Simulacao_da_Copa_de_Futebol_das_Olimpiadas.ipynb

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    "colab": {
      "name": "Simulacao da Copa de Futebol das Olimpiadas.ipynb",
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      "authorship_tag": "ABX9TyPaWbNWEwEBkYMB4AcaOL5O",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ricardorocha86/Python-para-Machine-Learning/blob/master/Simulacao_da_Copa_de_Futebol_das_Olimpiadas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GL0KK35drDQl"
      },
      "source": [
        "# **Simulação da Copa de Futebol das Olimpíadas!**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_nXOba9xrKYU"
      },
      "source": [
        "## **Objetivo**\n",
        "\n",
        "Investigar as probabilidades dos jogos de futebol na Copa das Olimpíadas de Tóquio 2021."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qm36EtrQO362"
      },
      "source": [
        "## **Perguntas** \n",
        "\n",
        "1. Qual seleção é mais provável ser campeã?\n",
        "2. Quais as probabilidades das seleções avançarem para segunda fase?\n",
        "3. Quais as probabilidades das seleções ficarem em 1º, 2º, 3º e 4º em seus grupos na primeira fase?\n",
        "4. A força de uma equipe é proporcional a sua probabilidade de vencer a competição? Se não, porquê?\n",
        "5. Qual a probabilidade de todos cabeça de chave passarem simultâneamente para a segunda fase? (Probabilidade da 'zebra')\n",
        "\n",
        "6. Qual a probabilidade de pelo menos uma das quatro seleções mais fracas ganhar a competição?\n",
        "7. Qual a probabilidade de vencer a competição por confederação?\n",
        "8. Qual a probabilidade de uma seleção sem medalhas em sua história vencer a competição?\n",
        "\n",
        "9. Se o Brasil jogar com a Alemanha, qual a probabilidade de acontecer um 7x1 para a Alemanha? E um 7x1 para o Brasil? Quais os placares mais prováveis?\n",
        "10. Qual a probabilidade de cada grupo apresentar um campeão?\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKY5q9vjjvAn"
      },
      "source": [
        "![](https://pbs.twimg.com/media/EzfBqoFXMAAdvOR?format=jpg&name=large)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LhDKOhiA8WpN"
      },
      "source": [
        "## Importação dos Dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUhDB4fBIb9r"
      },
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from scipy.stats import poisson\n",
        "from collections import Counter"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibBGCgoiRu3r"
      },
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "from oauth2client.client import GoogleCredentials\n",
        "import gspread\n",
        "gc = gspread.authorize(GoogleCredentials.get_application_default())\n",
        "wb = gc.open_by_url('https://docs.google.com/spreadsheets/d/1dJrLGRMZvk-NU1O2AyNKFoIgTtrvosBKpof-fzBOWVE/edit#gid=0')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xxiRbXHFikqD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "7459b1ec-69d1-4614-f4d0-76a6117d5936"
      },
      "source": [
        "planilha = wb.worksheet('seleções')\n",
        "dados_cru = planilha.get_all_values()\n",
        "dados = pd.DataFrame(dados_cru)\n",
        "dados.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1</th>\n",
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              "      <th>5</th>\n",
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              "      <th>10</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>País</td>\n",
              "      <td>Nome FIFA</td>\n",
              "      <td>Grupo</td>\n",
              "      <td>Confederação</td>\n",
              "      <td>Bandeira</td>\n",
              "      <td>Cabeça-de-chave</td>\n",
              "      <td>Ranking FIFA (07/04)</td>\n",
              "      <td>Pontuação FIFA (07/04)</td>\n",
              "      <td>Ouros</td>\n",
              "      <td>Pratas</td>\n",
              "      <td>Bronzes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>França</td>\n",
              "      <td>France</td>\n",
              "      <td>A</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>2</td>\n",
              "      <td>1757</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Brasil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>D</td>\n",
              "      <td>CONMEBOL</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>3</td>\n",
              "      <td>1742</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Espanha</td>\n",
              "      <td>Spain</td>\n",
              "      <td>C</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>6</td>\n",
              "      <td>1648</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>C</td>\n",
              "      <td>CONMEBOL</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>8</td>\n",
              "      <td>1641</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "          0          1      2   ...     8       9        10\n",
              "0       País  Nome FIFA  Grupo  ...  Ouros  Pratas  Bronzes\n",
              "1     França     France      A  ...      1       1        0\n",
              "2     Brasil     Brazil      D  ...      1       3        2\n",
              "3    Espanha      Spain      C  ...      1       2        0\n",
              "4  Argentina  Argentina      C  ...      2       2        0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
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        "outputId": "aff0e2d0-1a4b-42cd-be00-4b22807eafcf"
      },
      "source": [
        "dados.columns = dados.iloc[0]\n",
        "dados = dados.iloc[1:]\n",
        "dados"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>País</th>\n",
              "      <th>Nome FIFA</th>\n",
              "      <th>Grupo</th>\n",
              "      <th>Confederação</th>\n",
              "      <th>Bandeira</th>\n",
              "      <th>Cabeça-de-chave</th>\n",
              "      <th>Ranking FIFA (07/04)</th>\n",
              "      <th>Pontuação FIFA (07/04)</th>\n",
              "      <th>Ouros</th>\n",
              "      <th>Pratas</th>\n",
              "      <th>Bronzes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>França</td>\n",
              "      <td>France</td>\n",
              "      <td>A</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>2</td>\n",
              "      <td>1757</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Brasil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>D</td>\n",
              "      <td>CONMEBOL</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>3</td>\n",
              "      <td>1742</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Espanha</td>\n",
              "      <td>Spain</td>\n",
              "      <td>C</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>6</td>\n",
              "      <td>1648</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>C</td>\n",
              "      <td>CONMEBOL</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>8</td>\n",
              "      <td>1641</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>México</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>A</td>\n",
              "      <td>CONCACAF</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>11</td>\n",
              "      <td>1629</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Alemanha</td>\n",
              "      <td>Germany</td>\n",
              "      <td>D</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>12</td>\n",
              "      <td>1609</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Honduras</td>\n",
              "      <td>Honduras</td>\n",
              "      <td>B</td>\n",
              "      <td>CONCACAF</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>67</td>\n",
              "      <td>1361</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Japão</td>\n",
              "      <td>Japan</td>\n",
              "      <td>A</td>\n",
              "      <td>AFC</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>28</td>\n",
              "      <td>1509</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Romênia</td>\n",
              "      <td>Romania</td>\n",
              "      <td>B</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>43</td>\n",
              "      <td>1449</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Coréia do Sul</td>\n",
              "      <td>Korea Republic</td>\n",
              "      <td>B</td>\n",
              "      <td>AFC</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>39</td>\n",
              "      <td>1460</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Austrália</td>\n",
              "      <td>Australia</td>\n",
              "      <td>C</td>\n",
              "      <td>AFC</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>41</td>\n",
              "      <td>1457</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Egito</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>C</td>\n",
              "      <td>CAF</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>46</td>\n",
              "      <td>1432</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Costa do Marfim</td>\n",
              "      <td>Côte d'Ivoire</td>\n",
              "      <td>D</td>\n",
              "      <td>CAF</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>59</td>\n",
              "      <td>1384</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Arábia Saudita</td>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>D</td>\n",
              "      <td>AFC</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>65</td>\n",
              "      <td>1364</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>África do Sul</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>A</td>\n",
              "      <td>CAF</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>75</td>\n",
              "      <td>1325</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Nova Zelândia</td>\n",
              "      <td>New Zealand</td>\n",
              "      <td>B</td>\n",
              "      <td>OFC</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>122</td>\n",
              "      <td>1149</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0              País       Nome FIFA Grupo  ... Ouros Pratas Bronzes\n",
              "1            França          France     A  ...     1      1       0\n",
              "2            Brasil          Brazil     D  ...     1      3       2\n",
              "3           Espanha           Spain     C  ...     1      2       0\n",
              "4         Argentina       Argentina     C  ...     2      2       0\n",
              "5            México          Mexico     A  ...     1      0       0\n",
              "6          Alemanha         Germany     D  ...     1      1       1\n",
              "7          Honduras        Honduras     B  ...     0      0       0\n",
              "8             Japão           Japan     A  ...     0      0       0\n",
              "9           Romênia         Romania     B  ...     0      0       0\n",
              "10    Coréia do Sul  Korea Republic     B  ...     0      0       0\n",
              "11        Austrália       Australia     C  ...     0      0       0\n",
              "12            Egito           Egypt     C  ...     0      0       0\n",
              "13  Costa do Marfim   Côte d'Ivoire     D  ...     0      0       0\n",
              "14   Arábia Saudita    Saudi Arabia     D  ...     0      0       0\n",
              "15    África do Sul    South Africa     A  ...     0      0       0\n",
              "16    Nova Zelândia     New Zealand     B  ...     0      0       0\n",
              "\n",
              "[16 rows x 11 columns]"
            ]
          },
          "metadata": {
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          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tb3WqoRTkp6z"
      },
      "source": [
        "## Gráficos dos Grupos\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56t_MdOarPsP",
        "colab": {
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          "height": 946
        },
        "outputId": "1bd8ddda-f0ec-447d-86b2-ac82d3abb474"
      },
      "source": [
        "def plotgrupo(grupo = 'A', salvar = False):\n",
        "    d = dados.loc[dados['Grupo'] == grupo]\n",
        "    fig = plt.figure(figsize = (4, 3))\n",
        "    for i in range(4):\n",
        "        ax = plt.subplot(2, 2, i + 1) \n",
        "        a = plt.imread(d['Bandeira'].iloc[i])\n",
        "        ax.get_xaxis().set_visible(False)\n",
        "        ax.get_yaxis().set_visible(False)\n",
        "        plt.title(d['País'].iloc[i], size = 10)\n",
        "        plt.imshow(a)\n",
        "    plt.tight_layout(w_pad = 2) \n",
        "    plt.suptitle('Grupo ' + grupo, size = 14, y = 1.05)\n",
        "    if salvar:\n",
        "        plt.savefig('grupo' + grupo + '.png', transparent = True)\n",
        "\n",
        "plotgrupo('A', True)\n",
        "plotgrupo('B')\n",
        "plotgrupo('C')\n",
        "plotgrupo('D')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-oOpVX02Csl"
      },
      "source": [
        "## Criando a variável **Força** de cada seleção"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "USXZ3ztYnI1P",
        "outputId": "d12eee88-d9ed-4f82-bbf5-ddb08e46ac6a"
      },
      "source": [
        "dados.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16 entries, 1 to 16\n",
            "Data columns (total 11 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   País                    16 non-null     object\n",
            " 1   Nome FIFA               16 non-null     object\n",
            " 2   Grupo                   16 non-null     object\n",
            " 3   Confederação            16 non-null     object\n",
            " 4   Bandeira                16 non-null     object\n",
            " 5   Cabeça-de-chave         16 non-null     object\n",
            " 6   Ranking FIFA (07/04)    16 non-null     object\n",
            " 7   Pontuação FIFA (07/04)  16 non-null     object\n",
            " 8   Ouros                   16 non-null     object\n",
            " 9   Pratas                  16 non-null     object\n",
            " 10  Bronzes                 16 non-null     object\n",
            "dtypes: object(11)\n",
            "memory usage: 1.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFEgeUDomqvK"
      },
      "source": [
        "for i in ['Ranking FIFA (07/04)', 'Pontuação FIFA (07/04)', 'Ouros', 'Pratas', 'Bronzes']:\n",
        "    dados[i] = dados[i].astype(int)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYROsJ2TtBwv"
      },
      "source": [
        "M = max(dados['Pontuação FIFA (07/04)'])\n",
        "dados['Força'] = (1.2 * dados['Pontuação FIFA (07/04)']/M)**2"
      ],
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        },
        "id": "T9u2g3YLl5pg",
        "outputId": "81fb1694-a355-46e8-e8c8-6aaf1d9758f1"
      },
      "source": [
        "dados"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>País</th>\n",
              "      <th>Nome FIFA</th>\n",
              "      <th>Grupo</th>\n",
              "      <th>Confederação</th>\n",
              "      <th>Bandeira</th>\n",
              "      <th>Cabeça-de-chave</th>\n",
              "      <th>Ranking FIFA (07/04)</th>\n",
              "      <th>Pontuação FIFA (07/04)</th>\n",
              "      <th>Ouros</th>\n",
              "      <th>Pratas</th>\n",
              "      <th>Bronzes</th>\n",
              "      <th>Força</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>França</td>\n",
              "      <td>France</td>\n",
              "      <td>A</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>2</td>\n",
              "      <td>1757</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Brasil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>D</td>\n",
              "      <td>CONMEBOL</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>3</td>\n",
              "      <td>1742</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.415518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Espanha</td>\n",
              "      <td>Spain</td>\n",
              "      <td>C</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>6</td>\n",
              "      <td>1648</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1.266874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>C</td>\n",
              "      <td>CONMEBOL</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>8</td>\n",
              "      <td>1641</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1.256134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>México</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>A</td>\n",
              "      <td>CONCACAF</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>11</td>\n",
              "      <td>1629</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.237830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Alemanha</td>\n",
              "      <td>Germany</td>\n",
              "      <td>D</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>12</td>\n",
              "      <td>1609</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.207622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Honduras</td>\n",
              "      <td>Honduras</td>\n",
              "      <td>B</td>\n",
              "      <td>CONCACAF</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>67</td>\n",
              "      <td>1361</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.864043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Japão</td>\n",
              "      <td>Japan</td>\n",
              "      <td>A</td>\n",
              "      <td>AFC</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>28</td>\n",
              "      <td>1509</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.062178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Romênia</td>\n",
              "      <td>Romania</td>\n",
              "      <td>B</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>43</td>\n",
              "      <td>1449</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.979390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Coréia do Sul</td>\n",
              "      <td>Korea Republic</td>\n",
              "      <td>B</td>\n",
              "      <td>AFC</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Sim</td>\n",
              "      <td>39</td>\n",
              "      <td>1460</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.994317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Austrália</td>\n",
              "      <td>Australia</td>\n",
              "      <td>C</td>\n",
              "      <td>AFC</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>41</td>\n",
              "      <td>1457</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.990235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Egito</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>C</td>\n",
              "      <td>CAF</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>46</td>\n",
              "      <td>1432</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.956544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Costa do Marfim</td>\n",
              "      <td>Côte d'Ivoire</td>\n",
              "      <td>D</td>\n",
              "      <td>CAF</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>59</td>\n",
              "      <td>1384</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.893493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Arábia Saudita</td>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>D</td>\n",
              "      <td>AFC</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>65</td>\n",
              "      <td>1364</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.867856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>África do Sul</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>A</td>\n",
              "      <td>CAF</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>75</td>\n",
              "      <td>1325</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.818937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Nova Zelândia</td>\n",
              "      <td>New Zealand</td>\n",
              "      <td>B</td>\n",
              "      <td>OFC</td>\n",
              "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
              "      <td>Não</td>\n",
              "      <td>122</td>\n",
              "      <td>1149</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.615827</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0              País       Nome FIFA Grupo  ... Pratas Bronzes     Força\n",
              "1            França          France     A  ...      1       0  1.440000\n",
              "2            Brasil          Brazil     D  ...      3       2  1.415518\n",
              "3           Espanha           Spain     C  ...      2       0  1.266874\n",
              "4         Argentina       Argentina     C  ...      2       0  1.256134\n",
              "5            México          Mexico     A  ...      0       0  1.237830\n",
              "6          Alemanha         Germany     D  ...      1       1  1.207622\n",
              "7          Honduras        Honduras     B  ...      0       0  0.864043\n",
              "8             Japão           Japan     A  ...      0       0  1.062178\n",
              "9           Romênia         Romania     B  ...      0       0  0.979390\n",
              "10    Coréia do Sul  Korea Republic     B  ...      0       0  0.994317\n",
              "11        Austrália       Australia     C  ...      0       0  0.990235\n",
              "12            Egito           Egypt     C  ...      0       0  0.956544\n",
              "13  Costa do Marfim   Côte d'Ivoire     D  ...      0       0  0.893493\n",
              "14   Arábia Saudita    Saudi Arabia     D  ...      0       0  0.867856\n",
              "15    África do Sul    South Africa     A  ...      0       0  0.818937\n",
              "16    Nova Zelândia     New Zealand     B  ...      0       0  0.615827\n",
              "\n",
              "[16 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mmf--NEC795M"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eeQBDnWcmOCv"
      },
      "source": [
        "## Início das funções da simulação\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KD9lAWE85gc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "397cc5b3-3467-400d-be85-d767dcbf1f8d"
      },
      "source": [
        "# Função que retorna o resultado V, D ou E para as entradas dos gols de cada equipe eu uma partida simulada\n",
        "\n",
        "def Resultado(gols1, gols2):\n",
        "    if gols1 > gols2:\n",
        "        res = 'V'\n",
        "    if gols1 < gols2:\n",
        "        res = 'D' \n",
        "    if gols1 == gols2:\n",
        "        res = 'E'       \n",
        "    return res\n",
        "\n",
        "Resultado(2,2)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'E'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMDTOhofnv4w",
        "outputId": "c3bfbdb4-2f7f-44c1-de40-8c245bbfab02"
      },
      "source": [
        "# Retorna os pontos de cada equipe ao fim da partida (primeira fase da copa)\n",
        "\n",
        "def Pontos(gols1, gols2):\n",
        "    rst = Resultado(gols1, gols2)\n",
        "    if rst == 'V':\n",
        "        pontos1, pontos2 = 3, 0\n",
        "    if rst == 'E':\n",
        "        pontos1, pontos2 = 1, 1\n",
        "    if rst == 'D':\n",
        "        pontos1, pontos2 = 0, 3\n",
        "    return pontos1, pontos2, rst\n",
        "\n",
        "Pontos(1, 2)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 3, 'D')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKo1SCQsnwMf",
        "outputId": "bf73b11c-f232-40ec-ef80-ca1838d258dd"
      },
      "source": [
        "# Função para apresentar os resultados de cada jogo. \n",
        "# Entre a força das duas equipes e retorna os detalhes da partida simulada pela VA Poisson\n",
        "\n",
        "def Jogo(time1, time2):\n",
        "    gols1 = int(poisson.rvs(mu = time1, size = 1))\n",
        "    gols2 = int(poisson.rvs(mu = time2, size = 1)) \n",
        "    saldo1 = gols1 - gols2\n",
        "    saldo2 = -saldo1\n",
        "    pontos1, pontos2, result = Pontos(gols1, gols2)\n",
        "    placar = '{}x{}'.format(gols1, gols2)\n",
        "    return [gols1, gols2, saldo1, saldo2, pontos1, pontos2, result, placar]\n",
        "\n",
        "Jogo(1.2, 0.8)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 1, 1, -1, 3, 0, 'V', '2x1']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "1vKf1oudI6AX",
        "outputId": "6452ad23-5a38-43b5-bdea-90d93b579752"
      },
      "source": [
        "dados.loc[dados['Grupo'] == 'A'][['País', 'Força']].reset_index(drop = True)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>País</th>\n",
              "      <th>Força</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>França</td>\n",
              "      <td>1.314534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>México</td>\n",
              "      <td>1.173534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Japão</td>\n",
              "      <td>1.046280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>África do Sul</td>\n",
              "      <td>0.860871</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0           País     Força\n",
              "0         França  1.314534\n",
              "1         México  1.173534\n",
              "2          Japão  1.046280\n",
              "3  África do Sul  0.860871"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_868PM6U9UA_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b47260f-0ccd-4538-bb71-8d96fc924536"
      },
      "source": [
        "def JogosGrupo(dados, grupo): \n",
        "    aux = dados.loc[dados['Grupo'] == grupo][['País', 'Força']].reset_index(drop = True)\n",
        "\n",
        "    time1, time2, time3, time4 = aux['Força']\n",
        "    nome1, nome2, nome3, nome4 = aux['País']\n",
        "\n",
        "    pt1, pt2, pt3, pt4 = 0, 0, 0, 0\n",
        "    gp1, gp2, gp3, gp4 = 0, 0, 0, 0\n",
        "    sg1, sg2, sg3, sg4 = 0, 0, 0, 0\n",
        "\n",
        "    jogo1 = Jogo(time1, time2)\n",
        "    jogo2 = Jogo(time3, time4)\n",
        "\n",
        "    jogo3 = Jogo(time1, time3)\n",
        "    jogo4 = Jogo(time2, time4)\n",
        "\n",
        "    jogo5 = Jogo(time1, time4)\n",
        "    jogo6 = Jogo(time2, time3)\n",
        "\n",
        "    gp1, gp2, sg1, sg2, pt1, pt2 = gp1 + jogo1[0], gp2 + jogo1[1], sg1 + jogo1[2], sg2 + jogo1[3], pt1 + jogo1[4], pt2 + jogo1[5]\n",
        "    gp3, gp4, sg3, sg4, pt3, pt4 = gp3 + jogo2[0], gp4 + jogo2[1], sg3 + jogo2[2], sg4 + jogo2[3], pt3 + jogo2[4], pt4 + jogo2[5]\n",
        "    gp1, gp3, sg1, sg3, pt1, pt3 = gp1 + jogo3[0], gp3 + jogo3[1], sg1 + jogo3[2], sg3 + jogo3[3], pt1 + jogo3[4], pt3 + jogo3[5]\n",
        "    gp2, gp4, sg2, sg4, pt2, pt4 = gp2 + jogo4[0], gp4 + jogo4[1], sg2 + jogo4[2], sg4 + jogo4[3], pt2 + jogo4[4], pt4 + jogo4[5]\n",
        "    gp1, gp4, sg1, sg4, pt1, pt4 = gp1 + jogo5[0], gp4 + jogo5[1], sg1 + jogo5[2], sg4 + jogo5[3], pt1 + jogo5[4], pt4 + jogo5[5]\n",
        "    gp2, gp3, sg2, sg3, pt2, pt3 = gp2 + jogo6[0], gp3 + jogo6[1], sg2 + jogo6[2], sg3 + jogo6[3], pt2 + jogo6[4], pt3 + jogo6[5]\n",
        "\n",
        "    partidas = [ nome1 + ' x ' + nome2, nome3 + ' x ' + nome4, nome1 + ' x ' + nome3, \n",
        "                 nome2 + ' x ' + nome4, nome1 + ' x ' + nome4, nome2 + ' x ' + nome3 ]\n",
        "    resultados = [ jogo1[6], jogo2[6], jogo3[6], jogo4[6], jogo5[6], jogo6[6] ]\n",
        "    placares = [ jogo1[-1], jogo2[-1], jogo3[-1], jogo4[-1], jogo5[-1], jogo6[-1] ]\n",
        "    linhas = aux['País']\n",
        "    cols = ['Pontos', 'Saldo de Gols', 'Gols Pró']\n",
        "    tab = pd.DataFrame([[pt1, pt2, pt3, pt4], [sg1, sg2, sg3, sg4], [gp1, gp2, gp3, gp4]], index = cols, columns = linhas).transpose()\n",
        "    \n",
        "    tab = tab.sort_values(['Pontos', 'Saldo de Gols', 'Gols Pró'], ascending = False)\n",
        "    tab['Posição'] = [1, 2, 3, 4]\n",
        "\n",
        "    jogos = pd.DataFrame([partidas, placares, resultados]).transpose()\n",
        "    jogos.columns = ['Partida', 'Placar', 'Resultado']\n",
        "\n",
        "    return [tab, jogos]\n",
        "\n",
        "JogosGrupo(dados, 'D')    "
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[                 Pontos  Saldo de Gols  Gols Pró  Posição\n",
              " País                                                     \n",
              " Brasil                7              8        11        1\n",
              " Alemanha              4              0         5        2\n",
              " Costa do Marfim       3             -2         4        3\n",
              " Arábia Saudita        3             -6         4        4,\n",
              "                             Partida Placar Resultado\n",
              " 0                 Brasil x Alemanha    1x1         E\n",
              " 1  Costa do Marfim x Arábia Saudita    0x2         D\n",
              " 2          Brasil x Costa do Marfim    3x2         V\n",
              " 3         Alemanha x Arábia Saudita    3x2         V\n",
              " 4           Brasil x Arábia Saudita    7x0         V\n",
              " 5        Alemanha x Costa do Marfim    1x2         D]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LE48fU1Yh5xD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "ac1b6f03-83a9-4c2a-ab00-89b927539e49"
      },
      "source": [
        "def JogoMataMata(sel1, sel2):\n",
        "    time1 = dados.loc[dados['País'] == sel1]['Força']\n",
        "    time2 = dados.loc[dados['País'] == sel2]['Força']\n",
        "    gols1 = int(poisson.rvs(mu = time1, size = 1))\n",
        "    gols2 = int(poisson.rvs(mu = time2, size = 1))  \n",
        "    resultado = Resultado(gols1, gols2)\n",
        "    if resultado == 'E':\n",
        "        resultado = random.sample(['V', 'D'], 1)\n",
        "    placar = '{}x{}'.format(gols1, gols2)\n",
        "    vencedor = sel1 if resultado == 'V' else sel2\n",
        "    return vencedor\n",
        " \n",
        "JogoMataMata('Brasil', 'Argentina')"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Brasil'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vxPoq-Zoh5uS"
      },
      "source": [
        "**Quartas de final**\n",
        "\n",
        "1: 1A vs 2B\\\n",
        "2: 1B vs 2A\\\n",
        "3: 1C vs 2D\\\n",
        "4: 1D vs *2C*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "EwZd1yevL_Mk",
        "outputId": "7eb0f251-c1e7-48ca-e611-b5463f43d1bd"
      },
      "source": [
        "    cols = ['1st', '2nd', '3th', '4th', 'Quartas', 'Semis', 'Final', 'Campeão']\n",
        "    lins = dados['País'].to_list()\n",
        "    n = len(lins)\n",
        "    m = len(cols)\n",
        "    aux = np.array(np.zeros(n*m).reshape(n, m))\n",
        "    info = pd.DataFrame(aux, columns = cols, index = lins) \n",
        "    info = info.astype(int)\n",
        "    info"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>1st</th>\n",
              "      <th>2nd</th>\n",
              "      <th>3th</th>\n",
              "      <th>4th</th>\n",
              "      <th>Quartas</th>\n",
              "      <th>Semis</th>\n",
              "      <th>Final</th>\n",
              "      <th>Campeão</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>França</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brasil</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Espanha</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>México</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alemanha</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Honduras</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japão</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romênia</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coréia do Sul</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austrália</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Egito</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Costa do Marfim</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arábia Saudita</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>África do Sul</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nova Zelândia</th>\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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 1st  2nd  3th  4th  Quartas  Semis  Final  Campeão\n",
              "França             0    0    0    0        0      0      0        0\n",
              "Brasil             0    0    0    0        0      0      0        0\n",
              "Espanha            0    0    0    0        0      0      0        0\n",
              "Argentina          0    0    0    0        0      0      0        0\n",
              "México             0    0    0    0        0      0      0        0\n",
              "Alemanha           0    0    0    0        0      0      0        0\n",
              "Honduras           0    0    0    0        0      0      0        0\n",
              "Japão              0    0    0    0        0      0      0        0\n",
              "Romênia            0    0    0    0        0      0      0        0\n",
              "Coréia do Sul      0    0    0    0        0      0      0        0\n",
              "Austrália          0    0    0    0        0      0      0        0\n",
              "Egito              0    0    0    0        0      0      0        0\n",
              "Costa do Marfim    0    0    0    0        0      0      0        0\n",
              "Arábia Saudita     0    0    0    0        0      0      0        0\n",
              "África do Sul      0    0    0    0        0      0      0        0\n",
              "Nova Zelândia      0    0    0    0        0      0      0        0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIWGuqgA9b7u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "160c7ce9-9781-4a10-973d-d89006cc36cb"
      },
      "source": [
        "def SimulaCopa(dados):\n",
        "    cols = ['1st', '2nd', '3th', '4th', 'Quartas', 'Semis', 'Final', 'Campeão']\n",
        "    lins = dados['País'].to_list()\n",
        "    n = len(lins)\n",
        "    m = len(cols)\n",
        "    aux = np.array(np.zeros(n*m).reshape(n, m))\n",
        "    info = pd.DataFrame(aux, columns = cols, index = lins) \n",
        "    info = info.astype(int)\n",
        "\n",
        "    top8 = []\n",
        "    for i in list('ABCD'):\n",
        "        a = JogosGrupo(dados, i)[0] \n",
        "        top8 += a.index[:2].tolist()\n",
        "        anomes = a.index.to_list() \n",
        "        info.at[anomes[0], '1st'] = 1\n",
        "        info.at[anomes[1], '2nd'] = 1\n",
        "        info.at[anomes[2], '3th'] = 1\n",
        "        info.at[anomes[3], '4th'] = 1\n",
        "\n",
        "    sf1 = JogoMataMata(top8[0], top8[3])\n",
        "    sf2 = JogoMataMata(top8[2], top8[1])\n",
        "    sf3 = JogoMataMata(top8[4], top8[7])\n",
        "    sf4 = JogoMataMata(top8[6], top8[5])\n",
        "\n",
        "    top4 = [sf1, sf2, sf3, sf4]\n",
        "\n",
        "    f1 = JogoMataMata(sf1, sf2)\n",
        "    f2 = JogoMataMata(sf3, sf4)\n",
        "\n",
        "    top2 = [f1, f2]\n",
        "\n",
        "    top1 = [JogoMataMata(f1, f2)]\n",
        "\n",
        "    info.at[top8, 'Quartas'] = 1\n",
        "    info.at[top4, 'Semis'] = 1\n",
        "    info.at[top2, 'Final'] = 1\n",
        "    info.at[top1, 'Campeão'] = 1\n",
        "    return info\n",
        "\n",
        "SimulaCopa(dados)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>1st</th>\n",
              "      <th>2nd</th>\n",
              "      <th>3th</th>\n",
              "      <th>4th</th>\n",
              "      <th>Quartas</th>\n",
              "      <th>Semis</th>\n",
              "      <th>Final</th>\n",
              "      <th>Campeão</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>França</th>\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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brasil</th>\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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Espanha</th>\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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>México</th>\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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alemanha</th>\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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Honduras</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japão</th>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romênia</th>\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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coréia do Sul</th>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austrália</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Egito</th>\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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Costa do Marfim</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arábia Saudita</th>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>África do Sul</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nova Zelândia</th>\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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 1st  2nd  3th  4th  Quartas  Semis  Final  Campeão\n",
              "França             0    0    1    0        0      0      0        0\n",
              "Brasil             1    0    0    0        1      0      0        0\n",
              "Espanha            1    0    0    0        1      0      0        0\n",
              "Argentina          0    1    0    0        1      1      1        0\n",
              "México             1    0    0    0        1      1      1        1\n",
              "Alemanha           0    0    1    0        0      0      0        0\n",
              "Honduras           0    0    0    1        0      0      0        0\n",
              "Japão              0    1    0    0        1      1      0        0\n",
              "Romênia            1    0    0    0        1      0      0        0\n",
              "Coréia do Sul      0    1    0    0        1      0      0        0\n",
              "Austrália          0    0    0    1        0      0      0        0\n",
              "Egito              0    0    1    0        0      0      0        0\n",
              "Costa do Marfim    0    0    0    1        0      0      0        0\n",
              "Arábia Saudita     0    1    0    0        1      1      0        0\n",
              "África do Sul      0    0    0    1        0      0      0        0\n",
              "Nova Zelândia      0    0    1    0        0      0      0        0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19Cs3L2F9UG4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "outputId": "a15fa21c-2ae2-49e0-d661-11bee9c67054"
      },
      "source": [
        "def SimulacaoTotal(dados, S = 1000): \n",
        "    print('Iniciando simulação...')\n",
        "    info = SimulaCopa(dados)\n",
        "    for i in range(S-1):\n",
        "        info += SimulaCopa(dados)\n",
        "        if (i+2)%(S/10) == 0:\n",
        "            print('Simulando Copa: {:.0f}% pronto'.format(100*((i+2)/S)))\n",
        "    return info.sort_values(by = 'Campeão', ascending = False)/S\n",
        "\n",
        "sim = SimulacaoTotal(dados, 1000)\n",
        "sim"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iniciando simulação...\n",
            "Simulando Copa: 10% pronto\n",
            "Simulando Copa: 20% pronto\n",
            "Simulando Copa: 30% pronto\n",
            "Simulando Copa: 40% pronto\n",
            "Simulando Copa: 50% pronto\n",
            "Simulando Copa: 60% pronto\n",
            "Simulando Copa: 70% pronto\n",
            "Simulando Copa: 80% pronto\n",
            "Simulando Copa: 90% pronto\n",
            "Simulando Copa: 100% pronto\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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>1st</th>\n",
              "      <th>2nd</th>\n",
              "      <th>3th</th>\n",
              "      <th>4th</th>\n",
              "      <th>Quartas</th>\n",
              "      <th>Semis</th>\n",
              "      <th>Final</th>\n",
              "      <th>Campeão</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Brasil</th>\n",
              "      <td>0.446</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.186</td>\n",
              "      <td>0.121</td>\n",
              "      <td>0.693</td>\n",
              "      <td>0.376</td>\n",
              "      <td>0.210</td>\n",
              "      <td>0.139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Espanha</th>\n",
              "      <td>0.319</td>\n",
              "      <td>0.265</td>\n",
              "      <td>0.220</td>\n",
              "      <td>0.196</td>\n",
              "      <td>0.584</td>\n",
              "      <td>0.296</td>\n",
              "      <td>0.161</td>\n",
              "      <td>0.118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>França</th>\n",
              "      <td>0.379</td>\n",
              "      <td>0.292</td>\n",
              "      <td>0.209</td>\n",
              "      <td>0.120</td>\n",
              "      <td>0.671</td>\n",
              "      <td>0.423</td>\n",
              "      <td>0.256</td>\n",
              "      <td>0.112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alemanha</th>\n",
              "      <td>0.273</td>\n",
              "      <td>0.285</td>\n",
              "      <td>0.242</td>\n",
              "      <td>0.200</td>\n",
              "      <td>0.558</td>\n",
              "      <td>0.294</td>\n",
              "      <td>0.151</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>0.329</td>\n",
              "      <td>0.279</td>\n",
              "      <td>0.234</td>\n",
              "      <td>0.158</td>\n",
              "      <td>0.608</td>\n",
              "      <td>0.295</td>\n",
              "      <td>0.154</td>\n",
              "      <td>0.099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austrália</th>\n",
              "      <td>0.181</td>\n",
              "      <td>0.237</td>\n",
              "      <td>0.270</td>\n",
              "      <td>0.312</td>\n",
              "      <td>0.418</td>\n",
              "      <td>0.204</td>\n",
              "      <td>0.113</td>\n",
              "      <td>0.074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>México</th>\n",
              "      <td>0.292</td>\n",
              "      <td>0.274</td>\n",
              "      <td>0.240</td>\n",
              "      <td>0.194</td>\n",
              "      <td>0.566</td>\n",
              "      <td>0.323</td>\n",
              "      <td>0.187</td>\n",
              "      <td>0.065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Egito</th>\n",
              "      <td>0.171</td>\n",
              "      <td>0.219</td>\n",
              "      <td>0.276</td>\n",
              "      <td>0.334</td>\n",
              "      <td>0.390</td>\n",
              "      <td>0.176</td>\n",
              "      <td>0.092</td>\n",
              "      <td>0.055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japão</th>\n",
              "      <td>0.220</td>\n",
              "      <td>0.262</td>\n",
              "      <td>0.270</td>\n",
              "      <td>0.248</td>\n",
              "      <td>0.482</td>\n",
              "      <td>0.276</td>\n",
              "      <td>0.145</td>\n",
              "      <td>0.046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Costa do Marfim</th>\n",
              "      <td>0.134</td>\n",
              "      <td>0.252</td>\n",
              "      <td>0.309</td>\n",
              "      <td>0.305</td>\n",
              "      <td>0.386</td>\n",
              "      <td>0.183</td>\n",
              "      <td>0.060</td>\n",
              "      <td>0.037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coréia do Sul</th>\n",
              "      <td>0.319</td>\n",
              "      <td>0.261</td>\n",
              "      <td>0.230</td>\n",
              "      <td>0.190</td>\n",
              "      <td>0.580</td>\n",
              "      <td>0.258</td>\n",
              "      <td>0.126</td>\n",
              "      <td>0.034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arábia Saudita</th>\n",
              "      <td>0.147</td>\n",
              "      <td>0.216</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.374</td>\n",
              "      <td>0.363</td>\n",
              "      <td>0.176</td>\n",
              "      <td>0.059</td>\n",
              "      <td>0.032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romênia</th>\n",
              "      <td>0.315</td>\n",
              "      <td>0.272</td>\n",
              "      <td>0.232</td>\n",
              "      <td>0.181</td>\n",
              "      <td>0.587</td>\n",
              "      <td>0.232</td>\n",
              "      <td>0.094</td>\n",
              "      <td>0.029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>África do Sul</th>\n",
              "      <td>0.109</td>\n",
              "      <td>0.172</td>\n",
              "      <td>0.281</td>\n",
              "      <td>0.438</td>\n",
              "      <td>0.281</td>\n",
              "      <td>0.142</td>\n",
              "      <td>0.073</td>\n",
              "      <td>0.029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Honduras</th>\n",
              "      <td>0.247</td>\n",
              "      <td>0.271</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.235</td>\n",
              "      <td>0.518</td>\n",
              "      <td>0.222</td>\n",
              "      <td>0.081</td>\n",
              "      <td>0.022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nova Zelândia</th>\n",
              "      <td>0.119</td>\n",
              "      <td>0.196</td>\n",
              "      <td>0.291</td>\n",
              "      <td>0.394</td>\n",
              "      <td>0.315</td>\n",
              "      <td>0.124</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   1st    2nd    3th    4th  Quartas  Semis  Final  Campeão\n",
              "Brasil           0.446  0.247  0.186  0.121    0.693  0.376  0.210    0.139\n",
              "Espanha          0.319  0.265  0.220  0.196    0.584  0.296  0.161    0.118\n",
              "França           0.379  0.292  0.209  0.120    0.671  0.423  0.256    0.112\n",
              "Alemanha         0.273  0.285  0.242  0.200    0.558  0.294  0.151    0.100\n",
              "Argentina        0.329  0.279  0.234  0.158    0.608  0.295  0.154    0.099\n",
              "Austrália        0.181  0.237  0.270  0.312    0.418  0.204  0.113    0.074\n",
              "México           0.292  0.274  0.240  0.194    0.566  0.323  0.187    0.065\n",
              "Egito            0.171  0.219  0.276  0.334    0.390  0.176  0.092    0.055\n",
              "Japão            0.220  0.262  0.270  0.248    0.482  0.276  0.145    0.046\n",
              "Costa do Marfim  0.134  0.252  0.309  0.305    0.386  0.183  0.060    0.037\n",
              "Coréia do Sul    0.319  0.261  0.230  0.190    0.580  0.258  0.126    0.034\n",
              "Arábia Saudita   0.147  0.216  0.263  0.374    0.363  0.176  0.059    0.032\n",
              "Romênia          0.315  0.272  0.232  0.181    0.587  0.232  0.094    0.029\n",
              "África do Sul    0.109  0.172  0.281  0.438    0.281  0.142  0.073    0.029\n",
              "Honduras         0.247  0.271  0.247  0.235    0.518  0.222  0.081    0.022\n",
              "Nova Zelândia    0.119  0.196  0.291  0.394    0.315  0.124  0.038    0.009"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CbnMJI4S9VBO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8e898a4-d606-46b5-b7b0-a397421fc703"
      },
      "source": [
        "def SimulaJogo(eq1, eq2, dados = dados, S = 1000):\n",
        "    forca1 = dados.loc[dados['País'] == eq1]['Força']\n",
        "    forca2 = dados.loc[dados['País'] == eq2]['Força']\n",
        "      \n",
        "    resultados = [] \n",
        "    placares = []\n",
        "    for i in range(S):\n",
        "        jogo = Jogo(forca1, forca2)\n",
        "        resultados.append(jogo[-2])\n",
        "        placares.append(jogo[-1])\n",
        "\n",
        "    probs = (pd.Series(Counter(resultados))/S).sort_index(ascending = False).to_frame()\n",
        "    probs.columns = ['Probabilidade']\n",
        "\n",
        "    placs = (pd.Series(Counter(placares))/S).sort_values(ascending = False).to_frame()\n",
        "    placs.columns = ['Probabilidade']\n",
        "\n",
        "    a = list(range(0,8))\n",
        "    poss_plac = ['{}x{}'.format(x,y) for x in a for y in a]\n",
        "\n",
        "    import numpy as np\n",
        "    aux1 = pd.Series(np.zeros(len(poss_plac)), index = poss_plac).to_frame()\n",
        "\n",
        "    prob_placares = pd.concat([aux1, placs], axis = 1).fillna(0).drop(0, axis = 1)\n",
        "    prob_placares\n",
        "\n",
        "    return [probs, prob_placares]\n",
        "\n",
        "jogo = SimulaJogo('Brasil', 'Alemanha')\n",
        "jogo"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[   Probabilidade\n",
              " V          0.406\n",
              " E          0.267\n",
              " D          0.327,      Probabilidade\n",
              " 0x0          0.078\n",
              " 0x1          0.084\n",
              " 0x2          0.069\n",
              " 0x3          0.021\n",
              " 0x4          0.005\n",
              " ..             ...\n",
              " 7x3          0.000\n",
              " 7x4          0.000\n",
              " 7x5          0.000\n",
              " 7x6          0.000\n",
              " 7x7          0.000\n",
              " \n",
              " [64 rows x 1 columns]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1NWhcd9T7xxP"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I77jf-ZB0y9g",
        "outputId": "0ee7a2d9-0c64-4706-8477-c754af4fbb7b"
      },
      "source": [
        "# Questão 1: Qual seleção é mais provável ser campeã?\n",
        "resposta = sim.index[0]\n",
        "print('Resposta 1:', resposta)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resposta 1: Brasil\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQZUHMiY1CbP",
        "outputId": "b38cbae9-7399-44ed-fab4-bdb2c4af2c95"
      },
      "source": [
        "# Questão 2: Quais as probabilidades das seleções avançarem para segunda fase?\n",
        "resposta = sim['Quartas']\n",
        "print('Resposta 2:')\n",
        "resposta"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resposta 2:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brasil             0.693\n",
              "Espanha            0.584\n",
              "França             0.671\n",
              "Alemanha           0.558\n",
              "Argentina          0.608\n",
              "Austrália          0.418\n",
              "México             0.566\n",
              "Egito              0.390\n",
              "Japão              0.482\n",
              "Costa do Marfim    0.386\n",
              "Coréia do Sul      0.580\n",
              "Arábia Saudita     0.363\n",
              "Romênia            0.587\n",
              "África do Sul      0.281\n",
              "Honduras           0.518\n",
              "Nova Zelândia      0.315\n",
              "Name: Quartas, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "56kcewbO1VJr",
        "outputId": "d8fbbc33-cff7-4022-9c39-2efdb468e5a5"
      },
      "source": [
        "# Questão 3: Quais as probabilidades das seleções ficarem em 1º, 2º, 3º e 4º em seus grupos na primeira fase?\n",
        "resposta = sim[['1st', '2nd', '3th', '4th']]\n",
        "print('Resposta 3:')\n",
        "resposta"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resposta 3:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\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>1st</th>\n",
              "      <th>2nd</th>\n",
              "      <th>3th</th>\n",
              "      <th>4th</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Brasil</th>\n",
              "      <td>0.446</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.186</td>\n",
              "      <td>0.121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Espanha</th>\n",
              "      <td>0.319</td>\n",
              "      <td>0.265</td>\n",
              "      <td>0.220</td>\n",
              "      <td>0.196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>França</th>\n",
              "      <td>0.379</td>\n",
              "      <td>0.292</td>\n",
              "      <td>0.209</td>\n",
              "      <td>0.120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alemanha</th>\n",
              "      <td>0.273</td>\n",
              "      <td>0.285</td>\n",
              "      <td>0.242</td>\n",
              "      <td>0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>0.329</td>\n",
              "      <td>0.279</td>\n",
              "      <td>0.234</td>\n",
              "      <td>0.158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austrália</th>\n",
              "      <td>0.181</td>\n",
              "      <td>0.237</td>\n",
              "      <td>0.270</td>\n",
              "      <td>0.312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>México</th>\n",
              "      <td>0.292</td>\n",
              "      <td>0.274</td>\n",
              "      <td>0.240</td>\n",
              "      <td>0.194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Egito</th>\n",
              "      <td>0.171</td>\n",
              "      <td>0.219</td>\n",
              "      <td>0.276</td>\n",
              "      <td>0.334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japão</th>\n",
              "      <td>0.220</td>\n",
              "      <td>0.262</td>\n",
              "      <td>0.270</td>\n",
              "      <td>0.248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Costa do Marfim</th>\n",
              "      <td>0.134</td>\n",
              "      <td>0.252</td>\n",
              "      <td>0.309</td>\n",
              "      <td>0.305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coréia do Sul</th>\n",
              "      <td>0.319</td>\n",
              "      <td>0.261</td>\n",
              "      <td>0.230</td>\n",
              "      <td>0.190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arábia Saudita</th>\n",
              "      <td>0.147</td>\n",
              "      <td>0.216</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romênia</th>\n",
              "      <td>0.315</td>\n",
              "      <td>0.272</td>\n",
              "      <td>0.232</td>\n",
              "      <td>0.181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>África do Sul</th>\n",
              "      <td>0.109</td>\n",
              "      <td>0.172</td>\n",
              "      <td>0.281</td>\n",
              "      <td>0.438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Honduras</th>\n",
              "      <td>0.247</td>\n",
              "      <td>0.271</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nova Zelândia</th>\n",
              "      <td>0.119</td>\n",
              "      <td>0.196</td>\n",
              "      <td>0.291</td>\n",
              "      <td>0.394</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   1st    2nd    3th    4th\n",
              "Brasil           0.446  0.247  0.186  0.121\n",
              "Espanha          0.319  0.265  0.220  0.196\n",
              "França           0.379  0.292  0.209  0.120\n",
              "Alemanha         0.273  0.285  0.242  0.200\n",
              "Argentina        0.329  0.279  0.234  0.158\n",
              "Austrália        0.181  0.237  0.270  0.312\n",
              "México           0.292  0.274  0.240  0.194\n",
              "Egito            0.171  0.219  0.276  0.334\n",
              "Japão            0.220  0.262  0.270  0.248\n",
              "Costa do Marfim  0.134  0.252  0.309  0.305\n",
              "Coréia do Sul    0.319  0.261  0.230  0.190\n",
              "Arábia Saudita   0.147  0.216  0.263  0.374\n",
              "Romênia          0.315  0.272  0.232  0.181\n",
              "África do Sul    0.109  0.172  0.281  0.438\n",
              "Honduras         0.247  0.271  0.247  0.235\n",
              "Nova Zelândia    0.119  0.196  0.291  0.394"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "tCpVVGSp1u8v",
        "outputId": "b7fe81e1-9083-4b83-99a1-facbba03c7d5"
      },
      "source": [
        "# Questão 4: A força de uma equipe é proporcional a sua probabilidade de vencer a competição? Se não, porquê?\n",
        "print('Resposta 4:') \n",
        "plt.scatter(dados['Força'], sim['Campeão']);"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resposta 4:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Id0P0s4L2Pfq",
        "outputId": "c8917e81-6a60-4960-d276-4bf28259cd64"
      },
      "source": [
        "# Questão 5: Qual a probabilidade de todos cabeça de chave passarem simultâneamente para a segunda fase? (Probabilidade da 'zebra')\n",
        "print('Resposta 5:') \n",
        "dados['2fase'] = (sim['1st'] + sim['2nd']).values\n",
        "dados[dados['Cabeça-de-chave'] == 'Sim']['2fase'].prod()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resposta 5:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10482283627200002"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-EGUa-q38pA",
        "outputId": "86c05f66-debc-439c-88cb-2253e8671f4a"
      },
      "source": [
        "# Questão 6: Qual a probabilidade de pelo menos uma das quatro seleções mais fracas ganhar a competição?\n",
        "print('Resposta 6:')  \n",
        "dados['Campeão'] = sim['Campeão'].values\n",
        "dados.sort_values(by = 'Ranking FIFA (07/04)', ascending = False).iloc[:4, :]['Campeão'].sum()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resposta 6:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.125"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Bo68CDis4Jd9",
        "outputId": "bc840f10-8003-4924-9207-92ee9b04e2b5"
      },
      "source": [
        "# Questão 7: Qual a probabilidade de vencer a competição por confederação?\n",
        "print('Resposta 7:')  \n",
        "dados['Campeão'].groupby(dados['Confederação']).sum().plot(kind = 'barh');"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resposta 7:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDxgbNDg4LcK",
        "outputId": "ad045272-edde-4b4f-b6b4-1a209f1fe07e"
      },
      "source": [
        "# Questão 8: Qual a probabilidade de uma seleção sem medalhas em sua história vencer a competição?\n",
        "print('Resposta 8:')  \n",
        "dados[dados['Ouros'] + dados['Pratas'] + dados['Bronzes'] == 0]['Campeão'].sum()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resposta 8:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.358"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "mX3OwqhU4Vxb",
        "outputId": "b9471668-fc93-435d-ab4a-9bcaeb0680b6"
      },
      "source": [
        "# Questão 9: Se o Brasil jogar com a Alemanha, qual a probabilidade de acontecer um 7x1 para a Alemanha? E um 7x1 para o Brasil? Quais os placares mais prováveis?\n",
        "print('Resposta 9:')  \n",
        "jogo = SimulaJogo('Brasil', 'Alemanha', S = 10000)\n",
        "jogo[1].tail(10)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resposta 9:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>Probabilidade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6x6</th>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6x7</th>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7x0</th>\n",
              "      <td>0.0002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7x1</th>\n",
              "      <td>0.0002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7x2</th>\n",
              "      <td>0.0002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7x3</th>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7x4</th>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7x5</th>\n",
              "      <td>0.0001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7x6</th>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7x7</th>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Probabilidade\n",
              "6x6         0.0000\n",
              "6x7         0.0000\n",
              "7x0         0.0002\n",
              "7x1         0.0002\n",
              "7x2         0.0002\n",
              "7x3         0.0000\n",
              "7x4         0.0000\n",
              "7x5         0.0001\n",
              "7x6         0.0000\n",
              "7x7         0.0000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "JNnZNUJg6GHu",
        "outputId": "658288b9-1ecd-48ba-f04e-0ff04268875a"
      },
      "source": [
        "jogo[1].head(16)"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Probabilidade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0x0</th>\n",
              "      <td>0.0759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0x1</th>\n",
              "      <td>0.0847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0x2</th>\n",
              "      <td>0.0501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0x3</th>\n",
              "      <td>0.0248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0x4</th>\n",
              "      <td>0.0055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0x5</th>\n",
              "      <td>0.0011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0x6</th>\n",
              "      <td>0.0005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0x7</th>\n",
              "      <td>0.0001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1x0</th>\n",
              "      <td>0.1119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1x1</th>\n",
              "      <td>0.1220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1x2</th>\n",
              "      <td>0.0757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1x3</th>\n",
              "      <td>0.0269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1x4</th>\n",
              "      <td>0.0091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1x5</th>\n",
              "      <td>0.0025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1x6</th>\n",
              "      <td>0.0004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1x7</th>\n",
              "      <td>0.0002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Probabilidade\n",
              "0x0         0.0759\n",
              "0x1         0.0847\n",
              "0x2         0.0501\n",
              "0x3         0.0248\n",
              "0x4         0.0055\n",
              "0x5         0.0011\n",
              "0x6         0.0005\n",
              "0x7         0.0001\n",
              "1x0         0.1119\n",
              "1x1         0.1220\n",
              "1x2         0.0757\n",
              "1x3         0.0269\n",
              "1x4         0.0091\n",
              "1x5         0.0025\n",
              "1x6         0.0004\n",
              "1x7         0.0002"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "PawCQWHV6SYM",
        "outputId": "69c550db-275a-434f-aead-530a245b29b6"
      },
      "source": [
        "#Os 10 placares mais prováveis\n",
        "jogo[1].sort_values(by = 'Probabilidade', ascending = False).head(10)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Probabilidade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1x1</th>\n",
              "      <td>0.1220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1x0</th>\n",
              "      <td>0.1119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2x1</th>\n",
              "      <td>0.0850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0x1</th>\n",
              "      <td>0.0847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0x0</th>\n",
              "      <td>0.0759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1x2</th>\n",
              "      <td>0.0757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2x0</th>\n",
              "      <td>0.0756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2x2</th>\n",
              "      <td>0.0516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0x2</th>\n",
              "      <td>0.0501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3x1</th>\n",
              "      <td>0.0426</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Probabilidade\n",
              "1x1         0.1220\n",
              "1x0         0.1119\n",
              "2x1         0.0850\n",
              "0x1         0.0847\n",
              "0x0         0.0759\n",
              "1x2         0.0757\n",
              "2x0         0.0756\n",
              "2x2         0.0516\n",
              "0x2         0.0501\n",
              "3x1         0.0426"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "RMYE0x_8S9bx",
        "outputId": "7fa320ce-69cc-45fb-b0b8-b60a64efabc3"
      },
      "source": [
        "jogo[0]"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Probabilidade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>V</th>\n",
              "      <td>0.4232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.2615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.3153</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Probabilidade\n",
              "V         0.4232\n",
              "E         0.2615\n",
              "D         0.3153"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "QJJs8FUKTA7-",
        "outputId": "03d4fcf1-6a45-47f2-e8ff-61529373c73c"
      },
      "source": [
        "# Questão 10: Qual a probabilidade de cada grupo apresentar um campeão?\n",
        "print('Resposta 10:')  \n",
        "dados['Campeão'].groupby(dados['Grupo']).sum().plot(kind = 'barh');"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resposta 10:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANIUlEQVR4nO3df6zd9V3H8efbW2nXGWqxnWI7uTRhIWAnhs4/iDhhS0CbAWZEYSGSjdhNN5dootZgovGfNcZoNJKQ+sc2MLP7JRPSbQmTaULiRm6x7IpJGS0lW3XiylLdINOWt3+cb8Pp7Y972nM+53vuu89HcnK/53t+fN7v+21f93s/3+/53shMJEk1/UDfBUiS2jHkJakwQ16SCjPkJakwQ16SClvVdwFLbdiwIefn5/suQ5JWlH379n07MzcuXT9zIT8/P8/CwkLfZUjSihIRL55pvdM1klSYIS9JhRnyklSYIS9JhRnyklSYIS9JhRnyklSYIS9JhRnyklSYIS9JhRnyklSYIS9Jhc3cBcoWjxxjfufevsuQNCWHd23vu4TS3JOXpMIMeUkqzJCXpMIMeUkqzJCXpMIMeUkqzJCXpMIMeUkqzJCXpMKafeI1Ik4Ai8APAseBh4A/z8zXWo0pSTpVy8savJqZ1wFExJuATwCXAn/YcExJ0pCpTNdk5kvADuBDERHTGFOSNMU5+cw8BMwBb5rWmJJ0sZuJA68RsSMiFiJi4cQrx/ouR5LKmFrIR8QW4ATw0tLHMnN3Zm7LzG1za9dNqyRJKm8qIR8RG4EHgb/KzJzGmJKktmfXvCEi9vP6KZQPA3/WcDxJ0hLNQj4z51q9tyRpNDNx4FWS1IYhL0mFGfKSVJghL0mFGfKSVJghL0mFGfKSVJghL0mFtfzE6wXZumkdC7u2912GJJXgnrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1Jhq/ouYKnFI8eY37m37zIkFXV41/a+S5gq9+QlqTBDXpIKM+QlqTBDXpIKM+QlqTBDXpIKM+QlqTBDXpIKM+QlqTBDXpIKax7yEfFjEbEnIg5GxL6I+HxEvKX1uJKkxteuiYgAHgE+npl3det+CvhR4LmWY0uS2l+g7Cbg/zLzwZMrMvOZxmNKkjqtp2t+Eti33JMiYkdELETEwolXjjUuSZIuHjNx4DUzd2fmtszcNrd2Xd/lSFIZrUP+WeD6xmNIks6idcg/AayOiB0nV0TEWyPixsbjSpJoHPKZmcAvAe/sTqF8FvgI8K2W40qSBpr/+b/M/Hfgl1uPI0k63UwceJUktWHIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFdb8PPnztXXTOhZ2be+7DEkqwT15SSpspJCPiM0R8UhE/FdEvBQRn42Iza2LkySNZ9Q9+Y8CjwKXAz8OPNatkyTNsFFDfmNmfjQzj3e3jwEbG9YlSZqAUUP+aETcExFz3e0e4GjLwiRJ4xs15N/H4EqS3+pudwLvbVWUJGkyRjqFMjNfBG5rXIskacJGPbtmS0Q8NnR2zd9HxJbWxUmSxjPqdM0ngE/x+tk1nwb+tlVRkqTJGDXk12bmw0Nn1/wNsKZlYZKk8Y16WYMvRMROYA+QwK8An4+IywAy8+VG9UmSxjBqyJ/8G63vX7L+Lgah7/y8JM2gUc+uubJ1IZKkyRsp5CPiV8+0PjMfmmw5kqRJGnW65m1Dy2uAdwBPA4a8JM2wUadrfnP4fkT8MIODsJKkGXah15P/HuA8vSTNuFHn5B9jcBYNDH4wXMPgw1GSpBk26pz8nw4tHwdezMxvNqhHkjRBy4Z8RMwBf5SZN02hHknSBC07J5+ZJ4DXImLdFOqRJE3QqNM13wUWI+JxBgddAcjMDzepSpI0EaOG/N91N3j9AGxMvhxJ0iSdM+Qj4nZgc2Y+0N1/isHfdk3g99qXJ0kax3Jz8r8LPDp0/xLgeuDngQ80qkmSNCHLTddckpnfGLr/ZHdZ4Zcj4o0N65IkTcBye/Lrh+9k5oeG7m6cfDmSpElabk/+qxHxa5n518MrI+L9wFMtClo8coz5nXtbvLV0Tod3be+7BGnilgv53wI+FxHvYXDVSRjMya8G7mhZmCRpfOcM+cx8CbghIm4Gru1W783MJ5pXJkka26iXGn4CMNglaYW50EsNS5JWAENekgoz5CWpMENekgoz5CWpMENekgprGvIRcSIi9kfEMxHxdETc0HI8SdKpRr2e/IV6NTOvA4iIW4CPAG9vPKYkqTPN6ZpLge9McTxJuui13pN/Q0TsB9YAlwM3n+lJEbED2AEwd6kXt5SkSWm9J/9qZl6XmVcDtwIPRcRpfzYwM3dn5rbM3Da31r8XLkmTMrXpmsz8Z2ADXodekqZmaiEfEVcDc8DRaY0pSRe7ac3JAwRwb2aeaDymJKnTNOQzc67l+0uSzs1PvEpSYYa8JBVmyEtSYYa8JBVmyEtSYYa8JBVmyEtSYYa8JBXW+hOv523rpnUs7NredxmSVIJ78pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYUZ8pJUmCEvSYWt6ruApRaPHGN+596+y5CkqTq8a3uT93VPXpIKM+QlqTBDXpIKM+QlqTBDXpIKM+QlqTBDXpIKM+QlqTBDXpIKax7yEXFHRGREXN16LEnSqaaxJ3838GT3VZI0RU1DPiJ+CPhZ4D7grpZjSZJO13pP/nbgi5n5HHA0Iq5vPJ4kaUjrkL8b2NMt7+EsUzYRsSMiFiJi4cQrxxqXJEkXj2aXGo6Iy4Cbga0RkcAckBHxO5mZw8/NzN3AboDVl1+Vp72ZJOmCtNyTvxN4ODOvyMz5zHwz8AJwY8MxJUlDWob83cAjS9Z9Fs+ykaSpaTZdk5k3nWHdX7YaT5J0Oj/xKkmFGfKSVJghL0mFGfKSVJghL0mFGfKSVJghL0mFGfKSVFizD0NdqK2b1rGwa3vfZUhSCe7JS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhhrwkFWbIS1JhkZl913CKiPgf4EDfdUzIBuDbfRcxAVX6gDq9VOkD7GVSrsjMjUtXztylhoEDmbmt7yImISIWKvRSpQ+o00uVPsBeWnO6RpIKM+QlqbBZDPndfRcwQVV6qdIH1OmlSh9gL03N3IFXSdLkzOKevCRpQgx5SSpsqiEfEbdGxIGIeD4idp7h8dUR8cnu8a9GxPzQY7/frT8QEbdMs+6lLrSPiJiPiFcjYn93e3DatS81Qi8/FxFPR8TxiLhzyWP3RsTXu9u906v6dGP2cWJomzw6varPbIRefjsi/i0ivhYR/xARVww9NjPbpKtnnF5mZruM0McHImKxq/XJiLhm6LF+syszp3ID5oCDwBbgEuAZ4Jolz/kN4MFu+S7gk93yNd3zVwNXdu8zN63aJ9jHPPCvfdQ9Ri/zwFuBh4A7h9ZfBhzqvq7vltevtD66x77b97Y4z15uAtZ2y78+9O9rZrbJuL3M0nYZsY9Lh5ZvA77YLfeeXdPck/8Z4PnMPJSZ/wvsAW5f8pzbgY93y58B3hER0a3fk5nfz8wXgOe79+vDOH3MmmV7yczDmfk14LUlr70FeDwzX87M7wCPA7dOo+gzGKePWTNKL1/OzFe6u18BNnfLs7RNYLxeZskoffz30N03AifPaOk9u6YZ8puAbwzd/2a37ozPyczjwDHgR0Z87bSM0wfAlRHxLxHxTxFxY+tilzHO93WlbZNzWRMRCxHxlYi4Y7Klnbfz7eU+4AsX+NrWxukFZme7jNRHRHwwIg4CfwJ8+Hxe29IsXtagsv8AfiIzj0bE9cDnIuLaJXsBmr4rMvNIRGwBnoiIxcw82HdRy4mIe4BtwNv7rmVcZ+llRW2XzHwAeCAi3gP8AdD7MRGY7p78EeDNQ/c3d+vO+JyIWAWsA46O+NppueA+ul/ZjgJk5j4G83NvaV7x2Y3zfV1p2+SsMvNI9/UQ8I/AT0+yuPM0Ui8R8U7gfuC2zPz++bx2isbpZZa2y/l+X/cAJ3/z6H+bTPHgxSoGB4Ku5PWDF9cuec4HOfWA5ae65Ws59eDFIfo78DpOHxtP1s3gIM4R4LI++hi1l6HnfozTD7y+wOAA3/puuZdexuxjPbC6W94AfJ0lB9VmrRcGYXcQuGrJ+pnZJhPoZWa2y4h9XDW0/C5goVvuPbum/c36ReC5bqPe3637YwY/wQHWAJ9mcHDiKWDL0Gvv7153APiFPjb2uH0A7waeBfYDTwPv6rOPEXt5G4N5xO8x+K3q2aHXvq/r8XngvSuxD+AGYLH7j7gI3LcCtsmXgP/s/h3tBx6dxW0yTi+ztl1G6OMvhv5vf5mhHwJ9Z5eXNZCkwvzEqyQVZshLUmGGvCQVZshLUmGGvCQVZshLUmGGvCQV9v+110NyrD6UCAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NKcdP2JS7vrW"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFa6dWlW5Njb"
      },
      "source": [
        "## **Planos de continuação!**\n",
        "2. Propor um modelo para a força dos times (regressão Poisson, por exemplo).\n",
        "3. Fazer webscrapping de fontes de dados para utilizar na modelagem da força do time.\n",
        "4. Montar um Dashboard dos resultados da simulação.\n",
        "5. Fazer gráficos automaticos com formato para redes sociais.\n"
      ]
    }
  ]
}