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kibice
2022-10-18 00:24:12 +02:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analiza zależności ilości kibiców w baseball mlb"
]
},
{
"cell_type": "code",
win streak analysis
2022-10-18 00:53:52 +02:00
"execution_count": 1,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"outputs": [
{
"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>Unnamed: 0</th>\n",
" <th>attendance</th>\n",
" <th>away_team</th>\n",
" <th>away_team_errors</th>\n",
" <th>away_team_hits</th>\n",
" <th>away_team_runs</th>\n",
" <th>date</th>\n",
" <th>field_type</th>\n",
" <th>game_type</th>\n",
" <th>home_team</th>\n",
" <th>...</th>\n",
" <th>temperature</th>\n",
" <th>wind_speed</th>\n",
" <th>wind_direction</th>\n",
" <th>sky</th>\n",
" <th>total_runs</th>\n",
" <th>game_hours_dec</th>\n",
" <th>season</th>\n",
" <th>home_team_win</th>\n",
" <th>home_team_loss</th>\n",
" <th>home_team_outcome</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>40030.0</td>\n",
" <td>New York Mets</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2016-04-03</td>\n",
" <td>on grass</td>\n",
" <td>Night Game</td>\n",
" <td>Kansas City Royals</td>\n",
" <td>...</td>\n",
" <td>74.0</td>\n",
" <td>14.0</td>\n",
" <td>from Right to Left</td>\n",
" <td>Sunny</td>\n",
" <td>7</td>\n",
" <td>3.216667</td>\n",
" <td>regular season</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Win</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>21621.0</td>\n",
" <td>Philadelphia Phillies</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2016-04-06</td>\n",
" <td>on grass</td>\n",
" <td>Night Game</td>\n",
" <td>Cincinnati Reds</td>\n",
" <td>...</td>\n",
" <td>55.0</td>\n",
" <td>24.0</td>\n",
" <td>from Right to Left</td>\n",
" <td>Overcast</td>\n",
" <td>5</td>\n",
" <td>2.383333</td>\n",
" <td>regular season</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Win</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>12622.0</td>\n",
" <td>Minnesota Twins</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2016-04-06</td>\n",
" <td>on grass</td>\n",
" <td>Night Game</td>\n",
" <td>Baltimore Orioles</td>\n",
" <td>...</td>\n",
" <td>48.0</td>\n",
" <td>7.0</td>\n",
" <td>out to Leftfield</td>\n",
" <td>Unknown</td>\n",
" <td>6</td>\n",
" <td>3.183333</td>\n",
" <td>regular season</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Win</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>18531.0</td>\n",
" <td>Washington Nationals</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>2016-04-06</td>\n",
" <td>on grass</td>\n",
" <td>Night Game</td>\n",
" <td>Atlanta Braves</td>\n",
" <td>...</td>\n",
" <td>65.0</td>\n",
" <td>10.0</td>\n",
" <td>from Right to Left</td>\n",
" <td>Cloudy</td>\n",
" <td>4</td>\n",
" <td>2.883333</td>\n",
" <td>regular season</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Loss</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>18572.0</td>\n",
" <td>Colorado Rockies</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>2016-04-06</td>\n",
" <td>on grass</td>\n",
" <td>Day Game</td>\n",
" <td>Arizona Diamondbacks</td>\n",
" <td>...</td>\n",
" <td>77.0</td>\n",
" <td>0.0</td>\n",
" <td>in unknown direction</td>\n",
" <td>In Dome</td>\n",
" <td>7</td>\n",
" <td>2.650000</td>\n",
" <td>regular season</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Loss</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2458</th>\n",
" <td>2458</td>\n",
" <td>31042.0</td>\n",
" <td>Toronto Blue Jays</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2016-04-03</td>\n",
" <td>on turf</td>\n",
" <td>Day Game</td>\n",
" <td>Tampa Bay Rays</td>\n",
" <td>...</td>\n",
" <td>72.0</td>\n",
" <td>0.0</td>\n",
" <td>in unknown direction</td>\n",
" <td>In Dome</td>\n",
" <td>8</td>\n",
" <td>2.850000</td>\n",
" <td>regular season</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Loss</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2459</th>\n",
" <td>2459</td>\n",
" <td>39500.0</td>\n",
" <td>St. Louis Cardinals</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2016-04-03</td>\n",
" <td>on grass</td>\n",
" <td>Day Game</td>\n",
" <td>Pittsburgh Pirates</td>\n",
" <td>...</td>\n",
" <td>39.0</td>\n",
" <td>14.0</td>\n",
" <td>out to Leftfield</td>\n",
" <td>Unknown</td>\n",
" <td>5</td>\n",
" <td>3.033333</td>\n",
" <td>regular season</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Win</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2460</th>\n",
" <td>2460</td>\n",
" <td>20098.0</td>\n",
" <td>San Francisco Giants</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>2016-04-06</td>\n",
" <td>on grass</td>\n",
" <td>Day Game</td>\n",
" <td>Milwaukee Brewers</td>\n",
" <td>...</td>\n",
" <td>66.0</td>\n",
" <td>0.0</td>\n",
" <td>in unknown direction</td>\n",
" <td>In Dome</td>\n",
" <td>7</td>\n",
" <td>3.316667</td>\n",
" <td>regular season</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Win</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2461</th>\n",
" <td>2461</td>\n",
" <td>17883.0</td>\n",
" <td>Detroit Tigers</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>2016-04-06</td>\n",
" <td>on grass</td>\n",
" <td>Day Game</td>\n",
" <td>Miami Marlins</td>\n",
" <td>...</td>\n",
" <td>71.0</td>\n",
" <td>0.0</td>\n",
" <td>in unknown direction</td>\n",
" <td>In Dome</td>\n",
" <td>10</td>\n",
" <td>3.366667</td>\n",
" <td>regular season</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Loss</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2462</th>\n",
" <td>2462</td>\n",
" <td>10298.0</td>\n",
" <td>Boston Red Sox</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>6</td>\n",
" <td>2016-04-06</td>\n",
" <td>on grass</td>\n",
" <td>Night Game</td>\n",
" <td>Cleveland Indians</td>\n",
" <td>...</td>\n",
" <td>60.0</td>\n",
" <td>7.0</td>\n",
" <td>out to Leftfield</td>\n",
" <td>Unknown</td>\n",
" <td>13</td>\n",
" <td>3.483333</td>\n",
" <td>regular season</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Win</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2463 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 attendance away_team away_team_errors \\\n",
"0 0 40030.0 New York Mets 1 \n",
"1 1 21621.0 Philadelphia Phillies 0 \n",
"2 2 12622.0 Minnesota Twins 0 \n",
"3 3 18531.0 Washington Nationals 0 \n",
"4 4 18572.0 Colorado Rockies 1 \n",
"... ... ... ... ... \n",
"2458 2458 31042.0 Toronto Blue Jays 2 \n",
"2459 2459 39500.0 St. Louis Cardinals 0 \n",
"2460 2460 20098.0 San Francisco Giants 0 \n",
"2461 2461 17883.0 Detroit Tigers 0 \n",
"2462 2462 10298.0 Boston Red Sox 1 \n",
"\n",
" away_team_hits away_team_runs date field_type game_type \\\n",
"0 7 3 2016-04-03 on grass Night Game \n",
"1 5 2 2016-04-06 on grass Night Game \n",
"2 5 2 2016-04-06 on grass Night Game \n",
"3 8 3 2016-04-06 on grass Night Game \n",
"4 8 4 2016-04-06 on grass Day Game \n",
"... ... ... ... ... ... \n",
"2458 7 5 2016-04-03 on turf Day Game \n",
"2459 5 1 2016-04-03 on grass Day Game \n",
"2460 6 3 2016-04-06 on grass Day Game \n",
"2461 13 7 2016-04-06 on grass Day Game \n",
"2462 10 6 2016-04-06 on grass Night Game \n",
"\n",
" home_team ... temperature wind_speed \\\n",
"0 Kansas City Royals ... 74.0 14.0 \n",
"1 Cincinnati Reds ... 55.0 24.0 \n",
"2 Baltimore Orioles ... 48.0 7.0 \n",
"3 Atlanta Braves ... 65.0 10.0 \n",
"4 Arizona Diamondbacks ... 77.0 0.0 \n",
"... ... ... ... ... \n",
"2458 Tampa Bay Rays ... 72.0 0.0 \n",
"2459 Pittsburgh Pirates ... 39.0 14.0 \n",
"2460 Milwaukee Brewers ... 66.0 0.0 \n",
"2461 Miami Marlins ... 71.0 0.0 \n",
"2462 Cleveland Indians ... 60.0 7.0 \n",
"\n",
" wind_direction sky total_runs game_hours_dec \\\n",
"0 from Right to Left Sunny 7 3.216667 \n",
"1 from Right to Left Overcast 5 2.383333 \n",
"2 out to Leftfield Unknown 6 3.183333 \n",
"3 from Right to Left Cloudy 4 2.883333 \n",
"4 in unknown direction In Dome 7 2.650000 \n",
"... ... ... ... ... \n",
"2458 in unknown direction In Dome 8 2.850000 \n",
"2459 out to Leftfield Unknown 5 3.033333 \n",
"2460 in unknown direction In Dome 7 3.316667 \n",
"2461 in unknown direction In Dome 10 3.366667 \n",
"2462 out to Leftfield Unknown 13 3.483333 \n",
"\n",
" season home_team_win home_team_loss home_team_outcome \n",
"0 regular season 1 0 Win \n",
"1 regular season 1 0 Win \n",
"2 regular season 1 0 Win \n",
"3 regular season 0 1 Loss \n",
"4 regular season 0 1 Loss \n",
"... ... ... ... ... \n",
"2458 regular season 0 1 Loss \n",
"2459 regular season 1 0 Win \n",
"2460 regular season 1 0 Win \n",
"2461 regular season 0 1 Loss \n",
"2462 regular season 1 0 Win \n",
"\n",
"[2463 rows x 26 columns]"
]
},
win streak analysis
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"execution_count": 1,
kibice
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"data = pd.read_csv(\"baseball_reference_2016_clean.csv\")\n",
"\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pogoda\n",
"\n",
"![image](sky.jpg)"
]
},
{
"cell_type": "code",
win streak analysis
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"execution_count": 2,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Sunny', 'Overcast', 'Unknown', 'Cloudy', 'In Dome', 'Drizzle',\n",
" 'Rain', 'Night'], dtype=object)"
]
},
win streak analysis
2022-10-18 00:53:52 +02:00
"execution_count": 2,
kibice
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['sky'].unique()"
]
},
{
"cell_type": "code",
win streak analysis
2022-10-18 00:53:52 +02:00
"execution_count": 3,
kibice
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"metadata": {},
"outputs": [],
"source": [
"sunny = data[data['sky'] == 'Sunny']\n",
"overcast = data[data['sky'] == 'Overcast']\n",
"cloudy = data[data['sky'] == 'Cloudy']\n",
"in_dome = data[data['sky'] == 'In Dome']\n",
"drizzle = data[data['sky'] == 'Drizzle']\n",
"rain = data[data['sky'] == 'Rain']\n",
"night = data[data['sky'] == 'Night']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Średnia ilość kibiców w zależności od pogody"
]
},
{
"cell_type": "code",
win streak analysis
2022-10-18 00:53:52 +02:00
"execution_count": 4,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
" \n",
"left = [1, 2, 3, 4, 5, 6, 7]\n",
"\n",
"height = [sunny['attendance'].mean(), overcast['attendance'].mean(), cloudy['attendance'].mean(), \n",
"in_dome['attendance'].mean(), drizzle['attendance'].mean(), rain['attendance'].mean(), night['attendance'].mean()]\n",
"\n",
"tick_label = ['sunny', 'overcast', 'cloudy', 'in dome', 'drizzle', 'rain', 'night']\n",
"\n",
"plt.bar(left, height, tick_label = tick_label,\n",
" width = 0.8, color = ['blue', 'green', 'red'])\n",
" \n",
"plt.xlabel('Weather')\n",
"plt.ylabel('Attendance')\n",
"plt.title('Attendance - Weather')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mediana"
]
},
{
"cell_type": "code",
win streak analysis
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"execution_count": 5,
kibice
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"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
" \n",
"left = [1, 2, 3, 4, 5, 6, 7]\n",
"\n",
"height = [sunny['attendance'].median(), overcast['attendance'].median(), cloudy['attendance'].median(), \n",
"in_dome['attendance'].median(), drizzle['attendance'].median(), rain['attendance'].median(), night['attendance'].median()]\n",
"\n",
"tick_label = ['sunny', 'overcast', 'cloudy', 'in dome', 'drizzle', 'rain', 'night']\n",
"\n",
"plt.bar(left, height, tick_label = tick_label,\n",
" width = 0.8, color = ['blue', 'green', 'red'])\n",
" \n",
"plt.xlabel('Weather')\n",
"plt.ylabel('Attendance')\n",
"plt.title('Attendance - Weather')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"W nocy prawdopodobnie najwięcej, gdyż większa grupa odbiorców ma dostęp do meczy online z całego świata. \n",
"Pod kopułą może być najmniej widzów, gdyż takie stadiony mają mniejsze trybuny."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dzień tygodnia\n",
"\n",
"![image2](week.jpg)"
]
},
{
"cell_type": "code",
win streak analysis
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"execution_count": 6,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Sunday', 'Wednesday', 'Tuesday', 'Monday', 'Thursday', 'Saturday',\n",
" 'Friday'], dtype=object)"
]
},
win streak analysis
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"execution_count": 6,
kibice
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['day_of_week'].unique()"
]
},
{
"cell_type": "code",
win streak analysis
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"execution_count": 7,
kibice
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"metadata": {},
"outputs": [],
"source": [
"monday = data[data['day_of_week'] == 'Monday']\n",
"tuesday = data[data['day_of_week'] == 'Tuesday']\n",
"wednesday = data[data['day_of_week'] == 'Wednesday']\n",
"thursday = data[data['day_of_week'] == 'Thursday']\n",
"friday = data[data['day_of_week'] == 'Friday']\n",
"saturday = data[data['day_of_week'] == 'Saturday']\n",
"sunday = data[data['day_of_week'] == 'Sunday']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Średnia ilość kibiców w danym dniu"
]
},
{
"cell_type": "code",
win streak analysis
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"execution_count": 8,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"outputs": [
{
"data": {
win streak analysis
2022-10-18 00:53:52 +02:00
"image/png": "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
kibice
2022-10-18 00:24:12 +02:00
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
" \n",
"left = [1, 2, 3, 4, 5, 6, 7]\n",
"\n",
"height = [monday['attendance'].mean(), tuesday['attendance'].mean(), wednesday['attendance'].mean(), \n",
"thursday['attendance'].mean(), friday['attendance'].mean(), saturday['attendance'].mean(), sunday['attendance'].mean()]\n",
"\n",
"tick_label = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\n",
"\n",
"plt.bar(left, height, tick_label = tick_label,\n",
" width = 0.8, color = ['blue', 'green', 'red'])\n",
" \n",
"plt.xlabel('Day')\n",
"plt.ylabel('Attendance')\n",
"plt.title('Attendance - Day')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mediana"
]
},
{
"cell_type": "code",
win streak analysis
2022-10-18 00:53:52 +02:00
"execution_count": 9,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"outputs": [
{
"data": {
win streak analysis
2022-10-18 00:53:52 +02:00
"image/png": "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
kibice
2022-10-18 00:24:12 +02:00
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
" \n",
"left = [1, 2, 3, 4, 5, 6, 7]\n",
"\n",
"height = [monday['attendance'].median(), tuesday['attendance'].median(), wednesday['attendance'].median(), \n",
"thursday['attendance'].median(), friday['attendance'].median(), saturday['attendance'].median(), sunday['attendance'].median()]\n",
"\n",
"tick_label = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\n",
"\n",
"plt.bar(left, height, tick_label = tick_label,\n",
" width = 0.8, color = ['blue', 'green', 'red'])\n",
" \n",
"plt.xlabel('Day')\n",
"plt.ylabel('Attendance')\n",
"plt.title('Attendance - Day')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Najwięcej kibiców jest w weekendy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zwycięstwo / porażka gospodarzy\n",
"![image3](win.jpg)"
]
},
{
"cell_type": "code",
win streak analysis
2022-10-18 00:53:52 +02:00
"execution_count": 10,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Win', 'Loss'], dtype=object)"
]
},
win streak analysis
2022-10-18 00:53:52 +02:00
"execution_count": 10,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['home_team_outcome'].unique()"
]
},
{
"cell_type": "code",
win streak analysis
2022-10-18 00:53:52 +02:00
"execution_count": 11,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"outputs": [],
"source": [
"win = data[data['home_team_outcome'] == 'Win']\n",
"loss = data[data['home_team_outcome'] == 'Loss']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Średnia ilość kibiców przy wygraniu/przegraniu gospodarzy"
]
},
{
"cell_type": "code",
win streak analysis
2022-10-18 00:53:52 +02:00
"execution_count": 12,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"left = [1, 2]\n",
"\n",
"height = [win['attendance'].mean(), loss['attendance'].mean()]\n",
"\n",
"tick_label = ['win', 'loss']\n",
"\n",
"plt.bar(left, height, tick_label = tick_label,\n",
" width = 0.8, color = ['blue', 'red'])\n",
" \n",
"plt.xlabel('Win')\n",
"plt.ylabel('Attendance')\n",
"plt.title('Attendance - Win')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mediana"
]
},
{
"cell_type": "code",
win streak analysis
2022-10-18 00:53:52 +02:00
"execution_count": 13,
kibice
2022-10-18 00:24:12 +02:00
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"left = [1, 2]\n",
"\n",
"height = [win['attendance'].median(), loss['attendance'].median()]\n",
"\n",
"tick_label = ['win', 'loss']\n",
"\n",
"plt.bar(left, height, tick_label = tick_label,\n",
" width = 0.8, color = ['blue', 'red'])\n",
" \n",
"plt.xlabel('Win')\n",
"plt.ylabel('Attendance')\n",
"plt.title('Attendance - Win')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nie ma to wpływu, raczej nie jest tak, że widać przegraną przed końcem i przez to kibice wychodzą. A nawet jeśli to działa to w miarę równomiernie w obie strony."
]
win streak analysis
2022-10-18 00:53:52 +02:00
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zwycięstwa w kolejnych meczach\n",
"\n",
"![image4](win-streak.png)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['New York Mets', 'Philadelphia Phillies', 'Minnesota Twins',\n",
" 'Washington Nationals', 'Colorado Rockies', 'Seattle Mariners',\n",
" 'Toronto Blue Jays', 'Los Angeles Dodgers', 'St. Louis Cardinals',\n",
" 'Chicago White Sox', 'Houston Astros', 'San Francisco Giants',\n",
" 'Detroit Tigers', 'Texas Rangers', 'San Diego Padres',\n",
" 'Los Angeles Angels of Anaheim', 'Miami Marlins',\n",
" 'Kansas City Royals', 'Pittsburgh Pirates', 'Cincinnati Reds',\n",
" 'Atlanta Braves', 'New York Yankees', 'Chicago Cubs',\n",
" 'Arizona Diamondbacks', 'Milwaukee Brewers', 'Baltimore Orioles',\n",
" 'Cleveland Indians', 'Oakland Athletics', 'Boston Red Sox',\n",
" 'Tampa Bay Rays'], dtype=object)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['away_team'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"mets = data[data['away_team'] == 'New York Mets']"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Attendance - Win/Lose')"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1728x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"left = [i for i in range(len(mets))]\n",
"\n",
"height = [i for i in mets['attendance']]\n",
"\n",
"tick_label = ['l' if [i for i in mets['home_team_outcome']][i] == 'Win' else 'w' for i in range(len(mets))]\n",
"\n",
"plt.figure(figsize=(24, 3)) # width:20, height:3\n",
"plt.bar(left, height, tick_label = tick_label,\n",
" width = 0.5, color = ['blue', 'red'])\n",
" \n",
"plt.xlabel('Win (w) or Lose (l)')\n",
"plt.ylabel('Attendance')\n",
"plt.title('Attendance - Win/Lose')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"philadelphia = data[data['away_team'] == 'Philadelphia Phillies']"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Attendance - Win/Lose')"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1728x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"left = [i for i in range(len(philadelphia))]\n",
"\n",
"height = [i for i in philadelphia['attendance']]\n",
"\n",
"tick_label = ['l' if [i for i in philadelphia['home_team_outcome']][i] == 'Win' else 'w' for i in range(len(philadelphia))]\n",
"\n",
"plt.figure(figsize=(24, 3)) # width:20, height:3\n",
"plt.bar(left, height, tick_label = tick_label,\n",
" width = 0.5, color = ['blue', 'red'])\n",
" \n",
"plt.xlabel('Win (w) or Lose (l)')\n",
"plt.ylabel('Attendance')\n",
"plt.title('Attendance - Win/Lose')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Czasami można wywnioskować, że po wygranym meczu przychodzi więcej kibiców na następny, ale nie zawsze, to raczej nie jest częsta zasada."
]
kibice
2022-10-18 00:24:12 +02:00
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.0 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.0"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "3dafdb3de6203a1118d6c063d9a807622a512a5be3d463a10b75ce9c56521739"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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