notebooks/exploring_endorsement_revenue_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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       "      <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",
       "    </tr>\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",
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       "  <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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       "      <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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x115c18898>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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rqraqMq+ttqpyUL8611RNzGvbczje6zHy6F+daysn52+v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QkXAGfrPT5Xs66+eADTau5PBWDDGHNo4b4AAMbY44yxGMZYzKVLl9ql4IR0JKqzpLuhOku6G6qz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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 0x115c18518>"
      ]
     },
     "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 0x1160abd30>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BQ8pn8XqapBMonuuaBWxj+6GKQ+o7aUH1OUnvBt5K0VV0DjCnH7oeJD0POJfi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\nERFRS/8fzH/6Gd0LjpwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115e90400>"
      ]
     },
     "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": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attendance_valuation_df.to_csv(\"../data/nba_2017_att_val.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
       "    </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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_valuation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valuations = attendance_valuation_df.pivot(\"TEAM\", \"AVG_MILLIONS\", \"VALUE_MILLIONS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117b87cf8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N9eKdJSLWi+IGa4tSfA5NYQ7XfC77Og3YnaKneP2b2f0H6BrFTQcXpaicnUxxE0EiokP5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\nKmPyWJIkSZIkSZJUxuSxJEmSJEmSJKmMPY8lSZIkSZIkNX81lQ6g5bH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Ti4TwCOBiYL3i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sspp/yW9dbbllNO/S2XXXoeAM8++zw919+OPtvsxV//+g9uuvGKSoYtSZIkSZKkOiyRyePMHA2s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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118759f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(25,17))\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": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = smf.ols('VALUE_MILLIONS ~AVG_MILLIONS', data=attendance_valuation_df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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:                Tue, 28 Nov 2017   Prob (F-statistic):            0.00255\n",
      "Time:                        09:03:50   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    -2408.0155   1141.235     -2.110      0.044   -4745.729     -70.302\n",
      "AVG_MILLIONS  2.104e+05   6.35e+04      3.314      0.003    8.03e+04    3.41e+05\n",
      "==============================================================================\n",
      "Omnibus:                        7.692   Durbin-Watson:                   1.930\n",
      "Prob(Omnibus):                  0.021   Jarque-Bera (JB):                6.354\n",
      "Skew:                           1.100   Prob(JB):                       0.0417\n",
      "Kurtosis:                       3.495   Cond. No.                         568.\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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c2ece48>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GTDSSbpW0bJjHrcB+ZYpxqKOAjohYFxE7gBuAE3d18FNPPcWtt94KQE9PD4VCgeXLlwOw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\nJ7C+ZvIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c2aff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(y=\"VALUE_MILLIONS\", x=\"AVG_MILLIONS\", data=attendance_valuation_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attendance_valuation_elo_df = attendance_valuation_df.merge(elo_df, how=\"inner\", on=\"TEAM\")"
   ]
  },
  {
   "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>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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_valuation_elo_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attendance_valuation_elo_df.to_csv(\"../data/nba_2017_att_val_elo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11cb76358>"
      ]
     },
     "execution_count": 30,
     "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 0x11cb76cf8>"
      ]
     },
     "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": 31,
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_elo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11c2f3160>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bE/z+StKPmdmzLD/G+Tcl7fLDj5NsreWni21e6u6to3fyvHHQHkn/T3Gbr1b+2P9DcV0r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OnlVNhbJZ1vuX8hO9b2HOdcTdIVSdvWe0Izq2Q3f8vM/tLM/tjMdnZvyMDGAs/TznKy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117dd2da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lmplot(x=\"ELO\", y=\"AVG_MILLIONS\", data=attendance_valuation_elo_df, hue=\"CONF\", size=12)\n",
    "ax.set(xlabel='ELO Score', ylabel='Average Attendence Per Game', title=\"NBA Team AVG Attendance vs ELO Ranking:  2016-2017 Season\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CONF\n",
       "East    1502\n",
       "West    1543\n",
       "Name: ELO, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_valuation_elo_df.groupby(\"CONF\")[\"ELO\"].median()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CONF\n",
       "East    0.017332\n",
       "West    0.018203\n",
       "Name: AVG_MILLIONS, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_valuation_elo_df.groupby(\"CONF\")[\"AVG_MILLIONS\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = smf.ols('AVG_MILLIONS ~ELO', data=attendance_valuation_elo_df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           AVG_MILLIONS   R-squared:                       0.082\n",
      "Model:                            OLS   Adj. R-squared:                  0.049\n",
      "Method:                 Least Squares   F-statistic:                     2.493\n",
      "Date:                Tue, 28 Nov 2017   Prob (F-statistic):              0.126\n",
      "Time:                        09:04:26   Log-Likelihood:                 148.99\n",
      "No. Observations:                  30   AIC:                            -294.0\n",
      "Df Residuals:                      28   BIC:                            -291.2\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0107      0.005      2.333      0.027       0.001       0.020\n",
      "ELO          4.79e-06   3.03e-06      1.579      0.126   -1.42e-06     1.1e-05\n",
      "==============================================================================\n",
      "Omnibus:                        0.339   Durbin-Watson:                   0.651\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())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k_means = KMeans(n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cluster_source = attendance_valuation_elo_df.loc[:,[\"AVG_MILLIONS\", \"ELO\", \"VALUE_MILLIONS\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeans = k_means.fit(cluster_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attendance_valuation_elo_df['cluster'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attendance_valuation_elo_df.to_csv(\"../data/nba_2017_att_val_elo_with_cluster.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11f12bac8>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SGsykUJIkSZIazKRQkiRJkhrMpFCSJEmSGsykUJIkSZIazKRQkiRJkhrMpFCS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\nus24TGkW/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f12bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lmplot(x=\"ELO\", y=\"AVG_MILLIONS\", data=attendance_valuation_elo_df,hue=\"cluster\", size=12, fit_reg=False)\n",
    "ax.set(xlabel='ELO Score', ylabel='Average Attendence Per Game', title=\"NBA Team AVG Attendance vs ELO Ranking Clustered on ELO, AVG, VALUE_MILLIONS:  2016-2017 Season\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto',\n",
       " 'cluster_centers_': array([[  1.99997500e-02,   1.50750000e+03,   2.85000000e+03],\n",
       "        [  1.74615238e-02,   1.49990476e+03,   9.60000000e+02],\n",
       "        [  1.79664000e-02,   1.52340000e+03,   1.82000000e+03]]),\n",
       " 'copy_x': True,\n",
       " 'inertia_': 1634038.009595013,\n",
       " 'init': 'k-means++',\n",
       " 'labels_': array([0, 2, 1, 1, 1, 2, 1, 0, 1, 0, 2, 0, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1,\n",
       "        1, 2, 1, 1, 1, 1, 1], dtype=int32),\n",
       " 'max_iter': 300,\n",
       " 'n_clusters': 3,\n",
       " 'n_init': 10,\n",
       " 'n_iter_': 1,\n",
       " 'n_jobs': 1,\n",
       " 'precompute_distances': 'auto',\n",
       " 'random_state': None,\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.99997500e-02,   1.50750000e+03,   2.85000000e+03],\n",
       "       [  1.74615238e-02,   1.49990476e+03,   9.60000000e+02],\n",
       "       [  1.79664000e-02,   1.52340000e+03,   1.82000000e+03]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cluster_1 = attendance_valuation_elo_df[\"cluster\"] == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "      <td>1</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",
       "      <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>0.813050</td>\n",
       "      <td>0.019830</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1600</td>\n",
       "      <td>East</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>0.843042</td>\n",
       "      <td>0.020562</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1545</td>\n",
       "      <td>East</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>0.746323</td>\n",
       "      <td>0.018203</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>1543</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>41</td>\n",
       "      <td>99</td>\n",
       "      <td>0.792029</td>\n",
       "      <td>0.019317</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>1563</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>41</td>\n",
       "      <td>99</td>\n",
       "      <td>0.755347</td>\n",
       "      <td>0.018423</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>1661</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>41</td>\n",
       "      <td>99</td>\n",
       "      <td>0.806605</td>\n",
       "      <td>0.019673</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1617</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>41</td>\n",
       "      <td>94</td>\n",
       "      <td>0.727875</td>\n",
       "      <td>0.017753</td>\n",
       "      <td>920.0</td>\n",
       "      <td>1352</td>\n",
       "      <td>East</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>41</td>\n",
       "      <td>94</td>\n",
       "      <td>0.663099</td>\n",
       "      <td>0.016173</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1482</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>41</td>\n",
       "      <td>94</td>\n",
       "      <td>0.708639</td>\n",
       "      <td>0.017283</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1340</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>41</td>\n",
       "      <td>92</td>\n",
       "      <td>0.684578</td>\n",
       "      <td>0.016697</td>\n",
       "      <td>880.0</td>\n",
       "      <td>1526</td>\n",
       "      <td>East</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>41</td>\n",
       "      <td>91</td>\n",
       "      <td>0.677314</td>\n",
       "      <td>0.016519</td>\n",
       "      <td>790.0</td>\n",
       "      <td>1482</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>41</td>\n",
       "      <td>91</td>\n",
       "      <td>0.710643</td>\n",
       "      <td>0.017332</td>\n",
       "      <td>780.0</td>\n",
       "      <td>1463</td>\n",
       "      <td>East</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>41</td>\n",
       "      <td>85</td>\n",
       "      <td>0.710557</td>\n",
       "      <td>0.017330</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1338</td>\n",
       "      <td>East</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>41</td>\n",
       "      <td>85</td>\n",
       "      <td>0.654306</td>\n",
       "      <td>0.015958</td>\n",
       "      <td>885.0</td>\n",
       "      <td>1479</td>\n",
       "      <td>East</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>41</td>\n",
       "      <td>85</td>\n",
       "      <td>0.648952</td>\n",
       "      <td>0.015828</td>\n",
       "      <td>785.0</td>\n",
       "      <td>1502</td>\n",
       "      <td>East</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>41</td>\n",
       "      <td>84</td>\n",
       "      <td>0.697107</td>\n",
       "      <td>0.017002</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1587</td>\n",
       "      <td>East</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>41</td>\n",
       "      <td>77</td>\n",
       "      <td>0.605585</td>\n",
       "      <td>0.014770</td>\n",
       "      <td>890.0</td>\n",
       "      <td>1552</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>41</td>\n",
       "      <td>77</td>\n",
       "      <td>0.607203</td>\n",
       "      <td>0.014809</td>\n",
       "      <td>770.0</td>\n",
       "      <td>1463</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>41</td>\n",
       "      <td>72</td>\n",
       "      <td>0.655141</td>\n",
       "      <td>0.015979</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1441</td>\n",
       "      <td>East</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      TEAM  GMS  PCT  TOTAL_MILLIONS  AVG_MILLIONS  \\\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   \n",
       "6      Cleveland Cavaliers   41  100        0.843042      0.020562   \n",
       "8    Oklahoma City Thunder   41  100        0.746323      0.018203   \n",
       "12  Portland Trail Blazers   41   99        0.792029      0.019317   \n",
       "13       San Antonio Spurs   41   99        0.755347      0.018423   \n",
       "14               Utah Jazz   41   99        0.806605      0.019673   \n",
       "15           Orlando Magic   41   94        0.727875      0.017753   \n",
       "16    New Orleans Pelicans   41   94        0.663099      0.016173   \n",
       "18            Phoenix Suns   41   94        0.708639      0.017283   \n",
       "19          Indiana Pacers   41   92        0.684578      0.016697   \n",
       "20       Memphis Grizzlies   41   91        0.677314      0.016519   \n",
       "21       Charlotte Hornets   41   91        0.710643      0.017332   \n",
       "22      Philadelphia 76ers   41   85        0.710557      0.017330   \n",
       "23           Atlanta Hawks   41   85        0.654306      0.015958   \n",
       "25         Milwaukee Bucks   41   85        0.648952      0.015828   \n",
       "26      Washington Wizards   41   84        0.697107      0.017002   \n",
       "27          Denver Nuggets   41   77        0.605585      0.014770   \n",
       "28  Minnesota Timberwolves   41   77        0.607203      0.014809   \n",
       "29         Detroit Pistons   41   72        0.655141      0.015979   \n",
       "\n",
       "    VALUE_MILLIONS   ELO  CONF  cluster  \n",
       "2           1075.0  1393  West        1  \n",
       "3           1350.0  1569  East        1  \n",
       "4           1125.0  1600  East        1  \n",
       "6           1200.0  1545  East        1  \n",
       "8           1025.0  1543  West        1  \n",
       "12          1050.0  1563  West        1  \n",
       "13          1175.0  1661  West        1  \n",
       "14           910.0  1617  West        1  \n",
       "15           920.0  1352  East        1  \n",
       "16           750.0  1482  West        1  \n",
       "18          1100.0  1340  West        1  \n",
       "19           880.0  1526  East        1  \n",
       "20           790.0  1482  West        1  \n",
       "21           780.0  1463  East        1  \n",
       "22           800.0  1338  East        1  \n",
       "23           885.0  1479  East        1  \n",
       "25           785.0  1502  East        1  \n",
       "26          1000.0  1587  East        1  \n",
       "27           890.0  1552  West        1  \n",
       "28           770.0  1463  West        1  \n",
       "29           900.0  1441  East        1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attendance_valuation_elo_df[cluster_1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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