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kibice
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analiza zależności ilości kibiców w baseball mlb"
]
},
{
"cell_type": "code",
update
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"execution_count": 5,
kibice
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"metadata": {},
"outputs": [
{
update
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"output_type": "execute_result",
kibice
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"data": {
"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]"
update
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],
"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>...</t
kibice
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},
"metadata": {},
update
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"execution_count": 5
kibice
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}
],
"source": [
"import pandas as pd\n",
"\n",
"data = pd.read_csv(\"baseball_reference_2016_clean.csv\")\n",
"\n",
"data"
]
},
update
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{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['Unnamed: 0', 'attendance', 'away_team', 'away_team_errors',\n",
" 'away_team_hits', 'away_team_runs', 'date', 'field_type', 'game_type',\n",
" 'home_team', 'home_team_errors', 'home_team_hits', 'home_team_runs',\n",
" 'start_time', 'venue', 'day_of_week', 'temperature', 'wind_speed',\n",
" 'wind_direction', 'sky', 'total_runs', 'game_hours_dec', 'season',\n",
" 'home_team_win', 'home_team_loss', 'home_team_outcome'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"data.columns"
]
},
kibice
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pogoda\n",
"\n",
"![image](sky.jpg)"
]
},
{
"cell_type": "code",
update
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"execution_count": 7,
kibice
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"metadata": {},
"outputs": [
{
update
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"output_type": "execute_result",
kibice
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"data": {
"text/plain": [
"array(['Sunny', 'Overcast', 'Unknown', 'Cloudy', 'In Dome', 'Drizzle',\n",
" 'Rain', 'Night'], dtype=object)"
]
},
"metadata": {},
update
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"execution_count": 7
kibice
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}
],
"source": [
"data['sky'].unique()"
]
},
{
"cell_type": "code",
update
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"execution_count": 8,
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",
update
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"execution_count": 9,
kibice
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"metadata": {},
"outputs": [
{
update
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"output_type": "display_data",
kibice
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"data": {
update
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kibice
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},
"metadata": {
"needs_background": "light"
update
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}
kibice
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}
],
"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",
update
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"execution_count": 10,
kibice
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"metadata": {},
"outputs": [
{
update
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"output_type": "display_data",
kibice
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"data": {
update
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kibice
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},
"metadata": {
"needs_background": "light"
update
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}
kibice
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}
],
"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",
update
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"execution_count": 11,
kibice
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"metadata": {},
"outputs": [
{
update
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"output_type": "execute_result",
kibice
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"data": {
"text/plain": [
"array(['Sunday', 'Wednesday', 'Tuesday', 'Monday', 'Thursday', 'Saturday',\n",
" 'Friday'], dtype=object)"
]
},
"metadata": {},
update
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"execution_count": 11
kibice
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}
],
"source": [
"data['day_of_week'].unique()"
]
},
{
"cell_type": "code",
update
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"execution_count": 17,
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",
update
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"execution_count": 13,
kibice
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"metadata": {},
"outputs": [
{
update
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"output_type": "display_data",
kibice
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"data": {
update
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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kibice
2022-10-18 00:24:12 +02:00
},
"metadata": {
"needs_background": "light"
update
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}
kibice
2022-10-18 00:24:12 +02:00
}
],
"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",
update
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"execution_count": 14,
kibice
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"metadata": {},
"outputs": [
{
update
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"output_type": "display_data",
kibice
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"data": {
update
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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kibice
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},
"metadata": {
"needs_background": "light"
update
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}
kibice
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}
],
"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",
update
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"execution_count": 15,
kibice
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"metadata": {},
"outputs": [
{
update
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"output_type": "execute_result",
kibice
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"data": {
"text/plain": [
"array(['Win', 'Loss'], dtype=object)"
]
},
"metadata": {},
update
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"execution_count": 15
kibice
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}
],
"source": [
"data['home_team_outcome'].unique()"
]
},
{
"cell_type": "code",
update
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"execution_count": 16,
kibice
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"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",
update
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"execution_count": 18,
kibice
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"metadata": {},
"outputs": [
{
update
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"output_type": "display_data",
kibice
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"data": {
update
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kibice
2022-10-18 00:24:12 +02:00
},
"metadata": {
"needs_background": "light"
update
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}
kibice
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}
],
"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",
update
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"execution_count": 19,
kibice
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"metadata": {},
"outputs": [
{
update
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"output_type": "display_data",
kibice
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"data": {
update
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kibice
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},
"metadata": {
"needs_background": "light"
update
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}
kibice
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}
],
"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
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zwycięstwa w kolejnych meczach\n",
"\n",
"![image4](win-streak.png)"
]
},
{
"cell_type": "code",
update
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"execution_count": 20,
win streak analysis
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"metadata": {},
"outputs": [
{
update
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"output_type": "execute_result",
win streak analysis
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"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)"
]
},
"metadata": {},
update
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"execution_count": 20
win streak analysis
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}
],
"source": [
"data['away_team'].unique()"
]
},
{
"cell_type": "code",
update
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"execution_count": 26,
win streak analysis
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"metadata": {},
"outputs": [],
"source": [
"mets = data[data['away_team'] == 'New York Mets']"
]
},
{
"cell_type": "code",
update
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"execution_count": 27,
win streak analysis
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"metadata": {},
"outputs": [
{
update
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"output_type": "execute_result",
win streak analysis
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"data": {
"text/plain": [
"Text(0.5, 1.0, 'Attendance - Win/Lose')"
]
},
"metadata": {},
update
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"execution_count": 27
win streak analysis
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},
{
update
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"output_type": "display_data",
win streak analysis
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"data": {
update
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"text/plain": "<Figure size 1728x216 with 1 Axes>",
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win streak analysis
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},
"metadata": {
"needs_background": "light"
update
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}
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}
],
"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",
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" width = 0.5, color = ['red' if [i for i in mets['home_team_outcome']][i] == 'Win' else 'blue' for i in range(len(mets))])\n",
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" \n",
"plt.xlabel('Win (w) or Lose (l)')\n",
"plt.ylabel('Attendance')\n",
"plt.title('Attendance - Win/Lose')"
]
},
{
"cell_type": "code",
update
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"execution_count": 28,
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"metadata": {},
"outputs": [],
"source": [
"philadelphia = data[data['away_team'] == 'Philadelphia Phillies']"
]
},
{
"cell_type": "code",
update
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"execution_count": 29,
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"metadata": {},
"outputs": [
{
update
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"output_type": "execute_result",
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"data": {
"text/plain": [
"Text(0.5, 1.0, 'Attendance - Win/Lose')"
]
},
"metadata": {},
update
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"execution_count": 29
win streak analysis
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},
{
update
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"output_type": "display_data",
win streak analysis
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"data": {
update
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win streak analysis
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},
"metadata": {
"needs_background": "light"
update
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}
win streak analysis
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}
],
"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",
update
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" width = 0.5, color = ['red' if [i for i in philadelphia['home_team_outcome']][i] == 'Win' else 'blue' for i in range(len(philadelphia))])\n",
win streak analysis
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" \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
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}
],
"metadata": {
"kernelspec": {
update
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"display_name": "Python 3 (ipykernel)",
kibice
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"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",
update
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"version": "3.8.10-final"
kibice
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},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "3dafdb3de6203a1118d6c063d9a807622a512a5be3d463a10b75ce9c56521739"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
update
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}
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