{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b196af77",
   "metadata": {},
   "source": [
    "## A study into success and popularity in the NBA\n",
    "\n",
    "#### Calvin Muller"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f45457",
   "metadata": {},
   "source": [
    "### Asking a question\n",
    "\n",
    "When thinking about any sports team, the underlying assumption is that the better the team gets, the more popular the team will become. This makes sense logically: people like to talk about winners. However, could it also be true that if a team is bad on an extreme level that people will also want to talk about this? This is the question that I am investigating: how does the amount of wins a team has impact the popularity of that team? My hypothesis going into it that teams that are on the extreme of both wins and losses will be talked about more than teams that are just mediocre.\n",
    "\n",
    "\n",
    "### Gathering the data\n",
    "\n",
    "In order to find this data, I utilized trends.google.com, where Google tracks how much interest is in a certain topic over time. This data provides search data over the last five years for each of the NBA teams I wanted data on (Eastern Conference teams), which contains the first half of the data I need. The other half is the success of the teams, so I went ahead and manually inputted win data for each of these teams over this five year period. This will hopefully give me data that I can plot and find correlation between these two variables. All of this data was gathered into an Excel spreadsheet which I turned into a .CSV file.\n",
    "\n",
    "In addition to the data that I gathered from these two sources, I created my own variable for extremity to add to this CSV, which I called \"Extremity Score.\" This was simply the absolute value of the wins subtracted by 41 wins, which is the NBA average. I then created another subjective extremity score that used the value of 32 wins instead, thinking that is a better indicator of what a mediocre team looks like."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b7df4bd",
   "metadata": {},
   "source": [
    "## Beginning to work with the data\n",
    "\n",
    "#### Bringing in the data\n",
    "\n",
    "Below I am bringing in the CSV data I gathered and putting it into first a normal pandas dataframe, and then putting it into a pandas pivot table, so that the data is easier to look at as it is grouped by teams. There is some data cleaning that happens here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28a94be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24f5a6de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Years</th>\n",
       "      <th>Searches</th>\n",
       "      <th>Wins</th>\n",
       "      <th>Team</th>\n",
       "      <th>Extremity Score</th>\n",
       "      <th>Extremity Score (32 wins)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Years  Searches  Wins       Team  Extremity Score  \\\n",
       "1  2017.0    1237.0  51.0  Cavaliers             10.0   \n",
       "2  2018.0    1072.0  50.0  Cavaliers              9.0   \n",
       "3  2019.0     234.0  19.0  Cavaliers             22.0   \n",
       "4  2020.0     179.0  19.0  Cavaliers             22.0   \n",
       "5  2021.0     255.0  22.0  Cavaliers             19.0   \n",
       "\n",
       "   Extremity Score (32 wins)  \n",
       "1                       19.0  \n",
       "2                       18.0  \n",
       "3                       13.0  \n",
       "4                       13.0  \n",
       "5                       10.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin_table = pd.read_csv('fin_table.csv')\n",
    "fin_table.dropna(subset = [\"Team\"], inplace=True)\n",
    "fin_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f0501643",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th>Searches</th>\n",
       "      <th>Wins</th>\n",
       "      <th>Extremity Score</th>\n",
       "      <th>Extremity Score (32 wins)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bucks</th>\n",
       "      <th>169.0</th>\n",
       "      <th>42.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>10.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218.0</th>\n",
       "      <th>44.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>12.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272.0</th>\n",
       "      <th>56.0</th>\n",
       "      <th>15.0</th>\n",
       "      <th>24.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478.0</th>\n",
       "      <th>60.0</th>\n",
       "      <th>19.0</th>\n",
       "      <th>28.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701.0</th>\n",
       "      <th>46.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>14.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Wizards</th>\n",
       "      <th>583.0</th>\n",
       "      <th>25.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>7.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905.0</th>\n",
       "      <th>32.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>0.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106.0</th>\n",
       "      <th>43.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>11.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204.0</th>\n",
       "      <th>34.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>2.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447.0</th>\n",
       "      <th>49.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>17.0</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 0 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [(Bucks, 169.0, 42.0, 1.0, 10.0), (Bucks, 218.0, 44.0, 3.0, 12.0), (Bucks, 272.0, 56.0, 15.0, 24.0), (Bucks, 478.0, 60.0, 19.0, 28.0), (Bucks, 701.0, 46.0, 5.0, 14.0), (Bulls, 1003.0, 22.0, 19.0, 10.0), (Bulls, 1144.0, 22.0, 19.0, 10.0), (Bulls, 1246.0, 27.0, 14.0, 5.0), (Bulls, 1432.0, 31.0, 10.0, 1.0), (Bulls, 1744.0, 41.0, 0.0, 9.0), (Cavaliers, 179.0, 19.0, 22.0, 13.0), (Cavaliers, 234.0, 19.0, 22.0, 13.0), (Cavaliers, 255.0, 22.0, 19.0, 10.0), (Cavaliers, 1072.0, 50.0, 9.0, 18.0), (Cavaliers, 1237.0, 51.0, 10.0, 19.0), (Celtics, 765.0, 48.0, 7.0, 16.0), (Celtics, 851.0, 36.0, 5.0, 4.0), (Celtics, 1136.0, 49.0, 8.0, 17.0), (Celtics, 1459.0, 55.0, 14.0, 23.0), (Celtics, 1601.0, 53.0, 12.0, 21.0), (Hawks, 239.0, 20.0, 21.0, 12.0), (Hawks, 334.0, 24.0, 17.0, 8.0), (Hawks, 368.0, 29.0, 12.0, 3.0), (Hawks, 377.0, 43.0, 2.0, 11.0), (Hawks, 696.0, 41.0, 0.0, 9.0), (Heat, 802.0, 41.0, 0.0, 9.0), (Heat, 851.0, 44.0, 3.0, 12.0), (Heat, 853.0, 40.0, 1.0, 8.0), (Heat, 934.0, 39.0, 2.0, 7.0), (Heat, 1076.0, 44.0, 3.0, 12.0), (Hornets, 1066.0, 23.0, 18.0, 9.0), (Hornets, 1218.0, 39.0, 2.0, 7.0), (Hornets, 1223.0, 36.0, 5.0, 4.0), (Hornets, 1271.0, 36.0, 5.0, 4.0), (Hornets, 1667.0, 33.0, 8.0, 1.0), (Knicks, 641.0, 21.0, 20.0, 11.0), (Knicks, 831.0, 29.0, 12.0, 3.0), (Knicks, 1015.0, 17.0, 24.0, 15.0), (Knicks, 1133.0, 31.0, 10.0, 1.0), (Knicks, 1355.0, 41.0, 0.0, 9.0), (Magic, 1058.0, 33.0, 8.0, 1.0), (Magic, 1189.0, 21.0, 20.0, 11.0), (Magic, 1625.0, 25.0, 16.0, 7.0), (Magic, 1666.0, 29.0, 12.0, 3.0), (Magic, 1870.0, 42.0, 1.0, 10.0), (Nets, 357.0, 28.0, 13.0, 4.0), (Nets, 439.0, 20.0, 21.0, 12.0), (Nets, 457.0, 35.0, 6.0, 3.0), (Nets, 732.0, 42.0, 1.0, 10.0), (Nets, 1276.0, 48.0, 7.0, 16.0), (Pacers, 622.0, 45.0, 4.0, 13.0), (Pacers, 746.0, 35.0, 6.0, 3.0), (Pacers, 904.0, 48.0, 7.0, 16.0), (Pacers, 955.0, 42.0, 1.0, 10.0), (Pacers, 1120.0, 48.0, 7.0, 16.0), (Pistons, 865.0, 20.0, 21.0, 12.0), (Pistons, 962.0, 37.0, 4.0, 5.0), (Pistons, 1078.0, 39.0, 2.0, 7.0), (Pistons, 1205.0, 20.0, 21.0, 12.0), (Pistons, 1278.0, 41.0, 0.0, 9.0), (Raptors, 71.0, 27.0, 14.0, 5.0), (Raptors, 95.0, 51.0, 10.0, 19.0), (Raptors, 101.0, 53.0, 12.0, 21.0), (Raptors, 168.0, 59.0, 18.0, 27.0), (Raptors, 484.0, 58.0, 17.0, 26.0), (Sixers, 644.0, 43.0, 2.0, 11.0), (Sixers, 675.0, 28.0, 13.0, 4.0), (Sixers, 1103.0, 52.0, 11.0, 20.0), (Sixers, 1237.0, 49.0, 8.0, 17.0), (Sixers, 1253.0, 51.0, 10.0, 19.0), (Wizards, 583.0, 25.0, 16.0, 7.0), (Wizards, 905.0, 32.0, 9.0, 0.0), (Wizards, 1106.0, 43.0, 2.0, 11.0), (Wizards, 1204.0, 34.0, 7.0, 2.0), (Wizards, 1447.0, 49.0, 8.0, 17.0)]\n",
       "\n",
       "[75 rows x 0 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_pivot = pd.pivot_table(fin_table, values = ['Searches', 'Wins'], index = ['Team', 'Searches', 'Wins', 'Extremity Score', 'Extremity Score (32 wins)'], columns = ['Years'], aggfunc=np.sum)\n",
    "pd_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b74d0399",
   "metadata": {},
   "source": [
    "#### Team dataframes\n",
    "I continue to shape the data for future analysis by creating separate dataframes for each team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad3a3b09",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "teams_list = list(fin_table.groupby(\"Team\"))\n",
    "\n",
    "bucks_df = pd.DataFrame(teams_list[0][1])\n",
    "bulls_df = pd.DataFrame(teams_list[1][1])\n",
    "cavs_df = pd.DataFrame(teams_list[2][1])\n",
    "celtics_df = pd.DataFrame(teams_list[3][1])\n",
    "hawks_df = pd.DataFrame(teams_list[4][1])\n",
    "heat_df = pd.DataFrame(teams_list[5][1])\n",
    "hornets_df = pd.DataFrame(teams_list[6][1])\n",
    "knicks_df = pd.DataFrame(teams_list[7][1])\n",
    "magic_df = pd.DataFrame(teams_list[8][1])\n",
    "nets_df = pd.DataFrame(teams_list[9][1])\n",
    "pacers_df = pd.DataFrame(teams_list[10][1])\n",
    "pistons_df = pd.DataFrame(teams_list[11][1])\n",
    "raptors_df = pd.DataFrame(teams_list[12][1])\n",
    "sixers_df = pd.DataFrame(teams_list[13][1])\n",
    "wizards_df = pd.DataFrame(teams_list[14][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee8d7dc1",
   "metadata": {},
   "source": [
    "## Analysis Plan\n",
    "\n",
    "To start analyzing this data to answer my question, I first want to visualize the data, specifically comparing wins to searches in scatter plots to see if there is any discernible patterns. I would then like to also plot extremity score to searches to start to look at whether or not higher extremity score, regardless of direction looks like it may have correlation with searches.\n",
    "\n",
    "After creating these visuals, I would like to start actual statistical analysis to look at the correlations between these different variables to determine whether or not there is this correlation between worse teams on an extreme level and an increase in popularity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0f724c1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b7f9069",
   "metadata": {},
   "source": [
    "Below I am bringing in the matplotlib package to give me tools to visualize the data. The first visualization I am making is a scatter-plot comparing a team's wins to how much a team is searched. The second visualization is a scatter plot comparing a team's extremity score and how much a team is searched.\n",
    "\n",
    "When looking at the first plot that deals with just wins, there does seem to be a little bit of shape to the data that shows a positive correlation so we will want to investigate that with a Pearson correlation test. Unfortunately, there doesn't seem to be too strong of an immediate correlation that can be seen from looking at the extremity score plot. We will have to do further analysis to determine if there is something here. It is also possible we will need to split this analysis up into a team-by-team analysis, so that we could take out any exterior variables that may change from team to team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b28914c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots(1, 2)\n",
    "ax1[0].scatter(fin_table['Wins'], fin_table['Searches'])\n",
    "ax1[0].set_title('Wins')\n",
    "ax1[1].scatter(fin_table['Extremity Score (32 wins)'], fin_table['Searches'], color = 'g')\n",
    "ax1[1].set_title('Extremity')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74bfb8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6d9ea99b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.061000743609234215, 0.6031377948065617)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(fin_table['Wins'], fin_table['Searches'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6905e1a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.17668327192330044, 0.1294234879803889)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(fin_table['Extremity Score (32 wins)'], fin_table['Searches'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251b7ed9",
   "metadata": {},
   "source": [
    "Because the results of this correlation analysis between wins and searches show a high p-value, it leads me to believe that there are other explanatory variables that aren't included in the data I have. This high p-value says that there is a relatively high probability that randomly distributed data would have this same level of correlation. To attempt to correct for this, I will try to create plots for each team and then take a look at the results.\n",
    "\n",
    "The p-value of the correlation between searches and the extremity score, while significantly lower, is still too high to pull any conclusions from."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c71cfe",
   "metadata": {},
   "source": [
    "### Individual Team Scatterplots\n",
    "\n",
    "Below I have plotted each team on a scatter plot with wins on the x-axis and searches on the y-axis, as well as I plotted each team on a scatter plot with the extremity score on the x-axis and searches on the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e4d79b4b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots(3, 5, figsize = (10, 10))\n",
    "ax2[0][0].scatter(bucks_df['Wins'], bucks_df['Searches'])\n",
    "ax2[0][0].set_title('Bucks')\n",
    "ax2[0][1].scatter(bulls_df['Wins'], bulls_df['Searches'])\n",
    "ax2[0][1].set_title('Bulls')\n",
    "ax2[0][2].scatter(cavs_df['Wins'], cavs_df['Searches'])\n",
    "ax2[0][2].set_title('Cavs')\n",
    "ax2[0][3].scatter(celtics_df['Wins'], celtics_df['Searches'])\n",
    "ax2[0][3].set_title('Celtics')\n",
    "ax2[0][4].scatter(hawks_df['Wins'], hawks_df['Searches'])\n",
    "ax2[0][4].set_title('Hawks')\n",
    "ax2[1][0].scatter(heat_df['Wins'], heat_df['Searches'])\n",
    "ax2[1][0].set_title('Heat')\n",
    "ax2[1][1].scatter(hornets_df['Wins'], hornets_df['Searches'])\n",
    "ax2[1][1].set_title('Hornets')\n",
    "ax2[1][2].scatter(knicks_df['Wins'], knicks_df['Searches'])\n",
    "ax2[1][2].set_title('Knicks')\n",
    "ax2[1][3].scatter(magic_df['Wins'], magic_df['Searches'])\n",
    "ax2[1][3].set_title('Magic')\n",
    "ax2[1][4].scatter(nets_df['Wins'], nets_df['Searches'])\n",
    "ax2[1][4].set_title('Nets')\n",
    "ax2[2][0].scatter(pacers_df['Wins'], pacers_df['Searches'])\n",
    "ax2[2][0].set_title('Pacers')\n",
    "ax2[2][1].scatter(pistons_df['Wins'], pistons_df['Searches'])\n",
    "ax2[2][1].set_title('Pistons')\n",
    "ax2[2][2].scatter(raptors_df['Wins'], raptors_df['Searches'])\n",
    "ax2[2][2].set_title('Raptors')\n",
    "ax2[2][3].scatter(sixers_df['Wins'], sixers_df['Searches'])\n",
    "ax2[2][3].set_title('Sixers')\n",
    "ax2[2][4].scatter(wizards_df['Wins'], wizards_df['Searches'])\n",
    "ax2[2][4].set_title('Wizards')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "42f2b84f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3, ax3 = plt.subplots(3, 5, figsize = (10, 10))\n",
    "ax3[0][0].scatter(bucks_df['Extremity Score (32 wins)'], bucks_df['Searches'])\n",
    "ax3[0][0].set_title('Bucks')\n",
    "ax3[0][1].scatter(bulls_df['Extremity Score (32 wins)'], bulls_df['Searches'])\n",
    "ax3[0][1].set_title('Bulls')\n",
    "ax3[0][2].scatter(cavs_df['Extremity Score (32 wins)'], cavs_df['Searches'])\n",
    "ax3[0][2].set_title('Cavs')\n",
    "ax3[0][3].scatter(celtics_df['Extremity Score (32 wins)'], celtics_df['Searches'])\n",
    "ax3[0][3].set_title('Celtics')\n",
    "ax3[0][4].scatter(hawks_df['Extremity Score (32 wins)'], hawks_df['Searches'])\n",
    "ax3[0][4].set_title('Hawks')\n",
    "ax3[1][0].scatter(heat_df['Extremity Score (32 wins)'], heat_df['Searches'])\n",
    "ax3[1][0].set_title('Heat')\n",
    "ax3[1][1].scatter(hornets_df['Extremity Score (32 wins)'], hornets_df['Searches'])\n",
    "ax3[1][1].set_title('Hornets')\n",
    "ax3[1][2].scatter(knicks_df['Extremity Score (32 wins)'], knicks_df['Searches'])\n",
    "ax3[1][2].set_title('Knicks')\n",
    "ax3[1][3].scatter(magic_df['Extremity Score (32 wins)'], magic_df['Searches'])\n",
    "ax3[1][3].set_title('Magic')\n",
    "ax3[1][4].scatter(nets_df['Extremity Score (32 wins)'], nets_df['Searches'])\n",
    "ax3[1][4].set_title('Nets')\n",
    "ax3[2][0].scatter(pacers_df['Extremity Score (32 wins)'], pacers_df['Searches'])\n",
    "ax3[2][0].set_title('Pacers')\n",
    "ax3[2][1].scatter(pistons_df['Extremity Score (32 wins)'], pistons_df['Searches'])\n",
    "ax3[2][1].set_title('Pistons')\n",
    "ax3[2][2].scatter(raptors_df['Extremity Score (32 wins)'], raptors_df['Searches'])\n",
    "ax3[2][2].set_title('Raptors')\n",
    "ax3[2][3].scatter(sixers_df['Extremity Score (32 wins)'], sixers_df['Searches'])\n",
    "ax3[2][3].set_title('Sixers')\n",
    "ax3[2][4].scatter(wizards_df['Extremity Score (32 wins)'], wizards_df['Searches'])\n",
    "ax3[2][4].set_title('Wizards')\n",
    "\n",
    "plt.show()"
   ]
  },
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    "### Conclusions from visualizations\n",
    "\n",
    "Upon looking at the various plots for each team, there are a few interpretations one could make. Looking at the charts that are plotting wins as the independent variable it seems that there is a visible positive correlation between wins and searches. However, when looking at the charts plotting the extremity variable, it is not clear that there is this same positive correlation that I initially thought there might be.\n",
    "\n",
    "Because the sample size is only 5 for each team, running a correlation test will not necessarily be valuable in providing accurate correlation. When you run it for some teams, it shows a very high correlation with low p-values, however for other teams it shows the opposite. As a result of this, the conclusions that we draw are going to have to come from just visualizing the data all together. Luckily the human brain is pretty good at picking out patterns, so we can have some degree of confidence in our conclusions.\n",
    "\n",
    "As it seems, the underlying assumption that wins positively correlate with popularity, or searches, can be affirmed. We see that with few exceptions, the team's searches go up as there win total rises. However, we do not see any sort of apparent correlation in these scatter plots of the extremity scores."
   ]
  },
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   "id": "c1e88df2",
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   "source": [
    "## Final Conclusions\n",
    "\n",
    "After gathering this data and making an attempt to visualize and analyze it, there are a few takeaways that we can have, even if they aren't as conclusive as I had first hoped. Firstly, with some degree of confidence, the data that I have gathered confirms the idea that there is a positive correlation between a team's success and how much they are searched. Secondly, there doesn't seem to be the correlation that I initially expected between a team's extremity score and how much they are searched. While this doesn't support my initial hypothesis, it does provide what seems to be the interesting answer: people just don't care about losing teams, even though there might be more interesting events surrounding them relative to mediocre teams.\n",
    "\n",
    "There are some limitations in this data that if I could overcome, might provide better future analysis. The first of these is that the data that Google provided on these searches only goes back five years (it goes back 20, but there is a noticeable up-tick in all Google searches around 2013, so including those other 12 years of data would likely provide incorrect conclusions). If there was some all inclusive, perfect popularity metric for NBA teams, this would provide larger sample sizes for analysis and higher quality analysis that doesn't leave out other explained variables.\n",
    "\n",
    "If I were to continue research into this topic, I would try to continue to look into what factors lead towards rise in a team's popularity. I would want to find specific events that can happen to NBA teams, like playoff victories, championships, All-Star players, city population, and draft position to see what impact each of these would have on the popularity. I would likely want to run some sort of regression on this data, so it could provide values for each of these variables. This could lead to very interesting conclusions that there are perhaps overlooked events that lead to a team being more popular."
   ]
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