88 KiB
88 KiB
Analiza zależności ilości kibiców w baseball mlb
import pandas as pd
data = pd.read_csv("baseball_reference_2016_clean.csv")
data| Unnamed: 0 | attendance | away_team | away_team_errors | away_team_hits | away_team_runs | date | field_type | game_type | home_team | ... | temperature | wind_speed | wind_direction | sky | total_runs | game_hours_dec | season | home_team_win | home_team_loss | home_team_outcome | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 40030.0 | New York Mets | 1 | 7 | 3 | 2016-04-03 | on grass | Night Game | Kansas City Royals | ... | 74.0 | 14.0 | from Right to Left | Sunny | 7 | 3.216667 | regular season | 1 | 0 | Win |
| 1 | 1 | 21621.0 | Philadelphia Phillies | 0 | 5 | 2 | 2016-04-06 | on grass | Night Game | Cincinnati Reds | ... | 55.0 | 24.0 | from Right to Left | Overcast | 5 | 2.383333 | regular season | 1 | 0 | Win |
| 2 | 2 | 12622.0 | Minnesota Twins | 0 | 5 | 2 | 2016-04-06 | on grass | Night Game | Baltimore Orioles | ... | 48.0 | 7.0 | out to Leftfield | Unknown | 6 | 3.183333 | regular season | 1 | 0 | Win |
| 3 | 3 | 18531.0 | Washington Nationals | 0 | 8 | 3 | 2016-04-06 | on grass | Night Game | Atlanta Braves | ... | 65.0 | 10.0 | from Right to Left | Cloudy | 4 | 2.883333 | regular season | 0 | 1 | Loss |
| 4 | 4 | 18572.0 | Colorado Rockies | 1 | 8 | 4 | 2016-04-06 | on grass | Day Game | Arizona Diamondbacks | ... | 77.0 | 0.0 | in unknown direction | In Dome | 7 | 2.650000 | regular season | 0 | 1 | Loss |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2458 | 2458 | 31042.0 | Toronto Blue Jays | 2 | 7 | 5 | 2016-04-03 | on turf | Day Game | Tampa Bay Rays | ... | 72.0 | 0.0 | in unknown direction | In Dome | 8 | 2.850000 | regular season | 0 | 1 | Loss |
| 2459 | 2459 | 39500.0 | St. Louis Cardinals | 0 | 5 | 1 | 2016-04-03 | on grass | Day Game | Pittsburgh Pirates | ... | 39.0 | 14.0 | out to Leftfield | Unknown | 5 | 3.033333 | regular season | 1 | 0 | Win |
| 2460 | 2460 | 20098.0 | San Francisco Giants | 0 | 6 | 3 | 2016-04-06 | on grass | Day Game | Milwaukee Brewers | ... | 66.0 | 0.0 | in unknown direction | In Dome | 7 | 3.316667 | regular season | 1 | 0 | Win |
| 2461 | 2461 | 17883.0 | Detroit Tigers | 0 | 13 | 7 | 2016-04-06 | on grass | Day Game | Miami Marlins | ... | 71.0 | 0.0 | in unknown direction | In Dome | 10 | 3.366667 | regular season | 0 | 1 | Loss |
| 2462 | 2462 | 10298.0 | Boston Red Sox | 1 | 10 | 6 | 2016-04-06 | on grass | Night Game | Cleveland Indians | ... | 60.0 | 7.0 | out to Leftfield | Unknown | 13 | 3.483333 | regular season | 1 | 0 | Win |
2463 rows × 26 columns
Pogoda
data['sky'].unique()array(['Sunny', 'Overcast', 'Unknown', 'Cloudy', 'In Dome', 'Drizzle',
'Rain', 'Night'], dtype=object)sunny = data[data['sky'] == 'Sunny']
overcast = data[data['sky'] == 'Overcast']
cloudy = data[data['sky'] == 'Cloudy']
in_dome = data[data['sky'] == 'In Dome']
drizzle = data[data['sky'] == 'Drizzle']
rain = data[data['sky'] == 'Rain']
night = data[data['sky'] == 'Night']Średnia ilość kibiców w zależności od pogody
import matplotlib.pyplot as plt
left = [1, 2, 3, 4, 5, 6, 7]
height = [sunny['attendance'].mean(), overcast['attendance'].mean(), cloudy['attendance'].mean(),
in_dome['attendance'].mean(), drizzle['attendance'].mean(), rain['attendance'].mean(), night['attendance'].mean()]
tick_label = ['sunny', 'overcast', 'cloudy', 'in dome', 'drizzle', 'rain', 'night']
plt.bar(left, height, tick_label = tick_label,
width = 0.8, color = ['blue', 'green', 'red'])
plt.xlabel('Weather')
plt.ylabel('Attendance')
plt.title('Attendance - Weather')
plt.show()Mediana
import matplotlib.pyplot as plt
left = [1, 2, 3, 4, 5, 6, 7]
height = [sunny['attendance'].median(), overcast['attendance'].median(), cloudy['attendance'].median(),
in_dome['attendance'].median(), drizzle['attendance'].median(), rain['attendance'].median(), night['attendance'].median()]
tick_label = ['sunny', 'overcast', 'cloudy', 'in dome', 'drizzle', 'rain', 'night']
plt.bar(left, height, tick_label = tick_label,
width = 0.8, color = ['blue', 'green', 'red'])
plt.xlabel('Weather')
plt.ylabel('Attendance')
plt.title('Attendance - Weather')
plt.show()W nocy prawdopodobnie najwięcej, gdyż większa grupa odbiorców ma dostęp do meczy online z całego świata.
Pod kopułą może być najmniej widzów, gdyż takie stadiony mają mniejsze trybuny.
Dzień tygodnia
data['day_of_week'].unique()array(['Sunday', 'Wednesday', 'Tuesday', 'Monday', 'Thursday', 'Saturday',
'Friday'], dtype=object)monday = data[data['day_of_week'] == 'Monday']
tuesday = data[data['day_of_week'] == 'Tuesday']
wednesday = data[data['day_of_week'] == 'Wednesday']
thursday = data[data['day_of_week'] == 'Thursday']
friday = data[data['day_of_week'] == 'Friday']
saturday = data[data['day_of_week'] == 'Saturday']
sunday = data[data['day_of_week'] == 'Sunday']Średnia ilość kibiców w danym dniu
import matplotlib.pyplot as plt
left = [1, 2, 3, 4, 5, 6, 7]
height = [monday['attendance'].mean(), tuesday['attendance'].mean(), wednesday['attendance'].mean(),
thursday['attendance'].mean(), friday['attendance'].mean(), saturday['attendance'].mean(), sunday['attendance'].mean()]
tick_label = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']
plt.bar(left, height, tick_label = tick_label,
width = 0.8, color = ['blue', 'green', 'red'])
plt.xlabel('Day')
plt.ylabel('Attendance')
plt.title('Attendance - Day')
plt.show()Mediana
import matplotlib.pyplot as plt
left = [1, 2, 3, 4, 5, 6, 7]
height = [monday['attendance'].median(), tuesday['attendance'].median(), wednesday['attendance'].median(),
thursday['attendance'].median(), friday['attendance'].median(), saturday['attendance'].median(), sunday['attendance'].median()]
tick_label = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']
plt.bar(left, height, tick_label = tick_label,
width = 0.8, color = ['blue', 'green', 'red'])
plt.xlabel('Day')
plt.ylabel('Attendance')
plt.title('Attendance - Day')
plt.show()Najwięcej kibiców jest w weekendy.
Zwycięstwo / porażka gospodarzy
data['home_team_outcome'].unique()array(['Win', 'Loss'], dtype=object)
win = data[data['home_team_outcome'] == 'Win']
loss = data[data['home_team_outcome'] == 'Loss']Średnia ilość kibiców przy wygraniu/przegraniu gospodarzy
left = [1, 2]
height = [win['attendance'].mean(), loss['attendance'].mean()]
tick_label = ['win', 'loss']
plt.bar(left, height, tick_label = tick_label,
width = 0.8, color = ['blue', 'red'])
plt.xlabel('Win')
plt.ylabel('Attendance')
plt.title('Attendance - Win')
plt.show()Mediana
left = [1, 2]
height = [win['attendance'].median(), loss['attendance'].median()]
tick_label = ['win', 'loss']
plt.bar(left, height, tick_label = tick_label,
width = 0.8, color = ['blue', 'red'])
plt.xlabel('Win')
plt.ylabel('Attendance')
plt.title('Attendance - Win')
plt.show()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.