Codecademy_NBA_Trends_Project_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Codecademy [NBA Trends Project](https://www.codecademy.com/projects/practice/nba-trends)\n",
    "\n",
    "*Analyze National Basketball Association (NBA) data to look at associations between teams, win-rates, playoff appearances, and more.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, you'll analyze data from the NBA (National Basketball Association) and explore possible associations. \n",
    "\n",
    "This data was originally sourced from 538's Analysis of the [Complete History Of The NBA](http://fivethirtyeight.com/interactives/the-complete-history-of-every-nba-team-by-elo) and contains the original, unmodified data from [Basketball Reference](http://www.basketball-reference.com/) as well as several additional variables 538 added to perform their own analysis. \n",
    "\n",
    "You can read more about the data and how it's being used by 538 [here](https://github.com/fivethirtyeight/data/tree/master/nba-elo). For this project we've limited the data to just 5 teams and 10 columns (plus one constructed column, `point_diff`, the difference between `pts` and `opp_pts`).\n",
    "\n",
    "You will create several charts and tables in this project, so you'll need to use `plt.clf()` between plots in your code so that the plots don't layer on top of one another.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import pearsonr, chi2_contingency\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to make the output look nicer\n",
    "np.set_printoptions(suppress=True, precision = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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",
       "        vertical-align: top;\n",
       "    }\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>game_id</th>\n",
       "      <th>year_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>game_location</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>pts</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>point_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>194611010TRH</td>\n",
       "      <td>1947</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>Huskies</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>66</td>\n",
       "      <td>W</td>\n",
       "      <td>0.359935</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>194611020CHS</td>\n",
       "      <td>1947</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>Stags</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>63</td>\n",
       "      <td>L</td>\n",
       "      <td>0.368899</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>194611020PRO</td>\n",
       "      <td>1947</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>Steamrollers</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>59</td>\n",
       "      <td>L</td>\n",
       "      <td>0.359935</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>194611050BOS</td>\n",
       "      <td>1947</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>Stags</td>\n",
       "      <td>H</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>57</td>\n",
       "      <td>L</td>\n",
       "      <td>0.620204</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>194611070STB</td>\n",
       "      <td>1947</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>Bombers</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>63</td>\n",
       "      <td>W</td>\n",
       "      <td>0.339290</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        game_id  year_id  fran_id      opp_fran game_location  is_playoffs  \\\n",
       "0  194611010TRH     1947   Knicks       Huskies             A            0   \n",
       "1  194611020CHS     1947   Knicks         Stags             A            0   \n",
       "2  194611020PRO     1947  Celtics  Steamrollers             A            0   \n",
       "3  194611050BOS     1947  Celtics         Stags             H            0   \n",
       "4  194611070STB     1947   Knicks       Bombers             A            0   \n",
       "\n",
       "   pts  opp_pts game_result  forecast  point_diff  \n",
       "0   68       66           W  0.359935           2  \n",
       "1   47       63           L  0.368899         -16  \n",
       "2   53       59           L  0.359935          -6  \n",
       "3   55       57           L  0.620204          -2  \n",
       "4   68       63           W  0.339290           5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba = pd.read_csv('nba_games.csv')\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subset Data to 2010 Season, 2014 Season\n",
    "nba_2010 = nba[nba.year_id == 2010]\n",
    "nba_2014 = nba[nba.year_id == 2014]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1\n",
    "The data has been subset for you into two smaller datasets: games from 2010 (named nba_2010) and games from 2014 (named nba_2014). To start, let’s focus on the 2010 data.\n",
    "\n",
    "Suppose you want to compare the knicks to the nets with respect to points earned per game. Using the pts column from the nba_2010 DataFrame, create two series named knicks_pts (fran_id = \"Knicks\") and nets_pts(fran_id = \"Nets\") that represent the points each team has scored in their games.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "knicks_pts = nba_2010[nba_2010.fran_id == 'Knicks']['pts']\n",
    "nets_pts = nba_2010[nba_2010.fran_id == 'Nets']['pts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2\n",
    "\n",
    "Calculate the difference between the two teams’ average points scored and save the result as diff_means_2010. Based on this value, do you think fran_id and pts are associated? Why or why not?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.731707317073173"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_means_2010 = knicks_pts.mean() - nets_pts.mean()\n",
    "diff_means_2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3\n",
    "Rather than comparing means, it’s useful look at the full distribution of values to understand whether a difference in means is meaningful. Create a set of overlapping histograms that can be used to compare the points scored for the Knicks compared to the Nets. Use the series you created in the previous step (1) and the code below to create the plot. Do the distributions appear to be the same?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(knicks_pts, alpha = .8, density = True, label = 'Knicks')\n",
    "plt.hist(nets_pts, alpha = .8, density = True, label = 'Nets')\n",
    "#note that density is used for newer version of matplotlib\n",
    "plt.legend()\n",
    "plt.title(\"2010 Season\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4\n",
    "Now, let’s compare the 2010 games to 2014. Replicate the steps from Tasks 2 and 3 using `nba_2014`. First, calculate the mean difference between the two teams points scored. Save and print the value as `diff_means_2014`. Did the difference in points get larger or smaller in 2014? Then, plot the overlapping histograms. Does the mean difference you calculated make sense?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.731707317073173\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knicks_pts_14 = nba_2014[nba_2014.fran_id == 'Knicks']['pts']\n",
    "nets_pts_14 = nba_2014[nba_2014.fran_id == 'Nets']['pts']\n",
    "\n",
    "diff_means_2014 = knicks_pts_14.mean() - nets_pts_14.mean()\n",
    "print(diff_means_2014)\n",
    "\n",
    "plt.hist(knicks_pts_14, alpha = .8, density = True, label = 'Knicks')\n",
    "plt.hist(nets_pts_14, alpha = .8, density = True, label = 'Nets')\n",
    "plt.legend()\n",
    "plt.title(\"2014 Season\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5\n",
    "For the remainder of this project, we’ll focus on data from 2010. Let’s now include all teams in the dataset and investigate the relationship between franchise and points scored per game.\n",
    "\n",
    "Using nba_2010, generate side-by-side boxplots with points scored (pts) on the y-axis and team (fran_id) on the x-axis. Is there any overlap between the boxes? Does this chart suggest that fran_id and pts are associated? Which pairs of teams, if any, earn different average scores per game?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = nba_2010, x = 'fran_id', y = 'pts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 6\n",
    "We'd like to know if teams tend to win more games at home compared to away.\n",
    "\n",
    "The variable, `game_result`, indicates whether a team won a particular game ('W' stands for “win” and 'L' stands for “loss”). The variable, `game_location`, indicates whether a team was playing at home or away ('H' stands for “home” and 'A' stands for “away”). \n",
    "\n",
    "Data scientists will often calculate a contingency table of frequencies to help them determine if categorical variables are associated. Calculate a table of frequencies that shows the counts of game_result and game_location.\n",
    "\n",
    "Save your result as `location_result_freq` and print your result. Based on this table, do you think the variables are associated?`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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",
       "        vertical-align: top;\n",
       "    }\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>game_location</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>133</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>92</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "game_location    A    H\n",
       "game_result            \n",
       "L              133  105\n",
       "W               92  120"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_result_freq = pd.crosstab(nba_2010.game_result, nba_2010.game_location)\n",
    "location_result_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 7\n",
    "\n",
    "Convert this table of frequencies to a table of proportions and save the result as `location_result_proportions`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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",
       "        vertical-align: top;\n",
       "    }\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>game_location</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>0.295556</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>0.204444</td>\n",
       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "game_location         A         H\n",
       "game_result                      \n",
       "L              0.295556  0.233333\n",
       "W              0.204444  0.266667"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_result_proportions = location_result_freq/len(nba_2010)\n",
    "location_result_proportions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 8\n",
    "Using the contingency table created in the previous exercise (Ex. 7), calculate the expected contingency table (if there were no association) and the Chi-Square statistic.\n",
    "\n",
    "Does the actual contingency table look similar to the expected table — or different? Based on this output, do you think there is an association between these variables?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[119. 119.]\n",
      " [106. 106.]]\n",
      "6.501704455367053\n"
     ]
    }
   ],
   "source": [
    "chi2, pval, dof, expected = chi2_contingency(location_result_freq)\n",
    "print(expected)\n",
    "print(chi2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*For a 2x2 table, Chi-squared greater than about 4 indicates an association. We've exceeded that!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 9\n",
    "\n",
    "For each game, 538 has calculated the probability that each team will win the game. We want to know if teams with a higher probability of winning (according to 538) also tend to win games by more points. \n",
    "\n",
    "In the data, 538's prediction is saved as `forecast`. The `point_diff` column gives the margin of victory/defeat for each team (positive values mean that the team won; negative values mean that they lost). \n",
    "\n",
    "Using `nba_2010`, calculate the covariance between `forecast` (538's projected win probability) and `point_diff` (the margin of victory/defeat) in the dataset. Save and print your result. Looking at the matrix, what is the covariance between these two variables?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[186.56,   1.37],\n",
       "       [  1.37,   0.05]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_diff_forecast_cov = np.cov(nba_2010.point_diff, nba_2010.forecast)\n",
    "point_diff_forecast_cov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 10\n",
    "\n",
    "Because 538’s forecast variable is reported as a probability (not a binary), we can calculate the strength of the correlation.\n",
    "\n",
    "Using nba_2010, calculate the correlation between `forecast` and `point_diff`. Call this `point_diff_forecast_corr`. Save and print your result. Does this value suggest an association between the two variables?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.44020887084680815, 9.410391573138826e-23)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_diff_forecast_corr = pearsonr(nba_2010.forecast, nba_2010.point_diff)\n",
    "point_diff_forecast_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 11\n",
    "\n",
    "Generate a scatter plot of `forecast` (on the x-axis) and `point_diff` (on the y-axis). Does the correlation value make sense?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf() #to clear the previous plot\n",
    "plt.scatter('forecast', 'point_diff', data=nba_2010)\n",
    "plt.xlabel('Forecasted Win Prob.')\n",
    "plt.ylabel('Point Differential')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}