ww_goal_location_example.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Goal Locations from Women's Worlds  "
      ],
      "metadata": {
        "id": "DhV0VUmxmR9P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook is meant as a quick and simple example of how the IIHF data found in my [github repo](https://github.com/the-bucketless/iihf.git) can be used. It'll look at where in the net goals from the Women's Worlds have been scored over the years."
      ],
      "metadata": {
        "id": "NbHoAA-Rmc4Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pulling in the Data"
      ],
      "metadata": {
        "id": "Rewt0nolm7Z3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first step is to grab the data from the repo. The following cell will pull in everything from the repo (including this notebook because recursion is fun). Afterward, the files can be located in the pane to the left."
      ],
      "metadata": {
        "id": "ef39_YS1m-WA"
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          "text": [
            "Cloning into 'iihf'...\n",
            "remote: Enumerating objects: 890, done.\u001b[K\n",
            "remote: Counting objects: 100% (283/283), done.\u001b[K\n",
            "remote: Compressing objects: 100% (178/178), done.\u001b[K\n",
            "remote: Total 890 (delta 129), reused 246 (delta 105), pack-reused 607\u001b[K\n",
            "Receiving objects: 100% (890/890), 6.23 MiB | 8.02 MiB/s, done.\n",
            "Resolving deltas: 100% (451/451), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/the-bucketless/iihf.git"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "czHaw5Qwnnou"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from glob import glob\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "qAnESV7BKqKV"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DataFrame of Relevant Data"
      ],
      "metadata": {
        "id": "vEq9x9oWnsOU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that all the data can be easily accessed, let's put anything needed in a pandas DataFrame. For this example, the only files we need are the goals files from all the Women's World Championships."
      ],
      "metadata": {
        "id": "Gekyg6e5n3p7"
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      "source": [
        "# The goals files have file names of the form goals{year}.\n",
        "# We only need the top level Women's Worlds tournaments\n",
        "# which are in the ww/ww directory.\n",
        "ww_files = glob(\"/content/iihf/data/ww/ww/goals*.csv\")\n",
        "\n",
        "# We'll concatenate all of the csv files into a single DataFrame \n",
        "# while keeping track of which year the goals are from.\n",
        "df = pd.concat(\n",
        "    [\n",
        "        pd.read_csv(ww_file)\n",
        "        # Strip any non-numeric characters from the file path\n",
        "        # to add a column for what year the goals are from.\n",
        "        .assign(year=int(\"\".join(c for c in ww_file if c.isdigit())))\n",
        "        for ww_file in ww_files\n",
        "    ],\n",
        "    ignore_index=True,\n",
        ")"
      ],
      "metadata": {
        "id": "tccThd_GKzzp"
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      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking the Data"
      ],
      "metadata": {
        "id": "FTlF3npVpThz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "With our DataFrame assembled, we can check out what the data looks like. Let's see what the first goal from every tournament was."
      ],
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        "id": "elUs6o6zpXsX"
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      "cell_type": "code",
      "source": [
        "ww_by_year = df.groupby(\"year\", as_index=False)\n",
        "\n",
        "ww_by_year.first()"
      ],
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              "   year                                   shot_chart_url  period team  \\\n",
              "0  2019  https://www.iihf.com//pdf/510/ihw510b01_77a_2_0     2.0  GER   \n",
              "1  2021  https://www.iihf.com//pdf/929/ihw929b01_77a_3_0     1.0  CZE   \n",
              "2  2022  https://www.iihf.com//pdf/164/ihw164a01_77a_3_0     1.0  USA   \n",
              "\n",
              "   shooter_number opponent  goalie_number         x         y team1 team2  \\\n",
              "0            16.0      SWE            1.0  4.002305  2.135536   GER   SWE   \n",
              "1             9.0      DEN           30.0  1.615729  0.907237   CZE   DEN   \n",
              "2            12.0      JPN           30.0  5.344756  3.632701   JPN   USA   \n",
              "\n",
              "                                            game_url  \n",
              "0  https://www.iihf.com//en/events/2019/ww/gamece...  \n",
              "1  https://www.iihf.com//en/events/2021/ww/gamece...  \n",
              "2  https://www.iihf.com//en/events/2022/ww/gamece...  "
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              "      <td>2019</td>\n",
              "      <td>https://www.iihf.com//pdf/510/ihw510b01_77a_2_0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>GER</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021</td>\n",
              "      <td>https://www.iihf.com//pdf/929/ihw929b01_77a_3_0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CZE</td>\n",
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              "      <td>CZE</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022</td>\n",
              "      <td>https://www.iihf.com//pdf/164/ihw164a01_77a_3_0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>12.0</td>\n",
              "      <td>JPN</td>\n",
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              "      <td>5.344756</td>\n",
              "      <td>3.632701</td>\n",
              "      <td>JPN</td>\n",
              "      <td>USA</td>\n",
              "      <td>https://www.iihf.com//en/events/2022/ww/gamece...</td>\n",
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      "source": [
        "We can also look at the goals per game at each tournament."
      ],
      "metadata": {
        "id": "TqWqQA86qE0-"
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        "(\n",
        "    ww_by_year\n",
        "    .agg(\n",
        "        n_goals=(\"year\", \"count\"), \n",
        "        n_games=(\"game_url\", \"nunique\"),\n",
        "    )\n",
        "    .assign(goals_per_game=lambda df_: df_.n_goals / df_.n_games)\n",
        ")"
      ],
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            "text/plain": [
              "   year  n_goals  n_games  goals_per_game\n",
              "0  2019      158       29        5.448276\n",
              "1  2021      145       31        4.677419\n",
              "2  2022      188       30        6.266667"
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And we should take a look at what the coordinates of the goals look like."
      ],
      "metadata": {
        "id": "BrJx5WC3rC_R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df.x, df.y, \".\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "6R_JxvVOq-AX",
        "outputId": "2c40d0dd-8b72-400a-cfa4-754fd35b29dc"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dots should be in the range of 0 to 6 on the x-axis and 0 to 4 on the y-axis (a net is 6x4'). However, there are a couple shot charts from the IIHF that appear to have goals outside the net. That could very well mean that I'm misinterpreting how these are being recorded in their PDFs, but I haven't figured out how.  \n",
        "\n",
        "Also, it may have been a better idea to name the columns y and z instead of x and y to allow for them to match the coordinates typically used on a rink. But at least this way is sensible for how we're going to plot things."
      ],
      "metadata": {
        "id": "ksPtgYkQrM9B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Drawing a Net"
      ],
      "metadata": {
        "id": "wtj7baAcqUtk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rather than just plot a bunch of points on a blank space, let's draw the goals between the posts of a net. There are a few ways to do this, I'm going to do it by setting the corner coordinates of the posts and using matplotlib's fill function to color them in. I'm also leaving square corners on the net instead of elbows for simplicity.  We're going to need to do this a few times, so we'll throw it in a function that we can call each time."
      ],
      "metadata": {
        "id": "v3BJC04FqW68"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NET_WIDTH = 6\n",
        "NET_HEIGHT = 4\n",
        "POST_WIDTH = (2 + 3 / 8) / 12\n",
        "\n",
        "\n",
        "def draw_net(ax=None):\n",
        "    if ax is None:\n",
        "        ax = plt.gca()\n",
        "\n",
        "    # The posts are drawn going from bottom left to the top \n",
        "    # then across and, eventually, all the way back around.\n",
        "    ax.fill(\n",
        "        [\n",
        "            -POST_WIDTH, -POST_WIDTH, NET_WIDTH + POST_WIDTH, NET_WIDTH + POST_WIDTH, \n",
        "            NET_WIDTH, NET_WIDTH, 0, 0,\n",
        "        ],\n",
        "        [\n",
        "            0, NET_HEIGHT + POST_WIDTH, NET_HEIGHT + POST_WIDTH, 0,\n",
        "            0, NET_HEIGHT, NET_HEIGHT, 0,\n",
        "        ],\n",
        "        color=\"red\",\n",
        "    )"
      ],
      "metadata": {
        "id": "rzQ59g0GPVDL"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "draw_net()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "mW55m5A2QvPI",
        "outputId": "3ed09f7d-b7bd-495b-fdf1-188b30bec599"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKWElEQVR4nO3d34ulB33H8c/X3dTYqM1Fhhjc0PWiCOKFkSFQUqSkWKIG20sDelEKe9OWSAtSe1P8B8SbUliStCmmihiFEuyPgBEbqNHZGKvJxiKiuGLcERFNLyLqtxdzQnaXSefMZs6c73FfLxgys3N49kMI7zz7zHP2qe4OAHO9at0DAPj/CTXAcEINMJxQAwwn1ADDnVzFQW+66aY+ffr0Kg4N8Gvp3LlzP+rurf2+t5JQnz59Ojs7O6s4NMCvpar67st9z6UPgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhlvJG15ekTe8IfnhD9e9AuDwbr45ee65Iz/svDNqkQY21Yr6NS/UAFxGqAGGE2qA4YQaYLilQ11VJ6rqq1X1yCoHAXC5w5xR35vk/KqGALC/pUJdVaeSvCfJfaudA8CVlj2j/liSDyX51Qq3ALCPA0NdVXcnudjd5w543Zmq2qmqnd3d3SMbuK9Xv3q1xwe40hq7s8xbyO9I8t6qeneS65O8vqo+3t3vv/RF3X02ydkk2d7e7iNfeqkXXkh6tb8FwGWq1vZbH3hG3d0f7u5T3X06yfuSfP7KSAOwOu6jBhjuUH97Xnd/IckXVrIEgH05owYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhjsw1FV1fVV9uaq+VlVPV9VHjmMYAHtOLvGaF5Lc2d3PV9V1SR6vqn/t7i+teBsAWSLU3d1Jnl98ed3io1c5CoCXLHWNuqpOVNVTSS4mebS7n1jtLABetFSou/uX3f22JKeS3F5Vb73yNVV1pqp2qmpnd3f3qHcCXLMOdddHd/8kyWNJ7trne2e7e7u7t7e2to5qH8A1b5m7Praq6sbF569J8s4kz656GAB7lrnr45YkD1bVieyF/VPd/chqZwHwomXu+vjvJLcdwxYA9uGdiQDDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMNyBoa6qW6vqsap6pqqerqp7j2MYAHtOLvGaXyT5q+5+sqpel+RcVT3a3c+seBsAWeKMurt/0N1PLj7/WZLzSd646mEA7DnUNeqqOp3ktiRP7PO9M1W1U1U7u7u7R7MOgOVDXVWvTfJwkg9290+v/H53n+3u7e7e3traOsqNANe0pUJdVddlL9IPdfdnVjsJgEstc9dHJbk/yfnu/ujqJwFwqWXOqO9I8oEkd1bVU4uPd694FwALB96e192PJ6lj2ALAPrwzEWA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYY7sBQV9UDVXWxqr5xHIMAuNwyZ9T/mOSuFe8A4GUcGOru/mKSHx/DFgD2cWTXqKvqTFXtVNXO7u7uUR0W4Jp3ZKHu7rPdvd3d21tbW0d1WIBrnrs+AIYTaoDhlrk97xNJ/ivJm6vqQlX96epnAfCikwe9oLvvOY4hAOzPpQ+A4YQaYDihBhhOqAGGE2qA4YQaYDihBhhOqAGGE2qA4YQaYDihBhhOqAGGE2qA4YQaYDihBhhOqAGGE2qA4YQaYDihBhhOqAGGE2qA4YQaYDihBhhOqAGGE2qA4YQaYDihBhhOqAGGE2qA4YQaYDihBhhOqAGGE2qA4YQaYDihBhhOqAGGE2qA4YQaYLilQl1Vd1XVN6vqW1X116seBcBLDgx1VZ1I8ndJ3pXkLUnuqaq3rHoYAHuWOaO+Pcm3uvvb3f3zJJ9M8kernQXAi5YJ9RuTfO+Sry8sfu0yVXWmqnaqamd3d/fqF91889G8BuAorbFNJ4/qQN19NsnZJNne3u6rPtBzzx3VJICjs8Y2LXNG/f0kt17y9anFrwFwDJYJ9VeS/E5VvamqfiPJ+5L8y2pnAfCiAy99dPcvqurPk/x7khNJHujup1e+DIAkS16j7u7PJfncircAsA/vTAQYTqgBhhNqgOGEGmC46r7696a87EGrdpN898gP/JKbkvxohcdftU3ev8nbE/vXaZO3J6vf/9vdvbXfN1YS6lWrqp3u3l73jqu1yfs3eXti/zpt8vZkvftd+gAYTqgBhtvUUJ9d94BXaJP3b/L2xP512uTtyRr3b+Q1aoBryaaeUQNcM4QaYLiNC/UmP2i3qh6oqotV9Y11bzmsqrq1qh6rqmeq6umqunfdmw6jqq6vqi9X1dcW+z+y7k2HVVUnquqrVfXIurccVlV9p6q+XlVPVdXOuvccRlXdWFWfrqpnq+p8Vf3usW/YpGvUiwft/k+Sd2bvkWBfSXJPdz+z1mFLqqp3JHk+yT9191vXvecwquqWJLd095NV9bok55L88Qb9u68kN3T381V1XZLHk9zb3V9a87SlVdVfJtlO8vruvnvdew6jqr6TZLu7N+4NL1X1YJL/7O77Fn8n/29290+Oc8OmnVFv9IN2u/uLSX687h1Xo7t/0N1PLj7/WZLz2efZmVP1nucXX163+NiYs5SqOpXkPUnuW/eWa0lV/VaSdyS5P0m6++fHHelk80K91IN2Wa2qOp3ktiRPrHfJ4SwuHTyV5GKSR7t7k/Z/LMmHkvxq3UOuUif5j6o6V1Vn1j3mEN6UZDfJPywuO91XVTcc94hNCzVrVlWvTfJwkg9290/XvecwuvuX3f227D338/aq2ojLT1V1d5KL3X1u3Vtegd/r7rcneVeSP1tcBtwEJ5O8Pcnfd/dtSf43ybH/bGzTQu1Bu2u0uLb7cJKHuvsz695ztRZ/dH0syV3r3rKkO5K8d3Gd95NJ7qyqj6930uF09/cX/7yY5LPZu4y5CS4kuXDJn74+nb1wH6tNC7UH7a7J4odx9yc5390fXfeew6qqraq6cfH5a7L3A+ln17tqOd394e4+1d2ns/ff/Oe7+/1rnrW0qrph8QPoLC4b/GGSjbjzqbufS/K9qnrz4pf+IMmx/wB9qWcmTrHpD9qtqk8k+f0kN1XVhSR/2933r3fV0u5I8oEkX19c502Sv1k8T3MT3JLkwcWdQ69K8qnu3rjb3DbUzUk+u/f/+pxM8s/d/W/rnXQof5HkocXJ4beT/MlxD9io2/MArkWbdukD4Joj1ADDCTXAcEINMJxQAwwn1ADDCTXAcP8Hp9JOx+RI6FUAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before we get to the goals by year, let's make sure we're plotting things in the right spot. We'll grab the goals from the 2022 final and make sure they match the goals as they're shown in the IIHF PDF. This can be accessed by the link in the shot_chart_url column. All the goals occurred in the 2nd period."
      ],
      "metadata": {
        "id": "pWFX3ocZtXa5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Games are scraped in the order their URLs are found. Typically, \n",
        "# this should result in the final being the last game found in the \n",
        "# DataFrame, but it might not always be a safe assumption to make.\n",
        "last_game_url = df.query(\"year == 2022\").game_url.iloc[-1]\n",
        "last_game = df.query(\"game_url == @last_game_url\")\n",
        "\n",
        "last_game"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "BAEoA2VbMKbk",
        "outputId": "d8cd0103-f721-407d-cbf4-97525c61e496"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      shot_chart_url  period team  \\\n",
              "488  https://www.iihf.com//pdf/164/ihw164131_77a_3_0     2.0  USA   \n",
              "489  https://www.iihf.com//pdf/164/ihw164131_77a_3_0     2.0  CAN   \n",
              "490  https://www.iihf.com//pdf/164/ihw164131_77a_3_0     2.0  CAN   \n",
              "\n",
              "     shooter_number opponent  goalie_number         x         y team1 team2  \\\n",
              "488            11.0      CAN           35.0  4.449788  0.485183   USA   CAN   \n",
              "489            19.0      USA           29.0  5.605786  0.293087   USA   CAN   \n",
              "490            19.0      USA           29.0  5.270175  3.325452   USA   CAN   \n",
              "\n",
              "                                              game_url  year  \n",
              "488  https://www.iihf.com//en/events/2022/ww/gamece...  2022  \n",
              "489  https://www.iihf.com//en/events/2022/ww/gamece...  2022  \n",
              "490  https://www.iihf.com//en/events/2022/ww/gamece...  2022  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3d53e754-abba-44ba-96a8-e1ef45f3129f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>shot_chart_url</th>\n",
              "      <th>period</th>\n",
              "      <th>team</th>\n",
              "      <th>shooter_number</th>\n",
              "      <th>opponent</th>\n",
              "      <th>goalie_number</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>game_url</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>488</th>\n",
              "      <td>https://www.iihf.com//pdf/164/ihw164131_77a_3_0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>11.0</td>\n",
              "      <td>CAN</td>\n",
              "      <td>35.0</td>\n",
              "      <td>4.449788</td>\n",
              "      <td>0.485183</td>\n",
              "      <td>USA</td>\n",
              "      <td>CAN</td>\n",
              "      <td>https://www.iihf.com//en/events/2022/ww/gamece...</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>489</th>\n",
              "      <td>https://www.iihf.com//pdf/164/ihw164131_77a_3_0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>CAN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>29.0</td>\n",
              "      <td>5.605786</td>\n",
              "      <td>0.293087</td>\n",
              "      <td>USA</td>\n",
              "      <td>CAN</td>\n",
              "      <td>https://www.iihf.com//en/events/2022/ww/gamece...</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490</th>\n",
              "      <td>https://www.iihf.com//pdf/164/ihw164131_77a_3_0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>CAN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>29.0</td>\n",
              "      <td>5.270175</td>\n",
              "      <td>3.325452</td>\n",
              "      <td>USA</td>\n",
              "      <td>CAN</td>\n",
              "      <td>https://www.iihf.com//en/events/2022/ww/gamece...</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d53e754-abba-44ba-96a8-e1ef45f3129f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3d53e754-abba-44ba-96a8-e1ef45f3129f 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-3d53e754-abba-44ba-96a8-e1ef45f3129f');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n",
        "\n",
        "teams = sorted(last_game.team.unique())\n",
        "\n",
        "for team, ax in zip(teams, axs.flatten()):\n",
        "    draw_net(ax)\n",
        "    ax.set_title(team, fontsize=16)\n",
        "    ax.axis(\"off\")\n",
        "\n",
        "for row in last_game.itertuples():\n",
        "    ax_idx = teams.index(row.team)\n",
        "    axs[ax_idx].text(\n",
        "        row.x, row.y, f\"o{int(row.shooter_number)}\", \n",
        "        ha=\"left\", va=\"center\", color=\"black\",\n",
        "        fontsize=16,\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "33OjjrRRUq7H",
        "outputId": "a2d40b39-d8ef-43dc-eb16-95ff9d7e1023"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looks good to me."
      ],
      "metadata": {
        "id": "UTUsECr7uSVB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Goals by Year"
      ],
      "metadata": {
        "id": "N45IVkoAuOnN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, let's take a look at where pucks have been finding their way past goalies by year. We'll also throw in a plot that contains all the goals from every year for good measure. I'm choosing to use kernel density estimate plots (kdeplots) for this.\n",
        "\n",
        "Since I'm only interested in shots that beat goaltenders, we'll also need to remove empty netters. The goalie's number is listed as -1 for empty net goals, so we'll remove those from the data.  \n",
        "\n",
        "And I'll make the number of levels in the kdeplots a parameter you can play with, in case that interests you."
      ],
      "metadata": {
        "id": "RUnn3CelumG6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "levels = 15    #@param {type: \"number\"}"
      ],
      "metadata": {
        "id": "Ns8ACEpKvpbm"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove empty net goals.\n",
        "goals_on_goalies = df.query(\"goalie_number != -1\")\n",
        "\n",
        "# One Axes object for each year plus one for the overall totals.\n",
        "years = sorted(df.year.unique())\n",
        "n_cols = len(years) + 1\n",
        "\n",
        "summary_title = \"Overall\"\n",
        "\n",
        "fig, axs = plt.subplots(1, n_cols, figsize=(n_cols * 6, 4), facecolor=\"white\")\n",
        "fig.suptitle(\"Women's World Championship Goal Locations\", fontsize=24)\n",
        "\n",
        "for year, ax in zip(years + [summary_title], axs.flatten()):\n",
        "    draw_net(ax)\n",
        "    ax.axis(\"off\")\n",
        "    ax.set_title(year, fontsize=16, y=-0.1, va=\"top\")\n",
        "\n",
        "    sns.kdeplot(\n",
        "        x=\"x\", y=\"y\", ax=ax, levels=levels, shade=True, clip=[(0, NET_WIDTH), (0, NET_HEIGHT)],\n",
        "        data=goals_on_goalies.query(\"year == @year\") if year != summary_title else goals_on_goalies, \n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "OpLB27TGX5s4",
        "outputId": "4b686226-79ba-4d68-8d36-257848dd54c9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looks like a lot of goals in the bottom part of the net, not so many directed at the goalie's chest/head. Five hole goals may also have been less common in 2022 than in previous years."
      ],
      "metadata": {
        "id": "CRQDBh_xwqMi"
      }
    }
  ]
}