000.0                        1.10  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_housing_win_df = pd.read_csv(\"../data/nba_2017_att_val_elo_win_housing.csv\");val_housing_win_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TEAM', 'GMS', 'PCT_ATTENDANCE', 'WINNING_SEASON',\n",
       "       'TOTAL_ATTENDANCE_MILLIONS', 'VALUE_MILLIONS', 'ELO', 'CONF', 'COUNTY',\n",
       "       'MEDIAN_HOME_PRICE_COUNTY_MILLIONS', 'COUNTY_POPULATION_MILLIONS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_housing_win_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         VALUE_MILLIONS   R-squared:                       0.811\n",
      "Model:                            OLS   Adj. R-squared:                  0.789\n",
      "Method:                 Least Squares   F-statistic:                     37.12\n",
      "Date:                Fri, 09 Feb 2018   Prob (F-statistic):           1.52e-09\n",
      "Time:                        15:30:59   Log-Likelihood:                -214.03\n",
      "No. Observations:                  30   AIC:                             436.1\n",
      "Df Residuals:                      26   BIC:                             441.7\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================================\n",
      "                                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "Intercept                         -1608.0453    622.423     -2.584      0.016   -2887.454    -328.636\n",
      "COUNTY_POPULATION_MILLIONS          111.0593     26.277      4.227      0.000      57.047     165.072\n",
      "TOTAL_ATTENDANCE_MILLIONS          2921.7506    874.687      3.340      0.003    1123.807    4719.695\n",
      "MEDIAN_HOME_PRICE_COUNTY_MILLIONS     0.0014      0.000      6.826      0.000       0.001       0.002\n",
      "==============================================================================\n",
      "Omnibus:                        1.898   Durbin-Watson:                   1.641\n",
      "Prob(Omnibus):                  0.387   Jarque-Bera (JB):                0.939\n",
      "Skew:                           0.402   Prob(JB):                        0.625\n",
      "Kurtosis:                       3.323   Cond. No.                     9.06e+06\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 9.06e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "results = smf.ols('VALUE_MILLIONS ~COUNTY_POPULATION_MILLIONS+TOTAL_ATTENDANCE_MILLIONS+MEDIAN_HOME_PRICE_COUNTY_MILLIONS', data=val_housing_win_df).fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/noahgift/.socialpowernba/lib/python3.6/site-packages/ggplot/utils.py:81: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n",
      "You can access Timestamp as pandas.Timestamp\n",
      "  pd.tslib.Timestamp,\n",
      "/Users/noahgift/.socialpowernba/lib/python3.6/site-packages/ggplot/stats/smoothers.py:4: FutureWarning: The pandas.lib module is deprecated and will be removed in a future version. These are private functions and can be accessed from pandas._libs.lib instead\n",
      "  from pandas.lib import Timestamp\n"
     ]
    },
    {
     "data": {
      "image/png": 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WKN163j/2m6kvYZVcgXRy9gJxSaqpqVEgENCUKVNSzrPcvn279u7dq3g8rkOHDmnJkiU6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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d6389e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223372036556891297)>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ggplot import *\n",
    "ggplot(val_housing_win_df, aes(x=\"TOTAL_ATTENDANCE_MILLIONS\", y=\"VALUE_MILLIONS\", color=\"WINNING_SEASON\")) + geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numerical_df = val_housing_win_df.loc[:,[\"TOTAL_ATTENDANCE_MILLIONS\", \"ELO\", \"VALUE_MILLIONS\", \"MEDIAN_HOME_PRICE_COUNTY_MILLIONS\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MinMaxScaler(copy=True, feature_range=(0, 1))\n",
      "[[ 1.          0.41898148  0.68627451  0.08776879]\n",
      " [ 0.72637903  0.18981481  0.2745098   0.11603661]\n",
      " [ 0.41067502  0.12731481  0.12745098  0.13419221]\n",
      " [ 0.70531986  0.53472222  0.23529412  0.16243496]\n",
      " [ 0.73232332  0.60648148  0.14705882  0.16306188]\n",
      " [ 0.62487072  0.68981481  0.49019608  0.31038806]\n",
      " [ 0.83819102  0.47916667  0.17647059  0.00476459]\n",
      " [ 0.6983872   1.          0.7254902   0.39188139]\n",
      " [ 0.49678606  0.47453704  0.10784314  0.04993825]\n",
      " [ 0.72417286  0.08333333  1.          1.        ]\n",
      " [ 0.54749962  0.57638889  0.56862745  0.23139615]\n",
      " [ 0.60477873  0.06712963  0.88235294  0.31038806]\n",
      " [ 0.65812204  0.52083333  0.11764706  0.184816  ]\n",
      " [ 0.52863955  0.74768519  0.16666667  0.08156228]\n",
      " [ 0.70957335  0.64583333  0.0627451   0.13983449]\n",
      " [ 0.43166712  0.03240741  0.06666667  0.10657639]\n",
      " [ 0.20301662  0.33333333  0.          0.10350448]\n",
      " [ 0.31881029  0.61111111  0.35294118  0.09062441]\n",
      " [ 0.36376665  0.00462963  0.1372549   0.10350448]\n",
      " [ 0.27883458  0.43518519  0.05098039  0.00946649]\n",
      " [ 0.25319364  0.33333333  0.01568627  0.01573569]\n",
      " [ 0.3708405   0.28935185  0.01176471  0.10977023]\n",
      " [ 0.37053693  0.          0.01960784  0.03140869]\n",
      " [ 0.17197852  0.32638889  0.05294118  0.14738888]\n",
      " [ 0.09538753  0.0787037   0.41176471  0.40756065]\n",
      " [ 0.15307963  0.37962963  0.01372549  0.        ]\n",
      " [ 0.32306025  0.57638889  0.09803922  0.27336844]\n",
      " [ 0.          0.49537037  0.05490196  0.21885775]\n",
      " [ 0.00571132  0.28935185  0.00784314  0.12758322]\n",
      " [ 0.17492596  0.23842593  0.05882353  0.10663281]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "print(scaler.fit(numerical_df))\n",
    "print(scaler.transform(numerical_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEAM</th>\n",
       "      <th>GMS</th>\n",
       "      <th>PCT_ATTENDANCE</th>\n",
       "      <th>WINNING_SEASON</th>\n",
       "      <th>TOTAL_ATTENDANCE_MILLIONS</th>\n",
       "      <th>VALUE_MILLIONS</th>\n",
       "      <th>ELO</th>\n",
       "      <th>CONF</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>MEDIAN_HOME_PRICE_COUNTY_MILLIONS</th>\n",
       "      <th>COUNTY_POPULATION_MILLIONS</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>41</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>0.888882</td>\n",
       "      <td>2500</td>\n",
       "      <td>1519</td>\n",
       "      <td>East</td>\n",
       "      <td>Cook</td>\n",
       "      <td>269900.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>41</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0.811366</td>\n",
       "      <td>1450</td>\n",
       "      <td>1420</td>\n",
       "      <td>West</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>314990.0</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>41</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>1075</td>\n",
       "      <td>1393</td>\n",
       "      <td>West</td>\n",
       "      <td>Sacremento</td>\n",
       "      <td>343950.0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.805400</td>\n",
       "      <td>1350</td>\n",
       "      <td>1569</td>\n",
       "      <td>East</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>389000.0</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.813050</td>\n",
       "      <td>1125</td>\n",
       "      <td>1600</td>\n",
       "      <td>East</td>\n",
       "      <td>York-County</td>\n",
       "      <td>390000.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TEAM  GMS  PCT_ATTENDANCE  WINNING_SEASON  \\\n",
       "0     Chicago Bulls   41             104               1   \n",
       "1  Dallas Mavericks   41             103               0   \n",
       "2  Sacramento Kings   41             101               0   \n",
       "3        Miami Heat   41             100               1   \n",
       "4   Toronto Raptors   41             100               1   \n",
       "\n",
       "   TOTAL_ATTENDANCE_MILLIONS  VALUE_MILLIONS   ELO  CONF       COUNTY  \\\n",
       "0                   0.888882            2500  1519  East         Cook   \n",
       "1                   0.811366            1450  1420  West       Dallas   \n",
       "2                   0.721928            1075  1393  West   Sacremento   \n",
       "3                   0.805400            1350  1569  East   Miami-Dade   \n",
       "4                   0.813050            1125  1600  East  York-County   \n",
       "\n",
       "   MEDIAN_HOME_PRICE_COUNTY_MILLIONS  COUNTY_POPULATION_MILLIONS  cluster  \n",
       "0                           269900.0                        5.20        1  \n",
       "1                           314990.0                        2.57        1  \n",
       "2                           343950.0                        1.51        0  \n",
       "3                           389000.0                        2.71        1  \n",
       "4                           390000.0                        1.10        1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "k_means = KMeans(n_clusters=3)\n",
    "kmeans = k_means.fit(scaler.transform(numerical_df))\n",
    "val_housing_win_df['cluster'] = kmeans.labels_\n",
    "val_housing_win_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "val_housing_win_df.to_csv(\"../data/nba_2017_att_val_elo_win_housing_cluster.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KMeans Cluster Plot of Team Valuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EnMxLM7V3RaHy38tv0PF9r++jzud1NrgrRKv9a/Zrwx83au+nexXwn3pLnPhW8ep1VU8N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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x139236c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (329003626)>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ggplot import *\n",
    "ggplot(val_housing_win_df, aes(x=\"TOTAL_ATTENDANCE_MILLIONS\", y=\"VALUE_MILLIONS\", color=\"cluster\")) +\\\n",
    "geom_point(size=400) + scale_color_gradient(low = 'red', high = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elbow method shows that 3 clusters is decent choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MFrv75HjGVQs/RQzslcMvrz2R339lGhlpxj89XMw/P6Iyj0gqUcJPMacdV8CL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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b3ed160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distortions = []\n",
    "for i in range(1, 11):\n",
    "    km = KMeans(n_clusters=i,\n",
    "            init='k-means++',\n",
    "            n_init=10,\n",
    "            max_iter=300,\n",
    "            random_state=0)\n",
    "    km.fit(scaler.transform(numerical_df))\n",
    "    distortions.append(km.inertia_)\n",
    "    \n",
    "plt.plot(range(1,11), distortions, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title(\"Team Valuation Elbow Method Cluster Analysis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Silhouette Plot\n",
    "### (Code loosely based on Python ML 2nd edition example)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=3,\n",
    "            init='k-means++',\n",
    "            n_init=10,\n",
    "            max_iter=300,\n",
    "            random_state=0)\n",
    "y_km = km.fit_predict(scaler.transform(numerical_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b3e6978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import cm\n",
    "from sklearn.metrics import silhouette_samples\n",
    "cluster_labels = np.unique(y_km)\n",
    "n_clusters = cluster_labels.shape[0]\n",
    "silhouette_vals = silhouette_samples(scaler.transform(numerical_df),\n",
    "                                     y_km,\n",
    "                                     metric='euclidean')\n",
    "y_ax_lower, y_ax_upper = 0, 0\n",
    "yticks = []\n",
    "for i, c in enumerate(cluster_labels):\n",
    "    c_silhouette_vals = silhouette_vals[y_km == c]\n",
    "    c_silhouette_vals.sort()\n",
    "    y_ax_upper += len(c_silhouette_vals)\n",
    "    color = cm.jet(float(i)/n_clusters)\n",
    "    plt.barh(range(y_ax_lower, y_ax_upper), c_silhouette_vals, height=1.0, edgecolor='none',color=color)\n",
    "    yticks.append((y_ax_lower + y_ax_upper)/2)\n",
    "    y_ax_lower += len(c_silhouette_vals)\n",
    "silhouette_avg = np.mean(silhouette_vals)\n",
    "plt.axvline(silhouette_avg,\n",
    "            color=\"red\",\n",
    "            linestyle=\"--\")\n",
    "plt.yticks(yticks, cluster_labels + 1)\n",
    "plt.ylabel('Cluster')\n",
    "plt.xlabel('Silhouette coefficient')\n",
    "plt.title('Silhouette Plot Team Valuation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agglomerative clustering (Hierachial) vs KMeans clustering\n",
    "\n",
    "### (Code loosely based on Python ML 2nd edition example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ylcvpFOdngVFmdihBIp8NnNs+0N3fAoa2vzazJ4DZSmaRruK48UmEclkkS3Kd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\nMCrOIiIiCaPiLCIikjD/H1yOfa5cQAYcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131f61160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))\n",
    "km = KMeans(n_clusters=2,\n",
    "            random_state=0)\n",
    "X = scaler.transform(numerical_df)\n",
    "y_km = km.fit_predict(X)\n",
    "ax1.scatter(X[y_km==0,0],\n",
    "            X[y_km==0,1],\n",
    "            c='lightblue',\n",
    "            edgecolor='black',\n",
    "            marker='o',\n",
    "            s=40,\n",
    "            label='cluster 1')\n",
    "ax1.scatter(X[y_km==1,0],\n",
    "            X[y_km==1,1],\n",
    "            c='red',\n",
    "            edgecolor='black',\n",
    "            marker='s',\n",
    "            s=40,\n",
    "            label='cluster 2')\n",
    "ax1.set_title('K-means clustering')\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "X = scaler.transform(numerical_df)\n",
    "ac = AgglomerativeClustering(n_clusters=2,\n",
    "                             affinity='euclidean',\n",
    "                             linkage='complete')\n",
    "y_ac = ac.fit_predict(X)\n",
    "ax2.scatter(X[y_ac==0,0],\n",
    "             X[y_ac==0,1],\n",
    "             c='lightblue',\n",
    "             edgecolor='black',\n",
    "             marker='o',\n",
    "            s=40,\n",
    "            label='cluster 1')\n",
    "ax2.scatter(X[y_ac==1,0],\n",
    "            X[y_ac==1,1],\n",
    "            c='red',\n",
    "            edgecolor='black',\n",
    "            marker='s',\n",
    "            s=40,\n",
    "            label='cluster 2')\n",
    "ax2.set_title('Agglomerative clustering')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3D Plot\n",
    "\n",
    "![Valuation 3D Plot](https://user-images.githubusercontent.com/58792/36056809-7f87a266-0dbc-11e8-8877-9bb87905adbd.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}