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import streamlit as st |
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st.set_page_config(layout="wide") |
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import numpy as np |
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import pandas as pd |
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import gspread |
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import pymongo |
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import time |
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@st.cache_resource |
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def init_conn(): |
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uri = st.secrets['mongo_uri'] |
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client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) |
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return client |
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client = init_conn() |
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percentages_format = {'Exposure': '{:.2%}'} |
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freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'} |
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dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] |
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fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] |
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st.markdown(""" |
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<style> |
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/* Tab styling */ |
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.stTabs [data-baseweb="tab-list"] { |
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gap: 8px; |
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padding: 4px; |
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} |
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.stTabs [data-baseweb="tab"] { |
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height: 50px; |
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white-space: pre-wrap; |
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background-color: #FFD700; |
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color: white; |
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border-radius: 10px; |
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gap: 1px; |
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padding: 10px 20px; |
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font-weight: bold; |
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transition: all 0.3s ease; |
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} |
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.stTabs [aria-selected="true"] { |
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background-color: #DAA520; |
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color: white; |
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} |
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.stTabs [data-baseweb="tab"]:hover { |
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background-color: #DAA520; |
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cursor: pointer; |
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} |
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</style>""", unsafe_allow_html=True) |
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@st.cache_data(ttl = 599) |
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def init_DK_seed_frames(sport, split): |
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if sport == 'NFL': |
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db = client["NFL_Database"] |
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elif sport == 'NBA': |
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db = client["NBA_DFS"] |
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collection = db[f"DK_{sport}_SD_seed_frame"] |
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cursor = collection.find().limit(split) |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] |
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DK_seed = raw_display.to_numpy() |
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return DK_seed |
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@st.cache_data(ttl = 599) |
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def init_DK_secondary_seed_frames(sport, split): |
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if sport == 'NFL': |
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db = client["NFL_Database"] |
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elif sport == 'NBA': |
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db = client["NBA_DFS"] |
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collection = db[f"DK_{sport}_Secondary_SD_seed_frame"] |
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cursor = collection.find().limit(split) |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] |
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DK_second_seed = raw_display.to_numpy() |
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return DK_second_seed |
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@st.cache_data(ttl = 599) |
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def init_DK_auxiliary_seed_frames(sport, split): |
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if sport == 'NFL': |
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db = client["NFL_Database"] |
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elif sport == 'NBA': |
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db = client["NBA_DFS"] |
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collection = db[f"DK_{sport}_Auxiliary_SD_seed_frame"] |
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cursor = collection.find().limit(split) |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] |
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DK_auxiliary_seed = raw_display.to_numpy() |
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return DK_auxiliary_seed |
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@st.cache_data(ttl = 599) |
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def init_FD_seed_frames(sport, split): |
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if sport == 'NFL': |
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db = client["NFL_Database"] |
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elif sport == 'NBA': |
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db = client["NBA_DFS"] |
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collection = db[f"FD_{sport}_SD_seed_frame"] |
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cursor = collection.find().limit(split) |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] |
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FD_seed = raw_display.to_numpy() |
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return FD_seed |
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@st.cache_data(ttl = 599) |
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def init_FD_secondary_seed_frames(sport, split): |
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if sport == 'NFL': |
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db = client["NFL_Database"] |
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elif sport == 'NBA': |
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db = client["NBA_DFS"] |
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collection = db[f"FD_{sport}_Secondary_SD_seed_frame"] |
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cursor = collection.find().limit(split) |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] |
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FD_second_seed = raw_display.to_numpy() |
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return FD_second_seed |
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@st.cache_data(ttl = 599) |
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def init_FD_auxiliary_seed_frames(sport, split): |
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if sport == 'NFL': |
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db = client["NFL_Database"] |
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elif sport == 'NBA': |
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db = client["NBA_DFS"] |
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collection = db[f"FD_{sport}_Auxiliary_SD_seed_frame"] |
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cursor = collection.find().limit(split) |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] |
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FD_auxiliary_seed = raw_display.to_numpy() |
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return FD_auxiliary_seed |
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@st.cache_data(ttl = 599) |
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def init_baselines(sport): |
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if sport == 'NFL': |
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db = client["NFL_Database"] |
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collection = db['DK_SD_NFL_ROO'] |
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cursor = collection.find() |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', |
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'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']] |
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raw_display['Small_Field_Own'] = raw_display['Large_Field_Own'] |
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raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100) |
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small_cpt_own_var = 300 / raw_display['small_CPT_Own_raw'].sum() |
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raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var |
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raw_display['cpt_Median'] = raw_display['Median'] * 1.25 |
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raw_display['STDev'] = raw_display['Median'] / 4 |
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raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 |
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dk_raw = raw_display.dropna(subset=['Median']) |
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collection = db['FD_SD_NFL_ROO'] |
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cursor = collection.find() |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', |
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'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']] |
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raw_display['Small_Field_Own'] = raw_display['Large_Field_Own'] |
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raw_display['small_CPT_Own'] = raw_display['CPT_Own'] |
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raw_display['cpt_Median'] = raw_display['Median'] |
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raw_display['STDev'] = raw_display['Median'] / 4 |
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raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 |
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fd_raw = raw_display.dropna(subset=['Median']) |
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elif sport == 'NBA': |
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db = client["NBA_DFS"] |
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collection = db['Player_SD_Range_Of_Outcomes'] |
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cursor = collection.find() |
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load_display = pd.DataFrame(list(cursor)) |
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load_display = load_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', |
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'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']] |
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raw_display = load_display[load_display['site'] == 'Draftkings'] |
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raw_display['Small_Field_Own'] = raw_display['Small_Own'] |
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raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100) |
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small_cpt_own_var = 300 / raw_display['small_CPT_Own_raw'].sum() |
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raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var |
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raw_display['cpt_Median'] = raw_display['Median'] * 1.25 |
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raw_display['STDev'] = raw_display['Median'] / 4 |
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raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 |
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dk_raw = raw_display.dropna(subset=['Median']) |
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raw_display = load_display[load_display['site'] == 'Fanduel'] |
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raw_display['Small_Field_Own'] = raw_display['Large_Own'] |
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raw_display['small_CPT_Own'] = raw_display['CPT_Own'] |
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raw_display['cpt_Median'] = raw_display['Median'] |
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raw_display['STDev'] = raw_display['Median'] / 4 |
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raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 |
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fd_raw = raw_display.dropna(subset=['Median']) |
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return dk_raw, fd_raw |
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@st.cache_data |
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def convert_df(array): |
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array = pd.DataFrame(array, columns=column_names) |
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return array.to_csv().encode('utf-8') |
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@st.cache_data |
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def calculate_DK_value_frequencies(np_array): |
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unique, counts = np.unique(np_array[:, :6], return_counts=True) |
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frequencies = counts / len(np_array) |
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combined_array = np.column_stack((unique, frequencies)) |
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return combined_array |
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@st.cache_data |
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def calculate_FD_value_frequencies(np_array): |
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unique, counts = np.unique(np_array[:, :5], return_counts=True) |
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frequencies = counts / len(np_array) |
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combined_array = np.column_stack((unique, frequencies)) |
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return combined_array |
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@st.cache_data |
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def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size): |
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SimVar = 1 |
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Sim_Winners = [] |
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vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__) |
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vec_cpt_projection_map = np.vectorize(maps_dict['cpt_projection_map'].__getitem__) |
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vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) |
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vec_cpt_stdev_map = np.vectorize(maps_dict['cpt_STDev_map'].__getitem__) |
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st.write('Simulating contest on frames') |
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while SimVar <= Sim_size: |
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fp_random = seed_frame[np.random.choice(seed_frame.shape[0], Contest_Size)] |
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sample_arrays1 = np.c_[ |
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fp_random, |
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np.sum(np.random.normal( |
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loc=np.concatenate([ |
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vec_cpt_projection_map(fp_random[:, 0:1]), |
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vec_projection_map(fp_random[:, 1:-7]) |
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], axis=1), |
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scale=np.concatenate([ |
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vec_cpt_stdev_map(fp_random[:, 0:1]), |
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vec_stdev_map(fp_random[:, 1:-7]) |
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], axis=1)), |
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axis=1) |
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] |
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sample_arrays = sample_arrays1 |
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final_array = sample_arrays[sample_arrays[:, 7].argsort()[::-1]] |
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best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]] |
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Sim_Winners.append(best_lineup) |
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SimVar += 1 |
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return Sim_Winners |
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try: |
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dk_raw, fd_raw = init_baselines('NFL') |
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except: |
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dk_raw, fd_raw = init_baselines('NBA') |
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tab1, tab2 = st.tabs(['Contest Sims', 'Data Export']) |
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with tab1: |
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with st.expander("Info and Filters"): |
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if st.button("Load/Reset Data", key='reset2'): |
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st.cache_data.clear() |
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for key in st.session_state.keys(): |
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del st.session_state[key] |
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dk_raw, fd_raw = init_baselines('NFL') |
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sim_sport_var1 = st.radio("What sport are you working with?", ('NBA', 'NFL'), key='sim_sport_var1') |
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dk_raw, fd_raw = init_baselines(sim_sport_var1) |
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sim_slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='sim_slate_var1') |
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sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1') |
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if sim_site_var1 == 'Draftkings': |
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raw_baselines = dk_raw |
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column_names = dk_columns |
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elif sim_site_var1 == 'Fanduel': |
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raw_baselines = fd_raw |
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column_names = fd_columns |
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contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom')) |
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if contest_var1 == 'Small': |
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Contest_Size = 1000 |
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st.write("Small field size is 1,000 entrants.") |
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raw_baselines['Own'] = raw_baselines['Small_Field_Own'] |
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raw_baselines['CPT_Own'] = raw_baselines['small_CPT_Own'] |
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elif contest_var1 == 'Medium': |
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Contest_Size = 5000 |
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st.write("Medium field size is 5,000 entrants.") |
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elif contest_var1 == 'Large': |
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Contest_Size = 10000 |
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st.write("Large field size is 10,000 entrants.") |
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elif contest_var1 == 'Custom': |
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Contest_Size = st.number_input("Insert contest size", value=100, min_value=1, max_value=100000) |
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strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very')) |
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if strength_var1 == 'Not Very': |
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sharp_split = 500000 |
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elif strength_var1 == 'Below Average': |
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sharp_split = 400000 |
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elif strength_var1 == 'Average': |
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sharp_split = 300000 |
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elif strength_var1 == 'Above Average': |
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sharp_split = 200000 |
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elif strength_var1 == 'Very': |
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sharp_split = 100000 |
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if st.button("Run Contest Sim"): |
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if 'working_seed' in st.session_state: |
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maps_dict = { |
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'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), |
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'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)), |
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'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), |
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'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), |
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'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), |
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'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])), |
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'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), |
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'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)), |
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'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev'])) |
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} |
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Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size) |
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Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) |
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Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) |
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Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 |
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Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str) |
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Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) |
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type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32} |
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Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) |
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st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) |
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st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) |
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st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() |
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st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() |
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else: |
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if sim_site_var1 == 'Draftkings': |
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if sim_slate_var1 == 'Showdown': |
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st.session_state.working_seed = init_DK_seed_frames(sim_sport_var1, sharp_split) |
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if sim_sport_var1 == 'NFL': |
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export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
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elif sim_sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif sim_slate_var1 == 'Secondary Showdown': |
|
|
st.session_state.working_seed = init_DK_secondary_seed_frames(sim_sport_var1, sharp_split) |
|
|
if sim_sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif sim_sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif sim_slate_var1 == 'Auxiliary Showdown': |
|
|
st.session_state.working_seed = init_DK_auxiliary_seed_frames(sim_sport_var1, sharp_split) |
|
|
if sim_sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif sim_sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
raw_baselines = dk_raw |
|
|
column_names = dk_columns |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
if sim_slate_var1 == 'Showdown': |
|
|
st.session_state.working_seed = init_FD_seed_frames(sim_sport_var1, sharp_split) |
|
|
if sim_sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sim_sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sim_slate_var1 == 'Secondary Showdown': |
|
|
st.session_state.working_seed = init_FD_secondary_seed_frames(sim_sport_var1, sharp_split) |
|
|
if sim_sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sim_sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sim_slate_var1 == 'Auxiliary Showdown': |
|
|
st.session_state.working_seed = init_FD_auxiliary_seed_frames(sim_sport_var1, sharp_split) |
|
|
if sim_sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sim_sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
raw_baselines = fd_raw |
|
|
column_names = fd_columns |
|
|
maps_dict = { |
|
|
'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), |
|
|
'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)), |
|
|
'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), |
|
|
'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), |
|
|
'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), |
|
|
'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])), |
|
|
'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), |
|
|
'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)), |
|
|
'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev'])) |
|
|
} |
|
|
Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size) |
|
|
Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) |
|
|
Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 |
|
|
Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str) |
|
|
|
|
|
|
|
|
if sim_site_var1 == 'Fanduel': |
|
|
dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank'] |
|
|
own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own'] |
|
|
calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc'] |
|
|
Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100 |
|
|
Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100 |
|
|
Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100 |
|
|
Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100 |
|
|
Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100 |
|
|
|
|
|
|
|
|
Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1)) + 0.0001 |
|
|
|
|
|
|
|
|
Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1) |
|
|
|
|
|
|
|
|
Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 59800) / 100) |
|
|
|
|
|
|
|
|
Sim_Winner_Frame['Dupes'] = np.where( |
|
|
np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0, |
|
|
0, |
|
|
np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1 |
|
|
) |
|
|
Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2 |
|
|
elif sim_site_var1 == 'Draftkings': |
|
|
dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank'] |
|
|
own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own'] |
|
|
calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc'] |
|
|
Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['FLEX5_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']).rank(pct=True) |
|
|
Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100 |
|
|
Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100 |
|
|
Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100 |
|
|
Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100 |
|
|
Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100 |
|
|
Sim_Winner_Frame['FLEX5_Own'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']) / 100 |
|
|
|
|
|
|
|
|
Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1)) |
|
|
|
|
|
|
|
|
Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1) |
|
|
|
|
|
|
|
|
Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 49800) / 100) |
|
|
|
|
|
|
|
|
Sim_Winner_Frame['Dupes'] = np.where( |
|
|
np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0, |
|
|
0, |
|
|
np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1 |
|
|
) |
|
|
Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2 |
|
|
Sim_Winner_Frame['Dupes'] = np.round(Sim_Winner_Frame['Dupes'], 0) |
|
|
Sim_Winner_Frame['Dupes'] = np.where( |
|
|
np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0, |
|
|
0, |
|
|
np.round(Sim_Winner_Frame['dupes_calc'], 0) |
|
|
) |
|
|
Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=dup_count_columns) |
|
|
Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=own_columns) |
|
|
Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=calc_columns) |
|
|
|
|
|
Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) |
|
|
|
|
|
|
|
|
type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32, 'Dupes': int} |
|
|
Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) |
|
|
st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() |
|
|
st.session_state.Sim_Winner_Export.iloc[:, 0:6] = st.session_state.Sim_Winner_Export.iloc[:, 0:6].apply(lambda x: x.map(export_id_dict)) |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() |
|
|
freq_copy = st.session_state.Sim_Winner_Display |
|
|
|
|
|
if sim_site_var1 == 'Draftkings': |
|
|
freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:5].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
freq_working['Freq'] = freq_working['Freq'].astype(int) |
|
|
freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map']) |
|
|
if sim_site_var1 == 'Draftkings': |
|
|
if sim_sport_var1 == 'NFL': |
|
|
freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5 |
|
|
elif sim_sport_var1 == 'NBA': |
|
|
freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) |
|
|
freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100 |
|
|
freq_working['Exposure'] = freq_working['Freq']/(1000) |
|
|
freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own'] |
|
|
freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map']) |
|
|
st.session_state.player_freq = freq_working.copy() |
|
|
|
|
|
if sim_site_var1 == 'Draftkings': |
|
|
cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
cpt_working['Freq'] = cpt_working['Freq'].astype(int) |
|
|
cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map']) |
|
|
if sim_sport_var1 == 'NFL': |
|
|
cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) |
|
|
elif sim_sport_var1 == 'NBA': |
|
|
cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) * 1.5 |
|
|
cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100 |
|
|
cpt_working['Exposure'] = cpt_working['Freq']/(1000) |
|
|
cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own'] |
|
|
cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map']) |
|
|
st.session_state.sp_freq = cpt_working.copy() |
|
|
|
|
|
if sim_site_var1 == 'Draftkings': |
|
|
flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
cpt_own_div = 600 |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:5].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
cpt_own_div = 500 |
|
|
flex_working['Freq'] = flex_working['Freq'].astype(int) |
|
|
flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map']) |
|
|
if sim_site_var1 == 'Draftkings': |
|
|
if sim_sport_var1 == 'NFL': |
|
|
flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5 |
|
|
elif sim_sport_var1 == 'NBA': |
|
|
flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) |
|
|
flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_Own_map']) / 100) |
|
|
flex_working['Exposure'] = flex_working['Freq']/(1000) |
|
|
flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own'] |
|
|
flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map']) |
|
|
st.session_state.flex_freq = flex_working.copy() |
|
|
|
|
|
if sim_site_var1 == 'Draftkings': |
|
|
team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,8:9].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,7:8].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
team_working['Freq'] = team_working['Freq'].astype(int) |
|
|
team_working['Exposure'] = team_working['Freq']/(1000) |
|
|
st.session_state.team_freq = team_working.copy() |
|
|
|
|
|
with st.container(): |
|
|
if st.button("Reset Sim", key='reset_sim'): |
|
|
for key in st.session_state.keys(): |
|
|
del st.session_state[key] |
|
|
if 'player_freq' in st.session_state: |
|
|
player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2') |
|
|
if player_split_var2 == 'Specific Players': |
|
|
find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique()) |
|
|
elif player_split_var2 == 'Full Players': |
|
|
find_var2 = st.session_state.player_freq.Player.values.tolist() |
|
|
|
|
|
if player_split_var2 == 'Specific Players': |
|
|
st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)] |
|
|
if player_split_var2 == 'Full Players': |
|
|
st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame |
|
|
if 'Sim_Winner_Display' in st.session_state: |
|
|
st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) |
|
|
if 'Sim_Winner_Export' in st.session_state: |
|
|
st.download_button( |
|
|
label="Export Full Frame", |
|
|
data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'), |
|
|
file_name='NFL_SD_consim_export.csv', |
|
|
mime='text/csv', |
|
|
) |
|
|
|
|
|
with st.container(): |
|
|
tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures', 'Team Exposures']) |
|
|
with tab1: |
|
|
if 'player_freq' in st.session_state: |
|
|
|
|
|
st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) |
|
|
st.download_button( |
|
|
label="Export Exposures", |
|
|
data=st.session_state.player_freq.to_csv().encode('utf-8'), |
|
|
file_name='player_freq_export.csv', |
|
|
mime='text/csv', |
|
|
key='overall' |
|
|
) |
|
|
with tab2: |
|
|
if 'sp_freq' in st.session_state: |
|
|
|
|
|
st.dataframe(st.session_state.sp_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) |
|
|
st.download_button( |
|
|
label="Export Exposures", |
|
|
data=st.session_state.sp_freq.to_csv().encode('utf-8'), |
|
|
file_name='cpt_freq.csv', |
|
|
mime='text/csv', |
|
|
key='sp' |
|
|
) |
|
|
with tab3: |
|
|
if 'flex_freq' in st.session_state: |
|
|
|
|
|
st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) |
|
|
st.download_button( |
|
|
label="Export Exposures", |
|
|
data=st.session_state.flex_freq.to_csv().encode('utf-8'), |
|
|
file_name='flex_freq.csv', |
|
|
mime='text/csv', |
|
|
key='flex' |
|
|
) |
|
|
with tab4: |
|
|
if 'team_freq' in st.session_state: |
|
|
|
|
|
st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True) |
|
|
st.download_button( |
|
|
label="Export Exposures", |
|
|
data=st.session_state.team_freq.to_csv().encode('utf-8'), |
|
|
file_name='team_freq.csv', |
|
|
mime='text/csv', |
|
|
key='team' |
|
|
) |
|
|
|
|
|
with tab2: |
|
|
with st.expander("Info and Filters"): |
|
|
if st.button("Load/Reset Data", key='reset1'): |
|
|
st.cache_data.clear() |
|
|
for key in st.session_state.keys(): |
|
|
del st.session_state[key] |
|
|
dk_raw, fd_raw = init_baselines('NFL') |
|
|
|
|
|
sport_var1 = st.radio("What sport are you working with?", ('NBA', 'NFL'), key='sport_var1') |
|
|
slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='slate_var1') |
|
|
sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000) |
|
|
|
|
|
site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1') |
|
|
if site_var1 == 'Draftkings': |
|
|
|
|
|
team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1') |
|
|
if team_var1 == 'Specific Teams': |
|
|
team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique()) |
|
|
elif team_var1 == 'Full Slate': |
|
|
team_var2 = dk_raw.Team.values.tolist() |
|
|
|
|
|
stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1') |
|
|
if stack_var1 == 'Specific Stack Sizes': |
|
|
stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0]) |
|
|
elif stack_var1 == 'Full Slate': |
|
|
stack_var2 = [5, 4, 3, 2, 1, 0] |
|
|
|
|
|
elif site_var1 == 'Fanduel': |
|
|
|
|
|
team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1') |
|
|
if team_var1 == 'Specific Teams': |
|
|
team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique()) |
|
|
elif team_var1 == 'Full Slate': |
|
|
team_var2 = fd_raw.Team.values.tolist() |
|
|
|
|
|
stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1') |
|
|
if stack_var1 == 'Specific Stack Sizes': |
|
|
stack_var2 = st.multiselect('Which stack sizes do you want?', options = [4, 3, 2, 1, 0]) |
|
|
elif stack_var1 == 'Full Slate': |
|
|
stack_var2 = [4, 3, 2, 1, 0] |
|
|
|
|
|
|
|
|
if st.button("Prepare data export", key='data_export'): |
|
|
if 'working_seed' in st.session_state: |
|
|
data_export = st.session_state.working_seed.copy() |
|
|
elif 'working_seed' not in st.session_state: |
|
|
if site_var1 == 'Draftkings': |
|
|
if slate_var1 == 'Showdown': |
|
|
st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif slate_var1 == 'Secondary Showdown': |
|
|
st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif slate_var1 == 'Auxiliary Showdown': |
|
|
st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
raw_baselines = dk_raw |
|
|
column_names = dk_columns |
|
|
elif site_var1 == 'Fanduel': |
|
|
if slate_var1 == 'Showdown': |
|
|
st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif slate_var1 == 'Secondary Showdown': |
|
|
st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif slate_var1 == 'Auxiliary Showdown': |
|
|
st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
raw_baselines = fd_raw |
|
|
column_names = fd_columns |
|
|
data_export = st.session_state.working_seed.copy() |
|
|
for col in range(6): |
|
|
data_export[:, col] = np.array([export_id_dict.get(x, x) for x in data_export[:, col]]) |
|
|
st.download_button( |
|
|
label="Export optimals set", |
|
|
data=convert_df(data_export), |
|
|
file_name='NFL_SD_optimals_export.csv', |
|
|
mime='text/csv', |
|
|
) |
|
|
|
|
|
if st.button("Load Data", key='load_data'): |
|
|
if site_var1 == 'Draftkings': |
|
|
if 'working_seed' in st.session_state: |
|
|
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)] |
|
|
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)] |
|
|
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) |
|
|
elif 'working_seed' not in st.session_state: |
|
|
if slate_var1 == 'Showdown': |
|
|
st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif slate_var1 == 'Secondary Showdown': |
|
|
st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif slate_var1 == 'Auxiliary Showdown': |
|
|
st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) |
|
|
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)] |
|
|
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)] |
|
|
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) |
|
|
|
|
|
elif site_var1 == 'Fanduel': |
|
|
if 'working_seed' in st.session_state: |
|
|
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)] |
|
|
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)] |
|
|
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) |
|
|
elif 'working_seed' not in st.session_state: |
|
|
if slate_var1 == 'Showdown': |
|
|
st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif slate_var1 == 'Secondary Showdown': |
|
|
st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif slate_var1 == 'Auxiliary Showdown': |
|
|
st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var) |
|
|
if sport_var1 == 'NFL': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
elif sport_var1 == 'NBA': |
|
|
export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) |
|
|
raw_baselines = fd_raw |
|
|
column_names = fd_columns |
|
|
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)] |
|
|
st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)] |
|
|
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) |
|
|
|
|
|
with st.container(): |
|
|
if 'data_export_display' in st.session_state: |
|
|
st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True) |
