estimativaCopa2022.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy.stats import poisson\n",
        "\n",
        "selecoes = pd.read_excel('/content/DadosCopaDoMundoQatar2022.xlsx', sheet_name='selecoes', index_col=0)\n",
        "jogos = pd.read_excel('/content/DadosCopaDoMundoQatar2022.xlsx', sheet_name='jogos')"
      ]
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        "selecoes.head()"
      ],
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              "           Grupo GrupoNumero NomeEmIngles  PosiçãoRankingFIFA  ValorDeMercado  \\\n",
              "Seleção                                                                         \n",
              "Catar          A          A1        Qatar                  48           18.33   \n",
              "Equador        A          A2      Ecuador                  44          125.80   \n",
              "Senegal        A          A3      Senegal                  18          286.50   \n",
              "Holanda        A          A4  Netherlands                   8          455.75   \n",
              "Inglaterra     B          B1      England                   5         1360.00   \n",
              "\n",
              "            PontosRankingFIFA Confederação  Copas   JogadorDestaque  \\\n",
              "Seleção                                                               \n",
              "Catar                    1442          AFC      0        Almoez Ali   \n",
              "Equador                  1464         UEFA      0  Pervis Estupiñán   \n",
              "Senegal                  1585          CAF      0        Sadio Mané   \n",
              "Holanda                  1679         UEFA      0   Virgil Van Dijk   \n",
              "Inglaterra               1737         UEFA      1        Harry Kane   \n",
              "\n",
              "                                          FotoJogadorDestaque  \\\n",
              "Seleção                                                         \n",
              "Catar       https://github.com/ricardorocha86/PrevisaoEspo...   \n",
              "Equador     https://github.com/ricardorocha86/PrevisaoEspo...   \n",
              "Senegal     https://github.com/ricardorocha86/PrevisaoEspo...   \n",
              "Holanda     https://github.com/ricardorocha86/PrevisaoEspo...   \n",
              "Inglaterra  https://github.com/ricardorocha86/PrevisaoEspo...   \n",
              "\n",
              "                                          LinkBandeiraPequena  \\\n",
              "Seleção                                                         \n",
              "Catar       https://cloudinary.fifa.com/api/v3/picture/fla...   \n",
              "Equador     https://cloudinary.fifa.com/api/v3/picture/fla...   \n",
              "Senegal     https://cloudinary.fifa.com/api/v3/picture/fla...   \n",
              "Holanda     https://cloudinary.fifa.com/api/v3/picture/fla...   \n",
              "Inglaterra  https://cloudinary.fifa.com/api/v3/picture/fla...   \n",
              "\n",
              "                                           LinkBandeiraGrande  \n",
              "Seleção                                                        \n",
              "Catar       https://flagdownload.com/wp-content/uploads/Fl...  \n",
              "Equador     https://flagdownload.com/wp-content/uploads/Fl...  \n",
              "Senegal     https://flagdownload.com/wp-content/uploads/Fl...  \n",
              "Holanda     https://flagdownload.com/wp-content/uploads/Fl...  \n",
              "Inglaterra  https://flagdownload.com/wp-content/uploads/Fl...  "
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              "      <th>Senegal</th>\n",
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              "      <td>1585</td>\n",
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              "      <td>https://github.com/ricardorocha86/PrevisaoEspo...</td>\n",
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              "      <th>Holanda</th>\n",
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              "      <td>8</td>\n",
              "      <td>455.75</td>\n",
              "      <td>1679</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>0</td>\n",
              "      <td>Virgil Van Dijk</td>\n",
              "      <td>https://github.com/ricardorocha86/PrevisaoEspo...</td>\n",
              "      <td>https://cloudinary.fifa.com/api/v3/picture/fla...</td>\n",
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              "      <th>Inglaterra</th>\n",
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              "      <td>B1</td>\n",
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              "      <td>5</td>\n",
              "      <td>1360.00</td>\n",
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            "text/plain": [
              "        data      hora grupo seleção1 seleção2         estádio\n",
              "0 2022-11-21  07:00:00     A  Senegal  Holanda      AL THUMAMA\n",
              "1 2022-11-21  13:00:00     A    Catar  Equador         AL KHOR\n",
              "2 2022-11-25  10:00:00     A    Catar  Senegal      AL THUMAMA\n",
              "3 2022-11-25  13:00:00     A  Holanda  Equador  INTER. KHALIFA\n",
              "4 2022-11-29  12:00:00     A  Holanda    Catar         AL KHOR"
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.poisson(lam=1, size=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ws4tBj-0mGOS",
        "outputId": "a110b5f6-c537-41be-da81-6ac2ad20f8ef"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fifa = selecoes['PontosRankingFIFA']\n",
        "# Transformaçao linear de escala numerica\n",
        "a, b = min(fifa), max(fifa)\n",
        "fa, fb = 0.15, 1\n",
        "b1 = (fb - fa)/(b-a)\n",
        "b0 = fb - b*b1\n",
        "forca = b0 + b1*fifa\n",
        "forca"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAYOexI5m-rw",
        "outputId": "110c679c-087e-4bdc-83c9-7b8483c23bd5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Seleção\n",
              "Catar             0.243596\n",
              "Equador           0.285618\n",
              "Senegal           0.516742\n",
              "Holanda           0.696292\n",
              "Inglaterra        0.807079\n",
              "Irã               0.467079\n",
              "Estados Unidos    0.612247\n",
              "País de Gales     0.511011\n",
              "Argentina         0.872022\n",
              "Arábia Saudita    0.232135\n",
              "México            0.640899\n",
              "Polônia           0.442247\n",
              "França            0.860562\n",
              "Dinamarca         0.669551\n",
              "Tunísia           0.369663\n",
              "Austrália         0.323820\n",
              "Espanha           0.768876\n",
              "Alemanha          0.658090\n",
              "Japão             0.459438\n",
              "Costa Rica        0.354382\n",
              "Bélgica           0.969438\n",
              "Canadá            0.304719\n",
              "Marrocos          0.465169\n",
              "Croácia           0.606517\n",
              "Brasil            1.000000\n",
              "Sérvia            0.449888\n",
              "Suíça             0.585506\n",
              "Camarões          0.325730\n",
              "Portugal          0.696292\n",
              "Gana              0.150000\n",
              "Uruguai           0.623708\n",
              "Coreia do Sul     0.404045\n",
              "Name: PontosRankingFIFA, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def MediasPoisson(selecao1, selecao2):\n",
        "  forca1 = forca[selecao1]\n",
        "  forca2 = forca[selecao2]\n",
        "  mgols = 2.75\n",
        "  l1 = mgols*forca1/(forca1 + forca2)\n",
        "  l2 = mgols - l1\n",
        "  return [l1, l2]\n",
        "\n",
        "def Resultados(gols1, gols2):\n",
        "  if gols1 > gols2:\n",
        "    resultado = 'V'\n",
        "  elif gols2 > gols1:\n",
        "    resultado = 'D'\n",
        "  else:\n",
        "    resultado = 'E'\n",
        "\n",
        "  return resultado\n",
        "\n",
        "def Pontos(gols1, gols2):\n",
        "  resultado = Resultados(gols1, gols2)\n",
        "  if resultado == 'V':\n",
        "    pontos1, pontos2 = 3, 0\n",
        "  if resultado == 'E':\n",
        "    pontos1, pontos2 = 1, 1\n",
        "  if resultado == 'D':\n",
        "    pontos1, pontos2 = 0, 3\n",
        "  return [pontos1, pontos2, resultado]"
      ],
      "metadata": {
        "id": "gMHc9lLJoTFZ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Jogo(selecao1, selecao2):\n",
        "  l1, l2 = MediasPoisson(selecao1, selecao2)\n",
        "  gols1 = int(np.random.poisson(lam=l1, size=1))\n",
        "  gols2 = int(np.random.poisson(lam=l2, size=1))\n",
        "  saldo1 = gols1 - gols2\n",
        "  saldo2 = -saldo1\n",
        "  pontos1, pontos2, resultado = Pontos(gols1, gols2)\n",
        "  placar = '{}x{}'.format(gols1, gols2)\n",
        "  return [gols1, gols2, saldo1, saldo2, pontos1, pontos2, resultado, placar]"
      ],
      "metadata": {
        "id": "works5VEl75a"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Jogo('Holanda', 'Argentina')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nVCi9itj3kef",
        "outputId": "74291305-b29a-42a2-c498-c8fd04c5df72"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, -1, 1, 0, 3, 'D', '0x1']"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Distribuicao(media):\n",
        "  probs = []\n",
        "  for i in range(7):\n",
        "    probs.append(poisson.pmf(i, media))\n",
        "  probs.append(1 - sum(probs))\n",
        "  return pd.Series(probs, index = ['0', '1', '2', '3', '4', '5', '6', '7+'])\n",
        "\n",
        "def ProbabilidadesPartidas(selecao1, selecao2):\n",
        "  l1, l2 = MediasPoisson(selecao1, selecao2)\n",
        "  d1, d2 = Distribuicao(l1), Distribuicao(l2)\n",
        "  matriz = np.outer(d1, d2)\n",
        "  vitoria = np.tril(matriz).sum() - np.trace(matriz) # Soma o triangulo inferior\n",
        "  derrota = np.triu(matriz).sum() - np.trace(matriz) # Soma o triangulo superior\n",
        "  empate = 1 - (vitoria + derrota)\n",
        "\n",
        "  probs = np.around([vitoria, empate, derrota], 3)\n",
        "  probsp = [f'{100*i:.1f}%' for i in probs]\n",
        "\n",
        "  nomes = ['0', '1', '2', '3', '4', '5', '6', '7+']\n",
        "  matriz = pd.DataFrame(matriz, columns=nomes, index=nomes)\n",
        "  matriz.index = pd.MultiIndex.from_product([[selecao1], matriz.index])\n",
        "  matriz.columns = pd.MultiIndex.from_product([[selecao2], matriz.columns])\n",
        "\n",
        "  output = {\n",
        "      'selecao1': selecao1, 'selecao2': selecao2,\n",
        "      'f1': forca[selecao1], 'f2': forca[selecao2],\n",
        "      'media1': l1, 'media2': l2, 'probabilidades': probsp,\n",
        "      'matriz': matriz\n",
        "  }\n",
        "  return output"
      ],
      "metadata": {
        "id": "cuqQBIcc-YxT"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Distribuicao(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_s9etFgw_Bvz",
        "outputId": "e8b0d14e-578f-4acb-94cb-8c173e8157a0"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     0.135335\n",
              "1     0.270671\n",
              "2     0.270671\n",
              "3     0.180447\n",
              "4     0.090224\n",
              "5     0.036089\n",
              "6     0.012030\n",
              "7+    0.004534\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ProbabilidadesPartidas('Brasil', 'Croácia')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKB8sNJtAAhA",
        "outputId": "bddd9a3a-7ac0-4ab8-bd96-14cf0321f945"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'selecao1': 'Brasil',\n",
              " 'selecao2': 'Croácia',\n",
              " 'f1': 1.0,\n",
              " 'f2': 0.6065168539325843,\n",
              " 'media1': 1.7117778710309135,\n",
              " 'media2': 1.0382221289690865,\n",
              " 'probabilidades': ['53.2%', '23.9%', '22.9%'],\n",
              " 'matriz':             Croácia                                                    \\\n",
              "                   0         1         2         3         4         5   \n",
              " Brasil 0   0.063928  0.066371  0.034454  0.011924  0.003095  0.000643   \n",
              "        1   0.109430  0.113613  0.058978  0.020411  0.005298  0.001100   \n",
              "        2   0.093660  0.097240  0.050478  0.017469  0.004534  0.000942   \n",
              "        3   0.053442  0.055484  0.028803  0.009968  0.002587  0.000537   \n",
              "        4   0.022870  0.023744  0.012326  0.004266  0.001107  0.000230   \n",
              "        5   0.007830  0.008129  0.004220  0.001460  0.000379  0.000079   \n",
              "        6   0.002234  0.002319  0.001204  0.000417  0.000108  0.000022   \n",
              "        7+  0.000690  0.000716  0.000372  0.000129  0.000033  0.000007   \n",
              " \n",
              "                                    \n",
              "                   6            7+  \n",
              " Brasil 0   0.000111  1.890827e-05  \n",
              "        1   0.000190  3.236676e-05  \n",
              "        2   0.000163  2.770235e-05  \n",
              "        3   0.000093  1.580676e-05  \n",
              "        4   0.000040  6.764415e-06  \n",
              "        5   0.000014  2.315835e-06  \n",
              "        6   0.000004  6.606992e-07  \n",
              "        7+  0.000001  2.040428e-07  }"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "jogos['Vitória'] = None\n",
        "jogos['Empate'] = None\n",
        "jogos['Derrota'] = None\n",
        "\n",
        "for i in range(jogos.shape[0]):\n",
        "    selecao1 = jogos['seleção1'][i]\n",
        "    selecao2 = jogos['seleção2'][i]\n",
        "    v, e, d = ProbabilidadesPartidas(selecao1, selecao2)['probabilidades']\n",
        "    jogos.at[i, 'Vitória'] = v\n",
        "    jogos.at[i, 'Empate'] = e\n",
        "    jogos.at[i, 'Derrota'] = d\n",
        "\n",
        "jogos.to_excel('outputEstimativaJogosCopa2022.xlsx')\n",
        "jogos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Dm-e9jiYKWSd",
        "outputId": "f9292387-5628-4fcf-fc40-b14988e77473"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         data      hora grupo        seleção1        seleção2         estádio  \\\n",
              "0  2022-11-21  07:00:00     A         Senegal         Holanda      AL THUMAMA   \n",
              "1  2022-11-21  13:00:00     A           Catar         Equador         AL KHOR   \n",
              "2  2022-11-25  10:00:00     A           Catar         Senegal      AL THUMAMA   \n",
              "3  2022-11-25  13:00:00     A         Holanda         Equador  INTER. KHALIFA   \n",
              "4  2022-11-29  12:00:00     A         Holanda           Catar         AL KHOR   \n",
              "5  2022-11-29  12:00:00     A         Equador         Senegal  INTER. KHALIFA   \n",
              "6  2022-11-21  10:00:00     B      Inglaterra             Irã  INTER. KHALIFA   \n",
              "7  2022-11-21  16:00:00     B  Estados Unidos   País de Gales       AL RAYYAN   \n",
              "8  2022-11-25  07:00:00     B   País de Gales             Irã       AL RAYYAN   \n",
              "9  2022-11-25  16:00:00     B      Inglaterra  Estados Unidos         AL KHOR   \n",
              "10 2022-11-29  16:00:00     B   País de Gales      Inglaterra       AL RAYYAN   \n",
              "11 2022-11-29  16:00:00     B             Irã  Estados Unidos      AL THUMAMA   \n",
              "12 2022-11-22  07:00:00     C       Argentina  Arábia Saudita   CID. EDUCAÇÃO   \n",
              "13 2022-11-22  13:00:00     C          México         Polônia       AL WAKRAH   \n",
              "14 2022-11-26  10:00:00     C         Polônia  Arábia Saudita   CID. EDUCAÇÃO   \n",
              "15 2022-11-26  16:00:00     C       Argentina          México  NAC. DE LUSAIL   \n",
              "16 2022-11-30  16:00:00     C         Polônia       Argentina   PORTO DE DOHA   \n",
              "17 2022-11-30  16:00:00     C  Arábia Saudita          México  NAC. DE LUSAIL   \n",
              "18 2022-11-22  10:00:00     D       Dinamarca         Tunísia   CID. EDUCAÇÃO   \n",
              "19 2022-11-22  16:00:00     D          França       Austrália       AL WAKRAH   \n",
              "20 2022-11-26  07:00:00     D         Tunísia       Austrália       AL WAKRAH   \n",
              "21 2022-11-26  13:00:00     D          França       Dinamarca   PORTO DE DOHA   \n",
              "22 2022-11-30  12:00:00     D         Tunísia          França   CID. EDUCAÇÃO   \n",
              "23 2022-11-30  12:00:00     D       Austrália       Dinamarca       AL WAKRAH   \n",
              "24 2022-11-23  10:00:00     E        Alemanha           Japão  INTER. KHALIFA   \n",
              "25 2022-11-23  13:00:00     E         Espanha      Costa Rica      AL THUMAMA   \n",
              "26 2022-11-27  07:00:00     E           Japão      Costa Rica       AL RAYYAN   \n",
              "27 2022-11-27  16:00:00     E         Espanha        Alemanha         AL KHOR   \n",
              "28 2022-12-01  16:00:00     E           Japão         Espanha  INTER. KHALIFA   \n",
              "29 2022-12-01  16:00:00     E      Costa Rica        Alemanha         AL KHOR   \n",
              "30 2022-11-23  07:00:00     F        Marrocos         Croácia         AL KHOR   \n",
              "31 2022-11-23  16:00:00     F         Bélgica          Canadá       AL RAYYAN   \n",
              "32 2022-11-27  10:00:00     F         Bélgica        Marrocos      AL THUMAMA   \n",
              "33 2022-11-27  13:00:00     F         Croácia          Canadá   INTE. KHALIFA   \n",
              "34 2022-12-01  12:00:00     F         Croácia         Bélgica       AL RAYYAN   \n",
              "35 2022-12-01  12:00:00     F          Canadá        Marrocos      AL THUMAMA   \n",
              "36 2022-11-24  07:00:00     G           Suíça        Camarões       AL WAKRAH   \n",
              "37 2022-11-24  16:00:00     G          Brasil          Sérvia     NAC. LUSAIL   \n",
              "38 2022-11-28  07:00:00     G        Camarões          Sérvia       AL WAKRAH   \n",
              "39 2022-11-28  13:00:00     G          Brasil           Suíça   PORTO DE DOHA   \n",
              "40 2022-12-02  16:00:00     G        Camarões          Brasil     NAC. LUSAIL   \n",
              "41 2022-12-02  16:00:00     G          Sérvia           Suíça   PORTO DE DOHA   \n",
              "42 2022-11-24  10:00:00     H         Uruguai   Coreia do Sul   CID. EDUCAÇÃO   \n",
              "43 2022-11-24  13:00:00     H        Portugal            Gana   PORTO DE DOHA   \n",
              "44 2022-11-28  10:00:00     H   Coreia do Sul            Gana   CID. EDUCAÇÃO   \n",
              "45 2022-11-28  16:00:00     H        Portugal         Uruguai     NAC. LUSAIL   \n",
              "46 2022-12-02  12:00:00     H   Coreia do Sul        Portugal   CID. EDUCAÇÃO   \n",
              "47 2022-12-02  12:00:00     H            Gana         Uruguai       AL WAKRAH   \n",
              "\n",
              "   Vitória Empate Derrota  \n",
              "0    28.3%  24.9%   46.8%  \n",
              "1    32.3%  25.4%   42.3%  \n",
              "2    17.2%  22.1%   60.6%  \n",
              "3    64.4%  21.0%   14.6%  \n",
              "4    68.4%  19.6%   12.0%  \n",
              "5    20.7%  23.3%   56.0%  \n",
              "6    54.6%  23.6%   21.8%  \n",
              "7    43.0%  25.3%   31.7%  \n",
              "8    40.1%  25.5%   34.4%  \n",
              "9    46.1%  25.0%   28.9%  \n",
              "10   24.0%  24.2%   51.8%  \n",
              "11   29.1%  25.0%   45.9%  \n",
              "12   74.2%  17.3%    8.5%  \n",
              "13   49.1%  24.6%   26.3%  \n",
              "14   57.5%  22.9%   19.5%  \n",
              "15   47.1%  24.9%   28.0%  \n",
              "16   18.8%  22.7%   58.5%  \n",
              "17   12.5%  19.9%   67.5%  \n",
              "18   56.0%  23.3%   20.7%  \n",
              "19   66.6%  20.3%   13.1%  \n",
              "20   41.4%  25.4%   33.1%  \n",
              "21   45.3%  25.1%   29.6%  \n",
              "22   15.4%  21.4%   63.2%  \n",
              "23   17.8%  22.3%   59.9%  \n",
              "24   48.7%  24.7%   26.6%  \n",
              "25   61.3%  22.0%   16.8%  \n",
              "26   45.5%  25.1%   29.4%  \n",
              "27   42.2%  25.4%   32.4%  \n",
              "28   22.6%  23.8%   53.6%  \n",
              "29   20.1%  23.1%   56.8%  \n",
              "30   29.2%  25.1%   45.7%  \n",
              "31   70.8%  18.7%   10.5%  \n",
              "32   60.1%  22.3%   17.6%  \n",
              "33   58.8%  22.6%   18.6%  \n",
              "34   23.7%  24.1%   52.2%  \n",
              "35   24.9%  24.4%   50.7%  \n",
              "36   55.8%  23.4%   20.8%  \n",
              "37   61.9%  21.8%   16.3%  \n",
              "38   27.6%  24.8%   47.6%  \n",
              "39   54.2%  23.7%   22.1%  \n",
              "40   11.0%  19.0%   70.0%  \n",
              "41   29.3%  25.1%   45.7%  \n",
              "42   51.1%  24.3%   24.6%  \n",
              "43   77.9%  15.6%    6.4%  \n",
              "44   66.9%  20.2%   12.9%  \n",
              "45   40.7%  25.5%   33.8%  \n",
              "46   21.8%  23.6%   54.5%  \n",
              "47    7.4%  16.5%   76.1%  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4b23680e-64ba-487a-b150-0b58ddd658e3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>data</th>\n",
              "      <th>hora</th>\n",
              "      <th>grupo</th>\n",
              "      <th>seleção1</th>\n",
              "      <th>seleção2</th>\n",
              "      <th>estádio</th>\n",
              "      <th>Vitória</th>\n",
              "      <th>Empate</th>\n",
              "      <th>Derrota</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-11-21</td>\n",
              "      <td>07:00:00</td>\n",
              "      <td>A</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>Holanda</td>\n",
              "      <td>AL THUMAMA</td>\n",
              "      <td>28.3%</td>\n",
              "      <td>24.9%</td>\n",
              "      <td>46.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-11-21</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>A</td>\n",
              "      <td>Catar</td>\n",
              "      <td>Equador</td>\n",
              "      <td>AL KHOR</td>\n",
              "      <td>32.3%</td>\n",
              "      <td>25.4%</td>\n",
              "      <td>42.3%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-11-25</td>\n",
              "      <td>10:00:00</td>\n",
              "      <td>A</td>\n",
              "      <td>Catar</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>AL THUMAMA</td>\n",
              "      <td>17.2%</td>\n",
              "      <td>22.1%</td>\n",
              "      <td>60.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-11-25</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>A</td>\n",
              "      <td>Holanda</td>\n",
              "      <td>Equador</td>\n",
              "      <td>INTER. KHALIFA</td>\n",
              "      <td>64.4%</td>\n",
              "      <td>21.0%</td>\n",
              "      <td>14.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-11-29</td>\n",
              "      <td>12:00:00</td>\n",
              "      <td>A</td>\n",
              "      <td>Holanda</td>\n",
              "      <td>Catar</td>\n",
              "      <td>AL KHOR</td>\n",
              "      <td>68.4%</td>\n",
              "      <td>19.6%</td>\n",
              "      <td>12.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2022-11-29</td>\n",
              "      <td>12:00:00</td>\n",
              "      <td>A</td>\n",
              "      <td>Equador</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>INTER. KHALIFA</td>\n",
              "      <td>20.7%</td>\n",
              "      <td>23.3%</td>\n",
              "      <td>56.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2022-11-21</td>\n",
              "      <td>10:00:00</td>\n",
              "      <td>B</td>\n",
              "      <td>Inglaterra</td>\n",
              "      <td>Irã</td>\n",
              "      <td>INTER. KHALIFA</td>\n",
              "      <td>54.6%</td>\n",
              "      <td>23.6%</td>\n",
              "      <td>21.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2022-11-21</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>B</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>País de Gales</td>\n",
              "      <td>AL RAYYAN</td>\n",
              "      <td>43.0%</td>\n",
              "      <td>25.3%</td>\n",
              "      <td>31.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2022-11-25</td>\n",
              "      <td>07:00:00</td>\n",
              "      <td>B</td>\n",
              "      <td>País de Gales</td>\n",
              "      <td>Irã</td>\n",
              "      <td>AL RAYYAN</td>\n",
              "      <td>40.1%</td>\n",
              "      <td>25.5%</td>\n",
              "      <td>34.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2022-11-25</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>B</td>\n",
              "      <td>Inglaterra</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>AL KHOR</td>\n",
              "      <td>46.1%</td>\n",
              "      <td>25.0%</td>\n",
              "      <td>28.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2022-11-29</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>B</td>\n",
              "      <td>País de Gales</td>\n",
              "      <td>Inglaterra</td>\n",
              "      <td>AL RAYYAN</td>\n",
              "      <td>24.0%</td>\n",
              "      <td>24.2%</td>\n",
              "      <td>51.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2022-11-29</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>B</td>\n",
              "      <td>Irã</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>AL THUMAMA</td>\n",
              "      <td>29.1%</td>\n",
              "      <td>25.0%</td>\n",
              "      <td>45.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2022-11-22</td>\n",
              "      <td>07:00:00</td>\n",
              "      <td>C</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Arábia Saudita</td>\n",
              "      <td>CID. EDUCAÇÃO</td>\n",
              "      <td>74.2%</td>\n",
              "      <td>17.3%</td>\n",
              "      <td>8.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2022-11-22</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>C</td>\n",
              "      <td>México</td>\n",
              "      <td>Polônia</td>\n",
              "      <td>AL WAKRAH</td>\n",
              "      <td>49.1%</td>\n",
              "      <td>24.6%</td>\n",
              "      <td>26.3%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2022-11-26</td>\n",
              "      <td>10:00:00</td>\n",
              "      <td>C</td>\n",
              "      <td>Polônia</td>\n",
              "      <td>Arábia Saudita</td>\n",
              "      <td>CID. EDUCAÇÃO</td>\n",
              "      <td>57.5%</td>\n",
              "      <td>22.9%</td>\n",
              "      <td>19.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2022-11-26</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>C</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>México</td>\n",
              "      <td>NAC. DE LUSAIL</td>\n",
              "      <td>47.1%</td>\n",
              "      <td>24.9%</td>\n",
              "      <td>28.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2022-11-30</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>C</td>\n",
              "      <td>Polônia</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>PORTO DE DOHA</td>\n",
              "      <td>18.8%</td>\n",
              "      <td>22.7%</td>\n",
              "      <td>58.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2022-11-30</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>C</td>\n",
              "      <td>Arábia Saudita</td>\n",
              "      <td>México</td>\n",
              "      <td>NAC. DE LUSAIL</td>\n",
              "      <td>12.5%</td>\n",
              "      <td>19.9%</td>\n",
              "      <td>67.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2022-11-22</td>\n",
              "      <td>10:00:00</td>\n",
              "      <td>D</td>\n",
              "      <td>Dinamarca</td>\n",
              "      <td>Tunísia</td>\n",
              "      <td>CID. EDUCAÇÃO</td>\n",
              "      <td>56.0%</td>\n",
              "      <td>23.3%</td>\n",
              "      <td>20.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2022-11-22</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>D</td>\n",
              "      <td>França</td>\n",
              "      <td>Austrália</td>\n",
              "      <td>AL WAKRAH</td>\n",
              "      <td>66.6%</td>\n",
              "      <td>20.3%</td>\n",
              "      <td>13.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2022-11-26</td>\n",
              "      <td>07:00:00</td>\n",
              "      <td>D</td>\n",
              "      <td>Tunísia</td>\n",
              "      <td>Austrália</td>\n",
              "      <td>AL WAKRAH</td>\n",
              "      <td>41.4%</td>\n",
              "      <td>25.4%</td>\n",
              "      <td>33.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2022-11-26</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>D</td>\n",
              "      <td>França</td>\n",
              "      <td>Dinamarca</td>\n",
              "      <td>PORTO DE DOHA</td>\n",
              "      <td>45.3%</td>\n",
              "      <td>25.1%</td>\n",
              "      <td>29.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2022-11-30</td>\n",
              "      <td>12:00:00</td>\n",
              "      <td>D</td>\n",
              "      <td>Tunísia</td>\n",
              "      <td>França</td>\n",
              "      <td>CID. EDUCAÇÃO</td>\n",
              "      <td>15.4%</td>\n",
              "      <td>21.4%</td>\n",
              "      <td>63.2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2022-11-30</td>\n",
              "      <td>12:00:00</td>\n",
              "      <td>D</td>\n",
              "      <td>Austrália</td>\n",
              "      <td>Dinamarca</td>\n",
              "      <td>AL WAKRAH</td>\n",
              "      <td>17.8%</td>\n",
              "      <td>22.3%</td>\n",
              "      <td>59.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2022-11-23</td>\n",
              "      <td>10:00:00</td>\n",
              "      <td>E</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>Japão</td>\n",
              "      <td>INTER. KHALIFA</td>\n",
              "      <td>48.7%</td>\n",
              "      <td>24.7%</td>\n",
              "      <td>26.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2022-11-23</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>E</td>\n",
              "      <td>Espanha</td>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>AL THUMAMA</td>\n",
              "      <td>61.3%</td>\n",
              "      <td>22.0%</td>\n",
              "      <td>16.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2022-11-27</td>\n",
              "      <td>07:00:00</td>\n",
              "      <td>E</td>\n",
              "      <td>Japão</td>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>AL RAYYAN</td>\n",
              "      <td>45.5%</td>\n",
              "      <td>25.1%</td>\n",
              "      <td>29.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2022-11-27</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>E</td>\n",
              "      <td>Espanha</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>AL KHOR</td>\n",
              "      <td>42.2%</td>\n",
              "      <td>25.4%</td>\n",
              "      <td>32.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2022-12-01</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>E</td>\n",
              "      <td>Japão</td>\n",
              "      <td>Espanha</td>\n",
              "      <td>INTER. KHALIFA</td>\n",
              "      <td>22.6%</td>\n",
              "      <td>23.8%</td>\n",
              "      <td>53.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2022-12-01</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>E</td>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>AL KHOR</td>\n",
              "      <td>20.1%</td>\n",
              "      <td>23.1%</td>\n",
              "      <td>56.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2022-11-23</td>\n",
              "      <td>07:00:00</td>\n",
              "      <td>F</td>\n",
              "      <td>Marrocos</td>\n",
              "      <td>Croácia</td>\n",
              "      <td>AL KHOR</td>\n",
              "      <td>29.2%</td>\n",
              "      <td>25.1%</td>\n",
              "      <td>45.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2022-11-23</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>F</td>\n",
              "      <td>Bélgica</td>\n",
              "      <td>Canadá</td>\n",
              "      <td>AL RAYYAN</td>\n",
              "      <td>70.8%</td>\n",
              "      <td>18.7%</td>\n",
              "      <td>10.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2022-11-27</td>\n",
              "      <td>10:00:00</td>\n",
              "      <td>F</td>\n",
              "      <td>Bélgica</td>\n",
              "      <td>Marrocos</td>\n",
              "      <td>AL THUMAMA</td>\n",
              "      <td>60.1%</td>\n",
              "      <td>22.3%</td>\n",
              "      <td>17.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2022-11-27</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>F</td>\n",
              "      <td>Croácia</td>\n",
              "      <td>Canadá</td>\n",
              "      <td>INTE. KHALIFA</td>\n",
              "      <td>58.8%</td>\n",
              "      <td>22.6%</td>\n",
              "      <td>18.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2022-12-01</td>\n",
              "      <td>12:00:00</td>\n",
              "      <td>F</td>\n",
              "      <td>Croácia</td>\n",
              "      <td>Bélgica</td>\n",
              "      <td>AL RAYYAN</td>\n",
              "      <td>23.7%</td>\n",
              "      <td>24.1%</td>\n",
              "      <td>52.2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2022-12-01</td>\n",
              "      <td>12:00:00</td>\n",
              "      <td>F</td>\n",
              "      <td>Canadá</td>\n",
              "      <td>Marrocos</td>\n",
              "      <td>AL THUMAMA</td>\n",
              "      <td>24.9%</td>\n",
              "      <td>24.4%</td>\n",
              "      <td>50.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2022-11-24</td>\n",
              "      <td>07:00:00</td>\n",
              "      <td>G</td>\n",
              "      <td>Suíça</td>\n",
              "      <td>Camarões</td>\n",
              "      <td>AL WAKRAH</td>\n",
              "      <td>55.8%</td>\n",
              "      <td>23.4%</td>\n",
              "      <td>20.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2022-11-24</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>G</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>Sérvia</td>\n",
              "      <td>NAC. LUSAIL</td>\n",
              "      <td>61.9%</td>\n",
              "      <td>21.8%</td>\n",
              "      <td>16.3%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2022-11-28</td>\n",
              "      <td>07:00:00</td>\n",
              "      <td>G</td>\n",
              "      <td>Camarões</td>\n",
              "      <td>Sérvia</td>\n",
              "      <td>AL WAKRAH</td>\n",
              "      <td>27.6%</td>\n",
              "      <td>24.8%</td>\n",
              "      <td>47.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2022-11-28</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>G</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>Suíça</td>\n",
              "      <td>PORTO DE DOHA</td>\n",
              "      <td>54.2%</td>\n",
              "      <td>23.7%</td>\n",
              "      <td>22.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2022-12-02</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>G</td>\n",
              "      <td>Camarões</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>NAC. LUSAIL</td>\n",
              "      <td>11.0%</td>\n",
              "      <td>19.0%</td>\n",
              "      <td>70.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2022-12-02</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>G</td>\n",
              "      <td>Sérvia</td>\n",
              "      <td>Suíça</td>\n",
              "      <td>PORTO DE DOHA</td>\n",
              "      <td>29.3%</td>\n",
              "      <td>25.1%</td>\n",
              "      <td>45.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2022-11-24</td>\n",
              "      <td>10:00:00</td>\n",
              "      <td>H</td>\n",
              "      <td>Uruguai</td>\n",
              "      <td>Coreia do Sul</td>\n",
              "      <td>CID. EDUCAÇÃO</td>\n",
              "      <td>51.1%</td>\n",
              "      <td>24.3%</td>\n",
              "      <td>24.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2022-11-24</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>H</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>Gana</td>\n",
              "      <td>PORTO DE DOHA</td>\n",
              "      <td>77.9%</td>\n",
              "      <td>15.6%</td>\n",
              "      <td>6.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2022-11-28</td>\n",
              "      <td>10:00:00</td>\n",
              "      <td>H</td>\n",
              "      <td>Coreia do Sul</td>\n",
              "      <td>Gana</td>\n",
              "      <td>CID. EDUCAÇÃO</td>\n",
              "      <td>66.9%</td>\n",
              "      <td>20.2%</td>\n",
              "      <td>12.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2022-11-28</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>H</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>Uruguai</td>\n",
              "      <td>NAC. LUSAIL</td>\n",
              "      <td>40.7%</td>\n",
              "      <td>25.5%</td>\n",
              "      <td>33.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2022-12-02</td>\n",
              "      <td>12:00:00</td>\n",
              "      <td>H</td>\n",
              "      <td>Coreia do Sul</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>CID. EDUCAÇÃO</td>\n",
              "      <td>21.8%</td>\n",
              "      <td>23.6%</td>\n",
              "      <td>54.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2022-12-02</td>\n",
              "      <td>12:00:00</td>\n",
              "      <td>H</td>\n",
              "      <td>Gana</td>\n",
              "      <td>Uruguai</td>\n",
              "      <td>AL WAKRAH</td>\n",
              "      <td>7.4%</td>\n",
              "      <td>16.5%</td>\n",
              "      <td>76.1%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b23680e-64ba-487a-b150-0b58ddd658e3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4b23680e-64ba-487a-b150-0b58ddd658e3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4b23680e-64ba-487a-b150-0b58ddd658e3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "s1 = 'Brasil'\n",
        "s2 = 'Croácia'\n",
        "jogo = ProbabilidadesPartidas(s1, s2)\n",
        "\n",
        "matriz = jogo['matriz']\n",
        "\n",
        "fig, ax0 = plt.subplots(figsize=(10,8))\n",
        "ax = sns.heatmap(matriz, annot=True, square=True, linewidth=2., cmap=\"YlGnBu\",\n",
        "                 annot_kws={'fontsize':12},\n",
        "                 yticklabels=['0', '1', '2', '3', '4', '5', '6', '7+'], \n",
        "                 xticklabels=['0', '1', '2', '3', '4', '5', '6', '7+'])\n",
        "for t in ax.texts: t.set_text(f'{str(round(100*float(t.get_text()),1))}%')\n",
        "plt.ylabel('Gols ' + s1)\n",
        "plt.xlabel('Gols ' + s2)\n",
        "fig.show()\n",
        "\n",
        "# st.write(fig)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "47VlDIm3GreV",
        "outputId": "1dfbe3a5-1398-4929-9d5f-654ad0424f7f"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}