Fall Yu-Gi-Oh Tournament Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yu-Gi-Oh Fall Tournament Data Analysis \n",
    "By: Jacob Powell, Date: December 5th, 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project we will be looking through some information on a local card shops Yu-Gi-Oh Tournaments. This data has been collected from the tournament managers and has been allowed to be used for this project. \n",
    "\n",
    "In this project we will be looking to find the outlier deck among the data given. While yet trying to decipher the information trying to find the deck that seems to be causing the most problems among the local tournaments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import matplotlib\n",
    "import numpy\n",
    "import matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this we will first start out at looking at our information from the excel sheet given and try to decipher the information. Once done we will try and form some way to determine a better measurement to help calculate outliers later on in this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = pandas.read_excel('FNG_Fall_Deck_Data.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking at the data I decided to compute the PPE or Point Per Entry Average so as to help in assisting with figuring the normality later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "table.Points = table.Points/3\n",
    "PPE = []\n",
    "for x in range(len(table)):\n",
    "    PPE.append(table.Points[x]/table.Entries[x])\n",
    "table['PPE'] = PPE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once done calculating the PPE I decided to calculate the normality so as to be able to use that information for a data matrix. This data matrix will help us determine the distances from each point and will allow us to see all potential possibilities in combination. However we must first determine the quantifiable factors for the data to be used to determine the normality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "quant = table.iloc[:, 1:]\n",
    "norm = (quant - quant.mean())/quant.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However before moving forward into making the matrix. I have to make a basic function that will help calculate the distance between two points. Once done I can form the matrix so as to help assist in showing the distances between the two points using eulicidean distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a Distance function:\n",
    "def dist(i,j):     # The Function that works with finding the distance between i and j\n",
    "    quant = table.iloc[:, 1:] #the quantitative cells\n",
    "    cat = table.iloc[:, :0]   #the categorical cells\n",
    "    norm = (quant - quant.mean())/quant.std() #normalized cells for the quant cells\n",
    "    hdistance = (cat.iloc[i] != cat.iloc[j]).sum()\n",
    "    \n",
    "    qdistance = ((norm.iloc[i] - norm.iloc[j]).abs()).sum()\n",
    "    return hdistance + qdistance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = pandas.DataFrame(index = table.index, columns = table.index, dtype = float)\n",
    "for i in range(len(matrix.index)):\n",
    "    for j in range(i):\n",
    "        matrix.iloc[i,j] = dist(i,j)\n",
    "        matrix.iloc[j,i] = dist(j,i)\n",
    "# matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the matrix is made we have to move on to deciphering this information and try to see what deck might be an outlier among the data. Once identified we can test it against the centroids and if it is higher or is the centroid of the highest group we can see that it would be a problematic deck for others to face at local tournaments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deciphering the Data Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the information from the matrix we will be trying to calculate outliers among the groups and form a basic heatmap and a hexbin heatmap, a heatmap that uses hexagonal shapes instead of breaking it into small sections of squares. With the use of these heatmaps we will be able to see the distinguishable factors among decks and their distances from one another due to their colorations.\n",
    "\n",
    "Yet when using the hexbin heatmap, we will be able to use it to see if the data follows any form of potential linearity. However when identifying the outliers, we can possibly remove them so as to help with the normality of the information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a470c057b8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "correlation = matrix.corr()\n",
    "matplotlib.pyplot.figure(figsize = (15,10))\n",
    "label_ticks = range(-1, 2)\n",
    "correlmap = seaborn.heatmap(correlation, vmin = -1, vmax = 1, xticklabels=correlation.index, yticklabels=correlation.index, cbar_kws={'ticks': label_ticks})\n",
    "correlmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x1a47120d048>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.pyplot.hexbin(correlation.index, correlation.index, cmap = matplotlib.pyplot.cm.Reds, gridsize= 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from our data above, shown by the heatmap and the hexbin heatmap our data has some varying changes in distance from one another while also following a basic linear form. However what does this mean for the actual data?\n",
    "\n",
    "The data now can help us show the form of linearity and some potential outliers among them: Danger Darkworld, Gouki Knightmare, Pendulum Magician, and Nekroz. With this list of potential outliers we can compare it to others verified by a basic scatter plot of normality and compare those points to a basic line of regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let us see if this information follows any form of normality in a normal probability plot. If it doesn't follow the line of regression we might have to reform our idea, and start again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1a46e100860>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg_param1 = numpy.polyfit(norm.Entries, norm.Points, 1)\n",
    "slope1 = reg_param1[0]\n",
    "intercept1 = reg_param1[1]\n",
    "estimate1 = slope1*norm.Entries + intercept1\n",
    "Error1 = norm.Points - estimate1\n",
    "\n",
    "scatter2 = norm.plot.scatter(x = 'Entries', y = 'Points', c = abs(Error1),figsize = (10,8), cmap = matplotlib.pyplot.cm.rainbow, alpha = 0.9)\n",
    "scatter2.add_line(matplotlib.lines.Line2D(norm.Entries, estimate1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the data most of the data entries follow the normality of the data, aside from one which seems to be an outlier. Mekk-Knight Invoked seems to be an outlier, however why is this?\n",
    "\n",
    "This may be due to several different factors, such as falsified information, deck builds, the possibility of merging two or more decks, etc. However it does seem extremely weird that this deck is this far away that it is registering in a different color spectrum than the rest of the decks.\n",
    "\n",
    "Side note: When the coloration of the dot becomes closer and closer to red, it means it is farther from the line of normality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.cluster\n",
    "import sklearn.manifold\n",
    "import nose.tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will try to form the clusters and figure out if there is anything that would be abnormal aside from our potential list of outliers. If a potential outlier is within its own group and is farther away from the other groups this will guarantee the hypothesis that it is the most problematic deck due to its strength, recovery, or general resourcefulness due to potential power creep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = sklearn.cluster.KMeans(n_clusters = 5)\n",
    "labels = km.fit_predict(norm)\n",
    "new_table = table.copy()\n",
    "new_table['Cluster'] = labels\n",
    "new_table = new_table.sort_values(by = 'Cluster', ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdsinits = norm.loc[:, ['Entries', 'Points']]\n",
    "ms = sklearn.manifold.MDS(n_components = 2, dissimilarity = 'euclidean', \\\n",
    "                         eps = 0, max_iter = 1000, n_init = 1)\n",
    "#---------------------------------------------------------------------------\n",
    "red_data = pandas.DataFrame(ms.fit_transform(norm, mdsinits), columns = ['X', 'Y'])\n",
    "dist_table = pandas.DataFrame(columns = ['Name', 'X', 'Y'])\n",
    "dist_table.Name = table.Name\n",
    "dist_table.X = red_data.X\n",
    "dist_table.Y = red_data.Y\n",
    "new_table['X'] = abs(red_data.X)\n",
    "new_table['Y'] = abs(red_data.Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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M1MhOmHnuQ9J85RaSEEI3cCfQNTfOX8cYP5zXeFq4EAJuAi9pofI8IpkCXhdjHAshVIC7QghfjDH+U45jSpIaLLeQxBgjMDb3bmXuzQd7S1KLyfVMagihHEL4NnAQuC3GeE+e40mSGi/XkMQYZ2OMW4H1wJUhhMtO/ZwQws0hhO0hhO3Dw8N5TkeSlIOGXNsZYzwGfBV44xl+75YY47YY47ahoaFGTEeSVEe5hSSEMBRCWDr36x7g9cAjeY0nSSpGnldtrQU+FUIokwXrr2KMX8hxPElSAfK8auu7wBV5vb4kqTm4/4UkKYkhkSQlMSSSpCSGRJKUxJBIkpIYEklSEkMiSUpiSCRJSQyJJCmJIZEkJTEkkqQkhkSSlMSQSJKSGBJJUhJDIklKYkgkSUkMiSQpiSGRJCUxJJKkJIZEkpTEkEiSkhgSSVISQyKdxbdGqrzi60dZedth3nLfCIena0VPSWpKhkQ6g6enatxwzwjfHZ3l8Ezky4dmeNN9x4ueltSUDIl0BncdmQHi99+fifCt41WOz3hUIp3KkEhn0N8RqMWTPxaB7nIoZD5SMzMk0hn80IoKL+3voGfub0hfGT54fg+dJUMinaqj6AlIzahSCtz5qkH+eM8kO8dnec3yCj+2pqvoaUlNyZBIZ9FdDrxvU0/R05CanktbkqQkhkSSlMSQSJKSGBJJUhJDIklKYkgkSUkMiSQpiSGRJCUxJJKkJIZEkpTEkEiSkhgSSVISQyJJSmJIJElJDIkkKYkhkSQlMSSSpCSGRJKU5KwhCSHcGkLY1LipSJIWoxc6Ivkk8OUQwodCCJUGzUeStMh0nO03Yox/FUL4B+A/AdtDCH8O1J73+7/TgPlJkprcWUMyZwY4AXQBAzwvJJIkwQuEJITwRuB3gL8DfiDGON6wWUmSFo0XOiL5EPATMcYHGzUZSdLi80LnSK5t5EQkSYuT95FIkpIYEklSEkMiSUpiSCRJSQyJJCmJIZEkJTEkkqQkhkSSlMSQSJKSGBJJUhJDIklKYkgkSUkMiSQpiSGRJCUxJJKkJIZEkpTEkEiSkhgSSVISQyJJSmJIJElJDIkkKYkhkSQlMSSSpCSGRJKUxJBIkpIYEklSEkMiSUpiSCRJSQyJJCmJIZEkJTEkkqQkhkSSlMSQSJKSGBJJUhJDIklKYkgkSUkMiSQpiSGRJCUxJJKkJIZEkpQkt5CEEDaEEP4xhPBwCOHBEMIv5DWWJKk4HTm+dhX45RjjAyGEAeD+EMJtMcaHchxTktRguR2RxBgPxBgfmPv1KPAwsC6v8SRJxWjIOZIQwibgCuCeRownSWqc3EMSQugHPgf8Yozx+Bl+/+YQwvYQwvbh4eG8pyNJqrNcQxJCqJBF5C9ijP/nTJ8TY7wlxrgtxrhtaGgoz+lIknKQ51VbAfgE8HCM8XfyGkeSVKw8j0iuAW4CXhdC+Pbc25tyHE+SVIDcLv+NMd4FhLxeX5LUHLyzXZKUxJBIkpIYEklSEkMiSUpiSCRJSQyJJCmJIZEkJTEkkqQkhkSSlMSQSJKSGBJJUhJDIklKYkgkSUkMiSQpiSGRJCUxJJKkJIZEkpTEkEiSkhgSSVISQyJJSmJIJElJDIkkKYkhkSQlMSSSpCSGRJKUxJBIkpIYEklSEkMiSUpiSCRJSQyJJCmJIZEkJTEkkqQkhkSSlMSQSJKSGBJJUhJDIklKYkgkSUkMiSQpiSGRJCUxJJKkJIZEkpTEkEiSkhgSSVISQyJJSmJIJElJDIkkKYkhkSQlMSSSpCSGRJKUxJBIkpIYEklSEkMiSUpiSCRJSQyJJCmJIZEkJTEkkqQkhkSSlMSQSJKSGBJJUhJDIklKYkgkSUkMiSQpiSGRJCUxJJKkJIZEkpTEkEiSkhgSSVISQyJJSmJIJElJDIkkKYkhkSQlMSSSpCSGRJKUxJBIkpIYEklSEkMiSUpiSCRJSTqKnoAWaOo4HHkcOrphxSVQKhc9I0ltypAsRiO74e7fBiLECAPr4NUfhHKl6JlJakMubS1G3/5jqE5AdRJmp+D4Xtj9taJnJalNGZLFaPLoye/XpmH8UDFzkdT2DMlitOwCCM87J1LuhOUXFjcfSW3NkCxGW38GBtZD6IBQgk2vhzU/WPSsJLUpT7YvRp0DcO2HYeZEdjRS7ix6RpLamCFZrEKAzv6iZyFJLm1JktIYEklSEkMiSUpiSCRJSQyJJCmJIZEkJfHyXzWFGLOtw2rVbCPjcpcbGkuLhSFRwx0/Dp/8wWzLsGv+MzAOR5/MQhICELOb9gc3wspLsrehl0LJP61SU/Kvphpq153wyeufe//2n4PBC+Dl7zh5F/zaLEwcgR1fhsf/IbuZ/6IbYcM10D3Y+HlLOrvczpGEEP4khHAwhPC9vMbQ4nJkx8kRedbIjtMfpVIqZ8FYsg6WboJKLzz4v+ErH4R992ZLYZKaQ54n2z8JvDHH19ciUZvNIvC131j4a1R6YOlLoNIP9/w+3PcxmD5RvzlKWrjcQhJjvBM4ktfra3GoVeGBP4bH/h4GN6S/XmcfLNsMBx6Ab340e+KwpGJ5+a9yE2vwrU/Cnrth6ebsZPnFbz398/rWzO91Q8hOxB/fC//0uzAzXpfpSlqgwkMSQrg5hLA9hLB9eHi46OmojnbdCbu+li1JhZB9bO0VcMW/z67KogQXvAm2/duFvf6S9XD0KfjeZz1nIhWp8Ku2Yoy3ALcAbNu2zW8HLeLEQfjuX2Qny8MpP64sGYLrPlSfcQY3wM6vwrorYdVl9XlNSfNT+BGJWk+M8O1PZQHp6M53rFCC3pXwwMez+1AkNV6el//+JXA3sCWEsDeE8N68xlJzOb4Xhh+E/nme+1ioriUwOQJPf6cx40k6WW5LWzHGd+X12mpuu74Gpcpz50UaoXsQHr81W+Jq5LiSXNpSnVWnspPsfasaO27XIIzshtF9jR1XkiFRnY0dyO4dOfVO9bw9exQysqex40oyJKqz0QPFXYpb7oQjjxczttTOCr/8V63l6I7sG3oRugbgyBPFjC01gyqz3M4j7GCYfrq4kctYxUDu43pEorqaGm38stazSh0wPVbM2FIz+Dzf5gF2c5gT7OIIf8o3GSX/6+INieoq1gocPBQ8vlSgGpFHeIYqtZM+toP8dwwxJKqrcldx38xjLRtfakdh7u1U5QZ8mzckRdh/L9z/h/C9v8geE9hCBjfAzEQxY8+Mw8B5xYwtFS0QeBWbqZA9o7pEoJsKF5P/tfiebG+0HV+Cxz4Ps9NACfb/E1z/m9nt2S1gcENxNwROj2WP5JXa1Q9xCSvp53EOMkgPr+FCusj/pKUhabTH/34uIgC17A6+/ffA5h8udFr10r8WiNklwI0OSgjZjsBSuwoEtrKBrdTh4T/z4NJWo8XZU96vZXfwtYjupbD0/Mav2FWnssuOl53f2HElGZLGW3f1yTdalDpg9RXFzafOQoALb2z8kwtPHITzfzj/3YYlnc6lrUa77N3ZA8iffgA6++HSdzVum9wGWXM5VPqyZ6p39uU/Xq2aHdhtvCb/sSSdzpA0WqkML/2J7K1FlTvhFf8S7vsYVDbnf67k+D648I0t12Np0TAk9VKdgqkRqM1kPx6HcvZjedfA6Y8IbAPrroR998Az3833BPjEUehbCZf8aH5jSHphhmShYoTxYTj8GIztza49fTYYkezOoBihVIKeIVh+IQxuho72uGMuBLj8Jvjar8P4YehdUf8xpk/A9Chc9SHPjUhFMiTzFSOMPAVPfyu7NKlUho4+6F5x5jWc2mx2pLLnLth7Nyy/GFa/Ijs/0uJ6lsHVH4C7PgLjh7JH4tbL9Fj2mlf9Iiy/oH6vK2n+2m/NJcX0GDx1Gzx1e7aU1b0CupZlJwXOdiKgNLfE1bMSOpfA4Ufhkc9l29QWtd96Ay1ZB9d+KPtPNLInffuUGLOt6qdG4FW/BGu31meekhbOkLxYx/dmARjdly1VVXrnfxa5VIae5dk6zK47srfZmez34tzNiS1o4Dx47W/AhlfDsZ0weWxhDZ0Zh2NPwdJN8Nr/ml0dJql4Lm29GCO7siORSn99FuPLXVmMjj05dyddFzz82Swm/Wvhyl/O1oVaSGc/XPFeOO9KePCz2X/Schf0DWW30pxNrMHEkexgsGsJbP1peMl1WZMlNQdDci5jT2dLWZWB+p4oDwG6V2ZLXcMPAnN3vI/uh+3/A679cP3GahIhZEcRq1+ePQDrqTtg//1zN/bH55a9YsyuWwgh++fKLXD+G2DVpS8cHUnF8K/lC6lOZctPHT35XG0VwtyWKc8/cRCzH9eL2KyqQULILmJbfiH8QC07aT66P7sbfnYmi0VnX3Zw1r/aeEjNzr+iL+TAfVCdyE6q56Wjh7lrhZ/7WGdfy0bkVKEEfauyt3PZczfc9oHsf8lVP5/d9CipeIbkbMaH4dDD0JNjRCA7Ez2yCyYOZ/EolWHrzfmOuQjtvgv+9Nrn3v+bn8pWHa/5QHFzkpTxqq2zOfQQlDsacFd6gPWvhrWvzLbNfdUHYNXLcx5z8bn1P5z+sa/+esOnIekMDMmZzExk93l0DjZowJBtFNW/GiaPNGjMxWX6xOkfa6Hd96VFzZCcydi+5y4daqTKEjj0SFvcqDhfW3/69I9tfE3j56HWUaPGKJMMM8pBRhlmlBEmmCXxrtk25DmSMxl7uphLhcoVmD4OMyfaYguV+bjuQ9mVXQ/ckl0mvP5quOnLRc9Ki80EM+zjKPs4xgiTnHSRy5wIDNDFGpawgeUM4EZu52JIzuTE08XuAjh5zJCcwZs/lr1J8zXKJI/yDPs4BkAXHfTTRYnTr46MRGaY5QmGeZyDrGSAS1jNCvw7eTaG5FSxln0jz/OS3xeeQOMfLyi1qBo1nuQQD3GAEoEBus8Yj+cLBDrpoJMOIpERJvg6T3A+Q7yUNVRwW4VTGZJT1WaBUOB9HCF7pomkJJPMcC87OcIJBuimvIBTwoFAL510U+EpDvE0I7yK81nictdJPNl+qnDKzYHFTKLg8aXFbZIZvsEORphgkJ4FReT5SgQG6aFKjbt4nBEm6jTT1mBITvXsJk+FXTkVs5PukhZkhlm+yZNMMM0A3YQ6/mDWSyeBwDfZwQlac7fuhTAkpwql7BkjswX9IQkh2+ZW0oI8zAFGmaA/p+WnHjqZpca32EOt8NWL5mBIzqR/DVQnixk7At2ttYW81CiHGONJDjFAT67j9NHFIcbYyaFcx1ksDMmZ9K2Z25W3wWanodI9t5GjpPmoEfkWu+mmcs4rs1IFAv108SAHmMSLY1orJLVq9qCLYzvnrr5aoIF12RJXymssxPQorLi0bXb+lerpEGOcYIZuGnOOsYMyNeL3701pZ61z+e/0KHzjN2FyBIjZ8tSrf3VhzxHp6IIVW7KHTjVqmenZpzotv7Ax40ktZgfDVBr8s3EPFZ7gIJtZmftRUDNrnSOS730me0LS7GR2onx0Hzz2twt/vRWXZMtbjToqmRqBZZu9o11agCmqHGSUXjobOm4nHUxS5RjjDR232bROSEb3nXxeo1aF0T0Lf72e5bDqFTB1NH1u5zI7nS2lrb0y/7GkFjRKdnFMPS/1nY/jFHRxTpNonZAMvgTC81bqSpXs+R4pVm+FrkGYHkt7nRcSI0wdg/XXeDQiLVCR38g7KHGYHL9HLAKtE5JL35WdJC93QqkTll0IF74l7TXLFdj0umzLkpkc7mSNESYPZ8toyy6o/+tLbeIY43QU9O2sQrnt73RvnZPtlV649j/BiYPZMlHvUH2ufupZARfcCDu+CNSg0pf+mpCdXJ84DMvOz45GvFJLWrAqtcKWtUoEZtr8GSatExLIAtK/pv6v278GLnoLPHVbdgTRtSztoVczEzAzBkOXwnlXZc9pl5S777GPI3MnxnupsI1NdXnddv8xsHWWtvLWOwRb3gbLt2RHEtOj89+Pa3YaJg4BES58U/Z0JiMiJatQJp5ju5KH2P/9iACMM8N97Eweu0Zs+63lW+uIJG8dXbDhNdn5jGe+A6N7syWpck/2IKxTj1JizM6vVCehNg3lblizDVa+dGH3t0g6o2X0sJvDL/g5hzhx2scm6nBX+jSzDDGQ/DqLmSGmX4cgAAAGtUlEQVRZiP612dvUCBx9MgvK+KG5mwrDc8e5sQaVfhjcCEs3ZxcDFPEIX6nF1XuX3/mYpcZyegsZu1n4XS1F1yCsuSJ7i7VsuWt2BojZ0Uml3yMPqQEG6CaSPSb3bEFZzQDPMHrSx/rrdAPjkpw3iWx2hqReQikLi6SG66SDtQxykFH6OfMPb1tYQwQOzsVkCd1sZUPSuFNU6aWTpYZEkha/81nJ/nNsoHgJa7iE+l3ZOck0l7OhsGW1ZuFVW5Jawgr6GKCbCaYbMt4Ms5QpsQ5XIgyJpJYQCFzBBqaoUsv5BsFIZIwpLmc9nS7sGBJJrWM5fVzEKo4zec77SlKMMsValrABn2YKniNpLhNHYM/XsxsX110FSzYWPSNp0dnCao5wgqOM53I11ThTdFPmFaxv+3MjzzIkzWL8ENz54ex5KrEGO2+HK9+fbeiouqjNupFAO+igzFVs5m6e5BgTLKnjPSYnmKJMiau5gJ4GP/ukmbm01Sx2fBGqE889KXF2Gh76q2Ln1CIe/Tv47aXwXyrwh5fDyO6iZ6S8ddLB1VzAagYYYYIZ0h5QN0uNY4zTQyfXciH9dNdppq3BkDSLmXE4dU232t4Py6mHw4/B596VbUJAhOGH4NM3Fj0rNUJl7sjkCjYyxQzHmWB2nifha0RGmWSMKV7KGq7nIvrOcp9KO3Npq1msuwqeeSA7EoHsuSrrrip2Ti1gz90nb4EWZ+Hwo1m3K+29q0VbCARewnKG6GcHw+zkMDVqVOigk44zPsNklhrTzDJNFYB1LOUiVjHY5jcdvhBD0ixWb4VL3w2P/U22mL/hWrjonxU9q0Wvf/XpHytXsj021T566eTlrOMS1nCAEfZzjCOMc4LZ086elAgso4/VDLCeZXRTKWTOi4khaSYbr8veVDcXvAE2Xgu7vp4djQC85X+mPU5Gi1eFMhtZzkaWE4lMUmWKGSLZXqsVyvTS6dVY82RI1NJCCX7yC/DYF2B0P6x/FazZWvSs1AwCgR4q9HjEkcyQqOWFEmx5a9GzkFqXB/iSpCSGRJKUxJBIkpJ4jkRSw01T5XYeYS9HWUk/P8LLvNFvETMkkhoqEvkM97KPEWapcZBR9nKUn+V6KrgZ2mLk0pakhhplkv1zEYFsG5Jxptl3jqcbqnkZEkkNFQhnfFKINwEuXoZEUkP108VmVnx/n6sygUF6WM/SgmemhfIciaSGCgTewTa+zhPs4ShD9HMDF1P259pFy5BIargyJW7g4qKnoTrxRwBJUhJDIklKYkgkSUkMiSQpiSGRJCUxJJKkJK1x+W91Ch7+X3B0B/SvhUvfBV2DRc9KktrC4g9JjHDPf4ORp6BWhdF9cPQJuOEjUO4senaS1PIW/9LW5DEY2ZlFBCDOwsyJ7OhEkpS7xR+SUIJTt4CLQHADOElqhMUfkq4lsPJlUJpbxgod0LMcll1Y7LwkqU0s/nMkIcC298ET/wBHHof+82DLP4fS4v9Xk6TFoDW+25Y64OIfLXoWktSWFv/SliSpUIZEkpSkNZa2pJw88134f3+Z3ZJ0xU/D0k1Fz0hqPoZEOovd34BPvwFmJrKrzO/5Xfg322HFRUXPTGouLm1JZ3H7r8DMOBCz+1ynRuGu3y56VlLzMSTSWUyNnvKBCFPHCpmK1NQMiXQWl78HKr3PvV/phZe/u7j5SM3KcyTSWVz9y9m2bfffkt2qdN2vwUt/vOhZSc3HkEhnEUpww69nb5LOLtelrRDCG0MIj4YQngghfDDPsSRJxcgtJCGEMvAx4EbgZcC7Qggvy2s8SVIx8jwiuRJ4Isb4ZIxxGvgs4IZYktRi8gzJOmDP897fO/cxSVILyTMkZ3qyVDztk0K4OYSwPYSwfXh4OMfpSJLykGdI9gIbnvf+emD/qZ8UY7wlxrgtxrhtaGgox+lIkvKQZ0juAy4KIWwOIXQC7wT+LsfxJEkFyO0+khhjNYTwc8D/BcrAn8QYH8xrPElSMXK9ITHGeCtwa55jSJKK5V5bkqQkhkSSlMSQSJKSGBJJUhJDIklKYkgkSUkMiSQpiSGRJCUxJJKkJIZEkpTEkEiSkhgSSVKSEONpz5oqTAhhmOzhV7uLnkuCjTj/oizmuYPzL5rzP91LYoznfFBUU4UEspi8mIk3K+dfnMU8d3D+RXP+C9eMS1vHip5AIudfnMU8d3D+RXP+C9SMIRkpegKJnH9xFvPcwfkXzfkvUDOG5JaiJ5DI+RdnMc8dnH/RnP8CNd05EknS4tKMRySSpEXEkEiSkhgSSVISQyJJSmJIJElJ/j9pRiQJ7oDyegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = new_table.plot.scatter(c= 'Cluster', x = 'X', y = 'Y', cmap = matplotlib.pyplot.cm.rainbow, figsize = (10,10))\n",
    "groups = new_table.groupby('Cluster')\n",
    "centroid = groups.mean()\n",
    "centroid.plot.scatter(c = centroid.index, x = 'X', y = 'Y', cmap = matplotlib.cm.rainbow, s = 1000, ax = plot, alpha = .5)\n",
    "plot.figure.axes[1].remove()\n",
    "plot.figure.axes[1].remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.672805</td>\n",
       "      <td>0.903930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.493840</td>\n",
       "      <td>3.207835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.497753</td>\n",
       "      <td>0.537653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.258460</td>\n",
       "      <td>0.654153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.052966</td>\n",
       "      <td>4.721010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X         Y\n",
       "Cluster                    \n",
       "0        0.672805  0.903930\n",
       "1        0.493840  3.207835\n",
       "2        1.497753  0.537653\n",
       "3        0.258460  0.654153\n",
       "4        1.052966  4.721010"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newCentTab = pandas.DataFrame(centroid.X, columns = ['X', 'Y'])\n",
    "newCentTab.Y = centroid.Y\n",
    "newCentTab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the data that each data point is part of its own group of data entries and is near a separate centroid. However Mekk-Knight Invoked seems to be the only one far enough away from the other points for it to be considered that it would have to be within its own group. Due to this it is the centroid of that singular group and it only further proves that Mekk-Knight Invoked is more powerful than any other deck currently in the locals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the data as shown above, we see that Mekk-Knight Invoked seems to be the one deck that is the most problematic in strength and resourcefulness. This is due to the fact of it straying so far away from the path of normality and it forcing itself to be part of its very own group. Due to this information, there are a few things in which we could do to limit its power. For one if it is running another strong draw power engine such as the Sky Striker engine we could remove it which might lower its recovery factor enough to where it could compete with other decks on a fair level. Another option is that the player might be cheating or abusing ceratin aspects of the game and cards so as to make them seem like they are the best player. Then finally it might be due to the fact that it is one of the newer decks and has a greater power creep than the rest, allowing it to out perform the older decks. It also has the capability to be merged with other deck engines to allow it to perform at a higher rate than it could alone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So overall Mekk-Knight Invoked is the problematic deck among the local tournament and the local meta. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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