Create app.py

app.py

@@ -0,0 +1,198 @@
+import pulp
+import numpy as np
+import pandas as pd
+import random
+import sys
+import openpyxl
+import re
+import time
+import streamlit as st
+import matplotlib
+from  matplotlib.colors import LinearSegmentedColormap
+from st_aggrid import GridOptionsBuilder, AgGrid, GridUpdateMode, DataReturnMode
+import json
+import requests
+import gspread
+import plotly.figure_factory as ff
+
+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")
+
+dk_player_url = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260'
+fd_player_url = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260'
+
+@st.cache_data
+def load_overall_stats(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.get_worksheet(8)
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    raw_display = raw_display.sort_values(by='Median', ascending=False)
+
+    return raw_display
+
+@st.cache_data
+def load_fd_overall_stats(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.get_worksheet(9)
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    raw_display = raw_display.sort_values(by='Median', ascending=False)
+
+    return raw_display
+
+raw_baselines = load_overall_stats(dk_player_url)
+
+tab1, tab2 = st.tabs(["Range of Outcomes Model", "Optimizer (Coming soon)"])
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+with tab1:
+
+    col1, col2 = st.columns([1, 5])
+
+    with col1:
+        if st.button("Load/Reset Data", key='reset1'):
+              st.cache_data.clear()
+              raw_baselines = load_overall_stats(dk_player_url)
+        site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1')
+        if site_var1 == 'Draftkings':
+            raw_baselines = load_overall_stats(dk_player_url)
+        elif site_var1 == 'Fanduel':
+            raw_baselines = load_fd_overall_stats(fd_player_url)
+        split_var1 = st.radio("Are you running the full slate or crtain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
+        if split_var1 == 'Specific Games':
+            team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
+        elif split_var1 == 'Full Slate Run':
+            team_var1 = raw_baselines.Team.values.tolist()
+        pos_var1 = st.selectbox('View specific position?', options = ['All', 'PG', 'SG', 'SF', 'PF', 'C'])
+
+    with col2:
+        hold_container = st.empty()
+        if st.button('Create Range of Outcomes for Slate'):
+            with hold_container:
+                        if site_var1 == 'Draftkings':
+                            raw_baselines = load_overall_stats(dk_player_url)
+                        elif site_var1 == 'Fanduel':
+                            raw_baselines = load_fd_overall_stats(fd_player_url)
+                        
+                        working_roo = raw_baselines
+                        working_roo = working_roo[working_roo['Team'].isin(team_var1)]
+                        own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                        min_dict = dict(zip(working_roo.Player, working_roo.Minutes))
+                        team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                        total_sims = 1000
+
+                        flex_file = working_roo[['Player', 'Position', 'Salary', 'Median']]
+                        flex_file.rename(columns={"Agg": "Median"}, inplace = True)
+                        flex_file['Floor'] = flex_file['Median']*.25
+                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Floor'])
+                        flex_file['STD'] = (flex_file['Median']/4)
+                        flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                        hold_file = flex_file
+                        overall_file = flex_file
+                        salary_file = flex_file
+
+                        overall_players = overall_file[['Player']]
+
+                        for x in range(0,total_sims):    
+                            salary_file[x] = salary_file['Salary']
+
+                        salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                        salary_file.astype('int').dtypes
+
+                        salary_file = salary_file.div(1000)
+
+                        for x in range(0,total_sims):    
+                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                        overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                        overall_file.astype('int').dtypes
+
+                        players_only = hold_file[['Player']]
+                        raw_lineups_file = players_only
+
+                        for x in range(0,total_sims):
+                            maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                            raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                            players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                        players_only=players_only.drop(['Player'], axis=1)
+                        players_only.astype('int').dtypes
+
+                        salary_2x_check = (overall_file - (salary_file*4))
+                        salary_3x_check = (overall_file - (salary_file*5))
+                        salary_4x_check = (overall_file - (salary_file*6))
+                        gpp_check = (overall_file - ((salary_file*5)+10))
+
+                        players_only['Average_Rank'] = players_only.mean(axis=1)
+                        players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                        players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                        players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                        players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                        players_only['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                        players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                        players_only['5x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+                        players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
+
+                        players_only['Player'] = hold_file[['Player']]
+
+                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
+
+                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                        final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
+
+                        final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                        final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
+                        final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                        final_Proj['Own'] = final_Proj['Own'].astype('float')
+                        final_Proj['LevX'] = ((final_Proj[['Top_finish', '4x%', 'Top_5_finish']].mean(axis=1))*100) - final_Proj['Own']
+                        final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
+
+                        final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%', 'Own', 'LevX', 'ValX']]
+                        final_Proj = final_Proj.set_index('Player')
+                        final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+            with hold_container:
+                hold_container = st.empty()
+                
+                if pos_var1 == 'All':
+                    final_Proj = final_Proj
+                elif pos_var1 != 'All':
+                    final_Proj = final_Proj[final_Proj['Position'].str.contains(pos_var1)]
+                st.dataframe(final_Proj.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(final_Proj),
+                        file_name='Custom_NBA_export.csv',
+                        mime='text/csv',
+            )
+
+with tab2:
+    st.info('Nothing here yet, but will be porting in a simple lineup optimizer soon')