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Shinichi-Nakagawa/npb2023_teamstats.ipynb

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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyNH0xadmgV+37TGJFn4SaFZ",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/Shinichi-Nakagawa/3ca01932532ba41ceaef94bd722107b9/npb2023_teamstats.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# プロ野球チーム成績からチームごとの指標を計算・可視化\n",
"\n",
"- チーム打撃・チーム投球成績をスクレイピング\n",
"- データ型を指定してCSVとして保存\n",
"- Plotlyで可視化"
],
"metadata": {
"id": "4a_TuwVg425x"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JKzLztF94NH4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a13a1762-a9bd-45e4-853b-f752c6747f0a"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"datetime.datetime(2023, 9, 30, 16, 48, 1, 665959, tzinfo=zoneinfo.ZoneInfo(key='Asia/Tokyo'))"
]
},
"metadata": {},
"execution_count": 1
}
],
"source": [
"import pandas as pd\n",
"pd.options.display.max_rows = 12\n",
"pd.options.display.max_columns = 50\n",
"from datetime import datetime\n",
"from zoneinfo import ZoneInfo\n",
"# JSTなので注意\n",
"now = datetime.now(ZoneInfo(\"Asia/Tokyo\"))\n",
"now"
]
},
{
"cell_type": "code",
"source": [
"!pip install requests-html"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JP4e1y8S4SEW",
"outputId": "c0754edf-3d1e-4788-8ad5-ed4bdff7b302"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting requests-html\n",
" Downloading requests_html-0.10.0-py3-none-any.whl (13 kB)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from requests-html) (2.31.0)\n",
"Collecting pyquery (from requests-html)\n",
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"Collecting fake-useragent (from requests-html)\n",
" Downloading fake_useragent-1.2.1-py3-none-any.whl (14 kB)\n",
"Collecting parse (from requests-html)\n",
" Downloading parse-1.19.1-py2.py3-none-any.whl (18 kB)\n",
"Collecting bs4 (from requests-html)\n",
" Downloading bs4-0.0.1.tar.gz (1.1 kB)\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Collecting w3lib (from requests-html)\n",
" Downloading w3lib-2.1.2-py3-none-any.whl (21 kB)\n",
"Collecting pyppeteer>=0.0.14 (from requests-html)\n",
" Downloading pyppeteer-1.0.2-py3-none-any.whl (83 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.4/83.4 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: appdirs<2.0.0,>=1.4.3 in /usr/local/lib/python3.10/dist-packages (from pyppeteer>=0.0.14->requests-html) (1.4.4)\n",
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"Collecting pyee<9.0.0,>=8.1.0 (from pyppeteer>=0.0.14->requests-html)\n",
" Downloading pyee-8.2.2-py2.py3-none-any.whl (12 kB)\n",
"Requirement already satisfied: tqdm<5.0.0,>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from pyppeteer>=0.0.14->requests-html) (4.66.1)\n",
"Collecting urllib3<2.0.0,>=1.25.8 (from pyppeteer>=0.0.14->requests-html)\n",
" Downloading urllib3-1.26.16-py2.py3-none-any.whl (143 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.1/143.1 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting websockets<11.0,>=10.0 (from pyppeteer>=0.0.14->requests-html)\n",
" Downloading websockets-10.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (106 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.8/106.8 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"Requirement already satisfied: lxml>=2.1 in /usr/local/lib/python3.10/dist-packages (from pyquery->requests-html) (4.9.3)\n",
"Collecting cssselect>=1.2.0 (from pyquery->requests-html)\n",
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"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=1.4->pyppeteer>=0.0.14->requests-html) (3.16.2)\n",
"Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->bs4->requests-html) (2.5)\n",
"Building wheels for collected packages: bs4\n",
" Building wheel for bs4 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for bs4: filename=bs4-0.0.1-py3-none-any.whl size=1256 sha256=a57aa6cbc305fbea83f24a8f493c7c38f67e7bb792776f245a68cd63e6526605\n",
" Stored in directory: /root/.cache/pip/wheels/25/42/45/b773edc52acb16cd2db4cf1a0b47117e2f69bb4eb300ed0e70\n",
"Successfully built bs4\n",
"Installing collected packages: pyee, parse, fake-useragent, websockets, w3lib, urllib3, cssselect, pyquery, pyppeteer, bs4, requests-html\n",
" Attempting uninstall: urllib3\n",
" Found existing installation: urllib3 2.0.4\n",
" Uninstalling urllib3-2.0.4:\n",
" Successfully uninstalled urllib3-2.0.4\n",
"Successfully installed bs4-0.0.1 cssselect-1.2.0 fake-useragent-1.2.1 parse-1.19.1 pyee-8.2.2 pyppeteer-1.0.2 pyquery-2.0.0 requests-html-0.10.0 urllib3-1.26.16 w3lib-2.1.2 websockets-10.4\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!python --version"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fc3LAHmr4Z6p",
"outputId": "e098d867-7526-412a-d0b9-e8c20ddd6c39"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Python 3.10.12\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## チーム成績の取得とデータ整形"
],
"metadata": {
"id": "u_pMYz7u5V2w"
}
},
{
"cell_type": "code",
"source": [
"# 取得対象のチーム成績\n",
"\n",
"TEAM_STATS_CENTRAL_PITCHING = \"https://npb.jp/bis/2023/stats/tmp_c.html\"\n",
"TEAM_STATS_CENTRAL_BATTING = \"https://npb.jp/bis/2023/stats/tmb_c.html\""
],
"metadata": {
"id": "7VfVrvE04de_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# ひとまず取得\n",
"from requests_html import HTMLSession\n",
"session = HTMLSession()"
],
"metadata": {
"id": "j1bFHn2o5FiU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# 投手&打者成績を取得する\n",
"from typing import Dict, Union, List\n",
"\n",
"COLUMNS_PITCING = ['team', 'era', 'g', 'win', 'lose', 'sv', 'hld', 'hldp', 'cg', 'sho', 'no_walks', 'win_p', 'batter_faces', 'ip', 'ip_2', 'h', 'hr', 'bb', 'ibb', 'hbp', 'so', 'wp', 'balk', 'r', 'er']\n",
"COLUMNS_BATTING = ['team', 'ba', 'g', 'pa', 'ab', 'r', 'h', '_2b', '_3b', 'hr', 'tb', 'rbi', 'sb', 'cs', 'sh', 'sf', 'bb', 'ibb', 'hbp', 'so', 'dp', 'slg', 'obp']\n",
"\n",
"def stats(url: str, columns: List[str], session: HTMLSession) -> Dict[str, Union[str, int, float]]:\n",
" r = session.get(url)\n",
" rows: Dict[str, Union[str, int, float]] = {}\n",
" table = r.html.xpath(\"/html/body/div/div/div/div[3]/table\", first=True)\n",
" for i, tr in enumerate(table.find(\"tr\")):\n",
" # header is skip\n",
" if i == 0:\n",
" continue\n",
" values = {}\n",
" for ii, td in enumerate(tr.find('td')):\n",
" if ii == 0:\n",
" _a = td.find('a', first=True)\n",
" values[columns[ii]] = str(_a.text).replace('\\u3000', '')\n",
" else:\n",
" values[columns[ii]] = td.text\n",
" rows[values['team']] = values\n",
" return rows\n"
],
"metadata": {
"id": "450Uyp_076n_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"stats_p = stats(TEAM_STATS_CENTRAL_PITCHING, COLUMNS_PITCING, session)\n",
"stats_b = stats(TEAM_STATS_CENTRAL_BATTING, COLUMNS_BATTING, session)"
],
"metadata": {
"id": "OykoFv6rBXnz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "JbH28iGdJ2Cr"
}
},
{
"cell_type": "code",
"source": [
"# pandasに読み込んで整形する\n",
"# 投手成績はひと工夫入れる\n",
"_stats_p = {}\n",
"for k, v in stats_p.items():\n",
" v['ip'] = v['ip'] + v['ip_2']\n",
" del v['ip_2']\n",
" _stats_p[k] = v\n",
"\n",
"df_stats_p = pd.DataFrame(_stats_p.values()).astype({'era': float, 'g': int, 'win': int, 'lose': int, 'sv': int, 'hld': int, 'hldp': int, 'cg': int, 'sho': int, 'no_walks': int, 'win_p': float, 'batter_faces': int, 'ip': float, 'h': int, 'hr': int, 'bb': int, 'ibb': int, 'hbp': int, 'so': int, 'wp': int, 'balk': int, 'r': int, 'er': int})"
],
"metadata": {
"id": "CQC6u3ngF88I"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_stats_p"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"id": "irC7spIMGsHM",
"outputId": "22e8e103-c852-4416-b3ef-e1f273a53b54"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" team era g win lose sv hld hldp cg sho no_walks win_p \\\n",
"0 阪神 2.63 140 84 51 44 126 147 10 18 24 0.622 \n",
"1 中日 3.09 140 55 80 36 142 163 4 7 9 0.407 \n",
"2 DeNA 3.20 140 72 65 41 118 139 8 14 16 0.526 \n",
"3 広島 3.21 141 73 64 45 122 148 9 14 11 0.533 \n",
"4 巨人 3.47 139 68 69 29 118 144 7 16 8 0.496 \n",
"5 ヤクルト 3.69 140 56 81 33 130 145 3 15 9 0.409 \n",
"\n",
" batter_faces ip h hr bb ibb hbp so wp balk r er \n",
"0 5104 1262.1 1075 85 310 17 41 988 22 2 412 369 \n",
"1 5270 1261.1 1097 76 435 18 50 1045 32 0 490 433 \n",
"2 5265 1263.1 1187 118 357 28 42 1143 28 0 493 449 \n",
"3 5282 1257.0 1173 108 392 28 42 955 31 3 501 449 \n",
"4 5164 1240.1 1097 128 396 17 64 1041 27 6 504 478 \n",
"5 5231 1241.2 1191 139 376 41 62 935 33 1 558 509 "
],
"text/html": [
"\n",
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" <div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" <thead>\n",
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" <th></th>\n",
" <th>team</th>\n",
" <th>era</th>\n",
" <th>g</th>\n",
" <th>win</th>\n",
" <th>lose</th>\n",
" <th>sv</th>\n",
" <th>hld</th>\n",
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" <th>cg</th>\n",
" <th>sho</th>\n",
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" <th>win_p</th>\n",
" <th>batter_faces</th>\n",
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" <th>hr</th>\n",
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" <th>ibb</th>\n",
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" <th>0</th>\n",
" <td>阪神</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>中日</td>\n",
" <td>3.09</td>\n",
" <td>140</td>\n",
" <td>55</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>DeNA</td>\n",
" <td>3.20</td>\n",
" <td>140</td>\n",
" <td>72</td>\n",
" <td>65</td>\n",
" <td>41</td>\n",
" <td>118</td>\n",
" <td>139</td>\n",
" <td>8</td>\n",
" <td>14</td>\n",
" <td>16</td>\n",
" <td>0.526</td>\n",
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" <td>42</td>\n",
" <td>1143</td>\n",
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" <td>493</td>\n",
" <td>449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>広島</td>\n",
" <td>3.21</td>\n",
" <td>141</td>\n",
" <td>73</td>\n",
" <td>64</td>\n",
" <td>45</td>\n",
" <td>122</td>\n",
" <td>148</td>\n",
" <td>9</td>\n",
" <td>14</td>\n",
" <td>11</td>\n",
" <td>0.533</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>巨人</td>\n",
" <td>3.47</td>\n",
" <td>139</td>\n",
" <td>68</td>\n",
" <td>69</td>\n",
" <td>29</td>\n",
" <td>118</td>\n",
" <td>144</td>\n",
" <td>7</td>\n",
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" <td>8</td>\n",
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" <th>5</th>\n",
" <td>ヤクルト</td>\n",
" <td>3.69</td>\n",
" <td>140</td>\n",
" <td>56</td>\n",
" <td>81</td>\n",
" <td>33</td>\n",
" <td>130</td>\n",
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"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
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"\n",
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" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
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"\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",
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" margin-bottom: 4px;\n",
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"\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-5356890f-faa9-445a-ba5a-17258490a815 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-5356890f-faa9-445a-ba5a-17258490a815');\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",
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"\n",
"\n",
"<div id=\"df-911f11c2-3166-4bec-8790-924609465726\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-911f11c2-3166-4bec-8790-924609465726')\"\n",
" title=\"Suggest charts.\"\n",
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"\n",
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" width=\"24px\">\n",
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" </g>\n",
"</svg>\n",
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" --bg-color: #E8F0FE;\n",
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" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
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" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
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"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
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" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
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" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-911f11c2-3166-4bec-8790-924609465726 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"df_stats_b = pd.DataFrame(stats_b.values()).astype({'ba': float, 'g': int, 'pa': int, 'ab': int, 'r': int, 'h': int, '_2b': int, '_3b': int, 'hr': int, 'tb': int, 'rbi': int, 'sb': int, 'cs': int, 'sh': int, 'sf': int, 'bb': int, 'ibb': int, 'hbp': int, 'so': int, 'dp': int, 'slg': float, 'obp': float})"
],
"metadata": {
"id": "pOCM8mnuGxTk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_stats_b"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"id": "hmnR7UhQHGJ8",
"outputId": "3241ae24-1a79-431d-f16e-3bcb340a46a0"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" team ba g pa ab r h _2b _3b hr tb rbi sb cs \\\n",
"0 巨人 0.253 139 5213 4700 517 1188 198 14 163 1903 505 47 29 \n",
"1 阪神 0.247 140 5369 4679 544 1158 177 34 81 1646 523 78 27 \n",
"2 DeNA 0.247 140 5225 4686 512 1156 227 18 104 1731 495 32 25 \n",
"3 広島 0.246 141 5169 4664 486 1146 196 17 94 1658 469 77 50 \n",
"4 ヤクルト 0.239 140 5247 4609 527 1103 208 11 122 1699 502 61 20 \n",
"5 中日 0.236 140 5156 4703 387 1108 185 19 70 1541 368 35 24 \n",
"\n",
" sh sf bb ibb hbp so dp slg obp \n",
"0 92 31 353 38 37 1087 91 0.405 0.308 \n",
"1 104 46 485 34 55 1144 89 0.352 0.323 \n",
"2 101 31 352 22 55 832 100 0.369 0.305 \n",
"3 93 20 345 25 47 1017 114 0.355 0.303 \n",
"4 113 33 442 23 50 1075 111 0.369 0.311 \n",
"5 90 22 303 18 38 1007 93 0.328 0.286 "
],
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" <td>4664</td>\n",
" <td>486</td>\n",
" <td>1146</td>\n",
" <td>196</td>\n",
" <td>17</td>\n",
" <td>94</td>\n",
" <td>1658</td>\n",
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" <td>77</td>\n",
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" <td>114</td>\n",
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" <td>0.303</td>\n",
" </tr>\n",
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" <td>140</td>\n",
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" <td>0.369</td>\n",
" <td>0.311</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>中日</td>\n",
" <td>0.236</td>\n",
" <td>140</td>\n",
" <td>5156</td>\n",
" <td>4703</td>\n",
" <td>387</td>\n",
" <td>1108</td>\n",
" <td>185</td>\n",
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" + ' to learn more about interactive tables.';\n",
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" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-ac6abeb4-e12d-42fc-894a-48e8eae895f0 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"# 必要な指標を作り込む（打撃）\n",
"df_stats_b['ops'] = round(df_stats_b['obp'] + df_stats_b['slg'], 3)\n",
"df_stats_b['ab_k'] = round(df_stats_b['ab'] / df_stats_b['so'], 2)\n",
"df_stats_b['pa_k'] = round(df_stats_b['pa'] / df_stats_b['so'], 2)\n",
"df_stats_b['ab_bb'] = round(df_stats_b['ab'] / df_stats_b['bb'], 2)\n",
"df_stats_b['pa_bb'] = round(df_stats_b['pa'] / df_stats_b['bb'], 2)\n",
"df_stats_b['bb_per_so'] = round((df_stats_b['bb'] + df_stats_b['hbp'] + df_stats_b['ibb']) / df_stats_b['so'], 3)"
],
"metadata": {
"id": "N-Wu8VzbKL59"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_stats_b"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"id": "9IolahbJMQdh",
"outputId": "07ead18b-9abf-4f08-9e70-4ed1be476d87"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" team ba g pa ab r h _2b _3b hr tb rbi sb cs \\\n",
"0 巨人 0.253 139 5213 4700 517 1188 198 14 163 1903 505 47 29 \n",
"1 阪神 0.247 140 5369 4679 544 1158 177 34 81 1646 523 78 27 \n",
"2 DeNA 0.247 140 5225 4686 512 1156 227 18 104 1731 495 32 25 \n",
"3 広島 0.246 141 5169 4664 486 1146 196 17 94 1658 469 77 50 \n",
"4 ヤクルト 0.239 140 5247 4609 527 1103 208 11 122 1699 502 61 20 \n",
"5 中日 0.236 140 5156 4703 387 1108 185 19 70 1541 368 35 24 \n",
"\n",
" sh sf bb ibb hbp so dp slg obp ops ab_k pa_k ab_bb \\\n",
"0 92 31 353 38 37 1087 91 0.405 0.308 0.713 4.32 4.80 13.31 \n",
"1 104 46 485 34 55 1144 89 0.352 0.323 0.675 4.09 4.69 9.65 \n",
"2 101 31 352 22 55 832 100 0.369 0.305 0.674 5.63 6.28 13.31 \n",
"3 93 20 345 25 47 1017 114 0.355 0.303 0.658 4.59 5.08 13.52 \n",
"4 113 33 442 23 50 1075 111 0.369 0.311 0.680 4.29 4.88 10.43 \n",
"5 90 22 303 18 38 1007 93 0.328 0.286 0.614 4.67 5.12 15.52 \n",
"\n",
" pa_bb bb_per_so \n",
"0 14.77 0.394 \n",
"1 11.07 0.502 \n",
"2 14.84 0.516 \n",
"3 14.98 0.410 \n",
"4 11.87 0.479 \n",
"5 17.02 0.357 "
],
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" <td>0.394</td>\n",
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" <th>1</th>\n",
" <td>阪神</td>\n",
" <td>0.247</td>\n",
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" <td>4.69</td>\n",
" <td>9.65</td>\n",
" <td>11.07</td>\n",
" <td>0.502</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>DeNA</td>\n",
" <td>0.247</td>\n",
" <td>140</td>\n",
" <td>5225</td>\n",
" <td>4686</td>\n",
" <td>512</td>\n",
" <td>1156</td>\n",
" <td>227</td>\n",
" <td>18</td>\n",
" <td>104</td>\n",
" <td>1731</td>\n",
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" <td>32</td>\n",
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" <td>31</td>\n",
" <td>352</td>\n",
" <td>22</td>\n",
" <td>55</td>\n",
" <td>832</td>\n",
" <td>100</td>\n",
" <td>0.369</td>\n",
" <td>0.305</td>\n",
" <td>0.674</td>\n",
" <td>5.63</td>\n",
" <td>6.28</td>\n",
" <td>13.31</td>\n",
" <td>14.84</td>\n",
" <td>0.516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>広島</td>\n",
" <td>0.246</td>\n",
" <td>141</td>\n",
" <td>5169</td>\n",
" <td>4664</td>\n",
" <td>486</td>\n",
" <td>1146</td>\n",
" <td>196</td>\n",
" <td>17</td>\n",
" <td>94</td>\n",
" <td>1658</td>\n",
" <td>469</td>\n",
" <td>77</td>\n",
" <td>50</td>\n",
" <td>93</td>\n",
" <td>20</td>\n",
" <td>345</td>\n",
" <td>25</td>\n",
" <td>47</td>\n",
" <td>1017</td>\n",
" <td>114</td>\n",
" <td>0.355</td>\n",
" <td>0.303</td>\n",
" <td>0.658</td>\n",
" <td>4.59</td>\n",
" <td>5.08</td>\n",
" <td>13.52</td>\n",
" <td>14.98</td>\n",
" <td>0.410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ヤクルト</td>\n",
" <td>0.239</td>\n",
" <td>140</td>\n",
" <td>5247</td>\n",
" <td>4609</td>\n",
" <td>527</td>\n",
" <td>1103</td>\n",
" <td>208</td>\n",
" <td>11</td>\n",
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" <td>23</td>\n",
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" <td>1075</td>\n",
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" <td>0.369</td>\n",
" <td>0.311</td>\n",
" <td>0.680</td>\n",
" <td>4.29</td>\n",
" <td>4.88</td>\n",
" <td>10.43</td>\n",
" <td>11.87</td>\n",
" <td>0.479</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>中日</td>\n",
" <td>0.236</td>\n",
" <td>140</td>\n",
" <td>5156</td>\n",
" <td>4703</td>\n",
" <td>387</td>\n",
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" let quickchartButtonEl =\n",
" document.querySelector('#df-be4b2bf7-7b77-4f4c-89cc-dcbe77e06967 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"# 必要な指標を作り込む（投手）\n",
"# memo: fipの補正値は適当（今年は打低すぎるので2.8とかでもいいかも)\n",
"df_stats_p['fip'] = round((3 * (df_stats_p['bb'] + df_stats_p['hbp'] - df_stats_p['ibb']) + (13 * df_stats_p['hr']) - (2 * df_stats_p['so'])) / df_stats_p['ip'] + 3, 2)\n",
"df_stats_p['so_p'] = round(((df_stats_p['so'] * 9) /df_stats_p['ip']), 2)\n",
"df_stats_p['bb_p'] = round(((df_stats_p['bb'] * 9) /df_stats_p['ip']), 2)\n",
"df_stats_p"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"id": "OCXwYiPnMRZg",
"outputId": "b3fa9004-5d90-4d31-bb66-7a1fe9d8b777"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" team era g win lose sv hld hldp cg sho no_walks win_p \\\n",
"0 阪神 2.63 140 84 51 44 126 147 10 18 24 0.622 \n",
"1 中日 3.09 140 55 80 36 142 163 4 7 9 0.407 \n",
"2 DeNA 3.20 140 72 65 41 118 139 8 14 16 0.526 \n",
"3 広島 3.21 141 73 64 45 122 148 9 14 11 0.533 \n",
"4 巨人 3.47 139 68 69 29 118 144 7 16 8 0.496 \n",
"5 ヤクルト 3.69 140 56 81 33 130 145 3 15 9 0.409 \n",
"\n",
" batter_faces ip h hr bb ibb hbp so wp balk r er \\\n",
"0 5104 1262.1 1075 85 310 17 41 988 22 2 412 369 \n",
"1 5270 1261.1 1097 76 435 18 50 1045 32 0 490 433 \n",
"2 5265 1263.1 1187 118 357 28 42 1143 28 0 493 449 \n",
"3 5282 1257.0 1173 108 392 28 42 955 31 3 501 449 \n",
"4 5164 1240.1 1097 128 396 17 64 1041 27 6 504 478 \n",
"5 5231 1241.2 1191 139 376 41 62 935 33 1 558 509 \n",
"\n",
" fip so_p bb_p \n",
"0 3.10 7.05 2.21 \n",
"1 3.24 7.46 3.10 \n",
"2 3.29 8.14 2.54 \n",
"3 3.57 6.84 2.81 \n",
"4 3.73 7.56 2.87 \n",
"5 3.91 6.78 2.73 "
],
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>team</th>\n",
" <th>era</th>\n",
" <th>g</th>\n",
" <th>win</th>\n",
" <th>lose</th>\n",
" <th>sv</th>\n",
" <th>hld</th>\n",
" <th>hldp</th>\n",
" <th>cg</th>\n",
" <th>sho</th>\n",
" <th>no_walks</th>\n",
" <th>win_p</th>\n",
" <th>batter_faces</th>\n",
" <th>ip</th>\n",
" <th>h</th>\n",
" <th>hr</th>\n",
" <th>bb</th>\n",
" <th>ibb</th>\n",
" <th>hbp</th>\n",
" <th>so</th>\n",
" <th>wp</th>\n",
" <th>balk</th>\n",
" <th>r</th>\n",
" <th>er</th>\n",
" <th>fip</th>\n",
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" <th>bb_p</th>\n",
" </tr>\n",
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" <td>阪神</td>\n",
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" <td>3.10</td>\n",
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" <th>1</th>\n",
" <td>中日</td>\n",
" <td>3.09</td>\n",
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" <td>55</td>\n",
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" <td>32</td>\n",
" <td>0</td>\n",
" <td>490</td>\n",
" <td>433</td>\n",
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" </tr>\n",
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" <th>2</th>\n",
" <td>DeNA</td>\n",
" <td>3.20</td>\n",
" <td>140</td>\n",
" <td>72</td>\n",
" <td>65</td>\n",
" <td>41</td>\n",
" <td>118</td>\n",
" <td>139</td>\n",
" <td>8</td>\n",
" <td>14</td>\n",
" <td>16</td>\n",
" <td>0.526</td>\n",
" <td>5265</td>\n",
" <td>1263.1</td>\n",
" <td>1187</td>\n",
" <td>118</td>\n",
" <td>357</td>\n",
" <td>28</td>\n",
" <td>42</td>\n",
" <td>1143</td>\n",
" <td>28</td>\n",
" <td>0</td>\n",
" <td>493</td>\n",
" <td>449</td>\n",
" <td>3.29</td>\n",
" <td>8.14</td>\n",
" <td>2.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>広島</td>\n",
" <td>3.21</td>\n",
" <td>141</td>\n",
" <td>73</td>\n",
" <td>64</td>\n",
" <td>45</td>\n",
" <td>122</td>\n",
" <td>148</td>\n",
" <td>9</td>\n",
" <td>14</td>\n",
" <td>11</td>\n",
" <td>0.533</td>\n",
" <td>5282</td>\n",
" <td>1257.0</td>\n",
" <td>1173</td>\n",
" <td>108</td>\n",
" <td>392</td>\n",
" <td>28</td>\n",
" <td>42</td>\n",
" <td>955</td>\n",
" <td>31</td>\n",
" <td>3</td>\n",
" <td>501</td>\n",
" <td>449</td>\n",
" <td>3.57</td>\n",
" <td>6.84</td>\n",
" <td>2.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>巨人</td>\n",
" <td>3.47</td>\n",
" <td>139</td>\n",
" <td>68</td>\n",
" <td>69</td>\n",
" <td>29</td>\n",
" <td>118</td>\n",
" <td>144</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>8</td>\n",
" <td>0.496</td>\n",
" <td>5164</td>\n",
" <td>1240.1</td>\n",
" <td>1097</td>\n",
" <td>128</td>\n",
" <td>396</td>\n",
" <td>17</td>\n",
" <td>64</td>\n",
" <td>1041</td>\n",
" <td>27</td>\n",
" <td>6</td>\n",
" <td>504</td>\n",
" <td>478</td>\n",
" <td>3.73</td>\n",
" <td>7.56</td>\n",
" <td>2.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ヤクルト</td>\n",
" <td>3.69</td>\n",
" <td>140</td>\n",
" <td>56</td>\n",
" <td>81</td>\n",
" <td>33</td>\n",
" <td>130</td>\n",
" <td>145</td>\n",
" <td>3</td>\n",
" <td>15</td>\n",
" <td>9</td>\n",
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" <td>376</td>\n",
" <td>41</td>\n",
" <td>62</td>\n",
" <td>935</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>558</td>\n",
" <td>509</td>\n",
" <td>3.91</td>\n",
" <td>6.78</td>\n",
" <td>2.73</td>\n",
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" fill: var(--disabled-fill-color);\n",
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" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
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" }\n",
"\n",
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" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
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" }\n",
"</style>\n",
"\n",
" <script>\n",
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" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
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"</div>\n",
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]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"# 計算したデータを保存（必要なときだけ有効化）\n",
"\n",
"df_stats_b.to_csv(f'{now.strftime(\"%Y%m%d_%H%M%S\")}_team_stats_batting.csv')\n",
"df_stats_p.to_csv(f'{now.strftime(\"%Y%m%d_%H%M%S\")}_team_stats_pitcing.csv')"
],
"metadata": {
"id": "1X5ENewMNN3N"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## 成績の可視化"
],
"metadata": {
"id": "EJYNVyRL5feF"
}
},
{
"cell_type": "code",
"source": [
"# Plotlyで可視化\n",
"!pip install plotly"
],
"metadata": {
"id": "673O5VslWJtp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9c2ca545-6cdf-4be8-8002-f4a0b814a82a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)\n",
"Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (8.2.3)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (23.1)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import plotly.express as px\n",
"\n",
"WIDTH = 1400\n",
"HEIGHT = 700\n",
"\n",
"fig = px.bar(df_stats_b.sort_values(['pa_bb'], ascending=False), x=['pa_k', 'pa_bb'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=f\"【チーム打撃成績】三振および四球獲得までの平均打席数 ※{now.strftime('%Y/%m/%d')}時点\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（pa_bbの昇順）')\n",
"fig.update_xaxes(title='pa_k（三振するまでの平均打席数）, ab_bb（四球獲得までの平均打席数）', dtick=2, range=(0, 18))\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 717
},
"id": "JBaL0L2O4ZeZ",
"outputId": "d1202b0a-fa34-414b-d423-0475421c609f"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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"<html>\n",
"<head><meta charset=\"utf-8\" /></head>\n",
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]
},
{
"cell_type": "code",
"source": [
"fig = px.bar(df_stats_p.sort_values(['bb_p'], ascending=False), x=['so_p', 'bb_p'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=f\"【チーム投手成績】奪三振率と与四球率の比較 ※{now.strftime('%Y/%m/%d')}時点\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（与四球率の昇順）')\n",
"fig.update_xaxes(title='so_p（奪三振率）, bb_p（与四球率）', dtick=1, range=(0, 9))\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 717
},
"id": "9QvKLZV34xnE",
"outputId": "6f41d82e-a0d1-4220-dd1e-a190e1edeafd"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "wmLKpx8gCBN6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## 以下, スライド素材用のデータ"
],
"metadata": {
"id": "UsUM_0NgGdC0"
}
},
{
"cell_type": "code",
"source": [
"import plotly.express as px\n",
"fig = px.bar(df_stats_b.sort_values(['pa_bb'], ascending=False), x=['pa_bb'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=f\"【チーム打撃成績】四球獲得に要した打席数（少ない=多く四球を獲得しているので優秀） ※{now.strftime('%Y/%m/%d')}時点\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（数字が優秀な順）')\n",
"fig.update_xaxes(title='四球獲得までの平均打席数（pa_bb）', dtick=2, range=(0, 18))\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 717
},
"outputId": "955fa72b-ab24-411b-b64c-1b428fff7ef3",
"id": "eOQlkn_AGZig"
},
"execution_count": null,
"outputs": [
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},
{
"cell_type": "code",
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"metadata": {
"id": "1j1Xkc_pGavG"
},
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"outputs": []
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{
"cell_type": "code",
"source": [
"import plotly.express as px\n",
"fig = px.bar(df_stats_b.sort_values(['pa_k'], ascending=True), x=['pa_k'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=f\"【チーム打撃成績】三振するまでの平均打席数（多い=三振が少ない） ※{now.strftime('%Y/%m/%d')}時点\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（数字が優秀な順）')\n",
"fig.update_xaxes(title='三振するまでの平均打席数（pa_k）', dtick=1, range=(0, 9))\n",
"fig.show()"
],
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"height": 717
},
"outputId": "29f943c7-5929-4ad6-cea8-3b03c880d2e3",
"id": "C8Ot08jqGa-E"
},
"execution_count": null,
"outputs": [
{
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{
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"source": [
"fig = px.bar(df_stats_p.sort_values(['so_p'], ascending=True), x=['so_p'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=f\"【チーム投手成績】奪三振率（高いと優秀） ※{now.strftime('%Y/%m/%d')}時点\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（数字が優秀な順）')\n",
"fig.update_xaxes(title='奪三振率（so_p）', dtick=1, range=(0, 9))\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 717
},
"id": "D5Kiz3vsHlt4",
"outputId": "aff8b8d0-a500-4327-b23c-9cb334e45ee5"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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{
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"source": [
"fig = px.bar(df_stats_p.sort_values(['bb_p'], ascending=False), x=['bb_p'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=f\"【チーム投手成績】与四球率（低いと優秀） ※{now.strftime('%Y/%m/%d')}時点\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（数字が優秀な順）')\n",
"fig.update_xaxes(title='与四球率（bb_p）', dtick=1, range=(0, 6))\n",
"fig.show()"
],
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 717
},
"id": "ov6fAwzFJOxH",
"outputId": "c400534c-19ba-4766-dd21-379f5939ff84"
},
"execution_count": null,
"outputs": [
{
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},
{
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"source": [],
"metadata": {
"id": "IykavUTpK7WH"
},
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"outputs": []
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]
}
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