Create app.py

app.py

@@ -0,0 +1,311 @@
+import pulp
+import numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+from itertools import combinations
+
+scope = ['https://www.googleapis.com/auth/spreadsheets',
+          "https://www.googleapis.com/auth/drive"]
+
+credentials = {
+  "type": "service_account",
+  "project_id": "sheets-api-connect-378620",
+  "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
+  "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
+  "client_id": "106625872877651920064",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
+}
+
+gc = gspread.service_account_from_dict(credentials)
+
+st.set_page_config(layout="wide")
+
+game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
+              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
+
+team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}',
+                   '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'}
+
+wrong_acro = ['WSH', 'AZ']
+right_acro = ['WAS', 'ARI']
+
+dk_player_projections = 'https://docs.google.com/spreadsheets/d/1MdzPFqIT0MFid2IhegWf39VNR8IXUyo_Fb5dolOSt3o/edit#gid=340831852'
+fd_player_projections = 'https://docs.google.com/spreadsheets/d/1MdzPFqIT0MFid2IhegWf39VNR8IXUyo_Fb5dolOSt3o/edit#gid=340831852'
+
+secondary_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1lP4t8N7UhjR94MEwPn6powRyLl_cQBDUMSCs6cbL9ms/edit#gid=340831852'
+secondary_fd_player_projections = 'https://docs.google.com/spreadsheets/d/1lP4t8N7UhjR94MEwPn6powRyLl_cQBDUMSCs6cbL9ms/edit#gid=340831852'
+
+all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=500994479'
+all_fd_player_projections = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=500994479'
+final_Proj = 0
+
+@st.cache_data
+def load_time():
+          sh = gc.open_by_url(dk_player_projections)
+          worksheet = sh.worksheet('Timestamp')
+          raw_stamp = worksheet.acell('a1').value
+          
+          t_stamp = f"Last update was at {raw_stamp}"
+          
+          return t_stamp
+
+@st.cache_data
+def set_slate_teams():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('Site_Info')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+
+    for checkVar in range(len(wrong_acro)):
+                    raw_display['FD Main'] = raw_display['FD Main'].replace(wrong_acro, right_acro)
+                    
+    for checkVar in range(len(wrong_acro)):
+                    raw_display['FD Secondary'] = raw_display['FD Secondary'].replace(wrong_acro, right_acro)
+                    
+    for checkVar in range(len(wrong_acro)):
+                    raw_display['FD Overall'] = raw_display['FD Overall'].replace(wrong_acro, right_acro)
+
+    return raw_display
+
+@st.cache_data
+def load_dk_player_projections(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('DK_Projections')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Median'])
+
+    return raw_display
+
+@st.cache_data
+def load_fd_player_projections(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('FD_Projections')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Median'])
+
+    return raw_display
+
+@st.cache_data
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+t_stamp = load_time()
+site_slates = set_slate_teams()
+col1, col2 = st.columns([1, 5])
+
+with col1:
+    st.info(t_stamp)
+    if st.button("Load/Reset Data", key='reset4'):
+          st.cache_data.clear()
+    slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'All Games'), key='slate_var1')
+    site_var2 = st.radio("What site are you playing?", ('Draftkings', 'Fanduel'), key='site_var2')
+    if slate_var1 == 'Main Slate':
+          if site_var2 == 'Draftkings':
+              slate_teams = site_slates['DK Main'].values.tolist()
+              raw_baselines = load_dk_player_projections(all_dk_player_projections)
+              raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+          elif site_var2 == 'Fanduel':
+              slate_teams = site_slates['FD Main'].values.tolist()
+              raw_baselines = load_fd_player_projections(all_fd_player_projections)
+              raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+    elif slate_var1 == 'Secondary Slate':
+          if site_var2 == 'Draftkings':
+              slate_teams = site_slates['DK Secondary'].values.tolist()
+              raw_baselines = load_dk_player_projections(all_dk_player_projections)
+              raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+          elif site_var2 == 'Fanduel':
+              slate_teams = site_slates['FD Secondary'].values.tolist()
+              raw_baselines = load_fd_player_projections(all_fd_player_projections)
+              raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+    elif slate_var1 == 'All Games':
+          if site_var2 == 'Draftkings':
+              slate_teams = site_slates['DK Overall'].values.tolist()
+              raw_baselines = load_dk_player_projections(all_dk_player_projections)
+              raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+          elif site_var2 == 'Fanduel':
+              slate_teams = site_slates['FD Overall'].values.tolist()
+              raw_baselines = load_fd_player_projections(all_fd_player_projections)
+              raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+    split_var2 = st.radio("Would you like to run stack analysis for the full slate or individual teams?", ('Full Slate Run', 'Specific Teams'), key='split_var2')
+    if split_var2 == 'Specific Teams':
+        team_var2 = st.multiselect('Which teams would you like to include in the analysis?', options = raw_baselines['Team'].unique(), key='team_var2')
+    elif split_var2 == 'Full Slate Run':
+        team_var2 = raw_baselines.Team.unique().tolist()
+    pos_split2 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split2')
+    if pos_split2 == 'Specific Positions':
+        pos_var2 = st.multiselect('What Positions would you like to view?', options = ['SP', 'P', 'C', '1B', '2B', '3B', 'SS', 'OF'])
+    elif pos_split2 == 'All Positions':
+        pos_var2 = 'All'
+    if site_var2 == 'Draftkings':
+        max_sal2 = st.number_input('Max Salary', min_value = 5000, max_value = 50000, value = 35000, step = 100, key='max_sal2')
+    elif site_var2 == 'Fanduel':
+        max_sal2 = st.number_input('Max Salary', min_value = 5000, max_value = 35000, value = 25000, step = 100, key='max_sal2')
+    size_var2 = st.selectbox('What size of stacks are you analyzing?', options = ['3-man', '4-man', '5-man'])
+    if size_var2 == '3-man':
+        stack_size = 3
+    if size_var2 == '4-man':
+        stack_size = 4
+    if size_var2 == '5-man':
+        stack_size = 5
+
+    team_dict = dict(zip(raw_baselines.Player, raw_baselines.Team))
+    proj_dict = dict(zip(raw_baselines.Player, raw_baselines.Median))
+    own_dict = dict(zip(raw_baselines.Player, raw_baselines.Own))
+    cost_dict = dict(zip(raw_baselines.Player, raw_baselines.Salary))
+
+with col2:
+    stack_hold_container = st.empty()
+    if st.button('Run stack analysis'):
+        comb_list = []
+        if pos_split2 == 'All Positions':
+            raw_baselines = raw_baselines
+        elif pos_split2 != 'All Positions':
+            raw_baselines = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var2))]
+
+        for cur_team in team_var2:
+            working_baselines = raw_baselines
+            working_baselines = working_baselines[working_baselines['Team'] == cur_team]
+            working_baselines = working_baselines[working_baselines['Position'] != 'SP']
+            working_baselines = working_baselines[working_baselines['Position'] != 'P']
+            order_list = working_baselines['Player']
+
+            comb = combinations(order_list, stack_size)
+
+            for i in list(comb):
+                comb_list.append(i)
+
+        comb_DF = pd.DataFrame(comb_list)
+
+        if stack_size == 3:
+            comb_DF['Team'] = comb_DF[0].map(team_dict)
+
+            comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                    comb_DF[1].map(proj_dict),
+                    comb_DF[2].map(proj_dict)])
+
+            comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                    comb_DF[1].map(cost_dict),
+                    comb_DF[2].map(cost_dict)])
+
+            comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                    comb_DF[1].map(own_dict),
+                    comb_DF[2].map(own_dict)])
+        elif stack_size == 4:
+            comb_DF['Team'] = comb_DF[0].map(team_dict)
+
+            comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                    comb_DF[1].map(proj_dict),
+                    comb_DF[2].map(proj_dict),
+                    comb_DF[3].map(proj_dict)])
+
+            comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                    comb_DF[1].map(cost_dict),
+                    comb_DF[2].map(cost_dict),
+                    comb_DF[3].map(cost_dict)])
+
+            comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                    comb_DF[1].map(own_dict),
+                    comb_DF[2].map(own_dict),
+                    comb_DF[3].map(own_dict)])
+        elif stack_size == 5:
+            comb_DF['Team'] = comb_DF[0].map(team_dict)
+
+            comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                    comb_DF[1].map(proj_dict),
+                    comb_DF[2].map(proj_dict),
+                    comb_DF[3].map(proj_dict),
+                    comb_DF[4].map(proj_dict)])
+
+            comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                    comb_DF[1].map(cost_dict),
+                    comb_DF[2].map(cost_dict),
+                    comb_DF[3].map(cost_dict),
+                    comb_DF[4].map(cost_dict)])
+
+            comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                    comb_DF[1].map(own_dict),
+                    comb_DF[2].map(own_dict),
+                    comb_DF[3].map(own_dict),
+                    comb_DF[4].map(own_dict)])
+
+        comb_DF = comb_DF.sort_values(by='Proj', ascending=False)
+        comb_DF = comb_DF.loc[comb_DF['Salary'] <= max_sal2]
+
+        cut_var = 0
+
+        if stack_size == 3:
+            while cut_var <= int(len(comb_DF)):
+                try:
+                    if int(cut_var) == 0:
+                        cur_proj = float(comb_DF.iat[cut_var,4])
+                        cur_own = float(comb_DF.iat[cut_var,6])
+                    elif int(cut_var) >= 1:
+                        check_own = float(comb_DF.iat[cut_var,6])
+                        if check_own > cur_own:
+                            comb_DF = comb_DF.drop([cut_var])
+                            cur_own = cur_own
+                            cut_var = cut_var - 1
+                            comb_DF = comb_DF.reset_index()
+                            comb_DF = comb_DF.drop(['index'], axis=1)
+                        elif check_own <= cur_own:
+                            cur_own = float(comb_DF.iat[cut_var,6])
+                            cut_var = cut_var
+                    cut_var += 1
+                except:
+                    cut_var += 1
+        elif stack_size == 4:
+            while cut_var <= int(len(comb_DF)):
+                try:
+                    if int(cut_var) == 0:
+                        cur_proj = float(comb_DF.iat[cut_var,5])
+                        cur_own = float(comb_DF.iat[cut_var,7])
+                    elif int(cut_var) >= 1:
+                        check_own = float(comb_DF.iat[cut_var,7])
+                        if check_own > cur_own:
+                            comb_DF = comb_DF.drop([cut_var])
+                            cur_own = cur_own
+                            cut_var = cut_var - 1
+                            comb_DF = comb_DF.reset_index()
+                            comb_DF = comb_DF.drop(['index'], axis=1)
+                        elif check_own <= cur_own:
+                            cur_own = float(comb_DF.iat[cut_var,7])
+                            cut_var = cut_var
+                    cut_var += 1
+                except:
+                    cut_var += 1
+        elif stack_size == 5:
+            while cut_var <= int(len(comb_DF)):
+                try:
+                    if int(cut_var) == 0:
+                        cur_proj = float(comb_DF.iat[cut_var,6])
+                        cur_own = float(comb_DF.iat[cut_var,8])
+                    elif int(cut_var) >= 1:
+                        check_own = float(comb_DF.iat[cut_var,8])
+                        if check_own > cur_own:
+                            comb_DF = comb_DF.drop([cut_var])
+                            cur_own = cur_own
+                            cut_var = cut_var - 1
+                            comb_DF = comb_DF.reset_index()
+                            comb_DF = comb_DF.drop(['index'], axis=1)
+                        elif check_own <= cur_own:
+                            cur_own = float(comb_DF.iat[cut_var,8])
+                            cut_var = cut_var
+                    cut_var += 1
+                except:
+                    cut_var += 1
+
+        with stack_hold_container:
+            stack_hold_container = st.empty()
+            st.dataframe(comb_DF.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(comb_DF),
+                file_name='MLB_Stack_Options_export.csv',
+                mime='text/csv',
+        )