coin_flip_ufc.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from faker import Faker\n",
    "import matplotlib.colors as colors"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_fighter_data(number_of_fighters=300):\n",
    "    \"\"\"\n",
    "    Generate simulated fight data for a number of fighters.\n",
    "\n",
    "    Parameters:\n",
    "    number_of_fighters (int): The total number of fighters. Default is 300.\n",
    "    prob_of_winning (float): The probability of a fighter winning a fight. This parameter is not currently used. Default is 0.5.\n",
    "    number_of_fights_in_career (int): The total number of fights each fighter has in their career. Default is 12.\n",
    "\n",
    "    Returns:\n",
    "    df (pd.DataFrame): A DataFrame with columns 'Wins' and 'Losses', each row representing a fighter.\n",
    "    \"\"\"\n",
    "    \n",
    "    # initialize dictionary\n",
    "    d = {}\n",
    "\n",
    "    # assign starting values\n",
    "    for i in range(number_of_fighters):\n",
    "        d[i] = [0, 0]\n",
    "\n",
    "    # simulate fight\n",
    "    for i in range(len(d)):\n",
    "        outcome = np.random.uniform(.4, .8)\n",
    "        for _ in range(np.random.randint(1, 13)):\n",
    "                if d[i][1] > 3:\n",
    "                     break\n",
    "                result = np.random.choice([0, 1], p=[outcome, 1-outcome])\n",
    "                d[i][result] += 1\n",
    "\n",
    "    # convert to DataFrame\n",
    "    df = pd.DataFrame(d.values()).sort_values(by=1)\n",
    "    df.columns = ['Wins', 'Losses']\n",
    "\n",
    "    return df\n",
    "\n",
    "def plot_fighter_data(number_of_fighters):\n",
    "\n",
    "    \"\"\"\n",
    "    This function generates and plots data for a specified number of fighters.\n",
    "\n",
    "    It calculates the win percentage of each fighter, rounds it to three decimal places,\n",
    "    and then creates a histogram plot of these win percentages with color coding by height.\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    df = generate_fighter_data(number_of_fighters=number_of_fighters)\n",
    "\n",
    "    # calculate win pct\n",
    "    df['win pct'] = df['Wins'] / (df['Wins'] + df['Losses'])\n",
    "\n",
    "    # Round to 3 significant figures\n",
    "    df['win pct'] = np.round(df['win pct'], 3)\n",
    "\n",
    "    plt.figure(figsize=(10, 6))  # Change the figure size here\n",
    "\n",
    "    # create the histogram plot with edge color and line width specified\n",
    "    n, bins, patches = plt.hist(df['win pct'], bins=10, density=True, edgecolor='black', linewidth=3, color='red')\n",
    "\n",
    "    plt.title('Distribution of Win Percentage', fontsize=18, color='white')\n",
    "    plt.xlabel('Win Percentage', fontsize=14, color='white')\n",
    "    plt.ylabel('Percentage', fontsize=14, color='white')\n",
    "\n",
    "    plt.xticks(fontsize=14, color='white')\n",
    "    plt.yticks(fontsize=14, color='white')\n",
    "\n",
    "    # We'll color code by height, but you could use any scalar\n",
    "    fracs = n / n.max()\n",
    "\n",
    "    # we need to normalize the data to 0..1 for the full range of the colormap\n",
    "    norm = colors.Normalize(fracs.min(), fracs.max())\n",
    "\n",
    "    # Loop through the rectangles (bins) and set the color accordingly\n",
    "    for thisfrac, thispatch in zip(fracs, patches):\n",
    "        color = plt.cm.viridis(norm(thisfrac))\n",
    "        thispatch.set_facecolor(color)\n",
    "\n",
    "    plt.grid(False)  # Optionally remove the grid\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def plot_win_loss_distribution(number_of_fighters):\n",
    "\n",
    "    \"\"\"\n",
    "     This function generates data for a specified number of fighters and plots the distribution\n",
    "    of wins and losses in a bar graph format.\n",
    "    \n",
    "    The function first calculates the counts of wins and losses, sorts these counts in ascending \n",
    "    order, and creates a bar plot. The plot's aesthetics, such as color and size, are configured \n",
    "    for better readability.\n",
    "    \"\"\"\n",
    "\n",
    "    df = generate_fighter_data(number_of_fighters=number_of_fighters)\n",
    "\n",
    "    # calculate counts\n",
    "    value_counts = df[['Wins', 'Losses']].value_counts()\n",
    "\n",
    "    # sort by index (wins)\n",
    "    value_counts = value_counts.sort_index(ascending=True)\n",
    "\n",
    "    # calculate total for percentages\n",
    "    total = value_counts.sum()\n",
    "\n",
    "    # create the bar plot with edge color and line width specified\n",
    "    ax = value_counts.plot(kind='bar', figsize=(15, 8), color='red', edgecolor='black', linewidth=3)\n",
    "\n",
    "    ax.set_title('Distribution of Wins and Losses', fontsize=18, color='white')\n",
    "    ax.set_xlabel('Outcome (Wins, Losses)', fontsize=14, color='white')\n",
    "    ax.set_ylabel('Count', fontsize=14, color='white')\n",
    "\n",
    "    ax.tick_params(axis='x', colors='white', labelsize=14)  # Change the color and size of the x-axis tick labels\n",
    "    ax.tick_params(axis='y', colors='white')  # Change the color of the y-axis tick labels\n",
    "\n",
    "    plt.grid(False)  # Optionally remove the grid\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 300 Fighters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_win_loss_distribution(300)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1000 fighters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_win_loss_distribution(1_000)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10,000 Fighters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_win_loss_distribution(10_000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['win pct'] = df['Wins']/(df['Wins'] + df['Losses'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fighter_data(10_000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake = Faker()\n",
    "\n",
    "male_names = [fake.name_male() for _ in range(5_000)]\n",
    "female_names = [fake.name_female() for _ in range(5_000)]\n",
    "\n",
    "# Combine the lists\n",
    "ufc_fighter_names = male_names + female_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = ufc_fighter_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Wins</th>\n",
       "      <th>Losses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mr. Barry Berry</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin Robbins</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Lewis</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Baker</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christopher Stewart</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexis Houston</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joanne Phillips</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Erin Schneider</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kelsey Ramirez</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanda Jackson</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Wins  Losses\n",
       "Mr. Barry Berry         3       0\n",
       "Kevin Robbins           2       0\n",
       "John Lewis              3       0\n",
       "James Baker             1       0\n",
       "Christopher Stewart     3       0\n",
       "...                   ...     ...\n",
       "Alexis Houston          4       4\n",
       "Joanne Phillips         2       4\n",
       "Erin Schneider          2       4\n",
       "Kelsey Ramirez          0       4\n",
       "Wanda Jackson           3       4\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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