Create app.py

app.py

@@ -0,0 +1,243 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+import plotly.figure_factory as ff
+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")
+
+master_hold = 'https://docs.google.com/spreadsheets/d/15flX6E7lPxu_HC7IOHpB3VEg2Am1AmtxTo9c2y_I-Mw/edit?gid=676575006#gid=676575006'
+
+@st.cache_resource(ttl = 300)
+def init_baselines():
+          sh = gc.open_by_url(master_hold)
+          worksheet = sh.worksheet('ADPs (model)')
+          adp_hold = pd.DataFrame(worksheet.get_all_records())
+          adp_hold = adp_hold[['Player', 'Team', 'Bye', 'Position', 'Position Rank', 'Underdog', 'MFL10', 'RTSPORTS', 'AVG', '2023 Proj', 'Proj ADP', 'Diff']]
+          adp_table = adp_hold.drop_duplicates(subset='Player')
+          
+          worksheet = sh.worksheet('Stacks (model)')
+          stacks_hold = pd.DataFrame(worksheet.get_all_records())
+          stacks_table = stacks_hold.drop_duplicates(subset='Team')
+          
+          worksheet = sh.worksheet('Player Level Projections')
+          proj_hold = pd.DataFrame(worksheet.get_all_records())
+          proj_table = proj_hold[['Player', 'Team', 'Pos', 'Pass', 'PassTD', 'Rush', 'RushTD', 'Receptions', 'Rec Yards', 'RecTD', 'Proj']]
+          
+          return adp_table, stacks_table, proj_table
+
+adp_table, stacks_table, proj_table = init_baselines()
+
+# tab1, tab2, tab3 = st.tabs(["ADPs and Ranks", "Team Projections", "Stack Tool", "Player Prop Simulations", "Stat Specific Simulations", "Bet Sheet"])
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+col1, col2 = st.columns([1, 5])
+
+with col1:
+    if st.button("Load/Reset Data", key='reset4'):
+          st.cache_data.clear()
+          adp_table, stacks_table, proj_table = init_baselines()
+    site_var2 = st.radio("What site are you playing?", ('Underdog', 'MFL10'), key='site_var2')
+    split_var2 = st.radio("Would you like to run stack analysis for the full slate or individual teams?", ('All Teams', '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 = adp_table['Team'].unique(), key='team_var2')
+    elif split_var2 == 'All Teams':
+        team_var2 = adp_table.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 = ['QB', 'RB', 'WR', 'TE'])
+    elif pos_split2 == 'All Positions':
+        pos_var2 = 'All'
+    if site_var2 == 'Underdog':
+        adp_dict = dict(zip(adp_table.Player, adp_table.Underdog))
+    elif site_var2 == 'MFL10':
+       adp_dict = dict(zip(adp_table.Player, adp_table.MFL10))
+    size_var2 = st.number_input('What size of stacks are you analyzing?', min_value = 3, max_value = 6, step=1)
+    stack_size = size_var2
+
+    team_dict = dict(zip(adp_table.Player, adp_table.Team))
+    proj_dict = dict(zip(adp_table.Player, adp_table.Median))
+    diff_dict = dict(zip(adp_table.Player, adp_table.Diff))
+
+with col2:
+    stack_hold_container = st.empty()
+    if st.button('Run stack analysis'):
+        comb_list = []
+        if pos_split2 == 'All Positions':
+            slate_teams = adp_table['Team'].values.tolist()
+            raw_baselines = adp_table.copy()
+        elif pos_split2 != 'All Positions':
+            slate_teams = adp_table['Team'].values.tolist()
+            raw_baselines = adp_table[adp_table['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]
+            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['ADPs'] = str(comb_DF[0].map(adp_dict)) +  ', ' + str(comb_DF[1].map(adp_dict)) +  ', ' + str(comb_DF[2].map(adp_dict))
+
+            comb_DF['Value'] = sum([comb_DF[0].map(diff_dict),
+                    comb_DF[1].map(diff_dict),
+                    comb_DF[2].map(diff_dict)]) * -1
+            
+        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['ADPs'] = str(comb_DF[0].map(adp_dict)) +  ', ' + str(comb_DF[1].map(adp_dict)) +  ', ' + str(comb_DF[2].map(adp_dict)) +  ', ' + str(comb_DF[3].map(adp_dict))
+
+            comb_DF['Value'] = sum([comb_DF[0].map(diff_dict),
+                    comb_DF[1].map(diff_dict),
+                    comb_DF[2].map(diff_dict),
+                    comb_DF[3].map(diff_dict)]) * -1
+            
+        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['ADPs'] = str(comb_DF[0].map(adp_dict)) +  ', ' + str(comb_DF[1].map(adp_dict)) +  ', ' + str(comb_DF[2].map(adp_dict)) +  ', ' + str(comb_DF[3].map(adp_dict)) +  ', ' + str(comb_DF[4].map(adp_dict))
+
+            comb_DF['Value'] = sum([comb_DF[0].map(diff_dict),
+                    comb_DF[1].map(diff_dict),
+                    comb_DF[2].map(diff_dict),
+                    comb_DF[3].map(diff_dict),
+                    comb_DF[4].map(diff_dict)]) * -1
+        
+        elif stack_size == 6:
+            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[5].map(proj_dict)])
+
+            comb_DF['ADPs'] = str(comb_DF[0].map(adp_dict)) +  ', ' + str(comb_DF[1].map(adp_dict)) +  ', ' + str(comb_DF[2].map(adp_dict)) +  ', ' + str(comb_DF[3].map(adp_dict)) +  ', ' + str(comb_DF[4].map(adp_dict)) +  ', ' + str(comb_DF[5].map(adp_dict))
+
+            comb_DF['Value'] = sum([comb_DF[0].map(diff_dict),
+                    comb_DF[1].map(diff_dict),
+                    comb_DF[2].map(diff_dict),
+                    comb_DF[3].map(diff_dict),
+                    comb_DF[4].map(diff_dict),
+                    comb_DF[5].map(diff_dict)]) * -1
+
+        comb_DF = comb_DF.sort_values(by='Proj', ascending=False)
+
+        # 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='NFL_Stack_Options_export.csv',
+                mime='text/csv',
+        )