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import pandas as pd
import requests
def grab_contest_data(sport, contest_name, contest_id_map, contest_date):
contest_id = contest_id_map[contest_name]
raw_url = f'https://dh5nxc6yx3kwy.cloudfront.net/contests/{sport.lower()}/{contest_date}/{contest_id}/'
data_url = raw_url + 'data/'
lineups_url = raw_url + 'lineups/'
def format_lineup_string(lineup_hash, positions):
"""Replaces colons in a lineup hash with sequential positions."""
# Remove the leading colon and split by the remaining colons
player_ids = lineup_hash.lstrip(':').split(':')
# For MLB, we need to handle the specific position order
if len(player_ids) == 9: # 9-player lineup
# Map positions to the correct order for a 9-player lineup
position_map = {
0: '1B ',
1: ' 2B ',
2: ' 3B ',
3: ' C ',
4: ' OF ',
5: ' OF ',
6: ' P ',
7: ' P ',
8: ' SS '
}
# Use the mapped positions instead of the full position list
actual_positions = [position_map[i] for i in range(len(player_ids))]
else:
# For other cases, use the standard position list
actual_positions = positions[:len(player_ids)]
# Combine positions and player IDs
combined_parts = []
for pos, pid in zip(actual_positions, player_ids):
if pid == '-1': # Handle empty slots
combined_parts.append(pos + '')
else:
combined_parts.append(pos + pid_map.get(pid, pid))
# Join them into a single string
return "".join(combined_parts)
lineups_json = requests.get(lineups_url).json()
data_json = requests.get(data_url).json()
lineup_data = []
player_data = []
position_inserts = ['1B ', ' 2B ', ' 3B ', ' C ', ' OF ', ' OF ', ' OF ', ' P ', ' P ', ' SS ']
for players, player_info in data_json['players'].items():
player_data.append({
'fullName': player_info['fullName'],
'playerId': player_info['playerId'],
'salary': player_info['salary'],
'currentTeam': player_info['currentTeam'],
'rosterPosition': player_info['rosterPosition'],
'ownership': player_info['ownership'],
'actualPoints': player_info['actualPoints']
})
players_df = pd.DataFrame(player_data)
players_df = players_df.sort_values(by='ownership', ascending=False).reset_index(drop=True)
players_df = players_df.rename(columns={'fullName': 'Player', 'rosterPosition': 'Roster Position', 'ownership': '%Drafted', 'actualPoints': 'FPTS', 'salary': 'Salary', 'currentTeam': 'Team'})
pid_map = dict(zip(players_df['playerId'].astype(str), players_df['Player']))
for lineup_hash, lineup_info in lineups_json['lineups'].items():
lineup_data.append({
'lineupHash': lineup_hash,
'points': lineup_info['points'],
'entryNameList': lineup_info['entryNameList'][0]
})
lineups_df = pd.DataFrame(lineup_data)
lineups_df = lineups_df.sort_values(by='points', ascending=False)
lineups_df = lineups_df.reset_index()
lineups_df['index'] = lineups_df.index + 1
lineups_df['TimeRemaining'] = str(0)
lineups_df['EntryId'] = lineups_df['index'].astype(str) + lineups_df['entryNameList'].astype(str)
lineups_df['lineupHash'] = ':' + lineups_df['lineupHash']
lineups_df = lineups_df.rename(columns={'index': 'Rank', 'points': 'Points', 'entryNameList': 'EntryName', 'lineupHash': 'Lineup'})
lineups_df['EntryName'] = lineups_df['EntryName'] + ' (1/1)'
lineups_df['Lineup'] = lineups_df['Lineup'].apply(lambda x: format_lineup_string(x, position_inserts))
lineups_df = lineups_df[['Rank', 'EntryId', 'EntryName', 'TimeRemaining', 'Points', 'Lineup']]
total_data = lineups_df.merge(players_df, how='left', left_index=True, right_index=True)
return total_data |