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import numpy as np |
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import pandas as pd |
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import streamlit as st |
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import pymongo |
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st.set_page_config(layout="wide") |
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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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db = client["MLB_Database"] |
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return db |
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db = init_conn() |
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player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', |
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'4x%': '{:.2%}', 'GPP%': '{:.2%}'} |
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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_resource(ttl = 60) |
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def init_stat_load(): |
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collection = db["Player_Range_Of_Outcomes"] |
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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', 'Salary', 'Floor', 'Median', 'Ceiling', 'Own%', 'Site', 'Slate']] |
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raw_display = raw_display.rename(columns={'Own%': 'Own'}) |
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initial_concat = raw_display.sort_values(by='Own', ascending=False) |
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return initial_concat |
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@st.cache_data |
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def convert_df_to_csv(df): |
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return df.to_csv().encode('utf-8') |
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proj_raw = init_stat_load() |
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st.header("MLB DFS Pivot Tool") |
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with st.expander("Info and Filters"): |
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if st.button("Load/Reset Data", key='reset1'): |
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st.cache_data.clear() |
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proj_raw, timestamp = init_stat_load() |
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t_stamp = f"Last Update: " + str(timestamp) + f" CST" |
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for key in st.session_state.keys(): |
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del st.session_state[key] |
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site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1') |
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slate_var1 = st.radio("What slate are you working with?", ('Main Slate', 'Secondary Slate'), key='slate_var1') |
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if site_var1 == 'Draftkings': |
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raw_baselines = proj_raw[proj_raw['Site'] == 'Draftkings'] |
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if slate_var1 == 'Main Slate': |
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raw_baselines = raw_baselines[raw_baselines['Slate'] == 'main_slate'] |
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elif slate_var1 == 'Secondary Slate': |
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raw_baselines = raw_baselines[raw_baselines['Slate'] == 'secondary_slate'] |
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raw_baselines = raw_baselines.sort_values(by='Own', ascending=False) |
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elif site_var1 == 'Fanduel': |
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raw_baselines = proj_raw[proj_raw['Site'] == 'Fanduel'] |
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if slate_var1 == 'Main Slate': |
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raw_baselines = raw_baselines[raw_baselines['Slate'] == 'main_slate'] |
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elif slate_var1 == 'Secondary Slate': |
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raw_baselines = raw_baselines[raw_baselines['Slate'] == 'secondary_slate'] |
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raw_baselines = raw_baselines.sort_values(by='Own', ascending=False) |
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check_seq = st.radio("Do you want to check a single player or the top 10 in ownership?", ('Single Player', 'Top X Owned'), key='check_seq') |
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if check_seq == 'Single Player': |
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player_check = st.selectbox('Select player to create comps', options = raw_baselines['Player'].unique(), key='dk_player') |
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elif check_seq == 'Top X Owned': |
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top_x_var = st.number_input('How many players would you like to check?', min_value = 1, max_value = 10, value = 5, step = 1) |
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Salary_var = st.number_input('Acceptable +/- Salary range', min_value = 0, max_value = 1000, value = 300, step = 100) |
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Median_var = st.number_input('Acceptable +/- Median range', min_value = 0, max_value = 10, value = 3, step = 1) |
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pos_var1 = st.radio("Compare to all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_var1') |
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if site_var1 == 'Draftkings': |
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if pos_var1 == 'Specific Positions': |
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pos_var_list = st.multiselect('Which positions would you like to include?', options = ['SP', 'C', '1B', '2B', '3B', 'SS', 'OF'], key='pos_var_list') |
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elif pos_var1 == 'All Positions': |
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pos_var_list = ['SP', 'C', '1B', '2B', '3B', 'SS', 'OF'] |
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elif site_var1 == 'Fanduel': |
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if pos_var1 == 'Specific Positions': |
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pos_var_list = st.multiselect('Which positions would you like to include?', options = ['P', 'C', '1B', '2B', '3B', 'SS', 'OF'], key='pos_var_list') |
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elif pos_var1 == 'All Positions': |
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pos_var_list = ['P', 'C', '1B', '2B', '3B', 'SS', 'OF'] |
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split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1') |
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if split_var1 == 'Specific Games': |
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team_var1 = st.multiselect('Which teams would you like to include?', options = raw_baselines['Team'].unique(), key='team_var1') |
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elif split_var1 == 'Full Slate Run': |
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team_var1 = raw_baselines.Team.values.tolist() |
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placeholder = st.empty() |
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displayholder = st.empty() |
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if st.button('Simulate appropriate pivots'): |
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with placeholder: |
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if site_var1 == 'Draftkings': |
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working_roo = raw_baselines |
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working_roo.replace('', 0, inplace=True) |
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if site_var1 == 'Fanduel': |
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working_roo = raw_baselines |
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working_roo.replace('', 0, inplace=True) |
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own_dict = dict(zip(working_roo.Player, working_roo.Own)) |
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team_dict = dict(zip(working_roo.Player, working_roo.Team)) |
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pos_dict = dict(zip(working_roo.Player, working_roo.Position)) |
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total_sims = 1000 |
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if check_seq == 'Single Player': |
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player_var = working_roo.loc[working_roo['Player'] == player_check] |
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player_var = player_var.reset_index() |
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working_roo = working_roo[working_roo['Position'].apply(lambda x: any(pos in x.split('/') for pos in pos_var_list))] |
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working_roo = working_roo[working_roo['Team'].isin(team_var1)] |
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working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)] |
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working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)] |
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flex_file = working_roo[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling']] |
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flex_file['STD'] = (flex_file['Median']/3) |
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flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] |
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hold_file = flex_file.copy() |
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overall_file = flex_file.copy() |
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salary_file = flex_file.copy() |
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overall_players = overall_file[['Player']] |
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for x in range(0,total_sims): |
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salary_file[x] = salary_file['Salary'] |
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salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) |
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salary_file = salary_file.div(1000) |
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for x in range(0,total_sims): |
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overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD']) |
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overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) |
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players_only = hold_file[['Player']] |
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raw_lineups_file = players_only |
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for x in range(0,total_sims): |
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maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))} |
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raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])]) |
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players_only[x] = raw_lineups_file[x].rank(ascending=False) |
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players_only=players_only.drop(['Player'], axis=1) |
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salary_2x_check = (overall_file - (salary_file*2)) |
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salary_3x_check = (overall_file - (salary_file*3)) |
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salary_4x_check = (overall_file - (salary_file*4)) |
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gpp_check = (overall_file - ((salary_file*5)+10)) |
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players_only['Average_Rank'] = players_only.mean(axis=1) |
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players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims |
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players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims |
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players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims |
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players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) |
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players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims) |
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players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims) |
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players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) |
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players_only['GPP%'] = gpp_check[gpp_check >= 1].count(axis=1)/float(total_sims) |
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players_only['Player'] = hold_file[['Player']] |
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final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%']] |
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final_Proj = pd.merge(hold_file, final_outcomes, on="Player") |
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final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%']] |
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final_Proj['Own'] = final_Proj['Player'].map(own_dict) |
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final_Proj['Team'] = final_Proj['Player'].map(team_dict) |
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final_Proj['Own'] = final_Proj['Own'].astype('float') |
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final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%', 'Own']] |
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final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True) |
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final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) |
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final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100 |
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final_Proj['ValX'] = ((final_Proj[['2x%', '3x%', '4x%']].mean(axis=1))*100) + final_Proj['LevX'] |
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final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX']) |
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final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX']) |
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final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%', 'Own', 'LevX', 'ValX']] |
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final_Proj = final_Proj.set_index('Player') |
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st.session_state.final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False) |
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elif check_seq == 'Top X Owned': |
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if pos_var1 == 'Specific Positions': |
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raw_baselines = raw_baselines[raw_baselines['Position'].apply(lambda x: any(pos in x.split('/') for pos in pos_var_list))] |
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player_check = raw_baselines['Player'].head(top_x_var).tolist() |
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st.write(player_check) |
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final_proj_list = [] |
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for players in player_check: |
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players_pos = pos_dict[players] |
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player_var = working_roo.loc[working_roo['Player'] == players] |
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player_var = player_var.reset_index() |
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working_roo_temp = working_roo[working_roo['Team'].isin(team_var1)] |
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working_roo_temp = working_roo_temp.loc[(working_roo_temp['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo_temp['Salary'] <= player_var['Salary'][0] + Salary_var)] |
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working_roo_temp = working_roo_temp.loc[(working_roo_temp['Median'] >= player_var['Median'][0] - Median_var) & (working_roo_temp['Median'] <= player_var['Median'][0] + Median_var)] |
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flex_file = working_roo_temp[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling']] |
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flex_file['STD'] = (flex_file['Median']/3) |
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flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] |
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hold_file = flex_file.copy() |
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overall_file = flex_file.copy() |
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salary_file = flex_file.copy() |
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overall_players = overall_file[['Player']] |
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for x in range(0,total_sims): |
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salary_file[x] = salary_file['Salary'] |
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salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) |
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salary_file = salary_file.div(1000) |
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for x in range(0,total_sims): |
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overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD']) |
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overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) |
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players_only = hold_file[['Player']] |
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raw_lineups_file = players_only |
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for x in range(0,total_sims): |
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maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))} |
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raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])]) |
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players_only[x] = raw_lineups_file[x].rank(ascending=False) |
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players_only=players_only.drop(['Player'], axis=1) |
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salary_2x_check = (overall_file - (salary_file*2)) |
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salary_3x_check = (overall_file - (salary_file*3)) |
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salary_4x_check = (overall_file - (salary_file*4)) |
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gpp_check = (overall_file - ((salary_file*5)+10)) |
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players_only['Average_Rank'] = players_only.mean(axis=1) |
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players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims |
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players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims |
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players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims |
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players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) |
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players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims) |
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players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims) |
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players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) |
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players_only['GPP%'] = gpp_check[gpp_check >= 1].count(axis=1)/float(total_sims) |
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players_only['Player'] = hold_file[['Player']] |
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final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%']] |
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final_Proj = pd.merge(hold_file, final_outcomes, on="Player") |
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final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%']] |
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final_Proj['Own'] = final_Proj['Player'].map(own_dict) |
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final_Proj['Team'] = final_Proj['Player'].map(team_dict) |
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final_Proj['Own'] = final_Proj['Own'].astype('float') |
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final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True) |
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final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) |
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final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100 |
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final_Proj['ValX'] = ((final_Proj[['2x%', '3x%', '4x%']].mean(axis=1))*100) + final_Proj['LevX'] |
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final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX']) |
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final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX']) |
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final_Proj['Pivot_source'] = players |
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final_Proj = final_Proj[['Player', 'Pivot_source', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%', 'Own', 'LevX', 'ValX']] |
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final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False) |
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final_proj_list.append(final_Proj) |
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st.write(f'finished run for {players}') |
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final_Proj_combined = pd.concat(final_proj_list) |
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final_Proj_combined = final_Proj_combined.sort_values(by='LevX', ascending=False) |
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final_Proj_combined = final_Proj_combined[final_Proj_combined['Player'] != final_Proj_combined['Pivot_source']] |
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st.session_state.final_Proj = final_Proj_combined.reset_index(drop=True) |
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placeholder.empty() |
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with displayholder.container(): |
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if 'final_Proj' in st.session_state: |
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st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) |
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st.download_button( |
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label="Export Tables", |
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data=convert_df_to_csv(st.session_state.final_Proj), |
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file_name='MLB_pivot_export.csv', |
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mime='text/csv', |
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) |
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else: |
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st.write("Run some pivots my dude/dudette") |
