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Update app.py
Browse files
app.py
CHANGED
@@ -1,8 +1,36 @@
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import streamlit as st
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import requests
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st.set_page_config(layout="wide")
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purge_cache = "https://sheetdb.io/api/v1/svino07zkd6j6/cache/purge/f8fc41b2"
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traderater = "https://www.fantasylife.com/api/projections/v1/nfl/ratemytrade/season/update"
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@@ -24,11 +52,14 @@ dev_agg_url = "https://fantasylife.dev.spotlightsportsb2b.com/api/projections/v1
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freedman_nfl_game_model = "https://www.fantasylife.com/api/projections/v1/nfl-odds/james/game/update"
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thor_ncaaf_game_model = "https://www.fantasylife.com/api/projections/v1/ncaafb-odds/james/game/update"
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headers = {
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'Authorization': 'Bearer 6984da1f-2c81-4140-8206-d018af38533f',
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}
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tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(['Season Long (Live Site)', 'Season Long (Dev Site)', 'Weekly', 'Game Model', 'Trade Rater', 'Rest of Season'])
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with tab1:
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with st.container():
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@@ -144,4 +175,246 @@ with tab6:
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if st.button("Rest of Season Update", key='reset13'):
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response = requests.post(ros_james_url, headers=headers)
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if response.status_code == 200:
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st.write("Uploading!")
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import streamlit as st
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import requests
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import requests
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import pandas as pd
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from pandas import DataFrame
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import numpy as np
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import gspread
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import pytz
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from datetime import datetime
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from datetime import date, timedelta
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import time
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from discordwebhook import Discord
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st.set_page_config(layout="wide")
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scope = ['https://www.googleapis.com/auth/spreadsheets',
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"https://www.googleapis.com/auth/drive"]
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credentials = {
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"type": "service_account",
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"project_id": "sheets-api-connect-378620",
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"private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
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"private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
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"client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
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"client_id": "106625872877651920064",
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"auth_uri": "https://accounts.google.com/o/oauth2/auth",
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"token_uri": "https://oauth2.googleapis.com/token",
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"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
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"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
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}
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gc = gspread.service_account_from_dict(credentials)
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purge_cache = "https://sheetdb.io/api/v1/svino07zkd6j6/cache/purge/f8fc41b2"
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traderater = "https://www.fantasylife.com/api/projections/v1/nfl/ratemytrade/season/update"
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freedman_nfl_game_model = "https://www.fantasylife.com/api/projections/v1/nfl-odds/james/game/update"
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thor_ncaaf_game_model = "https://www.fantasylife.com/api/projections/v1/ncaafb-odds/james/game/update"
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NCAAF_model_url = 'https://docs.google.com/spreadsheets/d/17QUsCEMVAFbOteenUbi18H2kgVYx4cwBGs9dFCi4ri4/edit?pli=1&gid=1637459210#gid=1637459210'
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pff_url = 'https://www.pff.com/api/scoreboard/schedule?league=ncaa&season=2024'
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headers = {
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'Authorization': 'Bearer 6984da1f-2c81-4140-8206-d018af38533f',
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}
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tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Season Long (Live Site)', 'Season Long (Dev Site)', 'Weekly', 'Game Model', 'Trade Rater', 'Rest of Season', 'NCAAF Script'])
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with tab1:
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with st.container():
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if st.button("Rest of Season Update", key='reset13'):
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response = requests.post(ros_james_url, headers=headers)
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if response.status_code == 200:
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st.write("Uploading!")
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with tab7:
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with st.container():
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col1, col2, col3 = st.columns([3, 3, 3])
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with col1:
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st.info("Update NCAAF schedule and ranks")
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if st.button("Update NCAAF", key='reset14'):
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sh = gc.open_by_url(NCAAF_model_url)
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worksheet = sh.worksheet('ATLranks')
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ranks_df = DataFrame(worksheet.get_all_records())
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ranks_dict = dict(zip(ranks_df.Team, ranks_df.ATL))
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conf_dict = dict(zip(ranks_df.Team, ranks_df.Conference))
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time.sleep(.5)
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worksheet = sh.worksheet('HFA')
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hfa_df = DataFrame(worksheet.get_all_records())
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hfa_dict = dict(zip(hfa_df.Team, hfa_df.HFA))
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time.sleep(.5)
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worksheet = sh.worksheet('Odds')
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odds_df = DataFrame(worksheet.get_all_records())
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odds_dict = dict(zip(odds_df.Point_Spread, odds_df.Favorite_Win_Chance))
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time.sleep(.5)
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worksheet = sh.worksheet('Acronyms')
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acros_df = DataFrame(worksheet.get_all_records())
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right_acro = acros_df['Team'].tolist()
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wrong_acro = acros_df['Acro'].tolist()
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time.sleep(.5)
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worksheet = sh.worksheet('Add games')
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add_games_df = DataFrame(worksheet.get_all_records())
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add_games_df.replace('', np.nan, inplace=True)
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time.sleep(.5)
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worksheet = sh.worksheet('Completed games')
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comp_games_df = DataFrame(worksheet.get_all_records())
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comp_games_df.replace('', np.nan, inplace=True)
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time.sleep(.5)
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worksheet = sh.worksheet('LY_scoring')
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lyscore_df = DataFrame(worksheet.get_all_records())
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for checkVar in range(len(wrong_acro)):
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lyscore_df['Team'] = lyscore_df['Team'].replace(wrong_acro, right_acro)
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PFA_dict = dict(zip(lyscore_df.Team, lyscore_df.PF_G_adj))
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PAA_dict = dict(zip(lyscore_df.Team, lyscore_df.PA_G_adj))
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# Send a GET request to the API
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response = requests.get(pff_url)
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# Check if the request was successful
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if response.status_code == 200:
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# Parse the JSON content
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data = response.json()
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# Extract the "weeks" object
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weeks = data.get('weeks', [])
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# Initialize an empty list to store game data
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games_list = []
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team_list = []
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# Iterate over each week and its games
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for week in weeks:
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week_number = week.get('week')
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for game in week.get('games', []):
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# Add week number to the game dictionary
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game['week'] = week_number
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away_franchise = game.get('away_franchise', {})
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away_franchise_groups = away_franchise.get('groups', {})
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away_conf = away_franchise_groups[0]['name']
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home_franchise = game.get('home_franchise', {})
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home_franchise_groups = home_franchise.get('groups', {})
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home_conf = home_franchise_groups[0]['name']
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# Flatten the away and home franchise data
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game_data = {
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'game_id': game.get('external_game_id'),
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'Day': game.get('kickoff_date'),
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'CST': game.get('kickoff_raw'),
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'away_id': away_franchise.get('abbreviation'),
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'Away': away_franchise.get('city'),
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'home_id': home_franchise.get('abbreviation'),
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'Home': home_franchise.get('city')
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}
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home_data = {
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'team': home_franchise.get('city'),
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'conf': home_conf
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}
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away_data = {
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'team': away_franchise.get('city'),
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'conf': away_conf
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}
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merged_data = game_data | game
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team_data = home_data | away_data
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games_list.append(merged_data)
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team_list.append(home_data)
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team_list.append(away_data)
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# Create a DataFrame from the games list
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df = pd.DataFrame(games_list)
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team_df = pd.DataFrame(team_list)
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team_df = team_df.drop_duplicates(subset=['team', 'conf'])
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# Display the DataFrame
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print(df)
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else:
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print(f"Failed to retrieve data. HTTP Status code: {response.status_code}")
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df_raw = df[['pff_week', 'game_id', 'away_id', 'home_id', 'Away', 'Home', 'point_spread', 'over_under', 'Day', 'CST']]
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df_raw['conf_game'] = np.nan
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df_raw['Away_ATL'] = np.nan
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df_raw['Home_ATL'] = np.nan
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df_raw['Home Spread'] = np.nan
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df_raw['Proj Total'] = np.nan
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df_raw['Neutral'] = np.nan
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df_raw['Notes'] = np.nan
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df_raw['over_under'].fillna("", inplace=True)
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df_raw['over_under'] = pd.to_numeric(df_raw['over_under'], errors='coerce')
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df_raw = df_raw[['pff_week', 'game_id', 'away_id', 'home_id', 'Away', 'Home', 'conf_game', 'Away_ATL', 'Home_ATL', 'point_spread', 'Home Spread', 'over_under', 'Proj Total', 'Day', 'CST', 'Neutral', 'Notes']]
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add_games_merge = add_games_df
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comp_games_merge = comp_games_df
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conf_adj = dict(zip(add_games_merge['game_id'], add_games_merge['conf_game']))
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df_merge_1 = pd.concat([add_games_merge, df_raw])
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df_cleaned = pd.concat([comp_games_merge, df_merge_1])
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df_cleaned = df_cleaned[['pff_week', 'game_id', 'away_id', 'home_id', 'Away', 'Home', 'point_spread', 'over_under', 'Day', 'CST']]
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df_cleaned = df_cleaned.drop_duplicates(subset=['game_id'])
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def cond_away_PFA(row, df):
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mask = (df['Away_ATL'] >= row['Away_ATL'] - 5) & (df['Away_ATL'] <= row['Away_ATL'] + 5)
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return df.loc[mask, 'Away_PFA'].mean()
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|
322 |
+
def cond_home_PFA(row, df):
|
323 |
+
mask = (df['Home_ATL'] >= row['Home_ATL'] - 5) & (df['Home_ATL'] <= row['Home_ATL'] + 5)
|
324 |
+
return df.loc[mask, 'Home_PFA'].mean()
|
325 |
+
|
326 |
+
def cond_away_PAA(row, df):
|
327 |
+
mask = (df['Away_ATL'] >= row['Away_ATL'] - 5) & (df['Away_ATL'] <= row['Away_ATL'] + 5)
|
328 |
+
return df.loc[mask, 'Away_PAA'].mean()
|
329 |
+
|
330 |
+
def cond_home_PAA(row, df):
|
331 |
+
mask = (df['Home_ATL'] >= row['Home_ATL'] - 5) & (df['Home_ATL'] <= row['Home_ATL'] + 5)
|
332 |
+
return df.loc[mask, 'Home_PAA'].mean()
|
333 |
+
|
334 |
+
for checkVar in range(len(wrong_acro)):
|
335 |
+
df_cleaned['Away'] = df_cleaned['Away'].replace(wrong_acro, right_acro)
|
336 |
+
df_cleaned['Home'] = df_cleaned['Home'].replace(wrong_acro, right_acro)
|
337 |
+
df_cleaned['Away_conf'] = df_cleaned['Away'].map(conf_dict)
|
338 |
+
df_cleaned['Home_conf'] = df_cleaned['Home'].map(conf_dict)
|
339 |
+
df_cleaned['conf_game_var'] = np.where((df_cleaned['Away_conf'] == df_cleaned['Home_conf']), 1, 0)
|
340 |
+
df_cleaned['conf_game'] = df_cleaned.apply(lambda row: conf_adj.get(row['game_id'], row['conf_game_var']), axis=1)
|
341 |
+
df_cleaned['Away_ATL'] = df_cleaned['Away'].map(ranks_dict)
|
342 |
+
df_cleaned['Home_ATL'] = df_cleaned['Home'].map(ranks_dict)
|
343 |
+
df_cleaned['Away_PFA'] = df_cleaned['Away'].map(PFA_dict)
|
344 |
+
df_cleaned['Home_PFA'] = df_cleaned['Home'].map(PFA_dict)
|
345 |
+
df_cleaned['Away_PAA'] = df_cleaned['Away'].map(PAA_dict)
|
346 |
+
df_cleaned['Home_PAA'] = df_cleaned['Home'].map(PAA_dict)
|
347 |
+
|
348 |
+
# Apply the function to each row in the DataFrame
|
349 |
+
df_cleaned['cond_away_PFA'] = df_cleaned.apply(lambda row: cond_away_PFA(row, df_cleaned), axis=1)
|
350 |
+
df_cleaned['cond_home_PFA'] = df_cleaned.apply(lambda row: cond_home_PFA(row, df_cleaned), axis=1)
|
351 |
+
df_cleaned['cond_away_PAA'] = df_cleaned.apply(lambda row: cond_away_PAA(row, df_cleaned), axis=1)
|
352 |
+
df_cleaned['cond_home_PAA'] = df_cleaned.apply(lambda row: cond_home_PAA(row, df_cleaned), axis=1)
|
353 |
+
|
354 |
+
df_cleaned['cond_away_PFA'] = np.where((df_cleaned['Away_ATL'] <= 0), 18, df_cleaned['cond_away_PFA'])
|
355 |
+
df_cleaned['cond_away_PAA'] = np.where((df_cleaned['Away_ATL'] <= 0), 36, df_cleaned['cond_away_PAA'])
|
356 |
+
df_cleaned['cond_home_PFA'] = np.where((df_cleaned['Home_ATL'] <= 0), 18, df_cleaned['cond_home_PFA'])
|
357 |
+
df_cleaned['cond_home_PAA'] = np.where((df_cleaned['Home_ATL'] <= 0), 36, df_cleaned['cond_home_PAA'])
|
358 |
+
|
359 |
+
df_cleaned['Away_PFA'] = df_cleaned['Away_PFA'].fillna(df_cleaned['cond_away_PFA'])
|
360 |
+
df_cleaned['Away_PAA'] = df_cleaned['Away_PAA'].fillna(df_cleaned['cond_away_PAA'])
|
361 |
+
df_cleaned['Home_PFA'] = df_cleaned['Home_PFA'].fillna(df_cleaned['cond_home_PFA'])
|
362 |
+
df_cleaned['Home_PAA'] = df_cleaned['Home_PAA'].fillna(df_cleaned['cond_home_PAA'])
|
363 |
+
|
364 |
+
df_cleaned['Away_PFA_adj'] = (df_cleaned['Away_PFA'] * .75 + df_cleaned['Home_PAA'] * .25)
|
365 |
+
df_cleaned['Home_PFA_adj'] = (df_cleaned['Home_PFA'] * .75 + df_cleaned['Away_PAA'] * .25)
|
366 |
+
df_cleaned['Away_PFA_cond'] = (df_cleaned['cond_away_PFA'] * .75 + df_cleaned['cond_home_PAA'] * .25)
|
367 |
+
df_cleaned['Home_PFA_cond'] = (df_cleaned['cond_home_PFA'] * .75 + df_cleaned['cond_away_PAA'] * .25)
|
368 |
+
|
369 |
+
df_cleaned['HFA'] = df_cleaned['Home'].map(hfa_dict)
|
370 |
+
df_cleaned['Neutral'] = np.nan
|
371 |
+
df_cleaned['Home Spread'] = ((df_cleaned['Home_ATL'] - df_cleaned['Away_ATL']) + df_cleaned['HFA']) * -1
|
372 |
+
df_cleaned['Win Prob'] = df_cleaned['Home Spread'].map(odds_dict)
|
373 |
+
df_cleaned['Spread Adj'] = np.nan
|
374 |
+
df_cleaned['Final Spread'] = np.nan
|
375 |
+
df_cleaned['Proj Total'] = df_cleaned['Away_PFA_adj'] + df_cleaned['Home_PFA_adj']
|
376 |
+
df_cleaned['Proj Total (adj)'] = np.where(df_cleaned['over_under'] != np.nan, (df_cleaned['over_under'] * .66 + df_cleaned['Proj Total'] * .34), df_cleaned['Proj Total'])
|
377 |
+
df_cleaned['Proj Total (adj)'] = df_cleaned['Proj Total (adj)'].fillna(df_cleaned['Proj Total'])
|
378 |
+
df_cleaned['Total Adj'] = np.nan
|
379 |
+
df_cleaned['Final Total'] = np.nan
|
380 |
+
df_cleaned['Notes'] = np.nan
|
381 |
+
|
382 |
+
export_df_1 = df_cleaned[['pff_week', 'game_id', 'away_id', 'home_id', 'Away', 'Home', 'conf_game', 'Away_ATL', 'Home_ATL', 'point_spread', 'Home Spread',
|
383 |
+
'over_under', 'Proj Total (adj)', 'Day', 'CST', 'Neutral', 'Notes']]
|
384 |
+
|
385 |
+
|
386 |
+
export_df_1.rename(columns={"pff_week": "week", "point_spread": "Vegas Spread", "over_under": "Vegas Total", "Proj Total (adj)": "Proj Total"}, inplace = True)
|
387 |
+
export_df_2 = add_games_df
|
388 |
+
export_df = export_df_1
|
389 |
+
export_df['week'] = pd.to_numeric(export_df['week'], errors='coerce')
|
390 |
+
export_df = export_df.drop_duplicates(subset=['week', 'Away', 'Home'])
|
391 |
+
export_df = export_df.sort_values(by='week', ascending=True)
|
392 |
+
|
393 |
+
export_df['Vegas Spread'] = pd.to_numeric(export_df['Vegas Spread'], errors='coerce')
|
394 |
+
export_df['Vegas Total'] = pd.to_numeric(export_df['Vegas Total'], errors='coerce')
|
395 |
+
export_df['Proj Total'] = pd.to_numeric(export_df['Proj Total'], errors='coerce')
|
396 |
+
export_df['Home Spread'] = pd.to_numeric(export_df['Home Spread'], errors='coerce')
|
397 |
+
export_df.replace([np.nan, np.inf, -np.inf], '', inplace=True)
|
398 |
+
export_df = export_df.drop_duplicates(subset=['week', 'away_id', 'home_id'])
|
399 |
+
|
400 |
+
sh = gc.open_by_url(NCAAF_model_url)
|
401 |
+
worksheet = sh.worksheet('Master_sched')
|
402 |
+
worksheet.batch_clear(['A:P'])
|
403 |
+
worksheet.update([export_df.columns.values.tolist()] + export_df.values.tolist())
|
404 |
+
|
405 |
+
sheet_list = ['W0', 'W1', 'W2', 'W3', 'W4', 'W5', 'W6', 'W7', 'W8', 'W9', 'W10', 'W11', 'W12', 'W13', 'W14']
|
406 |
+
# sheet_list = ['W0']
|
407 |
+
counter = 0
|
408 |
+
|
409 |
+
for sheet_name in sheet_list:
|
410 |
+
export_cull = export_df[export_df['week'] == str(counter)]
|
411 |
+
sh = gc.open_by_url(NCAAF_model_url)
|
412 |
+
worksheet = sh.worksheet(sheet_name)
|
413 |
+
worksheet.batch_clear(['A:P'])
|
414 |
+
worksheet.update([export_cull.columns.values.tolist()] + export_cull.values.tolist())
|
415 |
+
|
416 |
+
counter += 1
|
417 |
+
|
418 |
+
time.sleep(3.76)
|
419 |
+
|
420 |
+
st.write("Finished NCAAF script!")
|