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scottboston/mlb-sweep-analysis-clean-version-2.ipynb

Created May 23, 2021 21:05
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mlb-sweep-analysis-clean-version-2.ipynb
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"name": "MLB Sweep Analysis Clean Version 2.ipynb",
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"<a href=\"https://colab.research.google.com/gist/scottboston/b09e7f54d7adccdd26365daca3fc74d3/mlb-sweep-analysis-clean-version-2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
"cell_type": "code",
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"height": 17
},
"outputId": "8830f9fc-5031-42e6-ab76-0f103405fd25"
},
"source": [
"from IPython.core.display import display, HTML\n",
"display(HTML(\"<style>.container { width:95% !important; }</style>\"))"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
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{
"cell_type": "markdown",
"metadata": {
"id": "SAjIHxMCqx8H"
},
"source": [
"<h1>MLB Baseball Sweep Analysis</h1>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j2owKxFSqx8P"
},
"source": [
"Copyright (C) 2021 Scott Boston ([email protected]) \n",
"No part of this code nor logic maybe used without permission of the copyright owner.\n",
"\n",
"Property of Scott Boston \n",
"Author: Scott Boston \n",
"Date: April 27, 2021\n",
"\n",
"Datasource: http://sportsbookreviewsonline.com\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JcQdK6rpW7ia"
},
"source": [
"What percentage of the time do teams convert a sweep of a two-games or greater series? For instances, in a three game series, the home team has won the first two games, what percentage of time does the winning team go on to win the last game of a series to convert the sweep opportunity? I speculated that most of times teams do not go on complete the sweep. I was wrong. By analyzing the past eleven years of baseball records, I determined that sweep converts a little better than 54% of time over the past eleven seasons. Can we devise an strategy to beat the money line?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-lwlnnJ9zVfP"
},
"source": [
"Five Strategies to beat the money line for the last game of a series where a sweep is possible.\n",
"\n",
"\n",
"\n",
"1. **All Sweep Opportunities** - Betting on the winning team for all sweep opportunities\n",
"2. **Home Team Sweep Opportunities** - Betting on games where the home team has an opportunity to sweep\n",
"3. **Home Series Sweep and Home Favorite Opportunities** - Betting on games where home team has an opportunity to sweep and the home team is favorite by the given money line odds\n",
"4. **Away Series Sweep and Away Favorite Opportunities** - Betting on games where the away team has an opportunity to sweep and the away team is favorite by the given money line odds\n",
"5. **Sweep Favorite Combination** - Combining 3 and 4, betting on only the favorite team in a sweep opportunity\n",
"\n",
"Note: 2020 is COVID shorten baseball Season and 2021 is still ongoing.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "P33C3LpZqx8Q"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as mtick\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "blskYShBqx8R"
},
"source": [
"def getGamesDataFrame(df, year):\n",
" ''' Pivots source datarframe to return games dataframe '''\n",
"\n",
" # source datafrome\n",
" # df = pd.read_excel('https://www.sportsbookreviewsonline.com/scoresoddsarchives/mlb/mlb%20odds%202011.xlsx')\n",
" df = df.dropna(how=\"all\").copy()\n",
"\n",
" df[\"Date\"] = df[\"Date\"].astype(int) # To handle excel float type in some years\n",
" df[\"Date\"] = pd.to_datetime(\n",
" f\"{year}\" + df[\"Date\"].astype(str).str.rjust(4, \"0\"), format=\"%Y%m%d\"\n",
" )\n",
"\n",
" cols = [\"Date\", \"VH\", \"Team\", \"Final\", \"Close\"]\n",
" df = df[cols]\n",
"\n",
" df = df[df[\"VH\"].isin([\"V\", \"H\"])]\n",
"\n",
" # Flatten teams lines in to games\n",
" df[\"Game No\"] = (df[\"VH\"] == \"V\").cumsum()\n",
" df = df.set_index([\"Game No\", \"Date\", \"VH\"]).unstack()\n",
" df.columns = df.columns.map(\"_\".join)\n",
"\n",
" df = df.reset_index()\n",
"\n",
" # Define a series, if the same home and away teams have a date differ by more than two days\n",
" # then increment series identifier\n",
" df[\"Date Diff\"] = df.groupby([\"Team_H\", \"Team_V\"])[\"Date\"].diff()\n",
" df.loc[df[\"Date Diff\"] > pd.Timedelta(days=2), \"Date Diff\"] = np.nan\n",
" df[\"Series\"] = df.sort_values([\"Team_H\", \"Team_V\"])[\"Date Diff\"].isna().cumsum()\n",
"\n",
" df = df.sort_values([\"Team_H\", \"Team_V\", \"Date\"])\n",
"\n",
" # Home team won or Away team won\n",
" df[\"Home_Win\"] = df[\"Final_V\"] < df[\"Final_H\"]\n",
"\n",
" # Assign Favorite Betting Team and odds Favorite\n",
" df = df.assign(\n",
" Favorite=np.select(\n",
" [df[\"Close_H\"] > df[\"Close_V\"], df[\"Close_H\"] < df[\"Close_V\"]],\n",
" [\"Away\", \"Home\"],\n",
" \"Toss up\",\n",
" )\n",
" )\n",
"\n",
" # Look for potential sweep series\n",
" l1 = lambda x: (x.iloc[:-1].sum() == 0) | (x.iloc[:-1].sum() == x.iloc[:-1].count())\n",
" l1.__name__ = \"Potential_Sweep\"\n",
"\n",
" # Success sweep occurred\n",
" l2 = lambda x: (x.sum() == 0) | (x.sum() == x.count())\n",
" l2.__name__ = \"Sweep\"\n",
" df = df.merge(\n",
" df.groupby(\"Series\").agg(\n",
" Potential_Sweep=pd.NamedAgg(column=\"Home_Win\", aggfunc=l1),\n",
" Sweep=pd.NamedAgg(column=\"Home_Win\", aggfunc=l2),\n",
" Home_Away_Sweep=pd.NamedAgg(\n",
" column=\"Home_Win\", aggfunc=lambda x: \"Home\" if x.iloc[0] else \"Away\"\n",
" ),\n",
" No_Games_In_Series=pd.NamedAgg(column=\"Home_Win\", aggfunc='count')\n",
" ),\n",
" left_on=\"Series\",\n",
" right_index=True,\n",
" )\n",
" return df\n",
"\n",
"\n",
"def getSeriesDataFrame(df, min_no=1, max_no=7):\n",
" ''' Returns a dataframe with last game of each series where number of games in the series meets min and max values'''\n",
" return df.query(f'{min_no} <= No_Games_In_Series <= {max_no}').drop_duplicates(\"Series\", keep=\"last\")\n",
"\n",
"\n",
"def getSweepDataFrame(df):\n",
" ''' Returns a dataframe of only series with potential sweeps '''\n",
" Sweep_df = df.loc[df[\"Potential_Sweep\"]].copy()\n",
"\n",
" # Determines with team to bet on Home or away\n",
" Sweep_df[\"BetOdds\"] = np.where(\n",
" Sweep_df[\"Home_Away_Sweep\"] == \"Home\", Sweep_df[\"Close_H\"], Sweep_df[\"Close_V\"]\n",
" )\n",
"\n",
" Sweep_df['Bet'] = np.where(Sweep_df[\"BetOdds\"]<0, Sweep_df['BetOdds'],-100)\n",
"\n",
" #Sweep_df['Bet_Favorite'] = np.where(Sweep_df['Favorite'] == 'Home', Sweep_df['Close_H'], Sweep_df['Close_V'])\n",
"\n",
" # Betting logic on moneyline wagers\n",
" cond = [\n",
" (Sweep_df[\"BetOdds\"] < 0) & Sweep_df[\"Sweep\"],\n",
" (Sweep_df[\"BetOdds\"] < 0) & ~Sweep_df[\"Sweep\"],\n",
" (Sweep_df[\"BetOdds\"] > 0) & Sweep_df[\"Sweep\"],\n",
" (Sweep_df[\"BetOdds\"] > 0) & ~Sweep_df[\"Sweep\"],\n",
" ]\n",
" res = [100, Sweep_df[\"BetOdds\"], Sweep_df[\"BetOdds\"], -100]\n",
"\n",
" Sweep_df[\"Bet_Outcome\"] = np.select(cond, res)\n",
"\n",
" return Sweep_df\n",
"\n",
"\n",
"def plotBettingOutcomes(df, title, ax):\n",
" for c in df:\n",
" df[c].plot(\n",
" ax=ax,\n",
" markevery=[df[c].last_valid_index()],\n",
" marker=\"o\",\n",
" ms=10,\n",
" alpha=0.8,\n",
" markeredgecolor=\"k\",\n",
" )\n",
" ax.set_title(title, size=24)\n",
" fmt = \"${x:,.0f}\"\n",
" tick = mtick.StrMethodFormatter(fmt)\n",
" ax.yaxis.set_major_formatter(tick)\n",
" ax.legend()\n",
" ax.axhline(color=\"r\", alpha=0.8)\n",
" return ax\n",
"\n",
"\n",
"def drawBetSweepOutcomes(sweepdf, year):\n",
" fig, ax = plt.subplots(3, 1, figsize=(25, 10), gridspec_kw={'hspace':.3})\n",
"\n",
" df = (\n",
" sweep_df.sort_values(\"Date\")[\"Bet_Outcome\"]\n",
" .cumsum()\n",
" .reset_index(drop=True)\n",
" .to_frame()\n",
" )\n",
" title = \"All Sweep Opportunities\"\n",
" plotBettingOutcomes(df, title, ax[0])\n",
"\n",
" df = (\n",
" sweep_df.sort_values(\"Date\")\n",
" .loc[sweep_df[\"Home_Away_Sweep\"] == \"Home\", \"Bet_Outcome\"]\n",
" .cumsum()\n",
" .reset_index(drop=True)\n",
" .to_frame()\n",
" )\n",
" title = \"Home Series Sweep Opportunities\"\n",
" plotBettingOutcomes(df, title, ax[1])\n",
"\n",
" df = (\n",
" sweep_df.sort_values(\"Date\")\n",
" .loc[\n",
" (sweep_df[\"Favorite\"] == \"Home\") & (sweep_df[\"Home_Away_Sweep\"] == \"Home\"),\n",
" \"Bet_Outcome\",\n",
" ]\n",
" .reset_index(drop=True)\n",
" .cumsum()\n",
" .to_frame()\n",
" )\n",
" title = \"Home Series Sweep and Home Favorite Opportunities\"\n",
" plotBettingOutcomes(df, title, ax[2])\n",
"\n",
" [i.grid(axis=\"y\") for i in ax]\n",
" plt.gcf().suptitle(f'MLB Baseball Season {year}', size=24)\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "6_-bJZhmqx8S"
},
"source": [
"Get one year's data (Of course let's pull 2017)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UzBPsGoGqx8T"
},
"source": [
"year = 2017\n",
"rawdf = pd.read_excel(f'https://www.sportsbookreviewsonline.com/scoresoddsarchives/mlb/mlb%20odds%20{year}.xlsx', usecols=range(17))\n",
"\n",
"gamesdf = getGamesDataFrame(rawdf, year)\n",
"\n",
"seriesdf = getSeriesDataFrame(gamesdf)\n",
"\n",
"sweep_df = getSweepDataFrame(seriesdf)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "qq7rjnDBqx8T",
"outputId": "aac6bfd7-cbb4-4d2a-e8c3-d490c6203a56"
},
"source": [
"rawdf.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Rot</th>\n",
" <th>VH</th>\n",
" <th>Team</th>\n",
" <th>Pitcher</th>\n",
" <th>1st</th>\n",
" <th>2nd</th>\n",
" <th>3rd</th>\n",
" <th>4th</th>\n",
" <th>5th</th>\n",
" <th>6th</th>\n",
" <th>7th</th>\n",
" <th>8th</th>\n",
" <th>9th</th>\n",
" <th>Final</th>\n",
" <th>Open</th>\n",
" <th>Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>402</td>\n",
" <td>901</td>\n",
" <td>V</td>\n",
" <td>SFO</td>\n",
" <td>MBUMGARNER-L</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>-130</td>\n",
" <td>-144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>402</td>\n",
" <td>902</td>\n",
" <td>H</td>\n",
" <td>ARI</td>\n",
" <td>ZGREINKE-R</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>110</td>\n",
" <td>129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>402</td>\n",
" <td>903</td>\n",
" <td>V</td>\n",
" <td>CUB</td>\n",
" <td>JLESTER-L</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>-125</td>\n",
" <td>-126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>402</td>\n",
" <td>904</td>\n",
" <td>H</td>\n",
" <td>STL</td>\n",
" <td>CMARTINEZ-R</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>105</td>\n",
" <td>116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>402</td>\n",
" <td>905</td>\n",
" <td>V</td>\n",
" <td>NYY</td>\n",
" <td>MTANAKA-R</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-120</td>\n",
" <td>-110</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Rot VH Team Pitcher 1st ... 7th 8th 9th Final Open Close\n",
"0 402 901 V SFO MBUMGARNER-L 0 ... 1 0 1 5 -130 -144\n",
"1 402 902 H ARI ZGREINKE-R 0 ... 0 1 2 6 110 129\n",
"2 402 903 V CUB JLESTER-L 0 ... 0 0 3 3 -125 -126\n",
"3 402 904 H STL CMARTINEZ-R 0 ... 0 2 1 4 105 116\n",
"4 402 905 V NYY MTANAKA-R 0 ... 0 0 1 3 -120 -110\n",
"\n",
"[5 rows x 17 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 359
},
"id": "LW8r1NAcqx8U",
"outputId": "3c6f7c46-0f99-4206-c07d-0edf7b389b0e"
},
"source": [
"gamesdf.sort_values('Team_H').head(10)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Game No</th>\n",
" <th>Date</th>\n",
" <th>Team_H</th>\n",
" <th>Team_V</th>\n",
" <th>Final_H</th>\n",
" <th>Final_V</th>\n",
" <th>Close_H</th>\n",
" <th>Close_V</th>\n",
" <th>Date Diff</th>\n",
" <th>Series</th>\n",
" <th>Home_Win</th>\n",
" <th>Favorite</th>\n",
" <th>Potential_Sweep</th>\n",
" <th>Sweep</th>\n",
" <th>Home_Away_Sweep</th>\n",
" <th>No_Games_In_Series</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1470</th>\n",
" <td>1471</td>\n",
" <td>2017-07-24</td>\n",
" <td>ARI</td>\n",
" <td>ATL</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>-215</td>\n",
" <td>185</td>\n",
" <td>NaT</td>\n",
" <td>1</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>319</th>\n",
" <td>320</td>\n",
" <td>2017-04-27</td>\n",
" <td>ARI</td>\n",
" <td>SDG</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>-200</td>\n",
" <td>170</td>\n",
" <td>1 days</td>\n",
" <td>21</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>306</th>\n",
" <td>307</td>\n",
" <td>2017-04-26</td>\n",
" <td>ARI</td>\n",
" <td>SDG</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>-127</td>\n",
" <td>117</td>\n",
" <td>1 days</td>\n",
" <td>21</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>292</th>\n",
" <td>293</td>\n",
" <td>2017-04-25</td>\n",
" <td>ARI</td>\n",
" <td>SDG</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>-134</td>\n",
" <td>119</td>\n",
" <td>1 days</td>\n",
" <td>21</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>283</th>\n",
" <td>284</td>\n",
" <td>2017-04-24</td>\n",
" <td>ARI</td>\n",
" <td>SDG</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>-158</td>\n",
" <td>143</td>\n",
" <td>NaT</td>\n",
" <td>21</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>546</th>\n",
" <td>547</td>\n",
" <td>2017-05-14</td>\n",
" <td>ARI</td>\n",
" <td>PIT</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>-160</td>\n",
" <td>145</td>\n",
" <td>1 days</td>\n",
" <td>20</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>531</th>\n",
" <td>532</td>\n",
" <td>2017-05-13</td>\n",
" <td>ARI</td>\n",
" <td>PIT</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>-165</td>\n",
" <td>150</td>\n",
" <td>1 days</td>\n",
" <td>20</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>516</th>\n",
" <td>517</td>\n",
" <td>2017-05-12</td>\n",
" <td>ARI</td>\n",
" <td>PIT</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>-150</td>\n",
" <td>135</td>\n",
" <td>1 days</td>\n",
" <td>20</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>503</th>\n",
" <td>504</td>\n",
" <td>2017-05-11</td>\n",
" <td>ARI</td>\n",
" <td>PIT</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>-138</td>\n",
" <td>123</td>\n",
" <td>NaT</td>\n",
" <td>20</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1133</th>\n",
" <td>1134</td>\n",
" <td>2017-06-26</td>\n",
" <td>ARI</td>\n",
" <td>PHI</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>-215</td>\n",
" <td>185</td>\n",
" <td>1 days</td>\n",
" <td>19</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Away</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Game No Date Team_H ... Sweep Home_Away_Sweep No_Games_In_Series\n",
"1470 1471 2017-07-24 ARI ... False Home 3\n",
"319 320 2017-04-27 ARI ... False Home 4\n",
"306 307 2017-04-26 ARI ... False Home 4\n",
"292 293 2017-04-25 ARI ... False Home 4\n",
"283 284 2017-04-24 ARI ... False Home 4\n",
"546 547 2017-05-14 ARI ... False Home 4\n",
"531 532 2017-05-13 ARI ... False Home 4\n",
"516 517 2017-05-12 ARI ... False Home 4\n",
"503 504 2017-05-11 ARI ... False Home 4\n",
"1133 1134 2017-06-26 ARI ... False Away 4\n",
"\n",
"[10 rows x 16 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
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},
"id": "TLGOyOykqx8V",
"outputId": "0747e586-8e6c-44ed-e36c-1794a519d508"
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"source": [
"seriesdf.head()"
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"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<div>\n",
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" <th>Final_V</th>\n",
" <th>Close_H</th>\n",
" <th>Close_V</th>\n",
" <th>Date Diff</th>\n",
" <th>Series</th>\n",
" <th>Home_Win</th>\n",
" <th>Favorite</th>\n",
" <th>Potential_Sweep</th>\n",
" <th>Sweep</th>\n",
" <th>Home_Away_Sweep</th>\n",
" <th>No_Games_In_Series</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1497</th>\n",
" <td>1498</td>\n",
" <td>2017-07-26</td>\n",
" <td>ARI</td>\n",
" <td>ATL</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>-210</td>\n",
" <td>180</td>\n",
" <td>1 days</td>\n",
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" <td>False</td>\n",
" <td>Home</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1316</th>\n",
" <td>1317</td>\n",
" <td>2017-07-09</td>\n",
" <td>ARI</td>\n",
" <td>CIN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>-155</td>\n",
" <td>140</td>\n",
" <td>1 days</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
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" <td>False</td>\n",
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" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>93</td>\n",
" <td>2017-04-09</td>\n",
" <td>ARI</td>\n",
" <td>CLE</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>152</td>\n",
" <td>-172</td>\n",
" <td>1 days</td>\n",
" <td>3</td>\n",
" <td>True</td>\n",
" <td>Away</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>358</th>\n",
" <td>359</td>\n",
" <td>2017-04-30</td>\n",
" <td>ARI</td>\n",
" <td>COL</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>-121</td>\n",
" <td>111</td>\n",
" <td>1 days</td>\n",
" <td>4</td>\n",
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" <td>Home</td>\n",
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" <td>Away</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1220</th>\n",
" <td>1221</td>\n",
" <td>2017-07-02</td>\n",
" <td>ARI</td>\n",
" <td>COL</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>-116</td>\n",
" <td>106</td>\n",
" <td>1 days</td>\n",
" <td>5</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Away</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Game No Date Team_H ... Sweep Home_Away_Sweep No_Games_In_Series\n",
"1497 1498 2017-07-26 ARI ... False Home 3\n",
"1316 1317 2017-07-09 ARI ... False Home 3\n",
"92 93 2017-04-09 ARI ... True Home 3\n",
"358 359 2017-04-30 ARI ... False Away 3\n",
"1220 1221 2017-07-02 ARI ... False Away 3\n",
"\n",
"[5 rows x 16 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
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"outputId": "e4ebb0cb-0876-4e29-a3d9-4f5303eaba12"
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"source": [
"sweep_df.head()"
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"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th>Close_V</th>\n",
" <th>Date Diff</th>\n",
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" <th>Home_Win</th>\n",
" <th>Favorite</th>\n",
" <th>Potential_Sweep</th>\n",
" <th>Sweep</th>\n",
" <th>Home_Away_Sweep</th>\n",
" <th>No_Games_In_Series</th>\n",
" <th>BetOdds</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>93</td>\n",
" <td>2017-04-09</td>\n",
" <td>ARI</td>\n",
" <td>CLE</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>152</td>\n",
" <td>-172</td>\n",
" <td>1 days</td>\n",
" <td>3</td>\n",
" <td>True</td>\n",
" <td>Away</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>3</td>\n",
" <td>152</td>\n",
" <td>-100</td>\n",
" <td>152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>358</th>\n",
" <td>359</td>\n",
" <td>2017-04-30</td>\n",
" <td>ARI</td>\n",
" <td>COL</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>-121</td>\n",
" <td>111</td>\n",
" <td>1 days</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
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" <td>False</td>\n",
" <td>Away</td>\n",
" <td>3</td>\n",
" <td>111</td>\n",
" <td>-100</td>\n",
" <td>-100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2431</th>\n",
" <td>2432</td>\n",
" <td>2017-10-04</td>\n",
" <td>ARI</td>\n",
" <td>COL</td>\n",
" <td>11</td>\n",
" <td>8</td>\n",
" <td>-155</td>\n",
" <td>140</td>\n",
" <td>NaT</td>\n",
" <td>7</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>1</td>\n",
" <td>-155</td>\n",
" <td>-155</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>686</th>\n",
" <td>687</td>\n",
" <td>2017-05-24</td>\n",
" <td>ARI</td>\n",
" <td>CWS</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>-119</td>\n",
" <td>109</td>\n",
" <td>1 days</td>\n",
" <td>9</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>3</td>\n",
" <td>-119</td>\n",
" <td>-119</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>501</th>\n",
" <td>502</td>\n",
" <td>2017-05-10</td>\n",
" <td>ARI</td>\n",
" <td>DET</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>-136</td>\n",
" <td>121</td>\n",
" <td>1 days</td>\n",
" <td>10</td>\n",
" <td>True</td>\n",
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" <td>121</td>\n",
" <td>-100</td>\n",
" <td>-100</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Game No Date Team_H ... BetOdds Bet Bet_Outcome\n",
"92 93 2017-04-09 ARI ... 152 -100 152\n",
"358 359 2017-04-30 ARI ... 111 -100 -100\n",
"2431 2432 2017-10-04 ARI ... -155 -155 100\n",
"686 687 2017-05-24 ARI ... -119 -119 100\n",
"501 502 2017-05-10 ARI ... 121 -100 -100\n",
"\n",
"[5 rows x 19 columns]"
]
},
"metadata": {
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},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LOzio0Xpqx8V"
},
"source": [
"What percent of the time does a team actually convert a sweep?"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "X7HjZlykqx8W",
"outputId": "72fff52a-fb33-4f5e-f059-ac44dc86e6e2"
},
"source": [
"sweep_df['Sweep'].mean()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.5526315789473685"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
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},
"id": "kuL52nxfqx8W",
"outputId": "c537dabd-30ab-4f10-93e7-42245ecb6fd0"
},
"source": [
"sweep_df['Sweep'].value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True 231\n",
"False 187\n",
"Name: Sweep, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
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"id": "BdbWsjQmqx8W",
"outputId": "9b5dab52-a810-4b66-9686-069b55730bf5"
},
"source": [
"sweep_df = sweep_df.sort_values('Date')\n",
"sweep_df"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<div>\n",
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" <th>35</th>\n",
" <td>36</td>\n",
" <td>2017-04-05</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>32</td>\n",
" <td>2017-04-05</td>\n",
" <td>BAL</td>\n",
" <td>TOR</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>-117</td>\n",
" <td>107</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>33</td>\n",
" <td>2017-04-05</td>\n",
" <td>TEX</td>\n",
" <td>CLE</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>105</td>\n",
" <td>-115</td>\n",
" <td>1 days</td>\n",
" <td>729</td>\n",
" <td>False</td>\n",
" <td>Away</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
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" <td>3</td>\n",
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" <td>-115</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>47</td>\n",
" <td>2017-04-06</td>\n",
" <td>HOU</td>\n",
" <td>SEA</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>-157</td>\n",
" <td>142</td>\n",
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" <td>293</td>\n",
" <td>False</td>\n",
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" <td>4</td>\n",
" <td>-157</td>\n",
" <td>-157</td>\n",
" <td>-157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>40</td>\n",
" <td>2017-04-06</td>\n",
" <td>WAS</td>\n",
" <td>MIA</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>-175</td>\n",
" <td>155</td>\n",
" <td>1 days</td>\n",
" <td>792</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>3</td>\n",
" <td>-175</td>\n",
" <td>-175</td>\n",
" <td>-175</td>\n",
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" <tr>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2452</th>\n",
" <td>2453</td>\n",
" <td>2017-10-15</td>\n",
" <td>LOS</td>\n",
" <td>CUB</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>-170</td>\n",
" <td>150</td>\n",
" <td>1 days</td>\n",
" <td>364</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>2</td>\n",
" <td>-170</td>\n",
" <td>-170</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2457</th>\n",
" <td>2458</td>\n",
" <td>2017-10-18</td>\n",
" <td>NYY</td>\n",
" <td>HOU</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>-102</td>\n",
" <td>-108</td>\n",
" <td>1 days</td>\n",
" <td>500</td>\n",
" <td>True</td>\n",
" <td>Away</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>3</td>\n",
" <td>-102</td>\n",
" <td>-102</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2460</th>\n",
" <td>2461</td>\n",
" <td>2017-10-21</td>\n",
" <td>HOU</td>\n",
" <td>NYY</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>-142</td>\n",
" <td>127</td>\n",
" <td>1 days</td>\n",
" <td>289</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>2</td>\n",
" <td>-142</td>\n",
" <td>-142</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2462</th>\n",
" <td>2463</td>\n",
" <td>2017-10-25</td>\n",
" <td>LOS</td>\n",
" <td>HOU</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>101</td>\n",
" <td>-111</td>\n",
" <td>1 days</td>\n",
" <td>366</td>\n",
" <td>False</td>\n",
" <td>Away</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>Home</td>\n",
" <td>2</td>\n",
" <td>101</td>\n",
" <td>-100</td>\n",
" <td>-100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2466</th>\n",
" <td>2467</td>\n",
" <td>2017-10-31</td>\n",
" <td>LOS</td>\n",
" <td>HOU</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>-130</td>\n",
" <td>115</td>\n",
" <td>NaT</td>\n",
" <td>367</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>1</td>\n",
" <td>-130</td>\n",
" <td>-130</td>\n",
" <td>100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>418 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" Game No Date Team_H ... BetOdds Bet Bet_Outcome\n",
"35 36 2017-04-05 BOS ... -165 -165 100\n",
"31 32 2017-04-05 BAL ... -117 -117 100\n",
"32 33 2017-04-05 TEX ... -115 -115 100\n",
"46 47 2017-04-06 HOU ... -157 -157 -157\n",
"39 40 2017-04-06 WAS ... -175 -175 -175\n",
"... ... ... ... ... ... ... ...\n",
"2452 2453 2017-10-15 LOS ... -170 -170 100\n",
"2457 2458 2017-10-18 NYY ... -102 -102 100\n",
"2460 2461 2017-10-21 HOU ... -142 -142 100\n",
"2462 2463 2017-10-25 LOS ... 101 -100 -100\n",
"2466 2467 2017-10-31 LOS ... -130 -130 100\n",
"\n",
"[418 rows x 19 columns]"
]
},
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},
"execution_count": 11
}
]
},
{
"cell_type": "code",
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"source": [
"sweep_df.query('Home_Away_Sweep == \"Home\"').head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th>35</th>\n",
" <td>36</td>\n",
" <td>2017-04-05</td>\n",
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" <th>46</th>\n",
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" <td>2017-04-06</td>\n",
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" <td>142</td>\n",
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" <td>-175</td>\n",
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" <td>-175</td>\n",
" <td>-175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>43</td>\n",
" <td>2017-04-06</td>\n",
" <td>MIN</td>\n",
" <td>KAN</td>\n",
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" <td>-107</td>\n",
" <td>-103</td>\n",
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" Game No Date Team_H ... BetOdds Bet Bet_Outcome\n",
"35 36 2017-04-05 BOS ... -165 -165 100\n",
"31 32 2017-04-05 BAL ... -117 -117 100\n",
"46 47 2017-04-06 HOU ... -157 -157 -157\n",
"39 40 2017-04-06 WAS ... -175 -175 -175\n",
"42 43 2017-04-06 MIN ... -107 -107 100\n",
"\n",
"[5 rows x 19 columns]"
]
},
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"tags": []
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"execution_count": 12
}
]
},
{
"cell_type": "code",
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},
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"outputId": "0c17f57d-e841-4647-c111-dae4ce7338ae"
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"source": [
"sweep_df.query('Home_Away_Sweep == \"Home\" and Favorite == \"Home\"')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th>35</th>\n",
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" <td>2017-04-05</td>\n",
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" </tr>\n",
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" <td>2017-04-06</td>\n",
" <td>HOU</td>\n",
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" <td>-157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>40</td>\n",
" <td>2017-04-06</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>43</td>\n",
" <td>2017-04-06</td>\n",
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" <td>-107</td>\n",
" <td>-103</td>\n",
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" <tr>\n",
" <th>2445</th>\n",
" <td>2446</td>\n",
" <td>2017-10-09</td>\n",
" <td>NYY</td>\n",
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" <td>7</td>\n",
" <td>3</td>\n",
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" <td>-150</td>\n",
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" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2451</th>\n",
" <td>2452</td>\n",
" <td>2017-10-14</td>\n",
" <td>HOU</td>\n",
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" <td>2</td>\n",
" <td>1</td>\n",
" <td>-118</td>\n",
" <td>108</td>\n",
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" <td>-118</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2452</th>\n",
" <td>2453</td>\n",
" <td>2017-10-15</td>\n",
" <td>LOS</td>\n",
" <td>CUB</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>-170</td>\n",
" <td>150</td>\n",
" <td>1 days</td>\n",
" <td>364</td>\n",
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" <td>True</td>\n",
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" <td>2</td>\n",
" <td>-170</td>\n",
" <td>-170</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2460</th>\n",
" <td>2461</td>\n",
" <td>2017-10-21</td>\n",
" <td>HOU</td>\n",
" <td>NYY</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>-142</td>\n",
" <td>127</td>\n",
" <td>1 days</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2466</th>\n",
" <td>2467</td>\n",
" <td>2017-10-31</td>\n",
" <td>LOS</td>\n",
" <td>HOU</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>-130</td>\n",
" <td>115</td>\n",
" <td>NaT</td>\n",
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" <td>True</td>\n",
" <td>Home</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>Home</td>\n",
" <td>1</td>\n",
" <td>-130</td>\n",
" <td>-130</td>\n",
" <td>100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>189 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" Game No Date Team_H ... BetOdds Bet Bet_Outcome\n",
"35 36 2017-04-05 BOS ... -165 -165 100\n",
"31 32 2017-04-05 BAL ... -117 -117 100\n",
"46 47 2017-04-06 HOU ... -157 -157 -157\n",
"39 40 2017-04-06 WAS ... -175 -175 -175\n",
"42 43 2017-04-06 MIN ... -107 -107 100\n",
"... ... ... ... ... ... ... ...\n",
"2445 2446 2017-10-09 NYY ... -150 -150 100\n",
"2451 2452 2017-10-14 HOU ... -118 -118 100\n",
"2452 2453 2017-10-15 LOS ... -170 -170 100\n",
"2460 2461 2017-10-21 HOU ... -142 -142 100\n",
"2466 2467 2017-10-31 LOS ... -130 -130 100\n",
"\n",
"[189 rows x 19 columns]"
]
},
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},
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}
]
},
{
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},
"source": [
"What is the subcategories and potential profits by Home/Away Team sweeping and betting favorite?"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 266
},
"id": "FNge3_D0qx8X",
"outputId": "3b94b144-7b8b-4064-bbdf-2fd8d070899e"
},
"source": [
"sweep_df.groupby(['Home_Away_Sweep', 'Favorite'])['Bet_Outcome'].agg(['sum','count'])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>sum</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Home_Away_Sweep</th>\n",
" <th>Favorite</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Away</th>\n",
" <th>Away</th>\n",
" <td>-236</td>\n",
" <td>79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Home</th>\n",
" <td>556</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Toss up</th>\n",
" <td>195</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Home</th>\n",
" <th>Away</th>\n",
" <td>-880</td>\n",
" <td>53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Home</th>\n",
" <td>1512</td>\n",
" <td>189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Toss up</th>\n",
" <td>-110</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sum count\n",
"Home_Away_Sweep Favorite \n",
"Away Away -236 79\n",
" Home 556 90\n",
" Toss up 195 4\n",
"Home Away -880 53\n",
" Home 1512 189\n",
" Toss up -110 3"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0_Ko_Fegqx8Y"
},
"source": [
"Let's look at bankroll requirements of a seasons of betting"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 663
},
"id": "Va4vIt7vqx8Y",
"outputId": "74ea196d-f7b8-4350-bc11-653c18777dde"
},
"source": [
"drawBetSweepOutcomes(sweep_df, year)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 1800x720 with 3 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "E0oETeB5ID1a"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "83MglLXuqx8Y"
},
"source": [
"Let's look at 11 seasons of data 2010 - 2021"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7pUGdovjqx8Y",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b3f8fa34-c26e-4dd4-baf6-2e688e2376e7"
},
"source": [
"l={}\n",
"g={}\n",
"for s in range(2010, 2022):\n",
" #print(s)\n",
" rawdf = pd.read_excel(f'https://www.sportsbookreviewsonline.com/scoresoddsarchives/mlb/mlb%20odds%20{s}.xlsx')\n",
" gamesdf = getGamesDataFrame(rawdf, s)\n",
" seriesdf = getSeriesDataFrame(gamesdf)\n",
" sweep_df = getSweepDataFrame(seriesdf)\n",
" l[s] = sweep_df\n",
" g[s] = gamesdf"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "be3497kKO_ba"
},
"source": [
"multiyear = pd.concat(l, keys=l.keys())\n",
"hws = pd.concat([d[d['Home_Away_Sweep'] == 'Home'] for k, d in l.items()], keys=l.keys())\n",
"bbs = pd.concat([d[(d['Favorite']=='Home')&(d['Home_Away_Sweep']=='Home')] for k, d in l.items()], keys=l.keys())\n",
"awafs = pd.concat([d[(d['Favorite'] == 'Away')&(d['Home_Away_Sweep'] == 'Away')] for k, d in l.items()], keys=l.keys())\n",
"sfcombo = pd.concat([bbs, awafs]).sort_index()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "Kud4j96Hqx8Z",
"outputId": "f5adfc38-4d66-4ddb-d37a-0feb7718de7f"
},
"source": [
"def plotBettingScenario(df, title, ax):\n",
" df = df.sort_values('Date')[['Bet_Outcome']].set_index(df.groupby(level=0).cumcount(), append=True)\\\n",
" .reset_index(level=1, drop=True)['Bet_Outcome'].unstack(0)\\\n",
" .cumsum()\n",
"\n",
" return plotBettingOutcomes(df, title, ax)\n",
"\n",
"df_dict = {}\n",
"df_dict['All Sweep Opportunities'] = multiyear\n",
"df_dict['Home Series Sweep Opportunities'] = hws\n",
"df_dict['Home Series Sweep and Home Favorite Opportunities'] = bbs\n",
"df_dict['Away Series Sweep and Away Favorite Opportunities'] = awafs\n",
"df_dict['Sweep Favorite Combination'] = sfcombo\n",
"\n",
"fig, ax = plt.subplots(len(df_dict),1, figsize=(25,30))\n",
"iax = iter(ax)\n",
"for title, df in df_dict.items():\n",
" plotBettingScenario(df, title, next(iax))\n",
"\n",
"_ = [i.grid(axis='y') for i in ax]"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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