MLB Clustering Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLB Player Analysis\n",
    "\n",
    "## Goal:\n",
    "How can we compare players of the past to players of today? Who is Hank Aaron most like today in terms of offensive statistics? Who are the players who are the cream of the crop?\n",
    "\n",
    "## Approach\n",
    "Since there is truly no right or wrong answer on determining a player's value, I decided to use a K means clustering algorithm to break out players in each year into different groups. Once the players are broken out, we can examine which group MVP candidates of that year fall into. I will run the analysis on all years prior to 2015 and then predict on the 2015 season to see which players are considered \"valuable\" by the clustering standards.\n",
    "\n",
    "From there, we can look to see which values have the highest correlation for being in the \"valuable\" cluster. Is batting average a high indicator? What about more advanded metris like OPS? BABIP? Or do traditional statistics like HR, H, R, and RBI give the best indication if a player is valuable?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard data science libraries\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "import numpy as np\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Statistics Libraries\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Machine learning libraries\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in the data\n",
    "This process does a for loop for all files that are in the current directory that end in a CSV (since those are the files that we are interested in loading). From there, I create global varables that have the same naming convention as the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salaries loaded successfully\n",
      "Schools loaded successfully\n",
      "People loaded successfully\n",
      "Batting loaded successfully\n",
      "CollegePlaying loaded successfully\n"
     ]
    }
   ],
   "source": [
    "# For loop for each file in the directory\n",
    "for file in os.listdir():\n",
    "    # Does the file end in csv?\n",
    "    if file.endswith('csv'):\n",
    "        # Get the file name and create a new global variable\n",
    "        file_name = file.replace('.csv','')\n",
    "        globals()['{}'.format(file_name)] = pd.read_csv(file)\n",
    "        print(file_name, 'loaded successfully')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
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       "       yearID teamID lgID   playerID    salary\n",
       "26423    2016    WAS   NL  strasst01  10400000\n",
       "26424    2016    WAS   NL  taylomi02    524000\n",
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       "26427    2016    WAS   NL  zimmery01  14000000"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salaries.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
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       "      <td>abilchrist</td>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>Abilene</td>\n",
       "      <td>TX</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adelphi</td>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>Garden City</td>\n",
       "      <td>NY</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>adrianmi</td>\n",
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       "      <td>University of Akron</td>\n",
       "      <td>Akron</td>\n",
       "      <td>OH</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>alabama</td>\n",
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       "     schoolID                     name_full         city state country\n",
       "0  abilchrist  Abilene Christian University      Abilene    TX     USA\n",
       "1     adelphi            Adelphi University  Garden City    NY     USA\n",
       "2    adrianmi                Adrian College       Adrian    MI     USA\n",
       "3       akron           University of Akron        Akron    OH     USA\n",
       "4     alabama         University of Alabama   Tuscaloosa    AL     USA"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Schools.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
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       "      <td>aardsda01</td>\n",
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       "      <td>12.0</td>\n",
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       "      <td>USA</td>\n",
       "      <td>CO</td>\n",
       "      <td>Denver</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>David</td>\n",
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       "      <td>75.0</td>\n",
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       "      <td>R</td>\n",
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       "      <td>2015-08-23</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>USA</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hank</td>\n",
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       "      <td>Henry Louis</td>\n",
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       "      <td>72.0</td>\n",
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       "      <td>R</td>\n",
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       "      <td>aaronto01</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>AL</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>GA</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Tommie</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Tommie Lee</td>\n",
       "      <td>190.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1962-04-10</td>\n",
       "      <td>1971-09-26</td>\n",
       "      <td>aarot101</td>\n",
       "      <td>aaronto01</td>\n",
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       "      <th>3</th>\n",
       "      <td>aasedo01</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>CA</td>\n",
       "      <td>Orange</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Don</td>\n",
       "      <td>Aase</td>\n",
       "      <td>Donald William</td>\n",
       "      <td>190.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1977-07-26</td>\n",
       "      <td>1990-10-03</td>\n",
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       "      <td>aasedo01</td>\n",
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       "      <td>abadan01</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>FL</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andy</td>\n",
       "      <td>Abad</td>\n",
       "      <td>Fausto Andres</td>\n",
       "      <td>184.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "      <td>2001-09-10</td>\n",
       "      <td>2006-04-13</td>\n",
       "      <td>abada001</td>\n",
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       "    playerID  birthYear  birthMonth  birthDay birthCountry birthState  \\\n",
       "0  aardsda01     1981.0        12.0      27.0          USA         CO   \n",
       "1  aaronha01     1934.0         2.0       5.0          USA         AL   \n",
       "2  aaronto01     1939.0         8.0       5.0          USA         AL   \n",
       "3   aasedo01     1954.0         9.0       8.0          USA         CA   \n",
       "4   abadan01     1972.0         8.0      25.0          USA         FL   \n",
       "\n",
       "    birthCity  deathYear  deathMonth  deathDay deathCountry deathState  \\\n",
       "0      Denver        NaN         NaN       NaN          NaN        NaN   \n",
       "1      Mobile        NaN         NaN       NaN          NaN        NaN   \n",
       "2      Mobile     1984.0         8.0      16.0          USA         GA   \n",
       "3      Orange        NaN         NaN       NaN          NaN        NaN   \n",
       "4  Palm Beach        NaN         NaN       NaN          NaN        NaN   \n",
       "\n",
       "  deathCity nameFirst nameLast       nameGiven  weight  height bats throws  \\\n",
       "0       NaN     David  Aardsma     David Allan   215.0    75.0    R      R   \n",
       "1       NaN      Hank    Aaron     Henry Louis   180.0    72.0    R      R   \n",
       "2   Atlanta    Tommie    Aaron      Tommie Lee   190.0    75.0    R      R   \n",
       "3       NaN       Don     Aase  Donald William   190.0    75.0    R      R   \n",
       "4       NaN      Andy     Abad   Fausto Andres   184.0    73.0    L      L   \n",
       "\n",
       "        debut   finalGame   retroID    bbrefID  \n",
       "0  2004-04-06  2015-08-23  aardd001  aardsda01  \n",
       "1  1954-04-13  1976-10-03  aaroh101  aaronha01  \n",
       "2  1962-04-10  1971-09-26  aarot101  aaronto01  \n",
       "3  1977-07-26  1990-10-03  aased001   aasedo01  \n",
       "4  2001-09-10  2006-04-13  abada001   abadan01  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "People.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105858</th>\n",
       "      <td>zimmery01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>WAS</td>\n",
       "      <td>NL</td>\n",
       "      <td>85</td>\n",
       "      <td>288</td>\n",
       "      <td>33</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105859</th>\n",
       "      <td>zobribe01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>CHN</td>\n",
       "      <td>NL</td>\n",
       "      <td>139</td>\n",
       "      <td>455</td>\n",
       "      <td>67</td>\n",
       "      <td>139</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>55</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105860</th>\n",
       "      <td>zuninmi01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>SEA</td>\n",
       "      <td>AL</td>\n",
       "      <td>113</td>\n",
       "      <td>373</td>\n",
       "      <td>37</td>\n",
       "      <td>75</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         playerID  yearID  stint teamID lgID    G   AB   R    H  2B  3B  HR  \\\n",
       "105856  zimmebr01    2018      1    CLE   AL   34  106  14   24   5   0   2   \n",
       "105857  zimmejo02    2018      1    DET   AL   25    2   0    0   0   0   0   \n",
       "105858  zimmery01    2018      1    WAS   NL   85  288  33   76  21   2  13   \n",
       "105859  zobribe01    2018      1    CHN   NL  139  455  67  139  28   3   9   \n",
       "105860  zuninmi01    2018      1    SEA   AL  113  373  37   75  18   0  20   \n",
       "\n",
       "         RBI   SB   CS  BB     SO  IBB  HBP   SH   SF  GIDP  \n",
       "105856   9.0  4.0  1.0   7   44.0  0.0  1.0  0.0  0.0   1.0  \n",
       "105857   0.0  0.0  0.0   0    2.0  0.0  0.0  0.0  0.0   0.0  \n",
       "105858  51.0  1.0  1.0  30   55.0  1.0  3.0  0.0  2.0  10.0  \n",
       "105859  58.0  3.0  4.0  55   60.0  1.0  2.0  1.0  7.0   8.0  \n",
       "105860  44.0  0.0  0.0  24  150.0  0.0  6.0  0.0  2.0   7.0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Batting.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>schoolID</th>\n",
       "      <th>yearID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>pennst</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>rice</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aardsda01</td>\n",
       "      <td>rice</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>gamiddl</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>gamiddl</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID schoolID  yearID\n",
       "0  aardsda01   pennst    2001\n",
       "1  aardsda01     rice    2002\n",
       "2  aardsda01     rice    2003\n",
       "3   abadan01  gamiddl    1992\n",
       "4   abadan01  gamiddl    1993"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CollegePlaying.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking at an example\n",
    "\n",
    "Since I was raised in Denver, the Colorado Rockies have been my favorite team to watch. Lets look at an example of one of Colorado's best players, Nolan Arenado. The data provided in the Batting table are all offensive statistics, but there are no \"metrics\" available like Batting Averag, On Base Percentage, Slugging Percentage, OPS, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
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       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
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       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97053</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
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       "      <td>NL</td>\n",
       "      <td>133</td>\n",
       "      <td>486</td>\n",
       "      <td>49</td>\n",
       "      <td>130</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98471</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>COL</td>\n",
       "      <td>NL</td>\n",
       "      <td>111</td>\n",
       "      <td>432</td>\n",
       "      <td>58</td>\n",
       "      <td>124</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99908</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>COL</td>\n",
       "      <td>NL</td>\n",
       "      <td>157</td>\n",
       "      <td>616</td>\n",
       "      <td>97</td>\n",
       "      <td>177</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>130.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>34</td>\n",
       "      <td>110.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101400</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>COL</td>\n",
       "      <td>NL</td>\n",
       "      <td>160</td>\n",
       "      <td>618</td>\n",
       "      <td>116</td>\n",
       "      <td>182</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68</td>\n",
       "      <td>103.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102889</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>COL</td>\n",
       "      <td>NL</td>\n",
       "      <td>159</td>\n",
       "      <td>606</td>\n",
       "      <td>100</td>\n",
       "      <td>187</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>37</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62</td>\n",
       "      <td>106.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104382</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>COL</td>\n",
       "      <td>NL</td>\n",
       "      <td>156</td>\n",
       "      <td>590</td>\n",
       "      <td>104</td>\n",
       "      <td>175</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>73</td>\n",
       "      <td>122.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         playerID  yearID  stint teamID lgID    G   AB    R    H  2B  3B  HR  \\\n",
       "97053   arenano01    2013      1    COL   NL  133  486   49  130  29   4  10   \n",
       "98471   arenano01    2014      1    COL   NL  111  432   58  124  34   2  18   \n",
       "99908   arenano01    2015      1    COL   NL  157  616   97  177  43   4  42   \n",
       "101400  arenano01    2016      1    COL   NL  160  618  116  182  35   6  41   \n",
       "102889  arenano01    2017      1    COL   NL  159  606  100  187  43   7  37   \n",
       "104382  arenano01    2018      1    COL   NL  156  590  104  175  38   2  38   \n",
       "\n",
       "          RBI   SB   CS  BB     SO   IBB  HBP   SH    SF  GIDP  \n",
       "97053    52.0  2.0  0.0  23   72.0   1.0  1.0  2.0   2.0  16.0  \n",
       "98471    61.0  2.0  1.0  25   58.0   1.0  4.0  1.0   5.0  13.0  \n",
       "99908   130.0  2.0  5.0  34  110.0  13.0  4.0  0.0  11.0  17.0  \n",
       "101400  133.0  2.0  3.0  68  103.0  10.0  2.0  0.0   8.0  17.0  \n",
       "102889  130.0  3.0  2.0  62  106.0   9.0  4.0  1.0   6.0  21.0  \n",
       "104382  110.0  2.0  2.0  73  122.0  10.0  3.0  1.0   6.0  16.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Batting[Batting['playerID'] == 'arenano01']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some Data Cleanup\n",
    "\n",
    "After doing some digging around, the stint category is one of the main identifier fields for a record in the table. For example, someone could be traded during the year, giving him 2 stints for the season. We want to look at the aggregate statistics for a season, not aggregate depending on which stint he was at during the year. So we are going to sum all of the fields together so everyone has 1 row per season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>98204</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
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       "      <td>14</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>99621</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>137</td>\n",
       "      <td>455</td>\n",
       "      <td>48</td>\n",
       "      <td>95</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101101</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2015</td>\n",
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       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>86</td>\n",
       "      <td>305</td>\n",
       "      <td>34</td>\n",
       "      <td>85</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102580</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>162</td>\n",
       "      <td>615</td>\n",
       "      <td>82</td>\n",
       "      <td>164</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104053</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>160</td>\n",
       "      <td>622</td>\n",
       "      <td>92</td>\n",
       "      <td>182</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105564</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>85</td>\n",
       "      <td>349</td>\n",
       "      <td>45</td>\n",
       "      <td>85</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105565</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>MIL</td>\n",
       "      <td>NL</td>\n",
       "      <td>46</td>\n",
       "      <td>124</td>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         playerID  yearID  stint teamID lgID    G   AB   R    H  2B  3B  HR  \\\n",
       "98204   schoojo01    2013      1    BAL   AL    5   14   5    4   0   0   1   \n",
       "99621   schoojo01    2014      1    BAL   AL  137  455  48   95  18   0  16   \n",
       "101101  schoojo01    2015      1    BAL   AL   86  305  34   85  17   0  15   \n",
       "102580  schoojo01    2016      1    BAL   AL  162  615  82  164  38   1  25   \n",
       "104053  schoojo01    2017      1    BAL   AL  160  622  92  182  35   0  32   \n",
       "105564  schoojo01    2018      1    BAL   AL   85  349  45   85  18   1  17   \n",
       "105565  schoojo01    2018      2    MIL   NL   46  124  16   25   4   0   4   \n",
       "\n",
       "          RBI   SB   CS  BB     SO  IBB   HBP   SH   SF  GIDP  \n",
       "98204     1.0  0.0  0.0   1    2.0  0.0   0.0  0.0  0.0   2.0  \n",
       "99621    45.0  2.0  0.0  13  122.0  0.0   8.0  5.0  0.0  12.0  \n",
       "101101   39.0  2.0  0.0   9   79.0  0.0   4.0  1.0  2.0   9.0  \n",
       "102580   82.0  1.0  2.0  21  137.0  0.0   8.0  0.0  3.0  16.0  \n",
       "104053  105.0  1.0  0.0  35  142.0  0.0  11.0  0.0  7.0  20.0  \n",
       "105564   40.0  0.0  1.0  12   74.0  1.0   3.0  1.0  2.0   8.0  \n",
       "105565   21.0  1.0  0.0   7   41.0  1.0   1.0  0.0  2.0   3.0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example before grouping data together\n",
    "Batting[Batting['playerID'] == 'schoojo01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "Batting_Grouped = Batting.groupby(['playerID','yearID'], as_index = False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79122</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79123</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>455</td>\n",
       "      <td>48</td>\n",
       "      <td>95</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79124</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>305</td>\n",
       "      <td>34</td>\n",
       "      <td>85</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>39.0</td>\n",
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       "      <td>9</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79125</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>615</td>\n",
       "      <td>82</td>\n",
       "      <td>164</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>137.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79126</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>622</td>\n",
       "      <td>92</td>\n",
       "      <td>182</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
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       "      <td>105.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79127</th>\n",
       "      <td>schoojo01</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>131</td>\n",
       "      <td>473</td>\n",
       "      <td>61</td>\n",
       "      <td>110</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID  yearID  stint    G   AB   R    H  2B  3B  HR    RBI   SB  \\\n",
       "79122  schoojo01    2013      1    5   14   5    4   0   0   1    1.0  0.0   \n",
       "79123  schoojo01    2014      1  137  455  48   95  18   0  16   45.0  2.0   \n",
       "79124  schoojo01    2015      1   86  305  34   85  17   0  15   39.0  2.0   \n",
       "79125  schoojo01    2016      1  162  615  82  164  38   1  25   82.0  1.0   \n",
       "79126  schoojo01    2017      1  160  622  92  182  35   0  32  105.0  1.0   \n",
       "79127  schoojo01    2018      3  131  473  61  110  22   1  21   61.0  1.0   \n",
       "\n",
       "        CS  BB     SO  IBB   HBP   SH   SF  GIDP  \n",
       "79122  0.0   1    2.0  0.0   0.0  0.0  0.0   2.0  \n",
       "79123  0.0  13  122.0  0.0   8.0  5.0  0.0  12.0  \n",
       "79124  0.0   9   79.0  0.0   4.0  1.0  2.0   9.0  \n",
       "79125  2.0  21  137.0  0.0   8.0  0.0  3.0  16.0  \n",
       "79126  0.0  35  142.0  0.0  11.0  0.0  7.0  20.0  \n",
       "79127  1.0  19  115.0  2.0   4.0  1.0  4.0  11.0  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example after grouping data together\n",
    "Batting_Grouped[Batting_Grouped['playerID'] == 'schoojo01']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New Metrics\n",
    "\n",
    "To give the data a little more depth, I am going to add in some more metrics that help evaluate the effectiveness of an offensive player.\n",
    "\n",
    "1. Batting Average (BA) - The percentage of time that they get a hit\n",
    "2. Total Basees (TB) - A weighted calculation to favor people who hit more extra base hits. Singles * 1 + Doubles * 2 + Triples * 3 + Home Runs * 4\n",
    "3. Slugging Percentage (SLG) - The total bases divided by the number of at bats\n",
    "4. On Base Percentage (OBP) - The percentage of time that they get on base\n",
    "5. On Base + Slugging (OPS) - This is a highly regarded statistic in baseball. It gives a way to measure the effectiveness by putting power and on base percentage on a more level playing field. This way, a big power hitter can be compared to a smaller player who puts the ball in play more often.\n",
    "6. Batting Average On Balls In Play (BABIP) - Percentage of time that a player gets a hit when they put the ball in play. This is a different way to gague how well they hit the ball by taking out strikeouts\n",
    "7. RBI/AB. The number of RBI's a player receives per at bat\n",
    "8. HR/G. The number of home runs a player averages each game\n",
    "9. R/G. The number of runs a player averages each game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def total_bases(row):\n",
    "    \"\"\" Creates the total bases calculation\"\"\"\n",
    "    doubles = row['2B']\n",
    "    triples = row['3B']\n",
    "    home_runs = row['HR']\n",
    "    \n",
    "    singles = row['H'] - doubles - triples - home_runs\n",
    "    \n",
    "    return singles + doubles*2 + triples*3 + home_runs*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batting_avg_ball_in_play(row):\n",
    "    \"\"\"\n",
    "    Calculates the batting average of balls in play\n",
    "    \n",
    "    BABIP = (H – HR)/(AB – K – HR + SF)\n",
    "    \n",
    "    \"\"\"\n",
    "    H = row['H']\n",
    "    HR = row['HR']\n",
    "    AB = row['AB']\n",
    "    K = row['SO']\n",
    "    SF = row['SH']\n",
    "    \n",
    "    return 0 if (AB - K - HR + SF) < 1 else (H - HR)/(AB - K - HR + SF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "Batting_Grouped['BA'] = Batting_Grouped['H'] / Batting_Grouped['AB']\n",
    "Batting_Grouped['TOTB'] = Batting_Grouped.apply(total_bases, axis = 1)\n",
    "Batting_Grouped['SLG'] = Batting_Grouped['TOTB'] / Batting_Grouped['AB']\n",
    "Batting_Grouped['OBP'] = (Batting_Grouped['H'] + Batting_Grouped['BB'] + Batting_Grouped['HBP']\n",
    "                          + Batting_Grouped['IBB']) / Batting_Grouped['AB']\n",
    "Batting_Grouped['OPS'] = Batting_Grouped['SLG'] + Batting_Grouped['OBP']\n",
    "Batting_Grouped['BABIP'] = Batting_Grouped.apply(batting_avg_ball_in_play, axis = 1)\n",
    "Batting_Grouped['RBI/AB'] = Batting_Grouped['RBI'] / Batting_Grouped['AB']\n",
    "Batting_Grouped['HR/G'] = Batting_Grouped['HR'] / Batting_Grouped['G']\n",
    "Batting_Grouped['R/G'] = Batting_Grouped['R'] / Batting_Grouped['G']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "      <th>BA</th>\n",
       "      <th>TOTB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>OPS</th>\n",
       "      <th>BABIP</th>\n",
       "      <th>RBI/AB</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2283</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2013</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.267490</td>\n",
       "      <td>197</td>\n",
       "      <td>0.405350</td>\n",
       "      <td>0.318930</td>\n",
       "      <td>0.724280</td>\n",
       "      <td>0.295567</td>\n",
       "      <td>0.106996</td>\n",
       "      <td>0.075188</td>\n",
       "      <td>0.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>432</td>\n",
       "      <td>58</td>\n",
       "      <td>124</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.287037</td>\n",
       "      <td>216</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.356481</td>\n",
       "      <td>0.856481</td>\n",
       "      <td>0.296919</td>\n",
       "      <td>0.141204</td>\n",
       "      <td>0.162162</td>\n",
       "      <td>0.522523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>616</td>\n",
       "      <td>97</td>\n",
       "      <td>177</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>130.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>34</td>\n",
       "      <td>110.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.287338</td>\n",
       "      <td>354</td>\n",
       "      <td>0.574675</td>\n",
       "      <td>0.370130</td>\n",
       "      <td>0.944805</td>\n",
       "      <td>0.290948</td>\n",
       "      <td>0.211039</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.617834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>618</td>\n",
       "      <td>116</td>\n",
       "      <td>182</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68</td>\n",
       "      <td>103.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.294498</td>\n",
       "      <td>352</td>\n",
       "      <td>0.569579</td>\n",
       "      <td>0.423948</td>\n",
       "      <td>0.993528</td>\n",
       "      <td>0.297468</td>\n",
       "      <td>0.215210</td>\n",
       "      <td>0.256250</td>\n",
       "      <td>0.725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>159</td>\n",
       "      <td>606</td>\n",
       "      <td>100</td>\n",
       "      <td>187</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>37</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62</td>\n",
       "      <td>106.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.308581</td>\n",
       "      <td>355</td>\n",
       "      <td>0.585809</td>\n",
       "      <td>0.432343</td>\n",
       "      <td>1.018152</td>\n",
       "      <td>0.323276</td>\n",
       "      <td>0.214521</td>\n",
       "      <td>0.232704</td>\n",
       "      <td>0.628931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>590</td>\n",
       "      <td>104</td>\n",
       "      <td>175</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>73</td>\n",
       "      <td>122.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.296610</td>\n",
       "      <td>331</td>\n",
       "      <td>0.561017</td>\n",
       "      <td>0.442373</td>\n",
       "      <td>1.003390</td>\n",
       "      <td>0.317865</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.243590</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerID  yearID  stint    G   AB    R    H  2B  3B  HR    RBI   SB  \\\n",
       "2283  arenano01    2013      1  133  486   49  130  29   4  10   52.0  2.0   \n",
       "2284  arenano01    2014      1  111  432   58  124  34   2  18   61.0  2.0   \n",
       "2285  arenano01    2015      1  157  616   97  177  43   4  42  130.0  2.0   \n",
       "2286  arenano01    2016      1  160  618  116  182  35   6  41  133.0  2.0   \n",
       "2287  arenano01    2017      1  159  606  100  187  43   7  37  130.0  3.0   \n",
       "2288  arenano01    2018      1  156  590  104  175  38   2  38  110.0  2.0   \n",
       "\n",
       "       CS  BB     SO   IBB  HBP   SH    SF  GIDP        BA  TOTB       SLG  \\\n",
       "2283  0.0  23   72.0   1.0  1.0  2.0   2.0  16.0  0.267490   197  0.405350   \n",
       "2284  1.0  25   58.0   1.0  4.0  1.0   5.0  13.0  0.287037   216  0.500000   \n",
       "2285  5.0  34  110.0  13.0  4.0  0.0  11.0  17.0  0.287338   354  0.574675   \n",
       "2286  3.0  68  103.0  10.0  2.0  0.0   8.0  17.0  0.294498   352  0.569579   \n",
       "2287  2.0  62  106.0   9.0  4.0  1.0   6.0  21.0  0.308581   355  0.585809   \n",
       "2288  2.0  73  122.0  10.0  3.0  1.0   6.0  16.0  0.296610   331  0.561017   \n",
       "\n",
       "           OBP       OPS     BABIP    RBI/AB      HR/G       R/G  \n",
       "2283  0.318930  0.724280  0.295567  0.106996  0.075188  0.368421  \n",
       "2284  0.356481  0.856481  0.296919  0.141204  0.162162  0.522523  \n",
       "2285  0.370130  0.944805  0.290948  0.211039  0.267516  0.617834  \n",
       "2286  0.423948  0.993528  0.297468  0.215210  0.256250  0.725000  \n",
       "2287  0.432343  1.018152  0.323276  0.214521  0.232704  0.628931  \n",
       "2288  0.442373  1.003390  0.317865  0.186441  0.243590  0.666667  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Batting_Grouped[Batting_Grouped['playerID'] == 'arenano01']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Data Cleanup\n",
    "\n",
    "Now that we have all the metrics in, we should only be looking at the metrics and fields that are known to be important to determing whether a player is valuable or not. HBP, IBB, SH and other metrics are either not thought to be important for offensive effeciency, or are being measured in the new calculations that were added.\n",
    "\n",
    "We are also going to fill any value that is NULL or Infinity as 0 since those happen when you are making calculations like 0/0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cleaned_Batting = Batting_Grouped[['playerID', 'yearID','G', 'AB', 'R', 'H', '2B', '3B', 'HR',\n",
    "       'RBI', 'BB','BA',\n",
    "       'TOTB', 'SLG', 'OBP', 'OPS', 'RBI/AB', 'HR/G', 'R/G', 'BABIP']].fillna(0).replace(np.inf, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>BB</th>\n",
       "      <th>BA</th>\n",
       "      <th>TOTB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>OPS</th>\n",
       "      <th>RBI/AB</th>\n",
       "      <th>HR/G</th>\n",
       "      <th>R/G</th>\n",
       "      <th>BABIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75225</th>\n",
       "      <td>rockepa01</td>\n",
       "      <td>1976</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.200000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73838</th>\n",
       "      <td>riceji01</td>\n",
       "      <td>1979</td>\n",
       "      <td>158</td>\n",
       "      <td>619</td>\n",
       "      <td>117</td>\n",
       "      <td>201</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "      <td>130.0</td>\n",
       "      <td>57</td>\n",
       "      <td>0.324717</td>\n",
       "      <td>369</td>\n",
       "      <td>0.596123</td>\n",
       "      <td>0.429725</td>\n",
       "      <td>1.025848</td>\n",
       "      <td>0.210016</td>\n",
       "      <td>0.246835</td>\n",
       "      <td>0.740506</td>\n",
       "      <td>0.335404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58124</th>\n",
       "      <td>mcnerje01</td>\n",
       "      <td>1967</td>\n",
       "      <td>56</td>\n",
       "      <td>123</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.227642</td>\n",
       "      <td>43</td>\n",
       "      <td>0.349593</td>\n",
       "      <td>0.317073</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.105691</td>\n",
       "      <td>0.053571</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.227273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89154</th>\n",
       "      <td>treshmi01</td>\n",
       "      <td>1946</td>\n",
       "      <td>80</td>\n",
       "      <td>217</td>\n",
       "      <td>28</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.216590</td>\n",
       "      <td>56</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.396313</td>\n",
       "      <td>0.654378</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.237374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>anderke01</td>\n",
       "      <td>1990</td>\n",
       "      <td>49</td>\n",
       "      <td>143</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>55</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.412587</td>\n",
       "      <td>0.797203</td>\n",
       "      <td>0.034965</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.338583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>affelje01</td>\n",
       "      <td>2005</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61687</th>\n",
       "      <td>morrijo01</td>\n",
       "      <td>1879</td>\n",
       "      <td>84</td>\n",
       "      <td>348</td>\n",
       "      <td>56</td>\n",
       "      <td>98</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.281609</td>\n",
       "      <td>126</td>\n",
       "      <td>0.362069</td>\n",
       "      <td>0.321839</td>\n",
       "      <td>0.683908</td>\n",
       "      <td>0.140805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.310127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94334</th>\n",
       "      <td>whittpo01</td>\n",
       "      <td>1915</td>\n",
       "      <td>128</td>\n",
       "      <td>448</td>\n",
       "      <td>46</td>\n",
       "      <td>126</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>29</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>152</td>\n",
       "      <td>0.339286</td>\n",
       "      <td>0.350446</td>\n",
       "      <td>0.689732</td>\n",
       "      <td>0.095982</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.359375</td>\n",
       "      <td>0.292056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12789</th>\n",
       "      <td>camermi01</td>\n",
       "      <td>2003</td>\n",
       "      <td>147</td>\n",
       "      <td>534</td>\n",
       "      <td>74</td>\n",
       "      <td>135</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>76.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.252809</td>\n",
       "      <td>230</td>\n",
       "      <td>0.430712</td>\n",
       "      <td>0.395131</td>\n",
       "      <td>0.825843</td>\n",
       "      <td>0.142322</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>0.503401</td>\n",
       "      <td>0.307895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74460</th>\n",
       "      <td>ritzke01</td>\n",
       "      <td>1992</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID  yearID    G   AB    R    H  2B  3B  HR    RBI  BB        BA  \\\n",
       "75225  rockepa01    1976    4    5    0    1   0   0   0    0.0   0  0.200000   \n",
       "73838   riceji01    1979  158  619  117  201  39   6  39  130.0  57  0.324717   \n",
       "58124  mcnerje01    1967   56  123    8   28   6   0   3   13.0   6  0.227642   \n",
       "89154  treshmi01    1946   80  217   28   47   5   2   0   21.0  36  0.216590   \n",
       "1852   anderke01    1990   49  143   16   44   6   1   1    5.0  13  0.307692   \n",
       "574    affelje01    2005   49    0    0    0   0   0   0    0.0   0  0.000000   \n",
       "61687  morrijo01    1879   84  348   56   98  18   5   0   49.0  14  0.281609   \n",
       "94334  whittpo01    1915  128  448   46  126  17   3   1   43.0  29  0.281250   \n",
       "12789  camermi01    2003  147  534   74  135  31   5  18   76.0  70  0.252809   \n",
       "74460   ritzke01    1992   23    0    0    0   0   0   0    0.0   0  0.000000   \n",
       "\n",
       "       TOTB       SLG       OBP       OPS    RBI/AB      HR/G       R/G  \\\n",
       "75225     1  0.200000  0.200000  0.400000  0.000000  0.000000  0.000000   \n",
       "73838   369  0.596123  0.429725  1.025848  0.210016  0.246835  0.740506   \n",
       "58124    43  0.349593  0.317073  0.666667  0.105691  0.053571  0.142857   \n",
       "89154    56  0.258065  0.396313  0.654378  0.096774  0.000000  0.350000   \n",
       "1852     55  0.384615  0.412587  0.797203  0.034965  0.020408  0.326531   \n",
       "574       0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "61687   126  0.362069  0.321839  0.683908  0.140805  0.000000  0.666667   \n",
       "94334   152  0.339286  0.350446  0.689732  0.095982  0.007812  0.359375   \n",
       "12789   230  0.430712  0.395131  0.825843  0.142322  0.122449  0.503401   \n",
       "74460     0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "          BABIP  \n",
       "75225  0.250000  \n",
       "73838  0.335404  \n",
       "58124  0.227273  \n",
       "89154  0.237374  \n",
       "1852   0.338583  \n",
       "574    0.000000  \n",
       "61687  0.310127  \n",
       "94334  0.292056  \n",
       "12789  0.307895  \n",
       "74460  0.000000  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cleaned_Batting.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalizing The Data\n",
    "\n",
    "From here, this is where we can really start to come up with some valuations. For each year, we want to see what percentile any player finished in one of the categories. You would assume that players who finish in high percentiles would be considered valuable.\n",
    "\n",
    "We go through each year, normalize the data by getting Z score (Z score is just a way to normalize the data, comparing data to the category's mean and varaince of the distribution), and then find the percentile of that Z score to make it easier to read. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the columns that we want to normalize\n",
    "normalize_columns =[col for col in Cleaned_Batting.columns if col not in ['playerID','yearID']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all of the years we're interested in, in order\n",
    "years = Cleaned_Batting.yearID.unique()[Cleaned_Batting.yearID.unique() < 2019]\n",
    "years.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Normalized Batting data frame\n",
    "Normalized_Batting = Cleaned_Batting[Cleaned_Batting['playerID'] == 1].copy()\n",
    "\n",
    "for year in years:\n",
    "    # Create a data frame for a specific year and only consider players with more than 100\n",
    "    # at bats so we don't dilute the data\n",
    "    df = Cleaned_Batting[(Cleaned_Batting['yearID'] == year) & (Cleaned_Batting['AB'] > 100)]\n",
    "    \n",
    "    #For each column, normalize the data, convert it to a percentile, and then replace it\n",
    "    for col in normalize_columns:\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(df[[col]])\n",
    "        df[col] = norm.cdf(scaler.transform(df[[col]]))\n",
    "        \n",
    "    Normalized_Batting = Normalized_Batting.append(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New look\n",
    "Now from the Normalized Batting data frame, we can see what percentile Nolan Arenado finished in that year for that given metric. For example, in 2015, Nolan finished in the 99th percentile in Slugging Percentage, Home Runs, and RBI's, which would indicate that he is a very powerful hitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>BB</th>\n",
       "      <th>BA</th>\n",
       "      <th>TOTB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>OPS</th>\n",
       "      <th>RBI/AB</th>\n",
       "      <th>HR/G</th>\n",
       "      <th>R/G</th>\n",
       "      <th>BABIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2283</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.772397</td>\n",
       "      <td>0.802843</td>\n",
       "      <td>0.585178</td>\n",
       "      <td>0.795578</td>\n",
       "      <td>0.856003</td>\n",
       "      <td>0.877934</td>\n",
       "      <td>0.489747</td>\n",
       "      <td>0.653572</td>\n",
       "      <td>0.341636</td>\n",
       "      <td>0.674912</td>\n",
       "      <td>0.757294</td>\n",
       "      <td>0.567961</td>\n",
       "      <td>0.255538</td>\n",
       "      <td>0.419742</td>\n",
       "      <td>0.424184</td>\n",
       "      <td>0.395229</td>\n",
       "      <td>0.431920</td>\n",
       "      <td>0.486679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.547418</td>\n",
       "      <td>0.689612</td>\n",
       "      <td>0.726811</td>\n",
       "      <td>0.756691</td>\n",
       "      <td>0.938823</td>\n",
       "      <td>0.522474</td>\n",
       "      <td>0.858207</td>\n",
       "      <td>0.783967</td>\n",
       "      <td>0.388274</td>\n",
       "      <td>0.849653</td>\n",
       "      <td>0.837384</td>\n",
       "      <td>0.951192</td>\n",
       "      <td>0.538472</td>\n",
       "      <td>0.863740</td>\n",
       "      <td>0.804074</td>\n",
       "      <td>0.918313</td>\n",
       "      <td>0.857996</td>\n",
       "      <td>0.480793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.907542</td>\n",
       "      <td>0.946265</td>\n",
       "      <td>0.977674</td>\n",
       "      <td>0.960878</td>\n",
       "      <td>0.993262</td>\n",
       "      <td>0.792687</td>\n",
       "      <td>0.999621</td>\n",
       "      <td>0.999544</td>\n",
       "      <td>0.552365</td>\n",
       "      <td>0.837220</td>\n",
       "      <td>0.995409</td>\n",
       "      <td>0.989129</td>\n",
       "      <td>0.603575</td>\n",
       "      <td>0.942947</td>\n",
       "      <td>0.994354</td>\n",
       "      <td>0.996297</td>\n",
       "      <td>0.944242</td>\n",
       "      <td>0.410093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.917976</td>\n",
       "      <td>0.943349</td>\n",
       "      <td>0.992873</td>\n",
       "      <td>0.963364</td>\n",
       "      <td>0.943524</td>\n",
       "      <td>0.971445</td>\n",
       "      <td>0.996940</td>\n",
       "      <td>0.998669</td>\n",
       "      <td>0.946259</td>\n",
       "      <td>0.884667</td>\n",
       "      <td>0.989605</td>\n",
       "      <td>0.983875</td>\n",
       "      <td>0.878797</td>\n",
       "      <td>0.973843</td>\n",
       "      <td>0.993741</td>\n",
       "      <td>0.985901</td>\n",
       "      <td>0.985566</td>\n",
       "      <td>0.474189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.921335</td>\n",
       "      <td>0.942151</td>\n",
       "      <td>0.967083</td>\n",
       "      <td>0.976784</td>\n",
       "      <td>0.989822</td>\n",
       "      <td>0.996181</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>0.998198</td>\n",
       "      <td>0.885279</td>\n",
       "      <td>0.941303</td>\n",
       "      <td>0.991399</td>\n",
       "      <td>0.982273</td>\n",
       "      <td>0.870130</td>\n",
       "      <td>0.970007</td>\n",
       "      <td>0.983108</td>\n",
       "      <td>0.942426</td>\n",
       "      <td>0.910694</td>\n",
       "      <td>0.712265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.906154</td>\n",
       "      <td>0.934356</td>\n",
       "      <td>0.983563</td>\n",
       "      <td>0.968954</td>\n",
       "      <td>0.968640</td>\n",
       "      <td>0.526137</td>\n",
       "      <td>0.996489</td>\n",
       "      <td>0.992915</td>\n",
       "      <td>0.956828</td>\n",
       "      <td>0.922374</td>\n",
       "      <td>0.988375</td>\n",
       "      <td>0.980437</td>\n",
       "      <td>0.926776</td>\n",
       "      <td>0.976827</td>\n",
       "      <td>0.954660</td>\n",
       "      <td>0.982615</td>\n",
       "      <td>0.966875</td>\n",
       "      <td>0.689224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerID  yearID         G        AB         R         H        2B  \\\n",
       "2283  arenano01    2013  0.772397  0.802843  0.585178  0.795578  0.856003   \n",
       "2284  arenano01    2014  0.547418  0.689612  0.726811  0.756691  0.938823   \n",
       "2285  arenano01    2015  0.907542  0.946265  0.977674  0.960878  0.993262   \n",
       "2286  arenano01    2016  0.917976  0.943349  0.992873  0.963364  0.943524   \n",
       "2287  arenano01    2017  0.921335  0.942151  0.967083  0.976784  0.989822   \n",
       "2288  arenano01    2018  0.906154  0.934356  0.983563  0.968954  0.968640   \n",
       "\n",
       "            3B        HR       RBI        BB        BA      TOTB       SLG  \\\n",
       "2283  0.877934  0.489747  0.653572  0.341636  0.674912  0.757294  0.567961   \n",
       "2284  0.522474  0.858207  0.783967  0.388274  0.849653  0.837384  0.951192   \n",
       "2285  0.792687  0.999621  0.999544  0.552365  0.837220  0.995409  0.989129   \n",
       "2286  0.971445  0.996940  0.998669  0.946259  0.884667  0.989605  0.983875   \n",
       "2287  0.996181  0.986507  0.998198  0.885279  0.941303  0.991399  0.982273   \n",
       "2288  0.526137  0.996489  0.992915  0.956828  0.922374  0.988375  0.980437   \n",
       "\n",
       "           OBP       OPS    RBI/AB      HR/G       R/G     BABIP  \n",
       "2283  0.255538  0.419742  0.424184  0.395229  0.431920  0.486679  \n",
       "2284  0.538472  0.863740  0.804074  0.918313  0.857996  0.480793  \n",
       "2285  0.603575  0.942947  0.994354  0.996297  0.944242  0.410093  \n",
       "2286  0.878797  0.973843  0.993741  0.985901  0.985566  0.474189  \n",
       "2287  0.870130  0.970007  0.983108  0.942426  0.910694  0.712265  \n",
       "2288  0.926776  0.976827  0.954660  0.982615  0.966875  0.689224  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Normalized_Batting[Normalized_Batting['playerID'] == 'arenano01']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# But does that mean Nolan Arenado is a valuable hitter in 2015?\n",
    "\n",
    "Nolan is in the upper percentiles for most the offensive categories, but are those the right categories to be considered a valuable hitter? What do other hitters finish at the top of who are considered for awards? This is where the clustering algorithm will come in. \n",
    "\n",
    "However, we don't want the clustering algorithm to break each cluster into equal parts. We want the truly valuable players all brought together and separated from the rest of the pack, which should be a smaller percentage compared to the rest of the population.\n",
    "\n",
    "First, we should see how many clusters we should start with. We will work with the data prior to 2015, and then test it out on the 2015 season to see if we agree with its predictions. We will use the elbow method (looking for the drop off to significantly stop in the visualization) to determine how many clusters to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qhHN3ebXqw2q5O71mlwIAACIMQS7CNbZ264/Lt2r73lazSwEAABGGIBfhRmYnyR5jU3k1/eQAAEBfBLkIZ4+xaVR2kiqqWbkKAAD6IshZQEFeinbVtsvnD5hdCgAAiCAEOQsozHepxxdQTVOX2aUAAIAIQvsRCzizcJieuOMiuRJizS4FAABEEIKcBThjY+SMjTG7DAAAEGGYWrWI9dvq9eyKbWaXAQAAIghBziJqm7v0rw1V6tjfY3YpAAAgQhDkLKIgL0WSVFlDGxIAANCLIGcRo3OSZUgqr6IxMAAA6EWQs4h4p135mYmqYEQOAAAcQJCzkHEj0swuAQAARBDaj1jInKmnml0CAACIIIzIAQAAWBRBzmIWLPpQz7++w+wyAABABCDIWYzPH9C2Pa1mlwEAACIAQc5iCvNd2lvfIU+P3+xSAACAyQhyFlOQm6JAMKjdte1mlwIAAExGkLOYgjyXJKmimn5yAAAMdQQ5i3ElxuriibnKTI0zuxQAAGAy+shZ0NcuH292CQAAIAIwImdRHft7WPAAAMAQR5CzoN217brj529pU0WT2aUAAAATEeQsKG9YouwxhspZ8AAAwJBGkLMgh92mkdnJrFwFAGCII8hZVEGeS7tq3PIHAmaXAgAATEKQs6jCvBR5fQHtq+80uxQAAGASgpxFnTYyVXOnj1NastPsUgAAgEnoI2dRKUlOfW5SvtllAAAAEzEiZ2HN7m6t39ZgdhkAAMAkBDkLW72pVk++/JE6u3vMLgUAAJiAIGdhhXkuSVIlbUgAABiSCHIWNjrXJUOinxwAAEMUQc7C4p125WUmssMDAABDFEHO4grzXKqoblMwGDS7FAAAMMhoP2JxV1wwWlddVCDDMMwuBQAADDKCnMUNS4k3uwQAAGASplajwL82VOmtD6vNLgMAAAwyglwUWLe1Xm9sqDK7DAAAMMgIclGgIM+lffUd8vb4zS4FAAAMIoJcFCjIc8kfCGp3XbvZpQAAgEFEkIsCBXkpkqTyKvrJAQAwlBDkokBKYqyy0xPk7vKaXQoAABhEtB+JEj/6xrmKsZHLAQAYSvjJHyUIcQAADD389I8Sze5uPfrn9/XhzkazSwEAAIOEIBclkhNitbOqTdv3tppdCgAAGCQEuSjhsNs0IitZFdWsXAUAYKggyEWRwjyXKmvd8gcCZpcCAAAGAUEuihTku+TtCaiqodPsUgAAwCAgyEWRU/JSNGFMuvyBoNmlAACAQUAfuSgyLDVe3/nyJLPLAAAAg4QRuSjk8frNLgEAAAwCglyU+deGKt22YJW6unvMLgUAAIQZQS7KZKXFKxAMqqKGNiQAAEQ7glyUGZPjkiHRTw4AgCGAIBdlEuLsyhuWSJADAGAIIMhFoYI8lyqq3QoGaUMCAEA0o/1IFJpSlKOR2cnyB4KyxxhmlwMAAMKEIBeFThuVptNGpZldBgAACDOmVqNUY9t+7a5tN7sMAAAQRgS5KPX7V7bo6bKtZpcBAADCiCAXpQryUrS3vkPeHnZ5AAAgWoU9yP30pz/VvHnzJElbtmzR7NmzNW3aNN13333y+XySpOrqas2ZM0fTp0/Xrbfeqs7OTkmS2+3WzTffrBkzZmjOnDlqaGiQJHm9Xt17772aMWOGvvjFL6q8vDzcH8NyCvNc8geC2lPXYXYpAAAgTMIa5N555x29/PLLocf33nuvHnzwQa1YsULBYFCLFi2SJD300EO6/vrrVVZWpgkTJmjhwoWSpCeeeELFxcVavny5rr32Wj388MOSpGeffVbx8fFavny55s+fr+9///vh/BiWVJDnkiSVV7eZXAkAAAiXsAW51tZWLViwQLfccoskqaqqSt3d3Zo0aZIkafbs2SorK1NPT4/Wrl2radOm9TkuSW+++aZmzZolSbriiiu0atUq9fT06M0339SVV14pSTrnnHPU3Nys6urqcH0US0pJcirDFadyGgMDABC1wtZ+5MEHH9Tdd9+tmpoaSVJ9fb0yMzND5zMzM1VXV6eWlhYlJSXJbrf3Of7p19jtdiUlJam5ufmw71VbW6u8vLzjri8jI+mkP2Okm/fVc5SREq/MtPiw/R6Zmclhe28MDq6h9XENrY3rZ31mXsOwBLkXXnhBubm5mjJlil566SVJUiAQkGF80pw2GAzKMIzQr4f69ONDX2Oz2T7zmoPH+6OpqUOBQHTvfJCR6JB8PjU0hKcNSWZmctjeG4ODa2h9XENr4/pZX7ivoc1mHHXwKSxBbtmyZWpoaNBVV12ltrY2dXV1yTCM0GIFSWpsbFRWVpbS09PV3t4uv9+vmJgYNTQ0KCsrS5KUlZWlxsZG5eTkyOfzqbOzU6mpqcrOzlZ9fb1GjhzZ573Ql8fr11sbq1WYn6IxuS6zywEAAAMsLPfI/eEPf9DSpUu1ZMkS3XHHHbr00kv1yCOPyOl0av369ZKkJUuWqKSkRA6HQ8XFxVq2bJkkafHixSopKZEklZaWavHixZJ6w2FxcbEcDodKS0u1ZMkSSdK6devkdDr7Na06VNhs0l/f2Kl12+rNLgUAAITBoPaRe+yxx/TII49o+vTp6urq0ty5cyVJP/jBD7Ro0SJdfvnlWrdune666y5J0p133qkPPvhAM2fO1J///Gc9+OCDkqQbb7xRXq9XM2fO1MMPP6xHH310MD+GZTjsMRqZnaRKFjwAABCVjGAwGN03ih3BULhHTpKe++d2vb2xRr+6+2LF9PM+wmPh3g7r4xpaH9fQ2rh+1mf2PXLs7BDlCvNc8vT4VdXQaXYpAABggBHkolxBnkuGpNrmLrNLAQAAAyxsfeQQGTJT4/Wru0sU7+RSAwAQbRiRi3KGYRDiAACIUgS5IWDbnhb97K8fqKvbZ3YpAABgABHkhgCfP6jNlc2qrKUNCQAA0YQgNwSMye1d8FBR1WZ2KQAAYAAR5IaAhDi7coclqpzGwAAARBWC3BBRkOtSRbVbQ7T/MwAAUYkgN0ScNipVI7KStN/jN7sUAAAwQOhLMURcMCFXF0zINbsMAAAwgBiRG2KGwv6yAAAMFQS5IeSPy7fqkefWm10GAAAYIAS5ISQx3q5dNe3q8XGfHAAA0YAgN4QU5qXIHwhqd12H2aUAAIABQJAbQgryXJJoDAwAQLQgyA0hqUlOZbicqqihMTAAANGA9iNDzNRzRireyWUHACAa8BN9iPnCOSPMLgEAAAwQplaHoJZ2j1raPWaXAQAAThJBbojp8fn13adW6/X1+8wuBQAAnCSC3BDjsMdoRFaSKqpZuQoAgNUR5IaggjyXKmva2a4LAACLI8gNQYV5KfL0+FXV2Gl2KQAA4CQQ5IagUGNgplcBALA02o8MQVlp8bp51ukaNzLN7FIAAMBJIMgNQYZh6PyiHLPLAAAAJ4mp1SGqpd2jNzdUab/HZ3YpAADgBBHkhqiqxg49s2KbKtl3FQAAyyLIDVEFub0LHsqrCXIAAFjVUYNcdXX1Ec+tWrVqwIvB4EmIcyg3I0EVVaxcBQDAqo4a5G677bbQ17fffnufcwsWLAhPRRg0BXkuVdS4FQzSGBgAACs6apA79Af83r17j3gO1lSQl6L2rh41uz1mlwIAAE7AUduPGIZx2K8P9xjWc/7p2Tp3fJYS4xxmlwIAAE7AUYMco27RLd5JG0EAAKzsqD/JA4GA2traFAwG5ff7Q19Lkt/vH5QCEV7vbKrV7rp2XXfZWLNLAQAA/XTUILd9+3adf/75ofB23nnnhc4xtRod9jV06I339+lLpYVy2OlGAwCAlRw1yG3dunWw6oBJCvJS5PPv0Z66dhXmp5hdDgAA6IdjDsEEg0H5fL3bOHV0dOjVV1/V7t27w14YBkdBHo2BAQCwqqMGuZ07d+qyyy7TW2+9pe7ubl177bVasGCBbrjhBv373/8erBoRRmnJTqUlO1VRTWNgAACs5qhB7tFHH9Vdd92lSy65RK+88ook6ZVXXtGiRYv0y1/+clAKRPiNH5VmdgkAAOAEHPUeuZqaGl155ZWSpPfee0+XXXaZbDabcnNz1dHRMSgFIvy+ccXpZpcAAABOwFFH5Gy2T05v2LBB55xzTuixx8NuAAAAAGY66ohcSkqKtm7dqo6ODjU0NISC3Pvvv6/s7OxBKRDhFwgG9ZPn3lfR6HRdddEYs8sBAADH6ahB7jvf+Y5uuukmdXR06J577lFCQoJ+97vf6amnntLChQsHq0aEmc0w1OMLaPveVrNLAQAA/XDUIDdp0iStWrVK7e3tysjIUEdHh7q7u/W3v/1No0ePHqQSMRgK8lxavalWgUBQNhvNngEAsIJjth+ZPn26Nm7cGGo/snTpUt144420H4kyhXkuebx+VTd2ml0KAAA4TrQfgaTeHR4kqaKGxsAAAFjFUYMc7UeGjuy0eJ1flK20ZKfZpQAAgONE+xFIkgzD0M2zinRGQYbZpQAAgONE+xH04e70Kt5pl8N+zG14AQCAyU6o/civf/1rPfnkk4NVIwbJ1t0tevQvG3TvdZM0fnS62eUAAIBjOK72I93d3XK5XJKkyZMn64UXXqD9SBQakZ0kSSqvdhPkAACwgKMGOUmKjY1VbGxs6PFZZ50V1oJgnsQ4h3LSE1RRzcpVAACsgBuh0EdhnksV1W0KBoNmlwIAAI6BIIc+CvJccnf1qLGt2+xSAADAMRDk0McZBRn62uWnKSHumLPuAADAZPy0Rh/DUuN1cWq82WUAAIDjwIgcPqO+db82bG8wuwwAAHAMBDl8xpsbqvTUkk3q8QXMLgUAABwFQQ6fUZDrks8f1J76drNLAQAAR0GQw2cU5qdIkiqq6CcHAEAkI8jhM9KSnUpLdqqihiAHAEAkI8jhsAryXCqvajO7DAAAcBS0H8FhXXvJKYpzxJhdBgAAOAqCHA4ri15yAABEPKZWcUQr1uzRu5trzS4DAAAcAUEOR/Tu5jq9tbHG7DIAAMAREORwRAV5LlXWuBUIBM0uBQAAHAZBDkdUkOdSt9evmqZOs0sBAACHQZDDERXkuSRJ5dX0kwMAIBIR5HBE2ekJSkmMVVun1+xSAADAYdB+BEdkMwz97LYLZbMZZpcCAAAOgxE5HBUhDgCAyEWQw1HVNnfpkT+t1/a9rWaXAgAAPoUgh6NKindox7427dhHkAMAINIQ5HBUSfEOZacnqIKVqwAARByCHI6pMM+l8mq3gkEaAwMAEEkIcjimgjyX3J1eNbm7zS4FAAAcgiCHYxo7PFUTCzPk6QmYXQoAADgEfeRwTCOyknTXtWeaXQYAAPgURuRw3Lq9PrNLAAAAhyDI4bi88s4u3fmLt9XjY3oVAIBIQZDDcclOS1CPL6C99R1mlwIAAA4gyOG4FOS5JEkV1W0mVwIAAA4iyOG4pLvilJoUS2NgAAAiSFiD3M9//nNdfvnlmjlzpv7whz9IklavXq1Zs2Zp6tSpWrBgQei5W7Zs0ezZszVt2jTdd9998vl6b6yvrq7WnDlzNH36dN16663q7OyUJLndbt18882aMWOG5syZo4aGhnB+FEgqzEshyAEAEEHCFuTWrFmjd999V3//+9/14osv6tlnn9XWrVs1f/58LVy4UMuWLdOmTZu0cuVKSdK9996rBx98UCtWrFAwGNSiRYskSQ899JCuv/56lZWVacKECVq4cKEk6YknnlBxcbGWL1+ua6+9Vg8//HC4PgoOuPjMPE0/fyQ7PAAAECHCFuTOPfdcPfPMM7Lb7WpqapLf75fb7daoUaM0YsQI2e12zZo1S2VlZaqqqlJ3d7cmTZokSZo9e7bKysrU09OjtWvXatq0aX2OS9Kbb76pWbNmSZKuuOIKrVq1Sj09PeH6OJA0sTBDn5uUL8MwzC4FAAAozA2BHQ6HfvGLX+j3v/+9pk+frvr6emVmZobOZ2Vlqa6u7jPHMzMzVVdXp5aWFiUlJclut/c5LqnPa+x2u5KSktTc3Kzs7Ozjqi0jI2mgPuaQUt3YIY/XrzF5KZKkzMxkkyvCyeIaWh/X0Nq4ftZn5jUM+84Od9xxh775zW/qlltu0a5du/qM5gSDQRmGoUAgcNjjB3891JFGg4LBoGy24x9gbGrqUCDAFGF/PfyHNUqKd+ie6yYrMzNZDQ3tZpeEk8A1tP7Gju0AACAASURBVD6uobVx/awv3NfQZjOOOvgUtqnV8vJybdmyRZIUHx+vqVOn6r333uuzKKGhoUFZWVnKycnpc7yxsVFZWVlKT09Xe3u7/H5/n+dLvaN5jY2NkiSfz6fOzk6lpqaG6+PggIK8FFXWuBXgPjkAAEwXtiC3b98+3X///fJ6vfJ6vXr99dd13XXXqbKyUrt375bf79fSpUtVUlKi/Px8OZ1OrV+/XpK0ZMkSlZSUyOFwqLi4WMuWLZMkLV68WCUlJZKk0tJSLV68WJK0bNkyFRcXy+FwhOvj4IDCPJf2e/yqaeoyuxQAAIa8sE2tlpaWauPGjbr66qsVExOjqVOnaubMmUpPT9ftt98uj8ej0tJSTZ8+XZL02GOP6f7771dHR4eKioo0d+5cSdIPfvADzZs3T0899ZRyc3P1+OOPS5LuvPNOzZs3TzNnzlRycrIee+yxcH0UHOLQxsCTxueYXA0AAEObERyivSS4R+7EBIJB3fHEWzpnfJbuufEc7u2wOO7PsT6uobVx/azP7Hvkwr7YAdHFZhi645qJykqLN7sUAACGPIIc+u3UESwqAQAgErDXKvqts7tHr67Zo901bNcFAICZGJFDv63ZUq/n39ip59/YqQyXU7NLCzWliIUPAAAMNkbk0C/vbK7VX1/fEXrc5Pbo6eVb9c7mWhOrAgBgaCLIoV9eWlkury/Q55jXF9BLK8tNqggAgKGLIId+aXJ7+nUcAACED0EO/ZLhcvbrOAAACB+CHPpldmmhYu19/7eJtds07byR+vM/t6vH5zepMgAAhh5WraJfDq5OfWlluZrdHqUfWLUaCAT12vp9qqx169uzJyolMdbkSgEAiH4EOfTblKIcTSnK+cy2JE5HjH679GP96Ol1uvPaiRqeeeQtRQAAwMljahUDpvi0LH1vzlnyBQL68bPrtXNfm9klAQAQ1QhyGFBjcl16YG6xJhZmKG9YgtnlAAAQ1QhyGHDprjjdctUEJcQ51OPzq+y9PfL5A8d+IQAA6BeCHMJqw45GLfrXTj3xwofq6u4xuxwAAKIKQQ5hde74bH398vHatqdVDz+7XnUtXWaXBABA1CDIIewumpire66bJHenVz96ep127Gs1uyQAAKICQQ6DYtzINN3/1WLlZCQoKd5hdjkAAEQFghwGTXZagubfcLZyMxIVDAa1dmu9AsGg2WUBAGBZBDkMKsMwJEmbK5v11OJNevKlj+Txsq0XAAAngiAHUxSNSddXPj9WH+xs1CPPrVezu9vskgAAsByCHExhGIa+UDxCd14zUfUt+/Xfz6xTZY3b7LIAALAUghxMNbFwmObfcLZi7Ta1d9FnDgCA/rCbXQAwPCtJD3/zfNljev9dUVnj1uic5ND9dAAA4PAYkUNEOBjidtW69aNn1ul3r2xRj49tvQAAOBqCHCLKqOxkXXXRGK3eVKvHnt8gd5fX7JIAAIhYBDlEFMMwdOWFY3TLVUXaVduuHz29TlWNnWaXBQBARCLIISKdOz5b37v+LHl9AW2ubDa7HAAAIhKLHRCxCvJc+tE3zlNiXO//ps3ubqW74kyuCgCAyMGIHCJaUrxDhmGooXW/Hvjde3ru1e3yB1gEAQCARJCDRWS44lRyZp5ef3+ffv7CRnV1+8wuCQAA0xHkYAk2m6EvXzpWN804TVt2t+jHf1qv+tb9ZpcFAICpCHKwlJIz8/SdL09SW4dH/1yz1+xyAAAwFYsdYDnjR6XpgZvOUVqSU5LU7fUpLpb/lQEAQw8jcrCkrNR4Oew2dXb36KE/rtOLK8sVCAbNLgsAgEFFkIOlOR0xGjciRa+8s1tPLd4kT4/f7JIAABg0BDlYmj3Gpq9OP01fvvQUvb+tQT997n21tHvMLgsAgEFBkIPlGYahaeeO1O1fmqiapi4998/tZpcEAMCg4A5xRI1JY4fp+zecpZTEWElSIBiUzTBMrgoAgPBhRA5RZWR2slKSnPIHAvr5Cxu1/N3dCrIIAgAQpQhyiEqBQFDxzhi98Ga5/rBsq3x+tvUCAEQfplYRlRz2GN18ZZFy0hP093/vUn3rft32xQlKTog1uzQAAAYMI3KIWjbD0NUXF+jmWaerotqtJ1/exDQrACCqMCKHqHd+UY6GpcbLHmPIYPEDACCKEOQwJJySnxL6+oV/7dSw1HhdMjnfxIoAADh5TK1iSPH5A6pq7NSzK7bpz69tVyDAVCsAwLoIchhS7DE23fGlifpC8Qi9tm6ffvHiRu33+MwuCwCAE0KQw5Bjsxn6yufH6sZp47Spoln/85cNCrAIAgBgQdwjhyHrksn5ykqLV1e3jx0gAACWRJDDkFY0Oj309epNNbIZhs4vyjGxIgAAjh9BDpAUDAb1zqZabd7VotrmLmWlxevlVRVqcnuU4XJqdmmhphDwAAARhiAHSDIMQ3dee6aeWbFNf//3LtkM6eCC1ia3R08v3ypJhDkAQERhsQNwgD3Gpq/NOE0JTrs+3ZXE6wvopZXl5hQGAMAREOSAQxiGoa4jtCNpcnsGuRoAAI6OIAd8SobLedjjrgTHIFcCAMDREeSAT5ldWqhY+2e/NdxdPfrff3wsd6fXhKoAAPgsFjsAn3JwQcNLK8tDq1avvGiMGlq7tfzd3fpwZ6Ouu2ysLpqYa3KlAIChjiAHHMaUopzDrlCdUpStZ1dsU1d3jwlVAQDQF0EO6IfcjETd+5XJOrij17uba1VZ066rLx6jeCffTgCAwcU9ckA/GYYhm613S6/qpk69tm6v7v/te1q3tV5B9mwFAAwighxwEmaXFGr+3LOVHO/QwsWbtOCFD1Xf0mV2WQCAIYIgB5ykwrwUPXBTsa67bKx27GvTvoZOs0sCAAwR3NQDDIAYm01TzxmhKUXZSorv7Te38oMqZaUlaPyoNJOrAwBEK4IcMICSE2IlSf5AQK+t26eqxk5NKcrWf1w6VimJsSZXBwCINkytAmEQY7Ppga8Wa9YFo7VmS73u+827+tf7+xT49CauAACcBIIcECaxjhh9saRAP/zPczUqJ1l/+ud2VTdy/xwAYOAwtQqEWW5Gou65bpJ21bZreFaSJOndj2t1ZuEwes8BAE4KP0WAQWAYhsbkuiRJ9a379b//+FgpibH6yudPVfG4TBmGYXKFAAArYmoVGGRZqfG678ZiuRJj9RS95wAAJ4EgB5igIM+lB75arK9cNlY797XpR8+sl7fHb3ZZAACLYWoVMEmMzaYvnDNCxadlaU9du2IdMQoGg9pV2x6ahgUA4GgIcoDJ0pKdSkt2SpLWb2vQwsWbdH5Rtr5M7zkAwDEwtQpEkImFGZp1wWit21qv+fSeAwAcA0EOiCAHe8899PVzNTonWc++ul1PLd5kdlkAgAjF1CoQgQ72nnvv47pQr7keX0A+f4DecwCAEH4iABHKMAydX5QTelz23m69saFKX7lsrM45LYvecwAAplYBq5hQkKGUxFj9eslmLVj0oeroPQcAQx5BDrCIMbkHes99fqx2VrXpgd+u0dsba8wuCwBgIqZWAQuJsdn0heIRKh6Xpb++sUP5mYmSpEAgKJuNqVYAGGoIcoAFpSU7dctVE0KPny7bqh5fQF++9BSlJDlNrAwAMJiYWgUsLhgMKt0Vp3Xb6jX/f9/TG/SeA4AhgyAHWJxhGLrqojH64X+ep9E5yfrTq9v18LPrVNPUaXZpAIAwI8gBUSInPUH3XDdJN886XZ37fXI6YswuCQAQZtwjB0SRg73nzh2fLZvNUDAY1P8u/ViTThlG7zkAiEKMyAFR6OAK1s5un2oau/TrJZv1OL3nACDqGMFgcEjeFd3U1MEN4ScpMzNZDQ3tZpeBYwgEgnrj/X16aVWFfP6gZk4ZpQyXU0verlSz26N0l1OzSws15ZBdJGAdfB9aG9fP+sJ9DW02QxkZSUc8z9QqEOVsNkOfLx6hsw/0nvvn2j3q8QfV4wtIkprcHj29fKskEeYAwGLCOrX6q1/9SjNnztTMmTP16KOPSpJWr16tWbNmaerUqVqwYEHouVu2bNHs2bM1bdo03XffffL5fJKk6upqzZkzR9OnT9ett96qzs7elXhut1s333yzZsyYoTlz5qihoSGcHwWwvIO955yx9lCIO8jrC+jFleUmVQYAOFFhC3KrV6/W22+/rZdfflmLFy/W5s2btXTpUs2fP18LFy7UsmXLtGnTJq1cuVKSdO+99+rBBx/UihUrFAwGtWjRIknSQw89pOuvv15lZWWaMGGCFi5cKEl64oknVFxcrOXLl+vaa6/Vww8/HK6PAkSVlnbPYY83uz36zd83a08d0zwAYBVhC3KZmZmaN2+eYmNj5XA4VFhYqF27dmnUqFEaMWKE7Ha7Zs2apbKyMlVVVam7u1uTJk2SJM2ePVtlZWXq6enR2rVrNW3atD7HJenNN9/UrFmzJElXXHGFVq1apZ6ennB9HCBqZLgOv/NDrMOmj3c1h0brtu9t1V/f2KHNlc3y9vgHs0QAwHEK2z1yY8eODX29a9cuLV++XDfccIMyMzNDx7OyslRXV6f6+vo+xzMzM1VXV6eWlhYlJSXJbrf3OS6pz2vsdruSkpLU3Nys7Ozs46rvaDcO4vhlZiabXQL66aYrivSrFz6U55Bw5nTE6NvXnqmSycMl9d5X9962Br2+vkor1uxVrN2mCYXDNHlcpmZeOEYOOz3qIgnfh9bG9bM+M69h2Bc77NixQ9/61rf03e9+VzExMdq1a1foXDAYlGEYCgQCffpbHTx+8NdDHakPVjAYlM12/AOMrFo9eay2sqaikamaO32cXlpZ3mfVatHIVDU1dYSed964TE2682Jt29uqTZVN2lzZrJ17WzRlfJZshqF/f1Qje4xNp49OU3JCrImfaGjj+9DauH7WF9WrVtevX6877rhD8+fP18yZM7VmzZo+ixIaGhqUlZWlnJycPscbGxuVlZWl9PR0tbe3y+/3KyYmJvR8qXc0r7GxUTk5OfL5fOrs7FRqamo4Pw4QNaYU5WhKUc4x/wJyxsZoYmGGJhZmSJK6untkO/CPqX+u26s9dR0yJI3KSVbRmHRNHpupgjzXYHwEAIDCeI9cTU2NbrvtNj322GOaOXOmJOnMM89UZWWldu/eLb/fr6VLl6qkpET5+flyOp1av369JGnJkiUqKSmRw+FQcXGxli1bJklavHixSkpKJEmlpaVavHixJGnZsmUqLi6Ww+EI18cBICkh7pPvsQe/eo7um3u2rrpojOx2m5a/u0crP6iS1DtCvurDatXTgBgAwipsDYF/9KMf6cUXX9TIkSNDx6677jqNHj1ajzzyiDwej0pLS/X9739fhmFo69atuv/++9XR0aGioiI98sgjio2NVVVVlebNm6empibl5ubq8ccfV0pKilpbWzVv3jzt3btXycnJeuyxxzR8+PDjro+p1ZPHlID1DeQ17OrukacnoLRkp6oaOvTA79b0/h6pcSoak6EJY9I1flSa4p20rxxIfB9aG9fP+syeWmVnB5ww/gKyvnBdw2AwqPqW/dpU2azNlc3asqdFHq9ft3/pDE0em6lmd7daO7wanZMc2k4MJ4bvQ2vj+lmf2UGOfxoDGHCGYSg7PUHZ6Qm67Ozh8vkDKq9q06ic3pVd//6oRi+/VanEOLtOH52uojHpmjAmXemuOJMrBwBrIcgBCDt7jE3jRqaFHn9ucr6y0hJCq2HXbq2Xw27Tr+66WA57jOpaupSa5JTTQZsTADgaghyAQZecEKvzTs/WeadnKxgMqqqxUzVNXaH+dL/5+2btre/Q2OGpmjCmd8RuRFbSEdsPAcBQFda9VgHgWAzD0PDMJJ1zWlbo2OzSQl129nC5u7x64c1y/d8/rNVvl34cOt+xn11cAEBiRA5ABCoana6i0en6snr3hv14V7NSk3q3Fmvt8Oi/fvVvjchKUlFBuiaMydAp+Sly2D/5d+k7m2v10spyNbk9yjjQ8HhKUY5JnwYAwocgByCipSU7deEZuaHHNpuhL5YUaFNls15ds1fL390jpyNG/+eLE3RGQYb+valGz5Ztk/fAnrFNbo+eXr5VkghzAKIOQQ6ApbgSYnXFBaN1xQWjtd/j09Y9Ldpc2az8YYmSpOdf2xEKcQd5fQG9tLKcIAcg6hDkAFhWvNOuyWMzNXlsZuhYZ7fvsM9tcnskSXUtXYqPtcuVyP6wAKyPIAcgqmS4nKHQ9unjkvSX13ZoY3mThqXEaXSuSwW5Lp2Sn6JThqcMdqkAcNJYtQogqswuLVSsve9fbbF2m2aXFkqSZl0wWv9xySkak+vSrhq3Fv1rp/62sjz03MVvVeiN9/epssYtn7/vFC0ARBpG5ABElYP3wR1p1WphfooK8z8ZfXN3edV5oJ1JIBDUWxtr1NLeO6JnjzE0IitJn5uUr4vPzOt9TjAoG/3sAEQIghyAqDOlKOe4Fza4EmLlSui9X85mM/TY/7lATe5uVda0q7LGrV017tDiibYOj+b/77salZ2sMbmu0H/pLifNigGYgiAHAIcwDEPDUuI1LCW+T5NiSfIHgjq/KEe7atx6de1e+QNBSdI3Z52uKUU5au3waE9dh8bkJis5gcUUAMKPIAcAxyndFacbp46TJPX4AtrX0KGKarfGjUiVJG0sb9IfD/SsG5YSFxqxu2hirpLiHabVDSB6EeQA4AQ47LZQUDvo3PFZykqNV2WNW5U1blVUu7V2a70uPKN3mvetjdXasa9NYw6sls3PTJQ9hjVnAE4cQQ4ABkhcrF2njUrTaaPSQsfau7yhadaWdo8+2NGotzfWSJLsMTYV5rn03esnyzAMtXd5lRjvYDEFgONGkAOAMDr0XrkrLxyjWReMVmNbd2jUztsTCC2U+MWLG1Xd2KnROQcXUiSrIC9FacnOz7zvwf1km90epbOfLDBkEeQAYBAZhqHM1Hhlpsbr3PHZfc5ddtZw7djXpooat1as2SN/IKhJpwzTHddMlCT9c91e5aYnqKGtW399fQf7yQIgyAFApDi/KEfnHwhiPT6/9tR3hKZZu7p9ev61HQoe4bVeX0Avsp8sMOQYwWDwSH8vRLWmpg4FAkPyow+YzMxkNTS0m10GTgLX0Fq6un3aXevW/zz/wRGfMywlTjkZCcpNT1RORoImFmQoIyVuEKtEf/A9aH3hvoY2m6GMjKQjnmdEDgAsIiHOrvGj04+4n2y8M0YFeS7VNndp+95WeXsCuuOaicpIidOW3S164V87lZuRoJz0BOVkJCo3PUE5GQmsnAUsjCAHABYzu7RQTy/fGrpHTurdT/aGqeNCU6uBYFCt7R4lxvX2rzMkJcbZtX1vq97ZXBd63f/92jkamZ2sTRVN2ljRdCDcJSonPUGpSbHsWAFEOIIcAFjMofvJHmnVqs0wlO76ZEr10LYoHq9fdS1dqm3uUk56giSpqrFTqz6slrfnk3AYFxujn912oeKddm3b0yJ3V49y0hOUnRavWEfMYHxUAMdAkAMACzq4n+yJ3J/jjI3RyOxkjcxODh2bdu5IfeGcEWpt96imuUu1TV1qautWvLP3x8S/NlRpzZZ6Sb2jexkpcRqZnaxvzz5DklTT1Km4WDujeMAgI8gBACR9MoqX7opT0ej0Pue+NmO8Lj9/lGqaekfyapu7dOhauT8u36od+9oUFxuj7PQE5aYnaOzwFF1y1nBJkj8QUIyNe/GAgUaQAwAc0+FG8Q71pdJC7a3vCIW8Hfta1e31h4Lc/N+8q2BQvQst0hOUm5GggrwUjco5/PsdbHjc5PYog4bHwBER5AAAJ+3UEak6dURqn2MHWzwFg0FddEauapq6VNPUpR37auTp8etzk/M1N2ecAoGgHvnTemWmxisnI0HtnV6t+rBGPX4aHgPHQpADAISFzdZ7r5xhGJp14ZjQ8WAwqJb2T9qndHl8inPatWNfq979uO4z7yP1Njx+dsU2dXt8ykjpnf7NTI2Xk0UXGOIIcgCAQWV8akVtUrxD//XlSZIkT49ft/5s5WFf1+3169lXt4cef/OK0zVlQo721LVr8VuVynDFHQh5TmWkxGn4sCQ5Ywl6iG4EOQBAxHA6Yo7Y8Djd5dR9Nxar2d2tJne3CvNSJEn7PT41tnVr295W7ff4Qs//3vWTNW5kmjaWN2nFmj19g54rToX5KYzowfIIcgCAiHKkhsdfKi1UWrJTaclOFeanhM6NG5mmH/7nuZJ6tzFrOhD0RmT1bmsUCATl9fm1qbJJbR3e0H61P7llirJS4/XmB1V6e2ON0l1xGnZI2DujIINdLxDxCHIAgIhyaMPj/q5aTYizKyEuKRTiJGnS2GGaNHaYJMnnD6i53aPmtm6lJzslSXGOGMXFxmhvXbs+2NEo34FFFv/vns9Jkl5cWa4PdjSG7s3LcDk1LCVe552ePZAfGzghBDkAQMQ52PB4oNljbMpKjVdWanzo2PlFOTr/wO8VDAbl7upRa7tHDnvvaFxmaryy0uLV5O5WeVWbOrt9ciXGhoLcr5dsUkW1WxkHevBlpMQpLyMh9J6H66F3sL3KkXbmAI4XQQ4AgAMMw1BKYqxSEmNDx0rOzFPJmXmhx91en9q7ekKPx41Ilc0w1OTu1ra9LWr52KNR2cmhIPfjZ9ersa07NHXr6fFr654W+fy9k7y0V8HJMIKHtuYeQpqaOkI9jnBiTmRrIEQWrqH1cQ0jj88f0H6PT8kJvWHwn+v2qrqxU01tvffu1TR1HfZ1NkMqzE9RWnLv/XkXnpErSaqscfeGy6RYdseIQOH+HrTZDGVkJB3xPCNyAAAMIHuMLRTiJOkLxSP6nP/6T9447OsCQSnGZmhXbbvSk3vbs/j8Af330+skSYYhpSY5lZrkVOmk3lFCnz+gddvqlZ4cp7Tk3nMHp4QxNBDkAAAYREdqr5Lhcuq715/1meN3XTtRze0etbg9amn3qKW9W0Zvr2W1tHv0m79/3Of5yQkO/cclp+jCM3Ll7vRq1YfVodW+B/+Li+XHf7TgSgIAMIiO1F5ldmnhZ55rj7FpYuGwI75XWrJTP/rGeWpp96i5vVst7R61tns0LKV3RK++Zb9eWlXxmdfdevUEnXNalvbVd+if6/YeEvLilJ7sVHZ6vBx2euxZAUEOAIBBdGh7lZNdtWqPsSlvWKLyhiUe9vwpw1P06/8qVWuH50DY6/11ZHbvPVctHR5trGiS+5D+epL0/RvO0tjhqfpgZ6NeXbOnN+C5PhnRGz8q7ZijegdX5va3hQz6hyAHAMAgO9heZTAWq8Q6YpSVlqCstITPnDujIEMLvn2RfP6A2jq8oZG9/APBMBgIyucPavveFrV2eOU/sEjw0VunKC7WrhVr9uj19ftCAe/gvXpxzhg99+r20KgjK3PDhyAHAMAQZ4+xKSOltwee9MmuGZNPzdTkUzMl9e6Q4e7qDXtpB5opZ6XF65ThKWpt92hXbbs27GiUzxdQmsvZZ+pYkry+gP6wbIs+3NmoW66aIEnatqdF7V09ciXGKjnBIVdirBKcdhkHbwLEMRHkAADAMdlsRmjV7EGTx2Zq8tjM0ONgMKjObp/u+Plbh30Pnz+o1g5v6PGra/dqw47GPs8ZlhKnR2+9QJL00qpyNbV1KzkhVq7EWLkSYpWZGqdxI9MOvF9gyG+jRpADAAADwjAMJcU7jroyd96cT1bm3jTjNF19sVfuLq/aO71yd/UoxvbJaFyL26Md+9rk7vSGRvgK81y6b26xJOmHf1ynhrb9ch0YzXMlxGrs8FRNP2+kJOnDnY2KtduUfOBcUrxDNlt0jfYR5AAAwIA63pW5yQmxfXrufdp/XnF66GuP1y931yf36UlS6aQ81bfsV3tXbxhsaN3f5/1+98oWdez/ZBcOQ9LFZ+bpphmn9Z5f+rHinfYDQc8hV0Ks8oYlKjs9QQf3SzjSNG+kbLNGkAMAAAPq0JW5A7Vq1Rkbo8zY+D7HLjt7+FFf8705Zx0Y6fOqvatH7k6v8jN7F3L4/AHtqGpTe5dX+z3+0GumnztS/3HpKer2+nX3L98+MK3r6P01IVbnjs9S+/6ePkHVzMUcBDkAADDgDq7MNVP+sETpCK1Z7DE2/eRbUyRJPT5/b9Dr8iohziFJCgalS88arrZOr9q7vGrt8GhvfYdG5SSr7L3dh13M8dLKcoIcAADAYHLYY5TuilG6Ky50LCHOrv+49JTDPv+5f24/7PHD3RcYbkN7qQcAAEA/Zbic/ToeTgQ5AACAfphdWqhYe98IdaRt1sKNqVUAAIB+GMht1k4WQQ4AAKCfBnObtaNhahUAAMCiCHIAAAAWRZADAACwKIIcAACARRHkAAAALIogBwAAYFEEOQAAAIsiyAEAAFgUQQ74/+3cXUhTbwDH8d+G84W6qhQDo6CCupBREDEKLIrITKJ1k0EQESZUVAglXuRNijNkEbFAkIKgV9KMWIGwCG0DqYtGjSgiJWmUNxKhSbrzvxt05f9M28Oj38/Vzi48XzyoP86RAQBgKYYcAACApRhyAAAAlmLIAQAAWIohBwAAYKkC0wGmeL0e0wkLAt9H+3EN7cc1tBvXz37/8hrO9rU9juM4/+zsAAAA+Gd4tAoAAGAphhwAAIClGHIAAACWYsgBAABYiiEHAABgKYYcAACApRhyAAAAlmLIAQAAWIohBwAAYCmGHAAAgKUYcnDt+vXrqqmpUU1NjTo6OkznYA5CoZCamppMZyAHsVhMwWBQ1dXVunz5sukc5KCvry/7uzQUCpnOwf/069cv7d+/X6Ojo5KkeDyu2tpa7dmzR+FwOO89DDm4Eo/HNTg4qN7eXj1+/Fjv379Xf3+/6SzkIJFIqLe313QGcvD161e1tLQoEonoyZMnSqVSevnypeksuDA5OanW1lbdvn1bfX19ev36teLxuOkszOLt27eqq6vT8PCwJOn3799qbm5WJBJRNBrVu3fv8v6zyJCDK6WlTEvXygAAA7NJREFUpWpqalJhYaF8Pp/Wrl2rb9++mc6CS+Pj4wqHw2poaDCdghz09/dr3759Ki8vl8/nUzgclt/vN50FF2ZmZpTJZDQ5Oanp6WlNT0+rqKjIdBZm8eDBA7W0tKisrEySlEwmtXr1aq1atUoFBQWqra3V8+fP89pUkNezwXrr16/Pvh4eHtazZ8909+5dg0XIxaVLl3T+/Hml02nTKcjByMiIfD6fGhoalE6ntWPHDp07d850FlxYunSpzp49q+rqapWUlGjLli3avHmz6SzMorW19a/jHz9+qLS0NHtcVlam79+/57WJO3LIyadPn3T8+HFduHBBa9asMZ0DFx4+fKiVK1cqEAiYTkGOZmZmlEgk1NbWpvv37yuZTPKY3DIfPnzQo0eP9OLFCw0MDMjr9aq7u9t0FlzKZDLyeDzZY8dx/jrOB4YcXHvz5o2OHTumxsZGHTx40HQOXIpGo3r16pUOHDiga9euKRaLqa2tzXQWXFixYoUCgYCWLVum4uJi7d69W8lk0nQWXBgcHFQgENDy5ctVWFioYDCooaEh01lwqby8XGNjY9njsbGx7GPXfOHRKlxJp9M6deqUwuEwd3QsdfPmzezrnp4eDQ0Nqbm52WAR3Nq5c6cuXryonz9/asmSJRoYGNCuXbtMZ8GFDRs26MqVK5qYmFBJSYlisZgqKytNZ8Elv9+vL1++aGRkRBUVFXr69KkOHTqU1waGHFzp7u7W1NSU2tvbs+8dPnxYdXV1BquAxcXv9+vEiRM6cuSI/vz5o23btuX9jwfmZvv27UqlUgoGg/L5fKqsrFR9fb3pLLhUVFSk9vZ2nTlzRlNTU6qqqtLevXvz2uBxHMfJ6xkBAAAwL/gfOQAAAEsx5AAAACzFkAMAALAUQw4AAMBSDDkAAABLMeQAYA5GR0e1adOmv96LRqPaunWrEomEoSoAiwWfIwcA8+jevXuKRCK6deuWNm7caDoHwALHkAOAedLV1aWenh7duXNHFRUVpnMALAI8WgWAedDR0aHOzk4dPXqUEQcgbxhyADBHExMT+vjxo7q6utTZ2alUKmU6CcAiwZADgDkqLi7WjRs3VFVVpZMnT+r06dMaHx83nQVgEWDIAcAceb1e+Xw+SVJ9fb3WrVunxsZGZTIZw2UAFjqGHADMI4/Ho1AopM+fP+vq1aumcwAscB7HcRzTEQAAAHCPO3IAAACWYsgBAABYiiEHAABgKYYcAACApRhyAAAAlmLIAQAAWIohBwAAYKn/APUDpq+c3oOlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = []\n",
    "cluster_data = Normalized_Batting[Normalized_Batting['yearID'] < 2015][normalize_columns]\n",
    "\n",
    "for k in range(1,11):\n",
    "    kmeans = KMeans(k)\n",
    "    \n",
    "    model = kmeans.fit(cluster_data)\n",
    "    \n",
    "    scores.append(abs(model.score(cluster_data)))\n",
    "    \n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(range(1,11), scores, linestyle='--', marker='o', color='b');\n",
    "plt.xlabel('K', fontsize = 12);\n",
    "plt.ylabel('SSE', fontsize = 12);\n",
    "plt.title('SSE vs. K', fontsize = 12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There really isn't a true \"elbow\", but we can see that the drop off starts to slow down right around 3 clusters, so we will start with that. 3 clusters is also easy to interpret (bad, average, great)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(3, random_state = 534)\n",
    "normalized_model = kmeans.fit(Normalized_Batting[Normalized_Batting['yearID'] < 2015][normalize_columns])\n",
    "Normalized_Batting['Cluster'] = kmeans.predict(Normalized_Batting[normalize_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>BB</th>\n",
       "      <th>BA</th>\n",
       "      <th>TOTB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>OPS</th>\n",
       "      <th>RBI/AB</th>\n",
       "      <th>HR/G</th>\n",
       "      <th>R/G</th>\n",
       "      <th>BABIP</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>abreujo02</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.893012</td>\n",
       "      <td>0.944184</td>\n",
       "      <td>0.950965</td>\n",
       "      <td>0.962630</td>\n",
       "      <td>0.941345</td>\n",
       "      <td>0.649517</td>\n",
       "      <td>0.980287</td>\n",
       "      <td>0.986017</td>\n",
       "      <td>0.645348</td>\n",
       "      <td>0.858115</td>\n",
       "      <td>0.978271</td>\n",
       "      <td>0.906075</td>\n",
       "      <td>0.770543</td>\n",
       "      <td>0.882056</td>\n",
       "      <td>0.896178</td>\n",
       "      <td>0.938147</td>\n",
       "      <td>0.894465</td>\n",
       "      <td>0.806352</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>ackledu01</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.487656</td>\n",
       "      <td>0.225228</td>\n",
       "      <td>0.240935</td>\n",
       "      <td>0.208401</td>\n",
       "      <td>0.227814</td>\n",
       "      <td>0.649517</td>\n",
       "      <td>0.452021</td>\n",
       "      <td>0.294393</td>\n",
       "      <td>0.261073</td>\n",
       "      <td>0.254296</td>\n",
       "      <td>0.272754</td>\n",
       "      <td>0.624293</td>\n",
       "      <td>0.207746</td>\n",
       "      <td>0.427178</td>\n",
       "      <td>0.577186</td>\n",
       "      <td>0.475624</td>\n",
       "      <td>0.148745</td>\n",
       "      <td>0.071072</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>adamsma01</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.086919</td>\n",
       "      <td>0.125338</td>\n",
       "      <td>0.105030</td>\n",
       "      <td>0.138707</td>\n",
       "      <td>0.171787</td>\n",
       "      <td>0.181075</td>\n",
       "      <td>0.252825</td>\n",
       "      <td>0.220055</td>\n",
       "      <td>0.152473</td>\n",
       "      <td>0.344339</td>\n",
       "      <td>0.143884</td>\n",
       "      <td>0.352155</td>\n",
       "      <td>0.168276</td>\n",
       "      <td>0.243647</td>\n",
       "      <td>0.691448</td>\n",
       "      <td>0.418345</td>\n",
       "      <td>0.108933</td>\n",
       "      <td>0.370038</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>adriaeh01</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.056884</td>\n",
       "      <td>0.062207</td>\n",
       "      <td>0.085144</td>\n",
       "      <td>0.063128</td>\n",
       "      <td>0.125445</td>\n",
       "      <td>0.315787</td>\n",
       "      <td>0.113006</td>\n",
       "      <td>0.101438</td>\n",
       "      <td>0.216305</td>\n",
       "      <td>0.023639</td>\n",
       "      <td>0.064060</td>\n",
       "      <td>0.029299</td>\n",
       "      <td>0.487597</td>\n",
       "      <td>0.112265</td>\n",
       "      <td>0.275835</td>\n",
       "      <td>0.065389</td>\n",
       "      <td>0.081919</td>\n",
       "      <td>0.022944</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>ahmedni01</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.754571</td>\n",
       "      <td>0.651723</td>\n",
       "      <td>0.548493</td>\n",
       "      <td>0.512608</td>\n",
       "      <td>0.443195</td>\n",
       "      <td>0.953454</td>\n",
       "      <td>0.409213</td>\n",
       "      <td>0.349629</td>\n",
       "      <td>0.456399</td>\n",
       "      <td>0.206100</td>\n",
       "      <td>0.507819</td>\n",
       "      <td>0.264474</td>\n",
       "      <td>0.152462</td>\n",
       "      <td>0.188358</td>\n",
       "      <td>0.146474</td>\n",
       "      <td>0.323001</td>\n",
       "      <td>0.397287</td>\n",
       "      <td>0.132838</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      playerID  yearID         G        AB         R         H        2B  \\\n",
       "214  abreujo02    2015  0.893012  0.944184  0.950965  0.962630  0.941345   \n",
       "280  ackledu01    2015  0.487656  0.225228  0.240935  0.208401  0.227814   \n",
       "423  adamsma01    2015  0.086919  0.125338  0.105030  0.138707  0.171787   \n",
       "563  adriaeh01    2015  0.056884  0.062207  0.085144  0.063128  0.125445   \n",
       "681  ahmedni01    2015  0.754571  0.651723  0.548493  0.512608  0.443195   \n",
       "\n",
       "           3B        HR       RBI        BB        BA      TOTB       SLG  \\\n",
       "214  0.649517  0.980287  0.986017  0.645348  0.858115  0.978271  0.906075   \n",
       "280  0.649517  0.452021  0.294393  0.261073  0.254296  0.272754  0.624293   \n",
       "423  0.181075  0.252825  0.220055  0.152473  0.344339  0.143884  0.352155   \n",
       "563  0.315787  0.113006  0.101438  0.216305  0.023639  0.064060  0.029299   \n",
       "681  0.953454  0.409213  0.349629  0.456399  0.206100  0.507819  0.264474   \n",
       "\n",
       "          OBP       OPS    RBI/AB      HR/G       R/G     BABIP  Cluster  \n",
       "214  0.770543  0.882056  0.896178  0.938147  0.894465  0.806352        0  \n",
       "280  0.207746  0.427178  0.577186  0.475624  0.148745  0.071072        1  \n",
       "423  0.168276  0.243647  0.691448  0.418345  0.108933  0.370038        1  \n",
       "563  0.487597  0.112265  0.275835  0.065389  0.081919  0.022944        1  \n",
       "681  0.152462  0.188358  0.146474  0.323001  0.397287  0.132838        2  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Normalized_Batting[Normalized_Batting['yearID'] == 2015].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the eyeball test, it looks like Cluster 0 has a player who finished high in some offensive categories, so let's take a look at the players in that cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>G</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>abreujo02</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.893012</td>\n",
       "      <td>0.944184</td>\n",
       "      <td>0.950965</td>\n",
       "      <td>0.962630</td>\n",
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       "      <td>0.986017</td>\n",
       "      <td>0.645348</td>\n",
       "      <td>0.858115</td>\n",
       "      <td>0.978271</td>\n",
       "      <td>0.906075</td>\n",
       "      <td>0.770543</td>\n",
       "      <td>0.882056</td>\n",
       "      <td>0.896178</td>\n",
       "      <td>0.938147</td>\n",
       "      <td>0.894465</td>\n",
       "      <td>0.806352</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>altuvjo01</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.893012</td>\n",
       "      <td>0.959705</td>\n",
       "      <td>0.942452</td>\n",
       "      <td>0.987649</td>\n",
       "      <td>0.984972</td>\n",
       "      <td>0.792687</td>\n",
       "      <td>0.664404</td>\n",
       "      <td>0.801746</td>\n",
       "      <td>0.533237</td>\n",
       "      <td>0.959704</td>\n",
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       "      <td>0.334579</td>\n",
       "      <td>0.505657</td>\n",
       "      <td>0.876007</td>\n",
       "      <td>0.783807</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>alvarpe01</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.871173</td>\n",
       "      <td>0.687952</td>\n",
       "      <td>0.710237</td>\n",
       "      <td>0.603955</td>\n",
       "      <td>0.483113</td>\n",
       "      <td>0.181075</td>\n",
       "      <td>0.958424</td>\n",
       "      <td>0.898336</td>\n",
       "      <td>0.790127</td>\n",
       "      <td>0.372314</td>\n",
       "      <td>0.760338</td>\n",
       "      <td>0.806642</td>\n",
       "      <td>0.654692</td>\n",
       "      <td>0.769483</td>\n",
       "      <td>0.942353</td>\n",
       "      <td>0.904490</td>\n",
       "      <td>0.496696</td>\n",
       "      <td>0.335571</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.907542</td>\n",
       "      <td>0.946265</td>\n",
       "      <td>0.977674</td>\n",
       "      <td>0.960878</td>\n",
       "      <td>0.993262</td>\n",
       "      <td>0.792687</td>\n",
       "      <td>0.999621</td>\n",
       "      <td>0.999544</td>\n",
       "      <td>0.552365</td>\n",
       "      <td>0.837220</td>\n",
       "      <td>0.995409</td>\n",
       "      <td>0.989129</td>\n",
       "      <td>0.603575</td>\n",
       "      <td>0.942947</td>\n",
       "      <td>0.994354</td>\n",
       "      <td>0.996297</td>\n",
       "      <td>0.944242</td>\n",
       "      <td>0.410093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4832</th>\n",
       "      <td>bautijo02</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.887820</td>\n",
       "      <td>0.875558</td>\n",
       "      <td>0.992659</td>\n",
       "      <td>0.814951</td>\n",
       "      <td>0.856224</td>\n",
       "      <td>0.649517</td>\n",
       "      <td>0.999183</td>\n",
       "      <td>0.996524</td>\n",
       "      <td>0.999927</td>\n",
       "      <td>0.462206</td>\n",
       "      <td>0.964287</td>\n",
       "      <td>0.961640</td>\n",
       "      <td>0.977524</td>\n",
       "      <td>0.980771</td>\n",
       "      <td>0.993850</td>\n",
       "      <td>0.995100</td>\n",
       "      <td>0.987390</td>\n",
       "      <td>0.070654</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerID  yearID         G        AB         R         H        2B  \\\n",
       "214   abreujo02    2015  0.893012  0.944184  0.950965  0.962630  0.941345   \n",
       "1469  altuvjo01    2015  0.893012  0.959705  0.942452  0.987649  0.984972   \n",
       "1533  alvarpe01    2015  0.871173  0.687952  0.710237  0.603955  0.483113   \n",
       "2285  arenano01    2015  0.907542  0.946265  0.977674  0.960878  0.993262   \n",
       "4832  bautijo02    2015  0.887820  0.875558  0.992659  0.814951  0.856224   \n",
       "\n",
       "            3B        HR       RBI        BB        BA      TOTB       SLG  \\\n",
       "214   0.649517  0.980287  0.986017  0.645348  0.858115  0.978271  0.906075   \n",
       "1469  0.792687  0.664404  0.801746  0.533237  0.959704  0.966243  0.767945   \n",
       "1533  0.181075  0.958424  0.898336  0.790127  0.372314  0.760338  0.806642   \n",
       "2285  0.792687  0.999621  0.999544  0.552365  0.837220  0.995409  0.989129   \n",
       "4832  0.649517  0.999183  0.996524  0.999927  0.462206  0.964287  0.961640   \n",
       "\n",
       "           OBP       OPS    RBI/AB      HR/G       R/G     BABIP  Cluster  \n",
       "214   0.770543  0.882056  0.896178  0.938147  0.894465  0.806352        0  \n",
       "1469  0.744624  0.780851  0.334579  0.505657  0.876007  0.783807        0  \n",
       "1533  0.654692  0.769483  0.942353  0.904490  0.496696  0.335571        0  \n",
       "2285  0.603575  0.942947  0.994354  0.996297  0.944242  0.410093        0  \n",
       "4832  0.977524  0.980771  0.993850  0.995100  0.987390  0.070654        0  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Normalized_Batting[(Normalized_Batting['yearID'] == 2015) & (Normalized_Batting['Cluster'] ==0)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalized Data Results\n",
    "Looking at the players in Cluster 2, we do get the top tier players including the MVP's Josh Donaldson and Bryce Harper. However, if we take a look at how many players are in each cluster, it is a little too evened out between the three. It says that Cluster 2 has 25% of the players from the 2015 season in that category. We want that cluster to only be the cream of the crop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.402353\n",
       "2    0.345882\n",
       "0    0.251765\n",
       "Name: Cluster, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Normalized_Batting[(Normalized_Batting['yearID'] == 2015)]['Cluster'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Approach # 2\n",
    "\n",
    "What if we really identify which players are the cream of the crop? I decided to make one tweak to this dataframe, and that was to replace each column with indicator flags whether they were in the top 10th percentile for that season. That way, it will be a lot more black and white on which players are considered the best in the league"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "Top10Percentiles_Batting = Normalized_Batting.copy()\n",
    "\n",
    "for col in normalize_columns:\n",
    "    Top10Percentiles_Batting[col] = np.array(Top10Percentiles_Batting[col] > .9,int)\n",
    "    \n",
    "Top10Percentiles_Batting['Total'] = Top10Percentiles_Batting[normalize_columns].sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>playerID</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2283</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerID  yearID  G  AB  R  H  2B  3B  HR  RBI  BB  BA  TOTB  SLG  OBP  \\\n",
       "2283  arenano01    2013  0   0  0  0   0   0   0    0   0   0     0    0    0   \n",
       "2284  arenano01    2014  0   0  0  0   1   0   0    0   0   0     0    1    0   \n",
       "2285  arenano01    2015  1   1  1  1   1   0   1    1   0   0     1    1    0   \n",
       "2286  arenano01    2016  1   1  1  1   1   1   1    1   1   0     1    1    0   \n",
       "2287  arenano01    2017  1   1  1  1   1   1   1    1   0   1     1    1    0   \n",
       "2288  arenano01    2018  1   1  1  1   1   0   1    1   1   1     1    1    1   \n",
       "\n",
       "      OPS  RBI/AB  HR/G  R/G  BABIP  Cluster  Total  \n",
       "2283    0       0     0    0      0        2      0  \n",
       "2284    0       0     1    0      0        0      3  \n",
       "2285    1       1     1    1      0        0     13  \n",
       "2286    1       1     1    1      0        0     15  \n",
       "2287    1       1     1    1      0        0     15  \n",
       "2288    1       1     1    1      0        0     16  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top10Percentiles_Batting[Top10Percentiles_Batting['playerID'] == 'arenano01']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see that in 2015, Nolan was in the top 10 percentile for 13 of the 18 categories we are interested in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets try the clustering method again but on the percentiles data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = []\n",
    "cluster_data = Top10Percentiles_Batting[Top10Percentiles_Batting['yearID'] < 2015][normalize_columns]\n",
    "\n",
    "for k in range(1,11):\n",
    "    kmeans = KMeans(k)\n",
    "    \n",
    "    model = kmeans.fit(cluster_data)\n",
    "    \n",
    "    scores.append(abs(model.score(cluster_data)))\n",
    "    \n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(range(1,11), scores, linestyle='--', marker='o', color='b');\n",
    "plt.xlabel('K', fontsize = 12);\n",
    "plt.ylabel('SSE', fontsize = 12);\n",
    "plt.title('SSE vs. K', fontsize = 12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, we are seeing that 3 clusters is a good options with this problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(3, random_state = 534)\n",
    "model = kmeans.fit(Top10Percentiles_Batting[Top10Percentiles_Batting['yearID'] < 2015][normalize_columns ])\n",
    "Top10Percentiles_Batting['Cluster'] = model.predict(Top10Percentiles_Batting[normalize_columns ])\n",
    "Top10Percentiles_Batting = pd.merge(Top10Percentiles_Batting, People)[['playerID','nameFirst','nameLast', 'yearID', 'G', 'AB', 'R', 'H', '2B', '3B', 'HR', 'RBI',\n",
    "       'BB', 'BA', 'TOTB', 'SLG', 'OBP', 'OPS', 'RBI/AB', 'HR/G', 'R/G',\n",
    "       'BABIP', 'Cluster', 'Total']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So in this example, it looks like Cluster 2 is the cluster that has the most top tier players, including Arenado!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>playerID</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>yearID</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>BB</th>\n",
       "      <th>BA</th>\n",
       "      <th>TOTB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>OPS</th>\n",
       "      <th>RBI/AB</th>\n",
       "      <th>HR/G</th>\n",
       "      <th>R/G</th>\n",
       "      <th>BABIP</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40101</th>\n",
       "      <td>goldspa01</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Goldschmidt</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40296</th>\n",
       "      <td>troutmi01</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Trout</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39292</th>\n",
       "      <td>vottojo01</td>\n",
       "      <td>Joey</td>\n",
       "      <td>Votto</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40258</th>\n",
       "      <td>rizzoan01</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>Rizzo</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40472</th>\n",
       "      <td>harpebr03</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Harper</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40407</th>\n",
       "      <td>donaljo02</td>\n",
       "      <td>Josh</td>\n",
       "      <td>Donaldson</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40375</th>\n",
       "      <td>cespeyo01</td>\n",
       "      <td>Yoenis</td>\n",
       "      <td>Cespedes</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40630</th>\n",
       "      <td>arenano01</td>\n",
       "      <td>Nolan</td>\n",
       "      <td>Arenado</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40361</th>\n",
       "      <td>carpema01</td>\n",
       "      <td>Matt</td>\n",
       "      <td>Carpenter</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38103</th>\n",
       "      <td>cruzne01</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>Cruz</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID nameFirst     nameLast  yearID  G  AB  R  H  2B  3B  HR  RBI  \\\n",
       "40101  goldspa01      Paul  Goldschmidt    2015  1   1  1  1   1   0   1    1   \n",
       "40296  troutmi01      Mike        Trout    2015  1   1  1  1   1   1   1    1   \n",
       "39292  vottojo01      Joey        Votto    2015  1   0  1  1   1   0   1    1   \n",
       "40258  rizzoan01   Anthony        Rizzo    2015  1   1  1  1   1   0   1    1   \n",
       "40472  harpebr03     Bryce       Harper    2015  0   0  1  1   1   0   1    1   \n",
       "40407  donaljo02      Josh    Donaldson    2015  1   1  1  1   1   0   1    1   \n",
       "40375  cespeyo01    Yoenis     Cespedes    2015  1   1  1  1   1   1   1    1   \n",
       "40630  arenano01     Nolan      Arenado    2015  1   1  1  1   1   0   1    1   \n",
       "40361  carpema01      Matt    Carpenter    2015  0   1  1  1   1   0   1    1   \n",
       "38103   cruzne01    Nelson         Cruz    2015  0   1  1  1   0   0   1    1   \n",
       "\n",
       "       BB  BA  TOTB  SLG  OBP  OPS  RBI/AB  HR/G  R/G  BABIP  Cluster  Total  \n",
       "40101   1   1     1    1    1    1       1     1    1      1        2     17  \n",
       "40296   1   1     1    1    1    1       0     1    1      0        2     16  \n",
       "39292   1   1     1    1    1    1       0     1    1      1        2     15  \n",
       "40258   1   0     1    1    1    1       1     1    1      0        2     15  \n",
       "40472   1   1     1    1    1    1       1     1    1      1        2     15  \n",
       "40407   1   0     1    1    0    1       1     1    1      0        2     14  \n",
       "40375   0   0     1    1    0    0       1     1    1      0        2     13  \n",
       "40630   0   0     1    1    0    1       1     1    1      0        2     13  \n",
       "40361   1   0     1    1    1    1       0     1    1      0        2     13  \n",
       "38103   1   1     1    1    0    1       0     1    1      1        2     13  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top10Percentiles_Batting[Top10Percentiles_Batting['yearID'] == 2015].sort_values('Total',ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what we wanted! Only 7.5% of the player population in 2015 fits into cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.818594\n",
       "1    0.102041\n",
       "2    0.079365\n",
       "Name: Cluster, dtype: float64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top10Percentiles_Batting[Top10Percentiles_Batting['yearID'] == 2015]['Cluster'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# So what makes Cluster 2 so special?\n",
    "We have shown that our clustering algorithm does a great job separating out the best players from the rest of the pack, but taking a closer look shows that not all players have the same fields filled out. These are still very good players, but what makes them so similar to the other big time players who lead in almost every offensive category?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>playerID</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>yearID</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>BB</th>\n",
       "      <th>BA</th>\n",
       "      <th>TOTB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>OPS</th>\n",
       "      <th>RBI/AB</th>\n",
       "      <th>HR/G</th>\n",
       "      <th>R/G</th>\n",
       "      <th>BABIP</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41315</th>\n",
       "      <td>sanomi01</td>\n",
       "      <td>Miguel</td>\n",
       "      <td>Sano</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40047</th>\n",
       "      <td>dudalu01</td>\n",
       "      <td>Lucas</td>\n",
       "      <td>Duda</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39898</th>\n",
       "      <td>stantmi03</td>\n",
       "      <td>Giancarlo</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38812</th>\n",
       "      <td>raburry01</td>\n",
       "      <td>Ryan</td>\n",
       "      <td>Raburn</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41058</th>\n",
       "      <td>peralda01</td>\n",
       "      <td>David</td>\n",
       "      <td>Peralta</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36871</th>\n",
       "      <td>cabremi01</td>\n",
       "      <td>Miguel</td>\n",
       "      <td>Cabrera</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39857</th>\n",
       "      <td>poseybu01</td>\n",
       "      <td>Buster</td>\n",
       "      <td>Posey</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39057</th>\n",
       "      <td>gonzaca01</td>\n",
       "      <td>Carlos</td>\n",
       "      <td>Gonzalez</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38538</th>\n",
       "      <td>braunry02</td>\n",
       "      <td>Ryan</td>\n",
       "      <td>Braun</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38537</th>\n",
       "      <td>braunry01</td>\n",
       "      <td>Ryan</td>\n",
       "      <td>Braun</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        playerID  nameFirst  nameLast  yearID  G  AB  R  H  2B  3B  HR  RBI  \\\n",
       "41315   sanomi01     Miguel      Sano    2015  0   0  0  0   0   0   0    0   \n",
       "40047   dudalu01      Lucas      Duda    2015  0   0  0  0   1   0   1    0   \n",
       "39898  stantmi03  Giancarlo   Stanton    2015  0   0  0  0   0   0   1    0   \n",
       "38812  raburry01       Ryan    Raburn    2015  0   0  0  0   0   0   0    0   \n",
       "41058  peralda01      David   Peralta    2015  0   0  0  0   0   1   0    1   \n",
       "36871  cabremi01     Miguel   Cabrera    2015  0   0  0  0   0   0   0    0   \n",
       "39857  poseybu01     Buster     Posey    2015  0   0  0  1   0   0   0    1   \n",
       "39057  gonzaca01     Carlos  Gonzalez    2015  0   0  1  0   0   0   1    1   \n",
       "38538  braunry02       Ryan     Braun    2015  0   0  1  0   0   0   1    1   \n",
       "38537  braunry01       Ryan     Braun    2015  0   0  1  0   0   0   1    1   \n",
       "\n",
       "       BB  BA  TOTB  SLG  OBP  OPS  RBI/AB  HR/G  R/G  BABIP  Cluster  Total  \n",
       "41315   0   0     0    1    1    1       1     1    0      1        2      6  \n",
       "40047   1   0     0    0    1    1       0     1    0      0        2      6  \n",
       "39898   0   0     0    1    0    1       1     1    1      0        2      6  \n",
       "38812   0   1     0    1    1    1       1     0    0      1        2      6  \n",
       "41058   0   1     0    1    0    1       1     0    0      1        2      7  \n",
       "36871   1   1     0    1    1    1       1     0    0      1        2      7  \n",
       "39857   0   1     1    0    1    1       1     0    0      0        2      7  \n",
       "39057   0   0     1    1    0    0       1     1    0      0        2      7  \n",
       "38538   0   0     1    0    0    0       1     1    1      0        2      7  \n",
       "38537   0   0     1    0    0    0       1     1    1      0        2      7  "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top10Percentiles_Batting[(Top10Percentiles_Batting['yearID'] == 2015) & (Top10Percentiles_Batting['Cluster'] == 2)\n",
    "                        ].sort_values('Total').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets compare how much each category is populated for each cluster in the 2015 season. As you can see, columns like RBI, OBP, OPS, SLG, and HR are fields that Cluster 2 outweigh Clusters 0 and 1 by a large margin. However, it looks like OPS has the largest spread between clusters, which is probably why OPS is considered one of the best metrics to use today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>BB</th>\n",
       "      <th>BA</th>\n",
       "      <th>TOTB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>OPS</th>\n",
       "      <th>RBI/AB</th>\n",
       "      <th>HR/G</th>\n",
       "      <th>R/G</th>\n",
       "      <th>BABIP</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearID</th>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015</th>\n",
       "      <th>0</th>\n",
       "      <td>0.005540</td>\n",
       "      <td>0.016620</td>\n",
       "      <td>0.008310</td>\n",
       "      <td>0.013850</td>\n",
       "      <td>0.033241</td>\n",
       "      <td>0.060942</td>\n",
       "      <td>0.041551</td>\n",
       "      <td>0.022161</td>\n",
       "      <td>0.019391</td>\n",
       "      <td>0.047091</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>0.038781</td>\n",
       "      <td>0.024931</td>\n",
       "      <td>0.024931</td>\n",
       "      <td>0.066482</td>\n",
       "      <td>0.047091</td>\n",
       "      <td>0.047091</td>\n",
       "      <td>0.063712</td>\n",
       "      <td>0.584488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.355556</td>\n",
       "      <td>0.288889</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.644444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>0.088889</td>\n",
       "      <td>0.355556</td>\n",
       "      <td>0.088889</td>\n",
       "      <td>6.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.342857</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>0.342857</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>10.542857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       G        AB         R         H        2B        3B  \\\n",
       "yearID Cluster                                                               \n",
       "2015   0        0.005540  0.016620  0.008310  0.013850  0.033241  0.060942   \n",
       "       1        0.488889  0.888889  0.666667  0.755556  0.622222  0.333333   \n",
       "       2        0.342857  0.428571  0.685714  0.514286  0.428571  0.085714   \n",
       "\n",
       "                      HR       RBI        BB        BA      TOTB       SLG  \\\n",
       "yearID Cluster                                                               \n",
       "2015   0        0.041551  0.022161  0.019391  0.047091  0.002770  0.038781   \n",
       "       1        0.222222  0.355556  0.288889  0.177778  0.644444  0.000000   \n",
       "       2        0.828571  0.857143  0.657143  0.342857  0.800000  0.742857   \n",
       "\n",
       "                     OBP       OPS    RBI/AB      HR/G       R/G     BABIP  \\\n",
       "yearID Cluster                                                               \n",
       "2015   0        0.024931  0.024931  0.066482  0.047091  0.047091  0.063712   \n",
       "       1        0.088889  0.000000  0.044444  0.088889  0.355556  0.088889   \n",
       "       2        0.628571  0.828571  0.685714  0.771429  0.628571  0.285714   \n",
       "\n",
       "                    Total  \n",
       "yearID Cluster             \n",
       "2015   0         0.584488  \n",
       "       1         6.111111  \n",
       "       2        10.542857  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top10Percentiles_Batting[(Top10Percentiles_Batting['yearID'] == 2015)\n",
    "                        ].groupby(['yearID', 'Cluster']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to look at this is through a heatmap. The values are shown as the percentage of players in that cluster to be in the top 10% of that offensive category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "plt.title('Offensive Categories vs Cluster Heat Map', fontsize = 14, fontweight = \"bold\")\n",
    "#sns.set(font_scale=1)  # crazy big\n",
    "sns.heatmap(Top10Percentiles_Batting[(Top10Percentiles_Batting['yearID'] == 2015)\n",
    "                        ].groupby(['Cluster']).mean(\n",
    "        )[['G', 'AB', 'R', 'H', '2B', '3B', 'HR', 'RBI',\n",
    "       'BB', 'BA', 'TOTB', 'SLG', 'OBP', 'OPS', 'RBI/AB', 'HR/G', 'R/G',\n",
    "       'BABIP']].transpose(), cmap = 'coolwarm', annot=True, );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Does salary line up with our findings?\n",
    "Another quick way to evaluate the value of players is to see which players are getting paid the most. You would assume that the players who are getting paid the most are the best players. To double check that we are looking at the right subset of players, we can see how these clusters compare in average salary over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will perge the Top 10 Percentiles data frame with the Salaries data. Similar to what we had to do with\n",
    "# the batting information, I decided to take the max salary for each person for each year. \n",
    "# From there, I took the average salary for each cluster\n",
    "mean_salaries = pd.merge(Top10Percentiles_Batting, Salaries[['yearID','playerID','salary']\n",
    "                        ].groupby(['yearID','playerID'], as_index = False).max()).groupby(['Cluster','yearID']\n",
    "                                                , as_index = False).mean()[['Cluster','yearID','salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (13,8))\n",
    "\n",
    "# For each cluster, plot the average salary over time\n",
    "for cluster in [0,1,2]:\n",
    "    df = mean_salaries[mean_salaries['Cluster'] == cluster][['yearID','salary']]\n",
    "    df.set_index('yearID', inplace = True)\n",
    "\n",
    "    (df['salary'] / 10000000).plot(label = 'Cluster ' +  str(cluster), linewidth = 4)\n",
    "    \n",
    "plt.legend(fontsize = 16,bbox_to_anchor=(1, 1))\n",
    "plt.ylabel(\"Average salary in millions of dollars\", fontsize = 14)\n",
    "plt.xlabel(\"Year\", fontsize = 14)\n",
    "plt.title(\"Average Salary Per Cluster Over Time\", fontsize = 14, fontweight = \"bold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like I anticipated, these trends have consistently shown since 1985 that Cluster 2 has been paid the most on average due to having the best players. It is also interesting to see the different times where clusters have come close to each other and which other times the clusters have had their biggest peaks (look at the early 2000's when the steriod era was at its peak. You needed players who could hit the ball a mile)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\", {'axes.grid' : True})\n",
    "Top10Percentiles_Batting[Top10Percentiles_Batting['Cluster'] == 2][\n",
    "    ['nameFirst','nameLast','yearID','Total']].groupby(['yearID']).mean()[['Total']\n",
    "    ].plot(figsize = (15,8), linewidth = 3,color='blue')\n",
    "plt.ylabel('Average Total Amount',fontsize = 16, fontweight = 'bold')\n",
    "plt.xlabel('Year',fontsize = 16, fontweight = 'bold')\n",
    "plt.yticks(fontsize = 12, fontweight = 'bold')\n",
    "plt.xticks(fontsize = 12, fontweight = 'bold')\n",
    "plt.title('Average Total Top 5% For Top Cluster',fontsize = 18, fontweight = 'bold');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Discussion\n",
    "\n",
    "The main question that we were trying to answer was \"Perform a player scoring to indicate the value of the players in the dataset\". We were able to answer these questions by doing the following:\n",
    "\n",
    "1. Normalize the data for each player each year to see what percentile they finished for that season\n",
    "2. Use clustering methods to find groups of players that fit together, and which ones were considered the best for that season (which happened to be Cluster 2)\n",
    "3. Show which offensive categories are most common in each cluster group of players, showing that players who are more advanced in the categories pertaining to power are considered to be the best hitters\n",
    "4. Show that over time, that group of high power players on average are paid at a higher rate than other players. We should assume that people who have to pay these players understand which ones are the best in the league\n",
    "\n",
    "\n",
    "# Why I chose clustering\n",
    "\n",
    "The clustering algorithm was my choice in method because there is no correct \"output\" that we can compare against. Clustering is great to split objects into different groups, which is how I could separate the top players from the rest of the population. I also made some other modifications, like using flags of the top 10th percentile for each offensive category to better limit the top players.\n",
    "\n",
    "# Limitations\n",
    "\n",
    "The limitations of the clustering algorithm at this point is that it can only look back at the full year. When the new year starts, everyone will have 0's in each category, making it difficult to understand who truly the best players are until the end of the year. \n",
    "\n",
    "Another limitation with determining which players are the best only using offensive categories are outside influences. For example Paul Goldshmidt showed every indication that he should have been the MVP with the data that was used. However, he is a first basemen and doesn't bring much value to defense like a middle infielder our outfielder, which is probably why he got beat out by Bryce Harper. Another outside influncer could be the location of where the player plays his home games. Do players from Colorado have a higher likelihood of being in the top cluster because they play at hitter friendly fields? \n",
    "\n",
    "# Next Steps\n",
    "\n",
    "Like I mentioned in the limitations section, doing more analysis on which players are in the top cluster could be rephrased to \"should they belong to the top cluster?\". By doing more analysis on players that have inflated offensive numbers because of their physical surroundings could be interesting to show how players would benefit / impede a players ability to be considered a top hitter. \n",
    "\n",
    "Another great analsis that could be done with the correct data could be a \"real time\" project to evaluate players throghout the season. This scenario could show what statistics a player would need X games into the season to be in the top cluster. Is a player on pace to be a top player this year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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