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get_arenafan_game_stats.py
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get_arenafan_game_stats.py
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import time
from datetime import datetime
import pandas as pd
import requests
from bs4 import BeautifulSoup
from dateutil import parser
from tqdm import tqdm
from arena_utls import raise_html_status_code, time_zone_whois
from get_arenafan_schedule import get_arenafan_schedule
def get_arena_game_stats(game_id: int):
"""
"""
col_names = ['season', 'game_id', 'team_id', 'team_name', 'loc', 'opp_team_id',
'opp_team_name', 'team_score', 'opp_score', 'is_overtime_game',
'player_id', 'player_name', 'passing_comp', 'passing_att',
'passing_comp_pct', 'passing_yds', 'passing_td', 'passing_int',
'passing_ypa', 'passing_ypc', 'passing_nfl_qbr', 'passing_cfb_qbr',
'rushing_att', 'rushing_yds', 'rushing_avg', 'rushing_td',
'receiving_rec', 'receiving_yds', 'receiving_avg', 'receiving_td',
'defense_tackles_solo', 'defense_tackles_ast', 'defense_tackles_total',
'defnese_passes_defended', 'defense_sacks', 'defense_sack_yds',
'defense_forced_fumbles', 'defense_fumble_recoveries',
'defense_interceptions', 'defense_interception_yds',
'defense_interception_avg', 'defense_interception_tds', 'kicking_XPM',
'kicking_XPA', 'kicking_XP%', 'kicking_FGM', 'kicking_FGA',
'kicking_FG%', 'kicking_2pt_M', 'kicking_2pt_A', 'kicking_2pt%',
'kicking_4pt_M', 'kicking_4pt_A', 'kicking_4pt%', 'kick_return_ret',
'kick_return_yds', 'kick_return_avg', 'kick_return_td',
'missed_fg_return_ret', 'missed_fg_return_yds', 'missed_fg_return_avg',
'missed_fg_return_td']
game_df = pd.DataFrame()
passing_df = pd.DataFrame()
rushing_df = pd.DataFrame()
receiving_df = pd.DataFrame()
tackling_df = pd.DataFrame()
interceptions_df = pd.DataFrame()
kicking_df = pd.DataFrame()
returns_df = pd.DataFrame()
season = 0
game_date = ""
is_overtime_game = False
away_team_name = ""
away_team_id = ""
away_team_logo_url = ""
away_team_score = 0
home_team_name = ""
home_team_id = ""
home_team_logo_url = ""
home_team_score = 0
time_zones = time_zone_whois()
print(f'\nGetting data for game #{game_id}.')
url = f"http://www.arenafan.com/statistics/?page=boxscore&gameid={game_id}"
headers = {
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"}
response = requests.get(url, headers=headers)
raise_html_status_code(response.status_code)
soup = BeautifulSoup(response.text, features='lxml')
time.sleep(1)
stat_tables = soup.find_all('div', {'class': 'twocolwrapper'})
for i in range(0, len(stat_tables)):
table_html = stat_tables[i]
if i == 0:
scores_table = table_html.find('div', {'class': 'coltwo'}).find(
'table', {'id': 'stats_left'})
# print(scores_table)
table_rows = scores_table.find_all('tr')
if len(table_rows) == 6:
a_cells = table_rows[3].find_all('td')
h_cells = table_rows[4].find_all('td')
away_team_name = str(a_cells[0].text.replace(
'\n', '').replace('\xa0', ''))
away_team_id = str(a_cells[0].find(
'a').get('href')).split('/')[2]
away_team_logo_url = a_cells[0].find('a').get('href')
away_team_score = int(a_cells[-1].text)
home_team_name = str(h_cells[0].text.replace(
'\n', '').replace('\xa0', ''))
home_team_id = str(h_cells[0].find(
'a').get('href')).split('/')[2]
home_team_logo_url = h_cells[0].find('a').get('href')
home_team_score = int(h_cells[-1].text)
season = int(away_team_logo_url.split('/')[-2])
game_info = str(table_rows[5].find(
'td')).replace('</b>', '').replace('\n', '').replace('<br>', '').replace('<br/>', '').split('<b>')
game_date = game_info[1].replace('Date: ', '')
game_date = game_date + \
str(game_info[2])
game_date = game_date.split(', Game Time: ')[0]
# game_date = parser.parse(game_date, tzinfos=time_zones)
game_date = datetime.strptime(game_date, "%A %B %d, %Y")
if len(h_cells) > 6:
is_overtime_game = True
else:
is_overtime_game = False
del scores_table, a_cells, h_cells
elif len(table_rows) == 5:
a_cells = table_rows[2].find_all('td')
h_cells = table_rows[3].find_all('td')
away_team_name = str(a_cells[0].text.replace(
'\n', '').replace('\xa0', ''))
away_team_id = str(a_cells[0].find(
'a').get('href')).split('/')[2]
away_team_logo_url = a_cells[0].find('a').get('href')
away_team_score = int(a_cells[-1].text)
home_team_name = str(h_cells[0].text.replace(
'\n', '').replace('\xa0', ''))
home_team_id = str(h_cells[0].find(
'a').get('href')).split('/')[2]
home_team_logo_url = h_cells[0].find('a').get('href')
home_team_score = int(h_cells[-1].text)
season = int(away_team_logo_url.split('/')[-2])
game_info = str(table_rows[4].find(
'td')).replace('</b>', '').replace('\n', '').replace('<br>', '').replace('<br/>', '').split('<b>')
game_date = game_info[1].replace('Date: ', '')
game_date = game_date + \
str(game_info[2].replace('Game Time: ', ''))
game_date = parser.parse(game_date)
if len(h_cells) > 6:
is_overtime_game = True
else:
is_overtime_game = False
del scores_table, a_cells, h_cells
elif len(table_rows) < 4:
raise NotImplementedError('It ain\'t ready yet.')
else:
raise LookupError(
f'Unhandled box score lookup for game ID #{game_id}.')
elif i == 1: # Passing
away_stats = table_html.find(
'div', {'class': 'colone'}).find('table').find_all('tr')
home_stats = table_html.find(
'div', {'class': 'coltwo'}).find('table').find_all('tr')
for j in range(2, len(away_stats)):
# player_stats = j.find_all('td')
p_stats = away_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = away_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': away_team_id, 'team_name': away_team_name,
'loc': 'A',
'opp_team_id': home_team_id, 'opp_team_name': home_team_name,
'team_score': away_team_score, 'opp_score': home_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
row_df['passing_comp'] = int(p_stats[1].text)
row_df['passing_att'] = int(p_stats[2].text)
row_df['passing_comp_pct'] = None
row_df['passing_yds'] = int(p_stats[3].text)
row_df['passing_td'] = int(p_stats[4].text)
row_df['passing_int'] = int(p_stats[5].text)
row_df['passing_ypa'] = None
row_df['passing_ypc'] = None
row_df['passing_nfl_qbr'] = None
row_df['passing_cfb_qbr'] = None
if len(row_df) > 0:
passing_df = pd.concat(
[passing_df, row_df], ignore_index=True)
del player_id, player_name, row_df
for j in range(2, len(home_stats)):
p_stats = home_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = home_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': home_team_id, 'team_name': home_team_name,
'loc': 'H',
'opp_team_id': away_team_id, 'opp_team_name': away_team_name,
'team_score': home_team_score, 'opp_score': away_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
row_df['passing_comp'] = int(p_stats[1].text)
row_df['passing_att'] = int(p_stats[2].text)
row_df['passing_comp_pct'] = None
row_df['passing_yds'] = int(p_stats[3].text)
row_df['passing_td'] = int(p_stats[4].text)
row_df['passing_int'] = int(p_stats[5].text)
row_df['passing_ypa'] = None
row_df['passing_ypc'] = None
row_df['passing_nfl_qbr'] = None
row_df['passing_cfb_qbr'] = None
if len(row_df) > 0:
passing_df = pd.concat(
[passing_df, row_df], ignore_index=True)
del player_id, player_name, row_df
elif i == 2: # Receiving
away_stats = table_html.find(
'div', {'class': 'colone'}).find('table').find_all('tr')
home_stats = table_html.find(
'div', {'class': 'coltwo'}).find('table').find_all('tr')
for j in range(2, len(away_stats)):
# player_stats = j.find_all('td')
p_stats = away_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = away_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': away_team_id, 'team_name': away_team_name,
'loc': 'A',
'opp_team_id': home_team_id, 'opp_team_name': home_team_name,
'team_score': away_team_score, 'opp_score': home_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
row_df['receiving_rec'] = int(p_stats[1].text)
row_df['receiving_yds'] = int(p_stats[2].text)
row_df['receiving_avg'] = None
row_df['receiving_td'] = int(p_stats[3].text)
if len(row_df) > 0:
receiving_df = pd.concat(
[receiving_df, row_df], ignore_index=True)
del player_id, player_name, row_df
for j in range(2, len(home_stats)):
p_stats = home_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = home_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': home_team_id, 'team_name': home_team_name,
'loc': 'H',
'opp_team_id': away_team_id, 'opp_team_name': away_team_name,
'team_score': home_team_score, 'opp_score': away_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
row_df['receiving_rec'] = int(p_stats[1].text)
row_df['receiving_yds'] = int(p_stats[2].text)
row_df['receiving_avg'] = None
row_df['receiving_td'] = int(p_stats[3].text)
if len(row_df) > 0:
receiving_df = pd.concat(
[receiving_df, row_df], ignore_index=True)
del player_id, player_name, row_df
# print(receiving_df)
elif i == 3: # Rushing
away_stats = table_html.find(
'div', {'class': 'colone'}).find('table').find_all('tr')
home_stats = table_html.find(
'div', {'class': 'coltwo'}).find('table').find_all('tr')
for j in range(2, len(away_stats)):
# player_stats = j.find_all('td')
p_stats = away_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = away_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': away_team_id, 'team_name': away_team_name,
'loc': 'A',
'opp_team_id': home_team_id, 'opp_team_name': home_team_name,
'team_score': away_team_score, 'opp_score': home_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
row_df['rushing_att'] = int(p_stats[1].text)
row_df['rushing_yds'] = int(p_stats[2].text)
row_df['rushing_avg'] = None
row_df['rushing_td'] = int(p_stats[3].text)
if len(row_df) > 0:
rushing_df = pd.concat(
[rushing_df, row_df], ignore_index=True)
del player_id, player_name, row_df
for j in range(2, len(home_stats)):
p_stats = home_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = home_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': home_team_id, 'team_name': home_team_name,
'loc': 'H',
'opp_team_id': away_team_id, 'opp_team_name': away_team_name,
'team_score': home_team_score, 'opp_score': away_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
row_df['rushing_att'] = int(p_stats[1].text)
row_df['rushing_yds'] = int(p_stats[2].text)
row_df['rushing_avg'] = None
row_df['rushing_td'] = int(p_stats[3].text)
if len(row_df) > 0:
rushing_df = pd.concat(
[rushing_df, row_df], ignore_index=True)
del player_id, player_name, row_df
# print(rushing_df)
elif i == 4: # Kicking
away_stats = table_html.find(
'div', {'class': 'colone'}).find('table').find_all('tr')
home_stats = table_html.find(
'div', {'class': 'coltwo'}).find('table').find_all('tr')
for j in range(2, len(away_stats)):
# player_stats = j.find_all('td')
p_stats = away_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = away_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': away_team_id, 'team_name': away_team_name,
'loc': 'A',
'opp_team_id': home_team_id, 'opp_team_name': home_team_name,
'team_score': away_team_score, 'opp_score': home_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
xps = p_stats[1].text
fg_3 = p_stats[2].text
fg_2 = p_stats[3].text
fg_4 = p_stats[4].text
row_df['kicking_XPM'] = int(xps.split('/')[0])
row_df['kicking_XPA'] = int(xps.split('/')[1])
row_df['kicking_XP%'] = 0
row_df['kicking_FGM'] = int(fg_3.split('/')[0])
row_df['kicking_FGA'] = int(fg_3.split('/')[1])
row_df['kicking_FG%'] = 0
row_df['kicking_2pt_M'] = int(fg_2.split('/')[0])
row_df['kicking_2pt_A'] = int(fg_2.split('/')[1])
row_df['kicking_2pt%'] = 0
row_df['kicking_4pt_M'] = int(fg_4.split('/')[0])
row_df['kicking_4pt_A'] = int(fg_4.split('/')[1])
row_df['kicking_4pt%'] = 0
if len(row_df) > 0:
kicking_df = pd.concat(
[kicking_df, row_df], ignore_index=True)
del player_id, player_name, row_df, xps, fg_2, fg_3, fg_4
for j in range(2, len(home_stats)):
p_stats = home_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = home_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': home_team_id, 'team_name': home_team_name,
'loc': 'H',
'opp_team_id': away_team_id, 'opp_team_name': away_team_name,
'team_score': home_team_score, 'opp_score': away_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
xps = p_stats[1].text
fg_3 = p_stats[2].text
fg_2 = p_stats[3].text
fg_4 = p_stats[4].text
row_df['kicking_XPM'] = int(xps.split('/')[0])
row_df['kicking_XPA'] = int(xps.split('/')[1])
row_df['kicking_XP%'] = 0
row_df['kicking_FGM'] = int(fg_3.split('/')[0])
row_df['kicking_FGA'] = int(fg_3.split('/')[1])
row_df['kicking_FG%'] = 0
row_df['kicking_2pt_M'] = int(fg_2.split('/')[0])
row_df['kicking_2pt_A'] = int(fg_2.split('/')[1])
row_df['kicking_2pt%'] = 0
row_df['kicking_4pt_M'] = int(fg_4.split('/')[0])
row_df['kicking_4pt_A'] = int(fg_4.split('/')[1])
row_df['kicking_4pt%'] = 0
if len(row_df) > 0:
kicking_df = pd.concat(
[kicking_df, row_df], ignore_index=True)
del player_id, player_name, row_df, xps, fg_2, fg_3, fg_4
# print(kicking_df)
elif i == 5: # Returns
away_stats = table_html.find(
'div', {'class': 'colone'}).find('table').find_all('tr')
home_stats = table_html.find(
'div', {'class': 'coltwo'}).find('table').find_all('tr')
for j in range(2, len(away_stats)):
# player_stats = j.find_all('td')
p_stats = away_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = away_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': away_team_id, 'team_name': away_team_name,
'loc': 'A',
'opp_team_id': home_team_id, 'opp_team_name': home_team_name,
'team_score': away_team_score, 'opp_score': home_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
kr_ret = str(p_stats[1].text).split('/')
mfg_ret = str(p_stats[2].text).split('/')
row_df['kick_return_ret'] = int(kr_ret[0])
row_df['kick_return_yds'] = int(kr_ret[1])
row_df['kick_return_avg'] = None
row_df['kick_return_td'] = int(kr_ret[2])
row_df['missed_fg_return_ret'] = int(mfg_ret[0])
row_df['missed_fg_return_yds'] = int(mfg_ret[1])
row_df['missed_fg_return_avg'] = None
row_df['missed_fg_return_td'] = int(mfg_ret[2])
if len(row_df) > 0:
returns_df = pd.concat(
[returns_df, row_df], ignore_index=True)
del player_id, player_name, row_df, kr_ret, mfg_ret
for j in range(2, len(home_stats)):
p_stats = home_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = home_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': home_team_id, 'team_name': home_team_name,
'loc': 'H',
'opp_team_id': away_team_id, 'opp_team_name': away_team_name,
'team_score': home_team_score, 'opp_score': away_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
kr_ret = str(p_stats[1].text).split('/')
mfg_ret = str(p_stats[2].text).split('/')
row_df['kick_return_ret'] = int(kr_ret[0])
row_df['kick_return_yds'] = int(kr_ret[1])
row_df['kick_return_avg'] = None
row_df['kick_return_td'] = int(kr_ret[2])
row_df['missed_fg_return_ret'] = int(mfg_ret[0])
row_df['missed_fg_return_yds'] = int(mfg_ret[1])
row_df['missed_fg_return_avg'] = None
row_df['missed_fg_return_td'] = int(mfg_ret[2])
if len(row_df) > 0:
returns_df = pd.concat(
[returns_df, row_df], ignore_index=True)
del player_id, player_name, row_df, kr_ret, mfg_ret
# print(returns_df)
elif i == 6: # Defensive (Tackling)
away_stats = table_html.find(
'div', {'class': 'colone'}).find('table').find_all('tr')
home_stats = table_html.find(
'div', {'class': 'coltwo'}).find('table').find_all('tr')
for j in range(2, len(away_stats)):
# player_stats = j.find_all('td')
p_stats = away_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = away_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': away_team_id, 'team_name': away_team_name,
'loc': 'A',
'opp_team_id': home_team_id, 'opp_team_name': home_team_name,
'team_score': away_team_score, 'opp_score': home_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
tack = str(p_stats[1].text).split('/')
sacs = str(p_stats[3].text).split('/')
ff_fr = str(p_stats[4].text).split('/')
row_df['defense_tackles_solo'] = int(tack[0])
row_df['defense_tackles_ast'] = int(tack[1])
row_df['defense_tackles_total'] = row_df['defense_tackles_ast'] + \
row_df['defense_tackles_solo']
row_df['defnese_passes_defended'] = int(p_stats[2].text)
row_df['defense_sacks'] = float(sacs[0])
row_df['defense_sack_yds'] = int(sacs[1])
row_df['defense_forced_fumbles'] = int(ff_fr[0])
row_df['defense_fumble_recoveries'] = int(ff_fr[1])
if len(row_df) > 0:
tackling_df = pd.concat(
[tackling_df, row_df], ignore_index=True)
del player_id, player_name, row_df
for j in range(2, len(home_stats)):
p_stats = home_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = home_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': home_team_id, 'team_name': home_team_name,
'loc': 'H',
'opp_team_id': away_team_id, 'opp_team_name': away_team_name,
'team_score': home_team_score, 'opp_score': away_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
tack = str(p_stats[1].text).split('/')
sacs = str(p_stats[3].text).split('/')
ff_fr = str(p_stats[4].text).split('/')
row_df['defense_tackles_solo'] = int(tack[0])
row_df['defense_tackles_ast'] = int(tack[1])
row_df['defense_tackles_total'] = row_df['defense_tackles_ast'] + \
row_df['defense_tackles_solo']
row_df['defnese_passes_defended'] = int(p_stats[2].text)
row_df['defense_sacks'] = float(sacs[0])
row_df['defense_sack_yds'] = int(sacs[1])
row_df['defense_forced_fumbles'] = int(ff_fr[0])
row_df['defense_fumble_recoveries'] = int(ff_fr[1])
if len(row_df) > 0:
tackling_df = pd.concat(
[tackling_df, row_df], ignore_index=True)
del player_id, player_name, row_df
# print(tackling_df)
elif i == 7: # Defensive (Interceptions)
away_stats = table_html.find(
'div', {'class': 'colone'}).find('table').find_all('tr')
home_stats = table_html.find(
'div', {'class': 'coltwo'}).find('table').find_all('tr')
for j in range(2, len(away_stats)):
# player_stats = j.find_all('td')
p_stats = away_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = away_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': away_team_id, 'team_name': away_team_name,
'loc': 'A',
'opp_team_id': home_team_id, 'opp_team_name': home_team_name,
'team_score': away_team_score, 'opp_score': home_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
row_df['defense_interceptions'] = int(p_stats[1].text)
row_df['defense_interception_yds'] = int(p_stats[2].text)
row_df['defense_interception_avg'] = None
row_df['defense_interception_tds'] = int(p_stats[3].text)
if len(row_df) > 0:
interceptions_df = pd.concat(
[interceptions_df, row_df], ignore_index=True)
del player_id, player_name, row_df
for j in range(2, len(home_stats)):
p_stats = home_stats[j].find_all('td')
try:
player_id = str(p_stats[0].find('a').get(
'href')).replace('/players/', '').replace('/', '')
except:
player_id = home_team_id * -1
player_name = p_stats[0].text
row_df = pd.DataFrame(
{
'season': season, 'game_id': game_id,
'team_id': home_team_id, 'team_name': home_team_name,
'loc': 'H',
'opp_team_id': away_team_id, 'opp_team_name': away_team_name,
'team_score': home_team_score, 'opp_score': away_team_score, 'is_overtime_game': is_overtime_game,
'player_id': player_id, 'player_name': player_name
},
index=[0])
row_df['defense_interceptions'] = int(p_stats[1].text)
row_df['defense_interception_yds'] = int(p_stats[2].text)
row_df['defense_interception_avg'] = None
row_df['defense_interception_tds'] = int(p_stats[3].text)
if len(row_df) > 0:
interceptions_df = pd.concat(
[interceptions_df, row_df], ignore_index=True)
del player_id, player_name, row_df
# print(interceptions_df)
print('')
if len(stat_tables) > 1:
# Passing/Rushing
if len(passing_df) > 0 and len(rushing_df) > 0:
game_df = pd.merge(passing_df, rushing_df, how='outer', on=['season', 'game_id', 'team_id', 'team_name', 'loc',
'opp_team_id', 'opp_team_name', 'team_score', 'opp_score', 'is_overtime_game', 'player_id', 'player_name'])
game_df.loc[game_df['passing_att'] > 0,
'passing_comp_pct'] = game_df['passing_comp'] / game_df['passing_att']
game_df.loc[game_df['passing_att'] > 0,
'passing_ypa'] = game_df['passing_yds'] / game_df['passing_att']
game_df.loc[game_df['passing_comp'] > 0,
'passing_ypc'] = game_df['passing_yds'] / game_df['passing_comp']
game_df.loc[game_df['passing_att'] > 0,
'passing_nfl_qbr'] = 0
game_df.loc[game_df['passing_att'] > 0,
'passing_cfb_qbr'] = 0
game_df.loc[game_df['rushing_att'] > 0,
'rushing_avg'] = game_df['rushing_yds'] / game_df['rushing_att']
# Receiving
if len(receiving_df) > 0:
game_df = game_df.merge(receiving_df, how='outer', on=['season', 'game_id', 'team_id', 'team_name', 'loc',
'opp_team_id', 'opp_team_name', 'team_score', 'opp_score', 'is_overtime_game', 'player_id', 'player_name'])
game_df.loc[game_df['receiving_rec'] > 0,
'receiving_avg'] = game_df['receiving_yds'] / game_df['receiving_rec']
# Tackling
if len(tackling_df) > 0:
game_df = game_df.merge(tackling_df, how='outer', on=['season', 'game_id', 'team_id', 'team_name', 'loc',
'opp_team_id', 'opp_team_name', 'team_score', 'opp_score', 'is_overtime_game', 'player_id', 'player_name'])
# Interceptions
if len(interceptions_df) > 0:
game_df = game_df.merge(interceptions_df, how='outer', on=['season', 'game_id', 'team_id', 'team_name', 'loc',
'opp_team_id', 'opp_team_name', 'team_score', 'opp_score', 'is_overtime_game', 'player_id', 'player_name'])
game_df.loc[game_df['defense_interceptions'] > 0,
'defense_interception_avg'] = game_df['defense_interception_yds'] / game_df['defense_interceptions']
# Kicking
if len(kicking_df) > 0:
game_df = game_df.merge(kicking_df, how='outer', on=['season', 'game_id', 'team_id', 'team_name', 'loc',
'opp_team_id', 'opp_team_name', 'team_score', 'opp_score', 'is_overtime_game', 'player_id', 'player_name'])
game_df.loc[game_df['kicking_XPA'] > 0,
'kicking_XP%'] = game_df['kicking_XPM'] / game_df['kicking_XPA']
game_df.loc[game_df['kicking_FGA'] > 0,
'kicking_FG%'] = game_df['kicking_FGM'] / game_df['kicking_FGA']
game_df.loc[game_df['kicking_2pt_A'] > 0,
'kicking_2pt%'] = game_df['kicking_2pt_M'] / game_df['kicking_2pt_A']
game_df.loc[game_df['kicking_2pt_A'] > 0,
'kicking_4pt%'] = game_df['kicking_4pt_M'] / game_df['kicking_4pt_A']
# Returns
if len(returns_df) > 0:
game_df = game_df.merge(returns_df, how='outer', on=['season', 'game_id', 'team_id', 'team_name', 'loc',
'opp_team_id', 'opp_team_name', 'team_score', 'opp_score', 'is_overtime_game', 'player_id', 'player_name'])
game_df.loc[game_df['kick_return_ret'] > 0,
'kick_return_avg'] = game_df['kick_return_yds'] / game_df['kick_return_ret']
game_df.loc[game_df['missed_fg_return_ret'] > 0,
'missed_fg_return_avg'] = game_df['missed_fg_return_yds'] / game_df['missed_fg_return_ret']
game_df = game_df.reindex(columns=col_names)
else:
print(f'No stats found for game #{game_id}.')
return game_df
def get_arena_season_game_stats(season: int):
season_df = pd.DataFrame()
game_df = pd.DataFrame()
sched_df = get_arenafan_schedule(season)
sched_df = sched_df.dropna(subset=['game_id'])
game_id_arr = sched_df['game_id'].to_list()
for i in tqdm(game_id_arr):
game_df = get_arena_game_stats(i)
season_df = pd.concat([season_df, game_df], ignore_index=True)
return season_df
if __name__ == "__main__":
# df = get_arena_game_stats(5689)
# print(df.columns)
for i in range(1986, 1990):
df = get_arena_season_game_stats(i)
if len(df) > 0:
df.to_csv(
f'arena_football_league/game_stats/player/csv/{i}_arena_player_game_stats.csv', index=False)
df.to_parquet(
f'arena_football_league/game_stats/player/parquet/{i}_arena_player_game_stats.parquet', index=False)