Project_Report (2).ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Predicting NBA Awards for the 2023 Season- DS2500 Final Project Report\n"
      ],
      "metadata": {
        "id": "5CuPq3ARL6Tq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Executive Summary\n",
        "\n",
        "The ultimate objective for this project was to predict the winners of 3 of this season's NBA awards: the Most Valuable Player, Defensive Player of the Year, and Sixth Man of the Year. Our first ML model, a K-NN Classifier, sorts a players season into an award deserving year or not, but was fairly inaccurate due to the low sample sizes of awards. The confusion matrix (see section on classifier cross validation) shows that only 10/30 MVPs were predicted correctly, and it ended up predicting 3 MVPs for the current season (section on classifier predictions for 2023). Our other ML model, a Random Forest Regressor, worked slightly better, predicting Michael Jordan to win 5 MVPs (he actually won 6), Lebron James to win 3 (he actually has won 4) and correctly predicting other historical MVP seasons (see historical RF Regression). The regressor also correctly predicted the winner of this year's DPOY, Jaren Jackson Jr, and it has the current favorite for the MVP, Joel Embiid, recieving the most votes. However, it is not as good with Sixth Man of the Year, as only one of the 3 favorites, Malcom Brogdon, recieved votes for the award in the model."
      ],
      "metadata": {
        "id": "FCiawoiUPfCH"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introduction\n",
        "\n",
        "The NBA is one of the most popular sports leagues in the world. Every year 5 end-of-season awards, voted by sportswriters and broadcasters from the US and Canada, are given out: MVP (Most Valuable Player), ROTY (Rookie of the Year), DPOY (Defensive Player of the Year), SMOTY (Sixth Man of the Year) and MIP (Most Improved Player). The deserving winners of these awards are always an intensly debated topic, and the voters can be prone to many biases that lead to potentially \"statistically undeserving\" winners, [such as in 2011 when LeBron James had a much better season than Derrick Rose, but Rose still won MVP](https://bleacherreport.com/articles/573923-the-eight-most-controversial-nba-mvp-wins-of-all-time) (obviously every player in contention has some merit). Especially with greater importance being afforded to advanced statistics in NBA gameplanning and playmaking, machine learning can be a useful tool in generating deserving winners of these awards. Using K-Nearest Neighbors and the 2022-23 NBA season's player statistics, we can find players with similar relative stats to previous award winners and group them together. Furthermore, regression can be used to predict the votes every player would get for each award."
      ],
      "metadata": {
        "id": "Re8F-zWb8Ktx"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Description\n",
        "\n",
        "In this project, we used two datasets. The first dataset has every NBA player's (with at least 60 games played) normalized statistics from this season. The second dataset has the same stats, but from the 1992-2022 seasons, and it is normalized by year (most years have 60 games as the cutoff, but for shortened seasons the cutoff is adjusted)."
      ],
      "metadata": {
        "id": "yzIPQMjWC5Nv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df_2023 = pd.read_csv('MVP Voting - 2023 Normalized.csv')\n",
        "df_1992_to_2022 = pd.read_csv('MVP Voting - All 30 Seasons.csv')"
      ],
      "metadata": {
        "id": "SwOkVr93FTK5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_2023.head()"
      ],
      "metadata": {
        "id": "JsYpSY4gFgUC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_1992_to_2022.head()"
      ],
      "metadata": {
        "id": "rvxiTVmQFiFR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Dictionary:\n",
        "\n",
        "\n",
        "| Feature Name | Explanation (Every feature with  \"Scaled\" is normalized per year)                                                                                                       |\n",
        "|--------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
        "| Year         | The second year of the NBA season in  which the statistics were  produced (i.e. the year 2023 has the  stats from the 2022-23 season)                                   |\n",
        "| Player       | The player who produced the stats                                                                                                                                       |\n",
        "| Age          | The age of the player on February 1st of the listed year                                                                                                                |\n",
        "| Tm           | The team of the player (TOT means that  the player played for multiple teams in a season)                                                                               |\n",
        "| G_scaled     | Total games played                                                                                                                                                      |\n",
        "| GS_scaled    | Games started                                                                                                                                                           |\n",
        "| MP_scaled    | Minutes played per game                                                                                                                                                 |\n",
        "| FG_scaled    | Field goals made per game                                                                                                                                               |\n",
        "| FGA_scaled   | Field goals attempted per game                                                                                                                                          |\n",
        "| FG%_scaled   | The percentage of field goals made                                                                                                                                      |\n",
        "| 3P_scaled    | Three-point field goals made per game                                                                                                                                   |\n",
        "| 3PA_scaled   | Three-point field goals attempted per game                                                                                                                              |\n",
        "| 3P%_scaled   | The percentage of three-point field goals  made per game                                                                                                                |\n",
        "| 2P_scaled    | Two-point field goals made per game                                                                                                                                     |\n",
        "| 2PA_scaled   | Two-point field goals attempted per game                                                                                                                                |\n",
        "| 2P%_scaled   | The percentage of two-point field goals made per game                                                                                                                   |\n",
        "| eFG%_scaled  | Effective field goal percentage, is calculated by (FG + (0.5 x 3P))/FGA. Is used to account  for a three-point field goal being worth more than a two-point field goal. |\n",
        "| FT_scaled    | Free throws made per game                                                                                                                                               |\n",
        "| FTA_scaled   | Free throws attempted per game                                                                                                                                          |\n",
        "| FT%_scaled   | The percentage of free throws made                                                                                                                                      |\n",
        "| ORB_scaled   | Offensive rebounds per game, meaning rebounds recovered when a player is on offense                                                                                     |\n",
        "| DRB_scaled   | Defensive rebounds per game, meaning rebounds recovered when a player is on defense                                                                                     |\n",
        "| TRB_scaled   | Total rebounds per game, offensive and defensive rebounds combined                                                                                                      |\n",
        "| AST_scaled   | Assists per game                                                                                                                                                        |\n",
        "| STL_scaled   | Steals per game                                                                                                                                                         |\n",
        "| BLK_scaled   | Blocks per game                                                                                                                                                         |\n",
        "| TOV_scaled   | Turnovers per game                                                                                                                                                      |\n",
        "| PF_scaled    | Personal fouls committed per game                                                                                                                                       |\n",
        "| PTS_scaled   | Points per game                                                                                                                                                         |\n",
        "| MVP          | The amount of votes the player recieved for the MVP award for that year          |\n",
        "| DPOY         | The amount of votes the player recieved for the DPOY award for that year  |\n",
        "| Sixth Man    | The amount of votes the player recieved for the 6MOY award for that year  |\n",
        "| Award        | The award the player recieved that year (None means that player did not get the MVP, DPOY, or 6MOY) |\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "MTI9ecKQFwuj"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualizations\n",
        "\n"
      ],
      "metadata": {
        "id": "81cU1wHsQVPO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "#Assign points scaled and assists scaled columns in the data frame as x_feat and y_feat respectively\n",
        "x_feat = 'PTS_scaled'\n",
        "y_feat = 'AST_scaled'\n",
        "\n",
        "#extract data\n",
        "x = df_2023.loc[:, x_feat]\n",
        "y = df_2023.loc[:, y_feat]\n",
        "\n",
        "#set labels and title\n",
        "title ='Normalized Points vs Normalized Assists for NBA Players 2022-23 Season (>=60 Games Played)'\n",
        "labels_dict = {'PTS_scaled':'Normalized Points', 'AST_scaled':'Normalized Assists'}\n",
        "\n",
        "#plot the qualified NBA players\n",
        "fig = px.scatter(df_2023, x=x, y=y, hover_data = ['Player'], title=title, labels = labels_dict)\n",
        "\n",
        "# show figure\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "jRl7DyyEQTwo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The scatterplot shows that for this year Joel Embiid was the highest scorer, while Trae Young had the most assists per game. Luka Doncic had a high standard deviation in both PPG and APG. Because the most important stats for MVP are PPG and APG, these players are all likely favorites for the award."
      ],
      "metadata": {
        "id": "MFc7pY8VYLnl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#find the PTS_scaled that are greater than or equal to 5.5 (i.e. find players with PTS per game that are 5.5 or more standard deviations above the mean)\n",
        "s_df = df_1992_to_2022.loc[:, 'PTS_scaled']\n",
        "\n",
        "s_bool = s_df >= 5.5\n",
        "\n",
        "df_pts_sd = df_1992_to_2022.loc[s_bool, ['Year', 'Player', 'PTS_scaled']]\n",
        "\n",
        "#change year to str data type\n",
        "df_pts_sd = df_pts_sd.astype({'Year': str})\n",
        "\n",
        "#create new column with player name and player year,\n",
        "df_pts_sd['Player + Year'] = df_pts_sd['Player'] + ' ' + df_pts_sd['Year']\n",
        "\n",
        "#sort by PTS_scaled\n",
        "df_pts_sd = df_pts_sd.sort_values('PTS_scaled')\n",
        "\n",
        "#create axes\n",
        "x_axis = df_pts_sd.loc[:, 'Player + Year']\n",
        "y_axis = df_pts_sd.loc[:, 'PTS_scaled']\n",
        "\n",
        "plt.bar(x_axis, y_axis)\n",
        "plt.gcf().set_size_inches(15, 7)\n",
        "plt.ylim((5.2, 6))\n",
        "plt.xlabel('Player and Year')\n",
        "plt.ylabel('Normalized Points')\n",
        "plt.suptitle('The Top 7 Scoring Seasons based on Normalized Point (For each year)')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kR5lVPwfSeY6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The barchart shows the best ever scoring seasons based on normalized points per game for that season. The data normalization is important because it eliminates trends in scoring in different years. According to the graph, James Harden had the best out of every normalized scoring year. Because PPG is the most important statistic in MVP voting, we would expect all of these seasons to have been, or close to being, MVP seasons."
      ],
      "metadata": {
        "id": "68uoTFE-YkOH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Method\n",
        "\n",
        "We are are using two Machine Learning methods, K-NN Classification and Random Forest Regression, in order to achieve our goal. Before we use the ML methods, though, we first normalized the data by year, so differences in year-by-year statistics trends do not skew it. We started by using the RF Regressor to find the most important features for each award, and we then combined those features into a list.\n",
        "\n",
        "Then, we went into the K-NN Classification (using the list of most important features as the stats the classifier would look at in order to classify each player) in order to find out what award, if any, players would be predicted to win. Before delving into the actual classifier, we used a graph of k value vs accuracy score to find the best k value to use. Initially, using the graph, we found that k=3 would be the best k to use. However, after we trained and cross validated the K-NN classifier on the 1992 to 2022 normalized data, we found that k=2 ended up prediciting more award winners correct (the difference in the accuracy score between k=3 and k=2 was less than 0.5%, so it was a insignificant difference regardless). So, for the 2023 predictions classifier, which was trained on the 1992 to 2022 data but predicted on the 2023 data, we used k=2, and found which players are predicted to win awards this season.\n",
        "\n",
        "The other ML method we used to make the award predictions was an RF Regressor. The 3 awards we are trying to predict also have data for which players recieved votes for the awards, so we needed a regressor to predict continous values. For the RF Regressor, we also trained and cross validated on the 1992 to 2022 normalized data, and found which players would have the most votes for each award every year. However, unlike the classifier, we created a separate model for each award. We found a cross validated r2 score of around .5 for each model, which although not great, does suggest some sort of relationship. Next, we predicted which players would have votes for all 3 awards this season, using the models that were trained on the previous 30 years of data.\n"
      ],
      "metadata": {
        "id": "Z98KwZlxq5jV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import all needed modules"
      ],
      "metadata": {
        "id": "TLxWG4ZQ5763"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import time\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from copy import copy"
      ],
      "metadata": {
        "id": "7i0HaVy75_Us"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Normalization and Web Scraping"
      ],
      "metadata": {
        "id": "qMV0pYrZ6fV5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "All the data normalized below is not the final data we used, however, this code is here to show that we normalized data that we decided to scrape and some of the code showing how we scraped it."
      ],
      "metadata": {
        "id": "Guyx4NPL6iFa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Normalize the 2023 Data\n"
      ],
      "metadata": {
        "id": "tR-YbIuPNmHz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import csv\n",
        "df_2023 = pd.read_csv('MVP Voting - 2023.csv')\n",
        "\n",
        "#chose the numeric numbers to normalize\n",
        "cols = ['G', 'GS', 'MP', 'FG', 'FGA', 'FG%',\n",
        "       '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%',\n",
        "       'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS']\n",
        "\n",
        "#normalize\n",
        "for feat in cols:\n",
        "    df_2023[feat] = df_2023[feat] / df_2023[feat].std()\n",
        "    df_2023.rename(columns={feat:f'{feat}_scaled'}, inplace=True)\n",
        "\n",
        "df_2023.head()"
      ],
      "metadata": {
        "id": "BlRX3WcBNmZe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Normalize 2012-2022 Data"
      ],
      "metadata": {
        "id": "GtALaAYiNzQk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#normalizing 2012-2022 data\n",
        "years = ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']\n",
        "\n",
        "#numeric stats\n",
        "cols = ['G', 'GS', 'MP', 'FG', 'FGA', 'FG%',\n",
        "       '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%',\n",
        "       'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS']\n",
        "\n",
        "#initialize new dataframe\n",
        "mvp_normalized_data = pd.DataFrame()\n",
        "\n",
        "#normalize by year\n",
        "for year in years:\n",
        "    df_normal = pd.DataFrame()\n",
        "    df_normal = pd.read_csv(f'MVP Voting - {year}.csv')\n",
        "\n",
        "    for feat in cols:\n",
        "        df_normal[feat] = df_normal[feat]/ df_normal[feat].std()\n",
        "        #rename the columns\n",
        "        df_normal.rename(columns={feat:f'{feat}_scaled'}, inplace=True)\n",
        "\n",
        "    #for first year set dataframe as the data from the first year, all other years add the data to the bottom\n",
        "    if year == '2022':\n",
        "        mvp_normalized_data = df_normal\n",
        "    else:\n",
        "        mvp_normalized_data = pd.concat([mvp_normalized_data, df_normal], axis=0)\n",
        "\n",
        "mvp_normalized_data.head()"
      ],
      "metadata": {
        "id": "6oKkgUU9Nypo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Web Scrapers\n"
      ],
      "metadata": {
        "id": "QGO8oxRuMvy_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4SldVQlLLmqE"
      },
      "outputs": [],
      "source": [
        "#taking data from 1992 to 2011\n",
        "year_list = ['1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n",
        "        '2005', '2006', '2007', '2008', '2009', '2010', '2011']\n",
        "\n",
        "mvp_1992_to_2011_data = pd.DataFrame()\n",
        "\n",
        "\n",
        "for year in year_list:\n",
        "    print(year)\n",
        "    #get table for each year\n",
        "    url = f'https://www.basketball-reference.com/leagues/NBA_{year}_per_game.html'\n",
        "    tables = pd.read_html(url)\n",
        "    df_year = tables[0]\n",
        "\n",
        "    #drop 'breaks' in table that appear for some reason\n",
        "    for idx in range(len(df_year)):\n",
        "        if df_year.loc[idx, 'Player'] == 'Player':\n",
        "            df_year.drop(labels=idx, axis=0, inplace=True)\n",
        "        else:\n",
        "            pass\n",
        "\n",
        "    #need to drop ranking and position\n",
        "    df_year.drop(labels='Rk',axis=1, inplace=True)\n",
        "    df_year.drop(labels='Pos',axis=1, inplace=True)\n",
        "\n",
        "    #stats that need to be turned into floats or ints\n",
        "    numerics = ['G', 'GS', 'MP', 'FG', 'FGA', 'FG%',\n",
        "       '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%',\n",
        "       'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS']\n",
        "    for col in numerics:\n",
        "        df_year[col] = pd.to_numeric(df_year[col])\n",
        "\n",
        "    #1999 season was shortened, so only need 36 games to qualify\n",
        "    if year == '1999':\n",
        "        df_games = df_year.loc[:, 'G']\n",
        "        s_bool = df_games >= 36\n",
        "\n",
        "        df_year_cond = df_year.loc[s_bool, :]\n",
        "\n",
        "    #every other year need 60 games to qualify\n",
        "    else:\n",
        "        df_games = df_year.loc[:, 'G']\n",
        "        s_bool = df_games >= 60\n",
        "\n",
        "        df_year_cond = df_year.loc[s_bool, :]\n",
        "\n",
        "\n",
        "    #add year column\n",
        "    df_year_cond['Year'] = year\n",
        "\n",
        "    #normalize data\n",
        "    for feat in numerics:\n",
        "        df_year_cond[feat] = df_year_cond[feat]/ df_year_cond[feat].std()\n",
        "        df_year_cond.rename(columns={feat:f'{feat}_scaled'}, inplace=True)\n",
        "\n",
        "    #if this is first year, then set equal to dataframe, if not, add to bottom\n",
        "    if len(mvp_1992_to_2011_data) == 0:\n",
        "        mvp_1992_to_2011_data = df_year_cond\n",
        "    else:\n",
        "        mvp_1992_to_2011_data = pd.concat([mvp_1992_to_2011_data, df_year_cond], axis=0)\n",
        "\n",
        "    # add a item delay to make sure we do not get an error\n",
        "    time.sleep(10)\n",
        "\n",
        "#export to csv\n",
        "mvp_1992_to_2011_data.to_csv('1992_to_2011.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The csvs above were not used for the rest of the project below, as we needed to manually input voting data. They are also not run in this output, as the output is not important to the rest of our project."
      ],
      "metadata": {
        "id": "vwnR2Zsc1aVW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Selection"
      ],
      "metadata": {
        "id": "RE4iewDpmYag"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We ran feature selection on the 30 seasons csv shown below in order to decide which features were most important in determining which players won each award. We decided that we would stick with the top 5 shown features for our classification and regression to really differentiate the set of players we were looking for and not to overwhelm our regression and classification ML methods."
      ],
      "metadata": {
        "id": "hljXr2fa5P-P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normalized_data = pd.read_csv('MVP Voting - All 30 Seasons.csv')\n",
        "normalized_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "PcB6S0HYma7u",
        "outputId": "95c94c2f-2e14-4e60-ff4a-09ff259f7c2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year                 Player  Age   Tm  G_scaled  GS_scaled  MP_scaled  \\\n",
              "0     1992             A.C. Green   28  LAL  12.31875    1.64491    4.20609   \n",
              "1     1992           A.J. English   24  WSB  12.16853    0.18622    2.44761   \n",
              "2     1992         Alaa Abdelnaby   23  POR  10.66624    0.03104    1.56837   \n",
              "3     1992           Alec Kessler   25  MIA  11.56761    0.12414    1.84165   \n",
              "4     1992       Alexander Volkov   27  ATL  11.56761    0.83797    2.34068   \n",
              "...    ...                    ...  ...  ...       ...        ...        ...   \n",
              "7401  2022           Jevon Carter   26  TOT  11.33658    0.10681    2.15983   \n",
              "7402  2022  Juan Toscano-Anderson   28  GSW  12.53895    0.21362    2.14406   \n",
              "7403  2022      Jordan McLaughlin   25  MIN  10.64952    0.10681    2.28595   \n",
              "7404  2022          Trent Forrest   23  UTA  10.30598    0.21362    2.01794   \n",
              "7405  2022          Dalano Banton   22  TOR  10.99305    0.03560    1.71840   \n",
              "\n",
              "      FG_scaled  FGA_scaled  FG%_scaled  ...  AST_scaled  STL_scaled  \\\n",
              "0       2.13325     2.22943    10.65897  ...     0.65121     2.04778   \n",
              "1       2.04247     2.36593     9.69608  ...     0.83727     0.74465   \n",
              "2       1.13471     1.16022    11.03965  ...     0.18606     0.74465   \n",
              "3       0.95315     1.13747     9.24822  ...     0.18606     0.37232   \n",
              "4       1.49781     1.68345     9.87522  ...     1.48847     1.67546   \n",
              "...         ...         ...         ...  ...         ...         ...   \n",
              "7401    0.73825     0.86307     5.44757  ...     0.74959     1.16487   \n",
              "7402    0.78746     0.72679     6.88337  ...     0.84953     2.03853   \n",
              "7403    0.68903     0.72679     6.19362  ...     1.44920     2.62096   \n",
              "7404    0.63981     0.59052     6.89745  ...     0.89950     1.45609   \n",
              "7405    0.63981     0.72679     5.78541  ...     0.74959     1.16487   \n",
              "\n",
              "      BLK_scaled  TOV_scaled  PF_scaled  PTS_scaled  MVP  DPOY  Sixth Man  \\\n",
              "0        0.56509     1.87094    2.41351     2.35245    0   0.0        0.0   \n",
              "1        0.14127     1.47002    2.83942     1.88542    0   0.0        0.0   \n",
              "2        0.28255     1.20275    2.69745     1.05514    0   0.0        0.0   \n",
              "3        0.56509     1.06911    3.40730     0.91676    0   0.0        0.0   \n",
              "4        0.56509     1.73730    3.26533     1.48758    0   0.0        0.0   \n",
              "...          ...         ...        ...         ...  ...   ...        ...   \n",
              "7401     0.48995     0.59109    1.65666     0.72692    0   0.0        0.0   \n",
              "7402     0.48995     1.06396    2.65065     0.70961    0   0.0        0.0   \n",
              "7403     0.48995     0.70931    1.49099     0.65769    0   0.0        0.0   \n",
              "7404     0.24497     0.94575    1.15966     0.57115    0   0.0        0.0   \n",
              "7405     0.48995     0.94575    1.82232     0.55385    0   0.0        0.0   \n",
              "\n",
              "      Award  \n",
              "0      None  \n",
              "1      None  \n",
              "2      None  \n",
              "3      None  \n",
              "4      None  \n",
              "...     ...  \n",
              "7401   None  \n",
              "7402   None  \n",
              "7403   None  \n",
              "7404   None  \n",
              "7405   None  \n",
              "\n",
              "[7406 rows x 33 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Player</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tm</th>\n",
              "      <th>G_scaled</th>\n",
              "      <th>GS_scaled</th>\n",
              "      <th>MP_scaled</th>\n",
              "      <th>FG_scaled</th>\n",
              "      <th>FGA_scaled</th>\n",
              "      <th>FG%_scaled</th>\n",
              "      <th>...</th>\n",
              "      <th>AST_scaled</th>\n",
              "      <th>STL_scaled</th>\n",
              "      <th>BLK_scaled</th>\n",
              "      <th>TOV_scaled</th>\n",
              "      <th>PF_scaled</th>\n",
              "      <th>PTS_scaled</th>\n",
              "      <th>MVP</th>\n",
              "      <th>DPOY</th>\n",
              "      <th>Sixth Man</th>\n",
              "      <th>Award</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1992</td>\n",
              "      <td>A.C. Green</td>\n",
              "      <td>28</td>\n",
              "      <td>LAL</td>\n",
              "      <td>12.31875</td>\n",
              "      <td>1.64491</td>\n",
              "      <td>4.20609</td>\n",
              "      <td>2.13325</td>\n",
              "      <td>2.22943</td>\n",
              "      <td>10.65897</td>\n",
              "      <td>...</td>\n",
              "      <td>0.65121</td>\n",
              "      <td>2.04778</td>\n",
              "      <td>0.56509</td>\n",
              "      <td>1.87094</td>\n",
              "      <td>2.41351</td>\n",
              "      <td>2.35245</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1992</td>\n",
              "      <td>A.J. English</td>\n",
              "      <td>24</td>\n",
              "      <td>WSB</td>\n",
              "      <td>12.16853</td>\n",
              "      <td>0.18622</td>\n",
              "      <td>2.44761</td>\n",
              "      <td>2.04247</td>\n",
              "      <td>2.36593</td>\n",
              "      <td>9.69608</td>\n",
              "      <td>...</td>\n",
              "      <td>0.83727</td>\n",
              "      <td>0.74465</td>\n",
              "      <td>0.14127</td>\n",
              "      <td>1.47002</td>\n",
              "      <td>2.83942</td>\n",
              "      <td>1.88542</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1992</td>\n",
              "      <td>Alaa Abdelnaby</td>\n",
              "      <td>23</td>\n",
              "      <td>POR</td>\n",
              "      <td>10.66624</td>\n",
              "      <td>0.03104</td>\n",
              "      <td>1.56837</td>\n",
              "      <td>1.13471</td>\n",
              "      <td>1.16022</td>\n",
              "      <td>11.03965</td>\n",
              "      <td>...</td>\n",
              "      <td>0.18606</td>\n",
              "      <td>0.74465</td>\n",
              "      <td>0.28255</td>\n",
              "      <td>1.20275</td>\n",
              "      <td>2.69745</td>\n",
              "      <td>1.05514</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1992</td>\n",
              "      <td>Alec Kessler</td>\n",
              "      <td>25</td>\n",
              "      <td>MIA</td>\n",
              "      <td>11.56761</td>\n",
              "      <td>0.12414</td>\n",
              "      <td>1.84165</td>\n",
              "      <td>0.95315</td>\n",
              "      <td>1.13747</td>\n",
              "      <td>9.24822</td>\n",
              "      <td>...</td>\n",
              "      <td>0.18606</td>\n",
              "      <td>0.37232</td>\n",
              "      <td>0.56509</td>\n",
              "      <td>1.06911</td>\n",
              "      <td>3.40730</td>\n",
              "      <td>0.91676</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1992</td>\n",
              "      <td>Alexander Volkov</td>\n",
              "      <td>27</td>\n",
              "      <td>ATL</td>\n",
              "      <td>11.56761</td>\n",
              "      <td>0.83797</td>\n",
              "      <td>2.34068</td>\n",
              "      <td>1.49781</td>\n",
              "      <td>1.68345</td>\n",
              "      <td>9.87522</td>\n",
              "      <td>...</td>\n",
              "      <td>1.48847</td>\n",
              "      <td>1.67546</td>\n",
              "      <td>0.56509</td>\n",
              "      <td>1.73730</td>\n",
              "      <td>3.26533</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>7401</th>\n",
              "      <td>2022</td>\n",
              "      <td>Jevon Carter</td>\n",
              "      <td>26</td>\n",
              "      <td>TOT</td>\n",
              "      <td>11.33658</td>\n",
              "      <td>0.10681</td>\n",
              "      <td>2.15983</td>\n",
              "      <td>0.73825</td>\n",
              "      <td>0.86307</td>\n",
              "      <td>5.44757</td>\n",
              "      <td>...</td>\n",
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              "      <td>1.16487</td>\n",
              "      <td>0.48995</td>\n",
              "      <td>0.59109</td>\n",
              "      <td>1.65666</td>\n",
              "      <td>0.72692</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7402</th>\n",
              "      <td>2022</td>\n",
              "      <td>Juan Toscano-Anderson</td>\n",
              "      <td>28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>12.53895</td>\n",
              "      <td>0.21362</td>\n",
              "      <td>2.14406</td>\n",
              "      <td>0.78746</td>\n",
              "      <td>0.72679</td>\n",
              "      <td>6.88337</td>\n",
              "      <td>...</td>\n",
              "      <td>0.84953</td>\n",
              "      <td>2.03853</td>\n",
              "      <td>0.48995</td>\n",
              "      <td>1.06396</td>\n",
              "      <td>2.65065</td>\n",
              "      <td>0.70961</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>7403</th>\n",
              "      <td>2022</td>\n",
              "      <td>Jordan McLaughlin</td>\n",
              "      <td>25</td>\n",
              "      <td>MIN</td>\n",
              "      <td>10.64952</td>\n",
              "      <td>0.10681</td>\n",
              "      <td>2.28595</td>\n",
              "      <td>0.68903</td>\n",
              "      <td>0.72679</td>\n",
              "      <td>6.19362</td>\n",
              "      <td>...</td>\n",
              "      <td>1.44920</td>\n",
              "      <td>2.62096</td>\n",
              "      <td>0.48995</td>\n",
              "      <td>0.70931</td>\n",
              "      <td>1.49099</td>\n",
              "      <td>0.65769</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>7404</th>\n",
              "      <td>2022</td>\n",
              "      <td>Trent Forrest</td>\n",
              "      <td>23</td>\n",
              "      <td>UTA</td>\n",
              "      <td>10.30598</td>\n",
              "      <td>0.21362</td>\n",
              "      <td>2.01794</td>\n",
              "      <td>0.63981</td>\n",
              "      <td>0.59052</td>\n",
              "      <td>6.89745</td>\n",
              "      <td>...</td>\n",
              "      <td>0.89950</td>\n",
              "      <td>1.45609</td>\n",
              "      <td>0.24497</td>\n",
              "      <td>0.94575</td>\n",
              "      <td>1.15966</td>\n",
              "      <td>0.57115</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>7405</th>\n",
              "      <td>2022</td>\n",
              "      <td>Dalano Banton</td>\n",
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              "      <td>10.99305</td>\n",
              "      <td>0.03560</td>\n",
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              "      <td>0.63981</td>\n",
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              "      <td>0.94575</td>\n",
              "      <td>1.82232</td>\n",
              "      <td>0.55385</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "def plot_feat_import(feat_list, feat_import, sort=True, limit=None):\n",
        "    \"\"\" plots feature importances in a horizontal bar chart\n",
        "\n",
        "    Args:\n",
        "        feat_list (list): str names of features\n",
        "        feat_import (np.array): feature importances (mean gini reduce)\n",
        "        sort (bool): if True, sorts features in decreasing importance\n",
        "            from top to bottom of plot\n",
        "        limit (int): if passed, limits the number of features shown\n",
        "            to this value\n",
        "    \"\"\"\n",
        "\n",
        "    if sort:\n",
        "        # sort features in decreasing importance\n",
        "        idx = np.argsort(feat_import).astype(int)\n",
        "        feat_list = [feat_list[_idx] for _idx in idx]\n",
        "        feat_import = feat_import[idx]\n",
        "\n",
        "    if limit is not None:\n",
        "\n",
        "        assert len(feat_list) == len(feat_import)\n",
        "        length = len(feat_list)\n",
        "\n",
        "        # limit to the first limit feature\n",
        "        feat_list = feat_list[(length-limit):length]\n",
        "        feat_import = feat_import[(length-limit):length]\n",
        "\n",
        "    # plot and label feature importance\n",
        "    plt.barh(feat_list, feat_import)\n",
        "    plt.gcf().set_size_inches(5, len(feat_list) / 2)\n",
        "    plt.xlabel('Feature importance\\n(Mean decrease in MSE across all Decision Trees)')"
      ],
      "metadata": {
        "id": "GvMbZ2_vmgv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above we used the plot_feature_importance function from lecture in lab, and applied it to the regressor data below.\n",
        "\n",
        "As a map, we always did MVP, DPOY, and SMOTY in that order."
      ],
      "metadata": {
        "id": "MbOLSGJi54Nx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# MVP feature selection\n",
        "\n",
        "# number of decision trees in the forest\n",
        "n_estimators = 100\n",
        "rf_reg = RandomForestRegressor(n_estimators=n_estimators)\n",
        "\n",
        "# fill na with 0 (this is because its usually %s where the denominator is 0)\n",
        "normalized_data = normalized_data.fillna(0)\n",
        "\n",
        "#x values\n",
        "x_feat_list = ['G_scaled', 'GS_scaled', 'MP_scaled',\n",
        "       'FG_scaled', 'FGA_scaled', 'FG%_scaled', '3P_scaled', '3PA_scaled',\n",
        "       '3P%_scaled', '2P_scaled', '2PA_scaled', '2P%_scaled', 'eFG%_scaled',\n",
        "       'FT_scaled', 'FTA_scaled', 'FT%_scaled', 'ORB_scaled', 'DRB_scaled',\n",
        "       'TRB_scaled', 'AST_scaled', 'BLK_scaled', 'BLK_scaled', 'TOV_scaled',\n",
        "       'PF_scaled', 'PTS_scaled']\n",
        "\n",
        "x = normalized_data.loc[:, x_feat_list].values\n",
        "\n",
        "# get the true y values\n",
        "y = normalized_data.loc[:, 'MVP'].values\n",
        "\n",
        "# fit random forest to data\n",
        "rf_reg.fit(x,y)\n",
        "\n",
        "# set and use plot_feat_import to plot the bar graph of feature importances\n",
        "sns.set()\n",
        "plot_feat_import(x_feat_list, rf_reg.feature_importances_, limit=5)\n",
        "plt.title('MVP Feature Importance')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "9hCF4lWBmjUf",
        "outputId": "4bc7cf70-4b43-4a6c-f2da-072bdb7cdff1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'MVP Feature Importance')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x250 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DPOY feature selection\n",
        "\n",
        "# number of decision trees in the forest\n",
        "n_estimators = 100\n",
        "rf_reg = RandomForestRegressor(n_estimators=n_estimators)\n",
        "\n",
        "# fill na with 0 (this is because its usually %s where the denominator is 0)\n",
        "normalized_data = normalized_data.fillna(0)\n",
        "\n",
        "#x values\n",
        "x_feat_list = ['G_scaled', 'GS_scaled', 'MP_scaled',\n",
        "       'FG_scaled', 'FGA_scaled', 'FG%_scaled', '3P_scaled', '3PA_scaled',\n",
        "       '3P%_scaled', '2P_scaled', '2PA_scaled', '2P%_scaled', 'eFG%_scaled',\n",
        "       'FT_scaled', 'FTA_scaled', 'FT%_scaled', 'ORB_scaled', 'DRB_scaled',\n",
        "       'TRB_scaled', 'AST_scaled', 'BLK_scaled', 'BLK_scaled', 'TOV_scaled',\n",
        "       'PF_scaled', 'PTS_scaled']\n",
        "\n",
        "x = normalized_data.loc[:, x_feat_list].values\n",
        "\n",
        "# get the true y values\n",
        "y = normalized_data.loc[:, 'DPOY'].values\n",
        "\n",
        "# fit random forest to data\n",
        "rf_reg.fit(x,y)\n",
        "\n",
        "# set and use plot_feat_import to plot the bar graph of feature importances\n",
        "sns.set()\n",
        "plot_feat_import(x_feat_list, rf_reg.feature_importances_, limit=6)\n",
        "plt.title('DPOY Feature Importance')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "yF0rZXkymmyS",
        "outputId": "62c37a3a-1a62-4a10-f74b-8713905d7fbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'DPOY Feature Importance')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sixth man feature importance\n",
        "\n",
        "# number of decision trees in the forest\n",
        "n_estimators = 100\n",
        "rf_reg = RandomForestRegressor(n_estimators=n_estimators)\n",
        "\n",
        "# fill na with 0 (this is because its usually %s where the denominator is 0)\n",
        "normalized_data = normalized_data.fillna(0)\n",
        "\n",
        "\n",
        "#x values\n",
        "x_feat_list = ['G_scaled', 'GS_scaled', 'MP_scaled',\n",
        "       'FG_scaled', 'FGA_scaled', 'FG%_scaled', '3P_scaled', '3PA_scaled',\n",
        "       '3P%_scaled', '2P_scaled', '2PA_scaled', '2P%_scaled', 'eFG%_scaled',\n",
        "       'FT_scaled', 'FTA_scaled', 'FT%_scaled', 'ORB_scaled', 'DRB_scaled',\n",
        "       'TRB_scaled', 'AST_scaled', 'BLK_scaled', 'BLK_scaled', 'TOV_scaled',\n",
        "       'PF_scaled', 'PTS_scaled']\n",
        "\n",
        "x = normalized_data.loc[:, x_feat_list].values\n",
        "\n",
        "# get the true y values\n",
        "y = normalized_data.loc[:, 'Sixth Man'].values\n",
        "\n",
        "# fit random forest to data\n",
        "rf_reg.fit(x,y)\n",
        "\n",
        "# set and use plot_feat_import to plot the bar graph of feature importances\n",
        "sns.set()\n",
        "plot_feat_import(x_feat_list, rf_reg.feature_importances_, limit=5)\n",
        "plt.title('Sixth Man Feature Importance')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "qZ3Afrd6mpnD",
        "outputId": "bc65ca83-53ff-4d02-9e86-7a2e0324695f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Sixth Man Feature Importance')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x250 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the classifier and regressor below, we used these top 5 feature graphs to decide which features to use for each award."
      ],
      "metadata": {
        "id": "EuGh20DD7_T2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classifier\n",
        "\n"
      ],
      "metadata": {
        "id": "aV7tmjGOOSV2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cross Validation and Testing on 1992-2022 Data\n"
      ],
      "metadata": {
        "id": "tH0M-yQnOadP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_2023 = pd.read_csv('MVP Voting - 2023 Normalized.csv')\n",
        "mvp_normalized_data = pd.read_csv('MVP Voting - All 30 Seasons.csv')"
      ],
      "metadata": {
        "id": "bpvcnMyfOmjJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_cv_acc_knn(x, y_true, k, n_splits=5):\n",
        "    \"\"\" computes cross validated accuracy of a KNN classifier\n",
        "\n",
        "    Args:\n",
        "        x (np.array): (n_sample, n_feat) features\n",
        "        y (np.array): (n_sample) target variable\n",
        "        k (int): number of nearest neighbors in k-NN classifier\n",
        "\n",
        "    Returns:\n",
        "        acc (float): cross validated accuracy\n",
        "    \"\"\"\n",
        "    # initialize a knn_classifier\n",
        "    knn_classifier = KNeighborsClassifier(n_neighbors=k)\n",
        "\n",
        "    # \"Stratified\" ensures (roughly) same number of species across folds\n",
        "    kfold = StratifiedKFold(n_splits=n_splits)\n",
        "\n",
        "    # initialize an empty array same size & datatype as y_true\n",
        "    y_pred = np.empty_like(y_true)\n",
        "    for train_idx, test_idx in kfold.split(x, y_true):\n",
        "        # split test / training data\n",
        "        x_train = x[train_idx, :]\n",
        "        x_test = x[test_idx, :]\n",
        "        y_true_train = y_true[train_idx]\n",
        "\n",
        "        # train on training data\n",
        "        knn_classifier.fit(x_train, y_true_train)\n",
        "\n",
        "        # predict on the testing data\n",
        "        y_pred[test_idx] = knn_classifier.predict(x_test)\n",
        "\n",
        "    return accuracy_score(y_true, y_pred)"
      ],
      "metadata": {
        "id": "qnHd-DZjO1Nl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above, we used the accuracy code from class. This is applied below, in order to find the best k value."
      ],
      "metadata": {
        "id": "UVj9Vh0y8Ke9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#these are the most important features (all the features in each of the awards' top 5 feature importance graphs combined)\n",
        "x_feat_list = ['PTS_scaled', 'AST_scaled', 'TRB_scaled', 'FG_scaled', 'GS_scaled', '2P_scaled', 'DRB_scaled', 'BLK_scaled', 'PF_scaled', 'TOV_scaled']\n",
        "\n",
        "y_feat = 'Award'\n",
        "\n",
        "#get values\n",
        "x = mvp_normalized_data.loc[:, x_feat_list].values\n",
        "y_true = mvp_normalized_data.loc[:, y_feat].values\n",
        "\n",
        "# compute cross validated accuracy of each k\n",
        "k_all = np.array(range(1, 20))\n",
        "acc = np.empty(k_all.shape, dtype=float)\n",
        "for idx, k in enumerate(k_all):\n",
        "    acc[idx] = get_cv_acc_knn(x, y_true, k, n_splits=5)\n",
        "\n",
        "#plot the cross validated accuracy of k=1 to k=20\n",
        "plt.plot(k_all, acc)\n",
        "plt.xlabel('k in k-NN')\n",
        "plt.ylabel('cross validated accuracy')\n",
        "plt.title('player classification optimization')\n",
        "plt.gcf().set_size_inches(10, 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "NUCr_K8pPYrA",
        "outputId": "29c48cb8-d3fa-4187-c8b9-1f9db85b213d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Even though according to the graph k=3 has a higher accuracy than k=2, k=2 ends up producing more correct award winners, so we use k=2 instead. Furthermore the difference in accuracy is .4%, which is a miniscule amount. The entire range on the y axis is less than .01, so we figure that the way to choose the k is insanely arbitrary."
      ],
      "metadata": {
        "id": "X1SvSbfLQorn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# set k value\n",
        "k = 2\n",
        "\n",
        "# set feature list for x values (features determined by feature importance above)\n",
        "new_feat_list = ['PTS_scaled', 'AST_scaled', 'TRB_scaled', 'FG_scaled', 'GS_scaled', '2P_scaled', 'DRB_scaled', 'BLK_scaled', 'PF_scaled', 'TOV_scaled']\n",
        "\n",
        "# set y value to the award\n",
        "y_feat = 'Award'\n",
        "\n",
        "#throw out rows with missing data\n",
        "mvp_normalized_data.dropna(how='any', inplace=True)\n",
        "\n",
        "x = mvp_normalized_data.loc[:, new_feat_list].values\n",
        "y_true = mvp_normalized_data.loc[:, y_feat].values\n",
        "\n",
        "# initialize a knn_classifier- look at k nearest neighbors\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k)\n",
        "\n",
        "# fit happens \"inplace\", we modify the internal state of knn_classifier (it remembers all the training samples)\n",
        "knn_classifier.fit(x, y_true)\n",
        "\n",
        "# construction of kfold object\n",
        "kfold = StratifiedKFold(n_splits=3)\n",
        "\n",
        "# allocate an empty array to store predictions in\n",
        "y_pred = copy(y_true)\n",
        "\n",
        "#create a copy of the dataset\n",
        "df_copy = copy(mvp_normalized_data)\n",
        "\n",
        "for train_idx, test_idx in kfold.split(x, y_true):\n",
        "    # build arrays which correspond to x, y train /test\n",
        "    x_test = x[test_idx, :]\n",
        "    x_train = x[train_idx, :]\n",
        "    y_true_train = y_true[train_idx]\n",
        "\n",
        "    # fit happens \"inplace\", we modify the internal state of knn_classifier to remember all the training samples\n",
        "    knn_classifier.fit(x_train, y_true_train)\n",
        "\n",
        "    # estimate each award\n",
        "    y_pred[test_idx] = knn_classifier.predict(x_test)\n",
        "\n",
        "#add the predictions to copied data set\n",
        "df_copy['Award Pred'] = y_pred"
      ],
      "metadata": {
        "id": "cLH5rDFLOsZC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# set the confusion matrix\n",
        "conf_mat = confusion_matrix(y_true=y_true, y_pred=y_pred)\n",
        "conf_mat_disp = ConfusionMatrixDisplay(conf_mat, display_labels=np.unique(y_true))\n",
        "conf_mat_disp.plot()\n",
        "\n",
        "# set plot size\n",
        "plt.gcf().set_size_inches(8, 8)\n",
        "\n",
        "# turns grid on confusion matrix off\n",
        "plt.grid(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "CIj9CNJmQiw7",
        "outputId": "84a5e8e8-1037-4475-f050-9147e54570db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Limit the df to only the players that are predicted to win awards\n",
        "s_df = df_copy.loc[:, 'Award Pred']\n",
        "s_bool = s_df != 'None'\n",
        "df_copy.loc[s_bool, :]"
      ],
      "metadata": {
        "id": "fJUVS-OIRAe9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "outputId": "476db140-67c2-44e3-d8fe-332d0c2a7a40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year            Player  Age   Tm  G_scaled  GS_scaled  MP_scaled  \\\n",
              "110   1992     John Stockton   29  UTA  12.31875    2.54495    4.34867   \n",
              "307   1993  Frank Brickowski   33  MIL  10.78311    2.06473    3.78643   \n",
              "321   1993   Hakeem Olajuwon   30  HOU  13.39720    2.64543    4.76319   \n",
              "339   1993     John Stockton   30  UTA  13.39720    2.64543    4.20849   \n",
              "367   1993    Lionel Simmons   24  SAC  11.27325    2.19377    4.37731   \n",
              "...    ...               ...  ...  ...       ...        ...        ...   \n",
              "6964  2020  Hassan Whiteside   30  POR  16.74216    2.27352    4.75918   \n",
              "6980  2021       Rudy Gobert   28  UTA  12.00608    2.88251    4.61034   \n",
              "7191  2022      Nikola Jokić   26  DEN  12.71071    2.63462    5.28133   \n",
              "7199  2022     DeMar DeRozan   32  CHI  13.05424    2.70583    5.69122   \n",
              "7288  2022      Clint Capela   27  ATL  12.71071    2.59902    4.35118   \n",
              "\n",
              "      FG_scaled  FGA_scaled  FG%_scaled  ...  STL_scaled  BLK_scaled  \\\n",
              "110     2.49635     2.61617    10.79333  ...     5.58487     0.42382   \n",
              "307     3.10633     2.82361    13.17371  ...     2.31673     1.09620   \n",
              "321     4.63699     4.33546    12.78696  ...     3.47510     6.57719   \n",
              "339     2.38602     2.44565    11.74757  ...     4.63346     0.46980   \n",
              "367     3.06131     3.40167    10.73234  ...     2.70285     0.93960   \n",
              "...         ...         ...         ...  ...         ...         ...   \n",
              "6964    2.96960     2.26725     8.97919  ...     1.10310     6.04963   \n",
              "6980    2.36729     1.72145     9.06206  ...     1.79575     6.50938   \n",
              "7191    5.06929     4.02008     8.20655  ...     4.36827     2.20476   \n",
              "7199    5.02008     4.58789     7.09452  ...     2.62096     0.73492   \n",
              "7288    2.46082     1.86241     8.62885  ...     2.03853     3.18466   \n",
              "\n",
              "      TOV_scaled  PF_scaled  PTS_scaled  MVP   DPOY  Sixth Man  Award  \\\n",
              "110      4.67735    4.11716     2.73299   18    0.0        0.0   None   \n",
              "307      4.12446    4.84384     2.86825    0    0.0        0.0   None   \n",
              "321      4.25750    4.97839     4.42967  647   73.0        0.0   DPOY   \n",
              "339      4.25750    3.63288     2.56276    1    0.0        0.0   None   \n",
              "367      3.72532    3.90198     3.03797    0    0.0        0.0   None   \n",
              "...          ...        ...         ...  ...    ...        ...    ...   \n",
              "6964     2.03517    5.14529     2.43515    0    3.0        0.0   None   \n",
              "6980     1.97303    4.04962     2.20747    8  464.0        0.0   DPOY   \n",
              "7191     4.49230    4.30730     4.69038  875    0.0        0.0    MVP   \n",
              "7199     2.83724    3.81031     4.82884    1    0.0        0.0   None   \n",
              "7288     0.70931    3.64464     1.92115    0    0.0        0.0   None   \n",
              "\n",
              "      Award Pred  \n",
              "110          MVP  \n",
              "307         DPOY  \n",
              "321          MVP  \n",
              "339          MVP  \n",
              "367         DPOY  \n",
              "...          ...  \n",
              "6964        DPOY  \n",
              "6980        DPOY  \n",
              "7191         MVP  \n",
              "7199         MVP  \n",
              "7288        DPOY  \n",
              "\n",
              "[87 rows x 34 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f1db4fdb-a207-4d41-8028-5a5460b543ca\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Year</th>\n",
              "      <th>Player</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tm</th>\n",
              "      <th>G_scaled</th>\n",
              "      <th>GS_scaled</th>\n",
              "      <th>MP_scaled</th>\n",
              "      <th>FG_scaled</th>\n",
              "      <th>FGA_scaled</th>\n",
              "      <th>FG%_scaled</th>\n",
              "      <th>...</th>\n",
              "      <th>STL_scaled</th>\n",
              "      <th>BLK_scaled</th>\n",
              "      <th>TOV_scaled</th>\n",
              "      <th>PF_scaled</th>\n",
              "      <th>PTS_scaled</th>\n",
              "      <th>MVP</th>\n",
              "      <th>DPOY</th>\n",
              "      <th>Sixth Man</th>\n",
              "      <th>Award</th>\n",
              "      <th>Award Pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>1992</td>\n",
              "      <td>John Stockton</td>\n",
              "      <td>29</td>\n",
              "      <td>UTA</td>\n",
              "      <td>12.31875</td>\n",
              "      <td>2.54495</td>\n",
              "      <td>4.34867</td>\n",
              "      <td>2.49635</td>\n",
              "      <td>2.61617</td>\n",
              "      <td>10.79333</td>\n",
              "      <td>...</td>\n",
              "      <td>5.58487</td>\n",
              "      <td>0.42382</td>\n",
              "      <td>4.67735</td>\n",
              "      <td>4.11716</td>\n",
              "      <td>2.73299</td>\n",
              "      <td>18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>MVP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>1993</td>\n",
              "      <td>Frank Brickowski</td>\n",
              "      <td>33</td>\n",
              "      <td>MIL</td>\n",
              "      <td>10.78311</td>\n",
              "      <td>2.06473</td>\n",
              "      <td>3.78643</td>\n",
              "      <td>3.10633</td>\n",
              "      <td>2.82361</td>\n",
              "      <td>13.17371</td>\n",
              "      <td>...</td>\n",
              "      <td>2.31673</td>\n",
              "      <td>1.09620</td>\n",
              "      <td>4.12446</td>\n",
              "      <td>4.84384</td>\n",
              "      <td>2.86825</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>DPOY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>321</th>\n",
              "      <td>1993</td>\n",
              "      <td>Hakeem Olajuwon</td>\n",
              "      <td>30</td>\n",
              "      <td>HOU</td>\n",
              "      <td>13.39720</td>\n",
              "      <td>2.64543</td>\n",
              "      <td>4.76319</td>\n",
              "      <td>4.63699</td>\n",
              "      <td>4.33546</td>\n",
              "      <td>12.78696</td>\n",
              "      <td>...</td>\n",
              "      <td>3.47510</td>\n",
              "      <td>6.57719</td>\n",
              "      <td>4.25750</td>\n",
              "      <td>4.97839</td>\n",
              "      <td>4.42967</td>\n",
              "      <td>647</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>DPOY</td>\n",
              "      <td>MVP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>1993</td>\n",
              "      <td>John Stockton</td>\n",
              "      <td>30</td>\n",
              "      <td>UTA</td>\n",
              "      <td>13.39720</td>\n",
              "      <td>2.64543</td>\n",
              "      <td>4.20849</td>\n",
              "      <td>2.38602</td>\n",
              "      <td>2.44565</td>\n",
              "      <td>11.74757</td>\n",
              "      <td>...</td>\n",
              "      <td>4.63346</td>\n",
              "      <td>0.46980</td>\n",
              "      <td>4.25750</td>\n",
              "      <td>3.63288</td>\n",
              "      <td>2.56276</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>MVP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>367</th>\n",
              "      <td>1993</td>\n",
              "      <td>Lionel Simmons</td>\n",
              "      <td>24</td>\n",
              "      <td>SAC</td>\n",
              "      <td>11.27325</td>\n",
              "      <td>2.19377</td>\n",
              "      <td>4.37731</td>\n",
              "      <td>3.06131</td>\n",
              "      <td>3.40167</td>\n",
              "      <td>10.73234</td>\n",
              "      <td>...</td>\n",
              "      <td>2.70285</td>\n",
              "      <td>0.93960</td>\n",
              "      <td>3.72532</td>\n",
              "      <td>3.90198</td>\n",
              "      <td>3.03797</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>DPOY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6964</th>\n",
              "      <td>2020</td>\n",
              "      <td>Hassan Whiteside</td>\n",
              "      <td>30</td>\n",
              "      <td>POR</td>\n",
              "      <td>16.74216</td>\n",
              "      <td>2.27352</td>\n",
              "      <td>4.75918</td>\n",
              "      <td>2.96960</td>\n",
              "      <td>2.26725</td>\n",
              "      <td>8.97919</td>\n",
              "      <td>...</td>\n",
              "      <td>1.10310</td>\n",
              "      <td>6.04963</td>\n",
              "      <td>2.03517</td>\n",
              "      <td>5.14529</td>\n",
              "      <td>2.43515</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>DPOY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6980</th>\n",
              "      <td>2021</td>\n",
              "      <td>Rudy Gobert</td>\n",
              "      <td>28</td>\n",
              "      <td>UTA</td>\n",
              "      <td>12.00608</td>\n",
              "      <td>2.88251</td>\n",
              "      <td>4.61034</td>\n",
              "      <td>2.36729</td>\n",
              "      <td>1.72145</td>\n",
              "      <td>9.06206</td>\n",
              "      <td>...</td>\n",
              "      <td>1.79575</td>\n",
              "      <td>6.50938</td>\n",
              "      <td>1.97303</td>\n",
              "      <td>4.04962</td>\n",
              "      <td>2.20747</td>\n",
              "      <td>8</td>\n",
              "      <td>464.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>DPOY</td>\n",
              "      <td>DPOY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7191</th>\n",
              "      <td>2022</td>\n",
              "      <td>Nikola Jokić</td>\n",
              "      <td>26</td>\n",
              "      <td>DEN</td>\n",
              "      <td>12.71071</td>\n",
              "      <td>2.63462</td>\n",
              "      <td>5.28133</td>\n",
              "      <td>5.06929</td>\n",
              "      <td>4.02008</td>\n",
              "      <td>8.20655</td>\n",
              "      <td>...</td>\n",
              "      <td>4.36827</td>\n",
              "      <td>2.20476</td>\n",
              "      <td>4.49230</td>\n",
              "      <td>4.30730</td>\n",
              "      <td>4.69038</td>\n",
              "      <td>875</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>MVP</td>\n",
              "      <td>MVP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7199</th>\n",
              "      <td>2022</td>\n",
              "      <td>DeMar DeRozan</td>\n",
              "      <td>32</td>\n",
              "      <td>CHI</td>\n",
              "      <td>13.05424</td>\n",
              "      <td>2.70583</td>\n",
              "      <td>5.69122</td>\n",
              "      <td>5.02008</td>\n",
              "      <td>4.58789</td>\n",
              "      <td>7.09452</td>\n",
              "      <td>...</td>\n",
              "      <td>2.62096</td>\n",
              "      <td>0.73492</td>\n",
              "      <td>2.83724</td>\n",
              "      <td>3.81031</td>\n",
              "      <td>4.82884</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>MVP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7288</th>\n",
              "      <td>2022</td>\n",
              "      <td>Clint Capela</td>\n",
              "      <td>27</td>\n",
              "      <td>ATL</td>\n",
              "      <td>12.71071</td>\n",
              "      <td>2.59902</td>\n",
              "      <td>4.35118</td>\n",
              "      <td>2.46082</td>\n",
              "      <td>1.86241</td>\n",
              "      <td>8.62885</td>\n",
              "      <td>...</td>\n",
              "      <td>2.03853</td>\n",
              "      <td>3.18466</td>\n",
              "      <td>0.70931</td>\n",
              "      <td>3.64464</td>\n",
              "      <td>1.92115</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>DPOY</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>87 rows × 34 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1db4fdb-a207-4d41-8028-5a5460b543ca')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code above has seperated all seasons in the 30 seasons dataframe into an award-worthy or non-award-worthy season. There isn't only one winner per season per award; rather all seasons were sorted into whether or not they deserved an award by the 30 season total standard."
      ],
      "metadata": {
        "id": "7Qbw2bK1-j_j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Historical Classifier Visualizations\n",
        "\n"
      ],
      "metadata": {
        "id": "vMcmEt9WkOxW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The goal with the code below is to make a visualization of the classifier's data above, and to graph all award worthy seasons."
      ],
      "metadata": {
        "id": "2lkimk2k-gUg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# make a copy of df_copy above and label it the award prediction for the classifier\n",
        "df_award_pred = copy(df_copy)\n",
        "\n",
        "#iterate through each row and remove the rows which have no awards for Award and Award Pred\n",
        "for idx, row in df_award_pred.iterrows():\n",
        "    if row['Award'] == 'None' and row['Award Pred'] == 'None':\n",
        "        df_award_pred = df_award_pred.drop(idx)"
      ],
      "metadata": {
        "id": "rievvxBFkUBz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#intialize empty lists for the 3 awards\n",
        "dpoy_list = list()\n",
        "mvp_list = list()\n",
        "sixth_man_list = list()\n",
        "\n",
        "#go through each row of the dataframe and add a players name to the respective award list\n",
        "for idx, row in df_copy.iterrows():\n",
        "    if row['Award Pred'] == 'MVP':\n",
        "        mvp_list.append(row['Player'])\n",
        "    if row['Award Pred'] == 'DPOY':\n",
        "        dpoy_list.append(row['Player'])\n",
        "    if row['Award Pred'] == 'Sixth Man':\n",
        "        sixth_man_list.append(row['Player'])"
      ],
      "metadata": {
        "id": "H-1TQpydlBRt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create the chart of MVP deserving seasons determined by the classifier\n",
        "plt.figure()\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('MVP Deserving Seasons')\n",
        "plt.xlabel('Player')\n",
        "plt.ylabel('Awards')\n",
        "for item in mvp_list:\n",
        "    plt.bar(item, mvp_list.count(item))\n",
        "\n",
        "#create the chart of DPOY deserving seasons determined by the classifier\n",
        "plt.figure()\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('DPOY Deserving Seasons')\n",
        "plt.xlabel('Player')\n",
        "plt.ylabel('Awards')\n",
        "for item in dpoy_list:\n",
        "    plt.bar(item, dpoy_list.count(item))\n",
        "\n",
        "#create the chart of Sixth Man deserving seasons determined by the classifier\n",
        "plt.figure()\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Sixth Man Deserving Seasons')\n",
        "plt.xlabel('Player')\n",
        "plt.ylabel('Awards')\n",
        "for item in sixth_man_list:\n",
        "    plt.bar(item, sixth_man_list.count(item))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "odtONtBSlKXO",
        "outputId": "ca229902-cb3a-4a48-d280-29596c3f21a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are some very interesting findings here. First of all, the amount of players deserving DPOY awards in extremely interesting. Some of the names are surprising -- Kobe Bryant, Clint Capela. However, we see some basketball defensive greats: Ben Wallace and Marcus Camby towering over the rest of the field, Rudy Gobert taking home two, and Shaq being worthy of one as well. The classifier cleary does not do so well with the SMOTY, as only 3 winners are predicted. The classifier also determines Lebron James to have 9 MVP wins, when in reality he only has 4, and does not have Michael Jordan to win a single MVP."
      ],
      "metadata": {
        "id": "9oEQ6GDl-86h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classifier Predictions for 2023"
      ],
      "metadata": {
        "id": "XhfkUQBsRThb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We then decided to see if there were any award worthy seasons in 2023, running the classifier and predicting the awards based off statistics from the 2022-23 NBA season."
      ],
      "metadata": {
        "id": "XpZC919Z_cwk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from copy import copy\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "#drop NaN\n",
        "df_2023.dropna(axis=0, inplace=True)\n",
        "mvp_normalized_data.dropna(axis=0, inplace=True)\n",
        "\n",
        "k = 2\n",
        "\n",
        "#feature list\n",
        "new_feat_list = ['PTS_scaled', 'AST_scaled', 'TRB_scaled', 'FG_scaled', 'GS_scaled', '2P_scaled', 'DRB_scaled', 'BLK_scaled', 'PF_scaled', 'TOV_scaled']\n",
        "\n",
        "y_feat = 'Award'\n",
        "\n",
        "#get the \"true\" data\n",
        "x_true = mvp_normalized_data.loc[:, new_feat_list].values\n",
        "y_true = mvp_normalized_data.loc[:, y_feat].values\n",
        "\n",
        "#get the data for the predicitons\n",
        "x_pred = df_2023.loc[:, new_feat_list]\n",
        "\n",
        "# initialize a knn_classifier\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k)\n",
        "\n",
        "# fit on the 1992-2022 stats\n",
        "knn_classifier.fit(x_true, y_true)\n",
        "\n",
        "# estimate awards\n",
        "y_pred = knn_classifier.predict(x_pred)\n",
        "\n",
        "#copy dataframe\n",
        "df_copy_pred = copy(df_2023)\n",
        "\n",
        "#add award predictions\n",
        "df_copy_pred['Award Pred'] = y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8W5VxUOxRRrf",
        "outputId": "2731c687-9621-4980-ce85-4dab5a3cf9c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:432: UserWarning: X has feature names, but KNeighborsClassifier was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#find predictions for this years wards\n",
        "s_df = df_copy_pred.loc[:, 'Award Pred']\n",
        "\n",
        "s_bool = s_df != 'None'\n",
        "\n",
        "df_copy_pred.loc[s_bool, :]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "Ab4XoIEKRjxO",
        "outputId": "a2b7bbcc-80cd-46a3-a46c-8dcc734074c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Year                 Player  Age   Tm   G_scaled  GS_scaled  MP_scaled  \\\n",
              "2   2023           Jayson Tatum   24  BOS  11.073352   2.645693   5.359932   \n",
              "29  2023        Lauri Markkanen   25  UTA   9.876233   2.359672   4.996793   \n",
              "34  2023           Nikola Jokić   27  DEN  10.325153   2.466930   4.895114   \n",
              "52  2023  Giannis Antetokounmpo   28  MIL   9.427313   2.252414   4.662705   \n",
              "97  2023            Ivica Zubac   25  LAC  11.372632   2.717198   4.154311   \n",
              "\n",
              "    FG_scaled  FGA_scaled  FG%_scaled  ...  ORB_scaled  DRB_scaled  \\\n",
              "2    4.285536    4.470805    6.480251  ...    1.416594    4.365779   \n",
              "29   3.804507    3.665636    6.939152  ...    2.575625    3.798795   \n",
              "34   4.110617    3.135920    8.788666  ...    3.090750    5.329653   \n",
              "52   4.897756    4.301295    7.690083  ...    2.833187    5.443049   \n",
              "97   1.880388    1.440828    8.816478  ...    3.992219    3.855493   \n",
              "\n",
              "    TRB_scaled  AST_scaled  STL_scaled  BLK_scaled  TOV_scaled  PF_scaled  \\\n",
              "2     3.733968    2.368566    3.253957    1.543777    3.626702   3.508180   \n",
              "29    3.649105    0.978321    1.774886    1.323237    2.376115   3.348717   \n",
              "34    5.006911    5.046076    3.845586    1.543777    4.502113   3.986568   \n",
              "52    5.006911    2.934963    2.366514    1.764316    4.877290   4.943345   \n",
              "97    4.200714    0.514906    1.183257    2.867014    1.875881   4.624419   \n",
              "\n",
              "    PTS_scaled  Award Pred  \n",
              "2     4.597636         MVP  \n",
              "29    3.910282        DPOY  \n",
              "34    3.742262         MVP  \n",
              "52    4.750381         MVP  \n",
              "97    1.649650        DPOY  \n",
              "\n",
              "[5 rows x 30 columns]"
            ],
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              "\n",
              "  <div id=\"df-50a5e777-9616-4854-9449-1acd8ba9c534\">\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Player</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tm</th>\n",
              "      <th>G_scaled</th>\n",
              "      <th>GS_scaled</th>\n",
              "      <th>MP_scaled</th>\n",
              "      <th>FG_scaled</th>\n",
              "      <th>FGA_scaled</th>\n",
              "      <th>FG%_scaled</th>\n",
              "      <th>...</th>\n",
              "      <th>ORB_scaled</th>\n",
              "      <th>DRB_scaled</th>\n",
              "      <th>TRB_scaled</th>\n",
              "      <th>AST_scaled</th>\n",
              "      <th>STL_scaled</th>\n",
              "      <th>BLK_scaled</th>\n",
              "      <th>TOV_scaled</th>\n",
              "      <th>PF_scaled</th>\n",
              "      <th>PTS_scaled</th>\n",
              "      <th>Award Pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023</td>\n",
              "      <td>Jayson Tatum</td>\n",
              "      <td>24</td>\n",
              "      <td>BOS</td>\n",
              "      <td>11.073352</td>\n",
              "      <td>2.645693</td>\n",
              "      <td>5.359932</td>\n",
              "      <td>4.285536</td>\n",
              "      <td>4.470805</td>\n",
              "      <td>6.480251</td>\n",
              "      <td>...</td>\n",
              "      <td>1.416594</td>\n",
              "      <td>4.365779</td>\n",
              "      <td>3.733968</td>\n",
              "      <td>2.368566</td>\n",
              "      <td>3.253957</td>\n",
              "      <td>1.543777</td>\n",
              "      <td>3.626702</td>\n",
              "      <td>3.508180</td>\n",
              "      <td>4.597636</td>\n",
              "      <td>MVP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2023</td>\n",
              "      <td>Lauri Markkanen</td>\n",
              "      <td>25</td>\n",
              "      <td>UTA</td>\n",
              "      <td>9.876233</td>\n",
              "      <td>2.359672</td>\n",
              "      <td>4.996793</td>\n",
              "      <td>3.804507</td>\n",
              "      <td>3.665636</td>\n",
              "      <td>6.939152</td>\n",
              "      <td>...</td>\n",
              "      <td>2.575625</td>\n",
              "      <td>3.798795</td>\n",
              "      <td>3.649105</td>\n",
              "      <td>0.978321</td>\n",
              "      <td>1.774886</td>\n",
              "      <td>1.323237</td>\n",
              "      <td>2.376115</td>\n",
              "      <td>3.348717</td>\n",
              "      <td>3.910282</td>\n",
              "      <td>DPOY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2023</td>\n",
              "      <td>Nikola Jokić</td>\n",
              "      <td>27</td>\n",
              "      <td>DEN</td>\n",
              "      <td>10.325153</td>\n",
              "      <td>2.466930</td>\n",
              "      <td>4.895114</td>\n",
              "      <td>4.110617</td>\n",
              "      <td>3.135920</td>\n",
              "      <td>8.788666</td>\n",
              "      <td>...</td>\n",
              "      <td>3.090750</td>\n",
              "      <td>5.329653</td>\n",
              "      <td>5.006911</td>\n",
              "      <td>5.046076</td>\n",
              "      <td>3.845586</td>\n",
              "      <td>1.543777</td>\n",
              "      <td>4.502113</td>\n",
              "      <td>3.986568</td>\n",
              "      <td>3.742262</td>\n",
              "      <td>MVP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2023</td>\n",
              "      <td>Giannis Antetokounmpo</td>\n",
              "      <td>28</td>\n",
              "      <td>MIL</td>\n",
              "      <td>9.427313</td>\n",
              "      <td>2.252414</td>\n",
              "      <td>4.662705</td>\n",
              "      <td>4.897756</td>\n",
              "      <td>4.301295</td>\n",
              "      <td>7.690083</td>\n",
              "      <td>...</td>\n",
              "      <td>2.833187</td>\n",
              "      <td>5.443049</td>\n",
              "      <td>5.006911</td>\n",
              "      <td>2.934963</td>\n",
              "      <td>2.366514</td>\n",
              "      <td>1.764316</td>\n",
              "      <td>4.877290</td>\n",
              "      <td>4.943345</td>\n",
              "      <td>4.750381</td>\n",
              "      <td>MVP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>2023</td>\n",
              "      <td>Ivica Zubac</td>\n",
              "      <td>25</td>\n",
              "      <td>LAC</td>\n",
              "      <td>11.372632</td>\n",
              "      <td>2.717198</td>\n",
              "      <td>4.154311</td>\n",
              "      <td>1.880388</td>\n",
              "      <td>1.440828</td>\n",
              "      <td>8.816478</td>\n",
              "      <td>...</td>\n",
              "      <td>3.992219</td>\n",
              "      <td>3.855493</td>\n",
              "      <td>4.200714</td>\n",
              "      <td>0.514906</td>\n",
              "      <td>1.183257</td>\n",
              "      <td>2.867014</td>\n",
              "      <td>1.875881</td>\n",
              "      <td>4.624419</td>\n",
              "      <td>1.649650</td>\n",
              "      <td>DPOY</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What we found is that there are 5 award worthy seasons: Jayson Tatum, Nikola Jokic, and Giannis Antetokounmpo worthy of MVP and Lauri Markkanen and Ivica Zubac worthy of DPOY. These are surprising results -- the fact that Tatum is in the top 3 and the two DPOY candidates are not extremely seriously considered for the award this year. But Nikola Jokic and Giannis are 2 of the 3 nominees for the award this year, so their inclusion is not surprising."
      ],
      "metadata": {
        "id": "09BGMLMq_uJQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Historical RF Regression Results"
      ],
      "metadata": {
        "id": "g2_Rv8NnmTd7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The classifier above did not satisfy us, and we wanted one winner per year. So we used the data for award votes to try and determine who would be the single winner for each season. One difference here is that each award gets its own regressor, which means tha the feature lists are more fine tuned, which we hoped would help the results."
      ],
      "metadata": {
        "id": "D_aiRvn7ANhk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normalized_data = pd.read_csv('MVP Voting - All 30 Seasons.csv')"
      ],
      "metadata": {
        "id": "TKnrym1hlF5n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# MVP Random Forest Regressor\n",
        "\n",
        "# define the splits, depth, and estimators values\n",
        "n_splits = 10\n",
        "n_estimators = 100\n",
        "\n",
        "# initialize k fold\n",
        "kfold = KFold(n_splits=n_splits, shuffle=True)\n",
        "rf_reg = RandomForestRegressor(n_estimators=n_estimators)\n",
        "\n",
        "#x values ta\n",
        "x_feat_list = ['PTS_scaled', 'FG_scaled', 'AST_scaled', 'GS_scaled', '2P_scaled']\n",
        "\n",
        "x = normalized_data.loc[:, x_feat_list].values\n",
        "\n",
        "# get the true y values\n",
        "y_true = normalized_data.loc[:, 'MVP'].values\n",
        "\n",
        "# fit happens \"inplace\", we modify the internal state of knn_classifier (it remembers all the training samples)\n",
        "rf_reg.fit(x, y_true)\n",
        "\n",
        "# allocate an empty array to store predictions in\n",
        "y_pred = np.empty_like(y_true)\n",
        "\n",
        "#create a copy of the dataset\n",
        "df_copy = copy(normalized_data)\n",
        "\n",
        "for train_idx, test_idx in kfold.split(x, y_true):\n",
        "    # build arrays which correspond to x, y train /test\n",
        "    x_test = x[test_idx, :]\n",
        "    x_train = x[train_idx, :]\n",
        "    y_true_train = y_true[train_idx]\n",
        "\n",
        "    # fit happens \"inplace\", we modify the internal state of knn_classifier to remember all the training samples\n",
        "    rf_reg.fit(x_train, y_true_train)\n",
        "\n",
        "    # estimate each award\n",
        "    y_pred[test_idx] = rf_reg.predict(x_test)\n",
        "\n",
        "#add the predictions to copied data set\n",
        "df_copy['MVP Pred'] = y_pred\n",
        "\n",
        "# sort data frame by top award vote winner per seasons\n",
        "mvp = df_copy.groupby('Year')['MVP Pred'].transform('max') == df_copy['MVP Pred']\n",
        "\n",
        "# create a count list that contains each player that won an mvp\n",
        "mvp_count_list = list()\n",
        "for idx, row in df_copy[mvp].iterrows():\n",
        "    mvp_count_list.append(row['Player'])"
      ],
      "metadata": {
        "id": "9bpUiWMFnT75"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# DPOY Random Forest Regressor\n",
        "\n",
        "# define the splits, depth, and estimators values\n",
        "n_splits = 10\n",
        "n_estimators = 100\n",
        "\n",
        "# initialize k fold\n",
        "kfold = KFold(n_splits=n_splits, shuffle=True)\n",
        "rf_reg = RandomForestRegressor(n_estimators=n_estimators)\n",
        "\n",
        "#x values\n",
        "x_feat_list = ['DRB_scaled', 'BLK_scaled', 'TRB_scaled', 'PF_scaled', 'TOV_scaled']\n",
        "\n",
        "x = normalized_data.loc[:, x_feat_list].values\n",
        "\n",
        "# get the true y values\n",
        "y_true = normalized_data.loc[:, 'DPOY'].values\n",
        "\n",
        "# fit happens \"inplace\", we modify the internal state of knn_classifier (it remembers all the training samples)\n",
        "rf_reg.fit(x, y_true)\n",
        "\n",
        "# allocate an empty array to store predictions in\n",
        "y_pred = np.empty_like(y_true)\n",
        "\n",
        "#create a copy of the dataset\n",
        "df_copy = copy(normalized_data)\n",
        "\n",
        "for train_idx, test_idx in kfold.split(x, y_true):\n",
        "    # build arrays which correspond to x, y train /test\n",
        "    x_test = x[test_idx, :]\n",
        "    x_train = x[train_idx, :]\n",
        "    y_true_train = y_true[train_idx]\n",
        "\n",
        "    # fit happens \"inplace\", we modify the internal state of knn_classifier to remember all the training samples\n",
        "    rf_reg.fit(x_train, y_true_train)\n",
        "\n",
        "    # estimate each award\n",
        "    y_pred[test_idx] = rf_reg.predict(x_test)\n",
        "\n",
        "#add the predictions to copied data set\n",
        "df_copy['DPOY Pred'] = y_pred\n",
        "\n",
        "# sort data frame by top award vote winner per seasons\n",
        "dpoy = df_copy.groupby('Year')['DPOY Pred'].transform('max') == df_copy['DPOY Pred']\n",
        "\n",
        "# create a count list of each player that won DPOY\n",
        "dpoy_count_list = list()\n",
        "for idx, row in df_copy[dpoy].iterrows():\n",
        "    dpoy_count_list.append(row['Player'])"
      ],
      "metadata": {
        "id": "LmwDdiN2nhER"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sixth Man Random Forest Regressor\n",
        "\n",
        "# define the splits, depth, and estimators values\n",
        "n_splits = 10\n",
        "n_estimators = 100\n",
        "\n",
        "# initialize k fold\n",
        "kfold = KFold(n_splits=n_splits, shuffle=True)\n",
        "rf_reg = RandomForestRegressor(n_estimators=n_estimators)\n",
        "\n",
        "#x values\n",
        "x_feat_list = ['PTS_scaled', 'GS_scaled', 'FG_scaled', 'AST_scaled', 'TOV_scaled']\n",
        "\n",
        "x = normalized_data.loc[:, x_feat_list].values\n",
        "\n",
        "# get the true y values\n",
        "y_true = normalized_data.loc[:, 'Sixth Man'].values\n",
        "\n",
        "# fit happens \"inplace\", we modify the internal state of knn_classifier (it remembers all the training samples)\n",
        "rf_reg.fit(x, y_true)\n",
        "\n",
        "# allocate an empty array to store predictions in\n",
        "y_pred = np.empty_like(y_true)\n",
        "\n",
        "#create a copy of the dataset\n",
        "df_copy = copy(normalized_data)\n",
        "\n",
        "for train_idx, test_idx in kfold.split(x, y_true):\n",
        "    # build arrays which correspond to x, y train /test\n",
        "    x_test = x[test_idx, :]\n",
        "    x_train = x[train_idx, :]\n",
        "    y_true_train = y_true[train_idx]\n",
        "\n",
        "    # fit happens \"inplace\", we modify the internal state of knn_classifier to remember all the training samples\n",
        "    rf_reg.fit(x_train, y_true_train)\n",
        "\n",
        "    # estimate each award\n",
        "    y_pred[test_idx] = rf_reg.predict(x_test)\n",
        "\n",
        "#add the predictions to copied data set\n",
        "df_copy['Sixth Man Pred'] = y_pred\n",
        "\n",
        "# sort data frame by top award vote winner per seasons\n",
        "sm = df_copy.groupby('Year')['Sixth Man Pred'].transform('max') == df_copy['Sixth Man Pred']\n",
        "\n",
        "# create a count list of each player that won sixth man of the year\n",
        "sm_count_list = list()\n",
        "for idx, row in df_copy[sm].iterrows():\n",
        "    sm_count_list.append(row['Player'])"
      ],
      "metadata": {
        "id": "2PrrDhVznwYH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create plot of MVP winners\n",
        "plt.figure()\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('1992-2022 MVP Winners')\n",
        "plt.xlabel('Player')\n",
        "plt.ylabel('MVPs')\n",
        "for item in mvp_count_list:\n",
        "    plt.bar(item, mvp_count_list.count(item))\n",
        "\n",
        "#create plot of DPOY winners\n",
        "plt.figure()\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('1992-2022 DPOY Winners')\n",
        "plt.xlabel('Player')\n",
        "plt.ylabel('DPOYs')\n",
        "for item in dpoy_count_list:\n",
        "    plt.bar(item, dpoy_count_list.count(item))\n",
        "\n",
        "#create plot of Sixth Man winners\n",
        "plt.figure()\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('1992-2022 Sixth Man Winners')\n",
        "plt.xlabel('Player')\n",
        "plt.ylabel('SMOTYs')\n",
        "for item in sm_count_list:\n",
        "    plt.bar(item, sm_count_list.count(item))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OhsGuChln4hx",
        "outputId": "3fff3097-ec84-4d72-914f-a971d464d900"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Because we are using cross-validation, the regressor will predict the awards differently each time it runs. So, here are the linked graphs that we are using for analysis of our results from the historical regression predictions.\n",
        "\n",
        "<a href=\"https://imgbb.com/\"><img src=\"https://i.ibb.co/vZgGNF7/1992-2022-MVP-votes.png\" alt=\"1992-2022-MVP-votes\" border=\"0\"></a>\n",
        "<a href=\"https://imgbb.com/\"><img src=\"https://i.ibb.co/98V0nPd/1992-2022-DPOY-Votes.png\" alt=\"1992-2022-DPOY-Votes\" border=\"0\"></a>\n",
        "<a href=\"https://imgbb.com/\"><img src=\"https://i.ibb.co/kHjznjL/1992-2022-SMOTY-Votes.png\" alt=\"1992-2022-SMOTY-Votes\" border=\"0\"></a>\n",
        "\n"
      ],
      "metadata": {
        "id": "LXRj0VTcQlav"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One this we noticed in the visualizations above is that there were multiple repeat MVP and DPOY winners, which is something we saw less with SMOTY. This is reflective of real life, where sixth man of the year winners often earn a starting spot, taking them out of contention for the award in the future, while MVPs and DPOYs often reign at the top or near the top of their leagues for at least a few years.\n",
        "\n",
        "These results pass the eye test as more accurate than the classifiers, with people like Lebron James and Michael Jordan taking home multiple MVPs and Ben Wallace and Dikembe Mutombo taking home multiple DPOYs."
      ],
      "metadata": {
        "id": "7XGydTHcBfN-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2023 Regressor Predictions"
      ],
      "metadata": {
        "id": "vkG22UExoh3B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we decided to fit the regressors to determine the top 10 vote getters for each award for the 2022-2023 NBA season."
      ],
      "metadata": {
        "id": "7MGzkGAdB-kh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_2023_stats = pd.read_csv('MVP Voting - 2023 Normalized.csv')"
      ],
      "metadata": {
        "id": "3AUJhnhwpz-w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2023 MVP Regressor Predictions\n",
        "\n",
        "# define the splits, depth, and estimators values\n",
        "n_splits = 10\n",
        "n_estimators = 100\n",
        "\n",
        "# initialize k fold\n",
        "kfold = KFold(n_splits=n_splits, shuffle=True)\n",
        "rf_reg = RandomForestRegressor(n_estimators=n_estimators)\n",
        "\n",
        "#x values\n",
        "x_feat_list = ['PTS_scaled', 'FG_scaled', 'AST_scaled', 'GS_scaled', '2P_scaled']\n",
        "\n",
        "x = normalized_data.loc[:, x_feat_list].values\n",
        "\n",
        "# get the true y values\n",
        "y_true = normalized_data.loc[:, 'MVP'].values\n",
        "\n",
        "# fit happens \"inplace\", we modify the internal state of knn_classifier (it remembers all the training samples)\n",
        "rf_reg.fit(x, y_true)\n",
        "\n",
        "# allocate an empty array to store predictions in\n",
        "y_pred = np.empty_like(y_true)\n",
        "\n",
        "#get the data for the predicitons\n",
        "x_pred = df_2023_stats.loc[:, x_feat_list]\n",
        "\n",
        "# estimate awards\n",
        "y_pred = rf_reg.predict(x_pred)\n",
        "\n",
        "#copy dataframe\n",
        "df_copy_pred = copy(df_2023_stats)\n",
        "\n",
        "#add award predictions\n",
        "df_copy_pred['MVP Pred'] = y_pred\n",
        "\n",
        "df_top_10_mvp = df_copy_pred.nlargest(n=10, columns = 'MVP Pred')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3zIqcMWptww",
        "outputId": "31f8a2e6-f18f-42eb-cfe0-2d757565e37e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:432: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2023 DPOY Regressor Predictions\n",
        "\n",
        "# define the splits, depth, and estimators values\n",
        "n_splits = 10\n",
        "n_estimators = 100\n",
        "\n",
        "# initialize k fold\n",
        "kfold = KFold(n_splits=n_splits, shuffle=True)\n",
        "rf_reg = RandomForestRegressor(n_estimators=n_estimators)\n",
        "\n",
        "#x values\n",
        "x_feat_list = ['DRB_scaled', 'BLK_scaled', 'TRB_scaled', 'PF_scaled', 'TOV_scaled']\n",
        "\n",
        "x = normalized_data.loc[:, x_feat_list].values\n",
        "\n",
        "# get the true y values\n",
        "y_true = normalized_data.loc[:, 'DPOY'].values\n",
        "\n",
        "# fit happens \"inplace\", we modify the internal state of knn_classifier (it remembers all the training samples)\n",
        "rf_reg.fit(x, y_true)\n",
        "\n",
        "# allocate an empty array to store predictions in\n",
        "y_pred = np.empty_like(y_true)\n",
        "\n",
        "#get the data for the predicitons\n",
        "x_pred = df_2023_stats.loc[:, x_feat_list]\n",
        "\n",
        "# estimate awards\n",
        "y_pred = rf_reg.predict(x_pred)\n",
        "\n",
        "#copy dataframe\n",
        "df_copy_pred = copy(df_2023_stats)\n",
        "\n",
        "#add award predictions\n",
        "df_copy_pred['DPOY Pred'] = y_pred\n",
        "\n",
        "df_top_10_dpoy = df_copy_pred.nlargest(n=10, columns = 'DPOY Pred')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gq-C8EBp2G1",
        "outputId": "1c2d4bd6-d63f-4cda-8323-deb11f001f0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:432: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2023 SMOTY Regressor Predictions\n",
        "\n",
        "# define the splits, depth, and estimators values\n",
        "n_splits = 10\n",
        "n_estimators = 100\n",
        "\n",
        "# initialize k fold\n",
        "kfold = KFold(n_splits=n_splits, shuffle=True)\n",
        "rf_reg = RandomForestRegressor(n_estimators=n_estimators)\n",
        "\n",
        "#x values\n",
        "x_feat_list = ['PTS_scaled', 'GS_scaled', 'FG_scaled', 'TOV_scaled', 'AST_scaled']\n",
        "\n",
        "x = normalized_data.loc[:, x_feat_list].values\n",
        "\n",
        "# get the true y values\n",
        "y_true = normalized_data.loc[:, 'Sixth Man'].values\n",
        "\n",
        "# fit happens \"inplace\", we modify the internal state of knn_classifier (it remembers all the training samples)\n",
        "rf_reg.fit(x, y_true)\n",
        "\n",
        "# allocate an empty array to store predictions in\n",
        "y_pred = np.empty_like(y_true)\n",
        "\n",
        "#get the data for the predicitons\n",
        "x_pred = df_2023_stats.loc[:, x_feat_list]\n",
        "\n",
        "# estimate awards\n",
        "y_pred = rf_reg.predict(x_pred)\n",
        "\n",
        "#copy dataframe\n",
        "df_copy_pred = copy(df_2023_stats)\n",
        "\n",
        "#add award predictions\n",
        "df_copy_pred['SM Pred'] = y_pred\n",
        "\n",
        "df_top_10_sm = df_copy_pred.nlargest(n=10, columns = 'SM Pred')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5DYdgfop8Rt",
        "outputId": "518098e8-65de-45ca-ca84-35c68e268aa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:432: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create plot of MVP vote receivers for 2023\n",
        "plt.figure()\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('MVP Votes 2023')\n",
        "plt.xlabel('Player')\n",
        "plt.ylabel('Votes')\n",
        "for idx, row in df_top_10_mvp.iterrows():\n",
        "    plt.bar(row['Player'], row['MVP Pred'])\n",
        "\n",
        "#create plot of top 10 DPOY vote recievers for 2023\n",
        "plt.figure()\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('DPOY Votes 2023')\n",
        "plt.xlabel('Player')\n",
        "plt.ylabel('Votes')\n",
        "for idx, row in df_top_10_dpoy.iterrows():\n",
        "    plt.bar(row['Player'], row['DPOY Pred'])\n",
        "\n",
        "#create plot of top 10 Sixth Man vote recievers for 2023\n",
        "plt.figure()\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Sixth Man Votes 2023')\n",
        "plt.xlabel('Player')\n",
        "plt.ylabel('Votes')\n",
        "for idx, row in df_top_10_sm.iterrows():\n",
        "    plt.bar(row['Player'], row['SM Pred'])"
      ],
      "metadata": {
        "id": "V-xYjdgKqC07",
        "outputId": "ed8eee4a-6f05-47bc-ab18-647a9ae9c53d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Just like the historical predictions, sometimes the RF Regression model will predict different results, so we are linking the graphs we are analyzing.\n",
        "\n",
        "<a href=\"https://imgbb.com/\"><img src=\"https://i.ibb.co/9Z6hgwt/MVP-Votes-2023.png\" alt=\"MVP-Votes-2023\" border=\"0\"></a>\n",
        "<a href=\"https://imgbb.com/\"><img src=\"https://i.ibb.co/GQRvrBP/2023-DPOY-Votes.png\" alt=\"2023-DPOY-Votes\" border=\"0\"></a>\n",
        "<a href=\"https://imgbb.com/\"><img src=\"https://i.ibb.co/D1jkfBN/2023-SMOTY-Votes.png\" alt=\"2023-SMOTY-Votes\" border=\"0\"></a>\n",
        "\n"
      ],
      "metadata": {
        "id": "DDVx60CIT6_u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Discussion\n"
      ],
      "metadata": {
        "id": "T-K2IauGR8ds"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Results\n",
        "\n",
        "The MVP and DPOY results were not shocking. Jaren Jackson Jr. actually already has won the DPOY. Joel Embiid, Shai Gilgeous-Alexander, and Luka Doncic were all at some point in the season very highly considered for the award, and Joel Embiid is the current media favorite.\n",
        "\n",
        "There are some really suprising results mixed in, however. For example, the Sixth Man vote list is confusing. Malcom Brogdon comes 4th as the real life favorite right now, and many of the names listed here aren't seriously considered for the award. Jalen Brunson and Kevin Love getting DPOY votes would be a shocker, as these two are seen as majorly offensive players."
      ],
      "metadata": {
        "id": "wKjf_doqCcn3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ethical Implications\n",
        "\n",
        "While our overall topic is fairly free of larger world consequences, there are some ethical implications of using a machine learning model to predict NBA end of season awards. Firstly, there is always a risk of bias, and a model based on past awards can further perpetuate existing biases. Furthermore, there has to be a transparecy of how the model works so that it doesn't immediately lead to mistrust of the system (such as what happened with the NBA Draft Lottery). Most prominently, many top NBA players' contracts and salaries are dependent upon the honors they recieve: if the model causes them to miss out on awards repeatedly, players stand to lose millions of dollars, and potentially even motivation for playing at high levels. It could change the way both players and organizations approach the regular season and cause play style changes across the league."
      ],
      "metadata": {
        "id": "K9Z8TaT7SDAB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Final Takeaway"
      ],
      "metadata": {
        "id": "SIaUINPX3_H7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We believe, while this could be used to predict NBA award winners on an amateur level, it should not be used by those deciding votes and should not be used for anything with serious implications, such as betting or live analysis. These results are not ground-breaking, unexpected, and should not be taken at face value. For this project to work, a significant amount of improvement on these methods should be done in order to create a more comprehensive model."
      ],
      "metadata": {
        "id": "1H_7Yyw_4CPm"
      }
    }
  ]
}