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Upload app.py

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  1. app.py +1131 -0
app.py ADDED
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1
+ import streamlit as st
2
+ st.set_page_config(layout="wide")
3
+
4
+ for name in dir():
5
+ if not name.startswith('_'):
6
+ del globals()[name]
7
+
8
+ import numpy as np
9
+ import pandas as pd
10
+ import streamlit as st
11
+ import gspread
12
+
13
+ @st.cache_resource
14
+ def init_conn():
15
+ scope = ['https://www.googleapis.com/auth/spreadsheets',
16
+ "https://www.googleapis.com/auth/drive"]
17
+
18
+ credentials = {
19
+ "type": "service_account",
20
+ "project_id": "sheets-api-connect-378620",
21
+ "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
22
+ "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQCtKa01beXwc88R\nnPZVQTNPVQuBnbwoOfc66gW3547ja/UEyIGAF112dt/VqHprRafkKGmlg55jqJNt\na4zceLKV+wTm7vBu7lDISTJfGzCf2TrxQYNqwMKE2LOjI69dBM8u4Dcb4k0wcp9v\ntW1ZzLVVuwTvmrg7JBHjiSaB+x5wxm/r3FOiJDXdlAgFlytzqgcyeZMJVKKBQHyJ\njEGg/1720A0numuOCt71w/2G0bDmijuj1e6tH32MwRWcvRNZ19K9ssyDz2S9p68s\nYDhIxX69OWxwScTIHLY6J2t8txf/XMivL/636fPlDADvBEVTdlT606n8CcKUVQeq\npUVdG+lfAgMBAAECggEAP38SUA7B69eTfRpo658ycOs3Amr0JW4H/bb1rNeAul0K\nZhwd/HnU4E07y81xQmey5kN5ZeNrD5EvqkZvSyMJHV0EEahZStwhjCfnDB/cxyix\nZ+kFhv4y9eK+kFpUAhBy5nX6T0O+2T6WvzAwbmbVsZ+X8kJyPuF9m8ldcPlD0sce\ntj8NwVq1ys52eosqs7zi2vjt+eMcaY393l4ls+vNq8Yf27cfyFw45W45CH/97/Nu\n5AmuzlCOAfFF+z4OC5g4rei4E/Qgpxa7/uom+BVfv9G0DIGW/tU6Sne0+37uoGKt\nW6DzhgtebUtoYkG7ZJ05BTXGp2lwgVcNRoPwnKJDxQKBgQDT5wYPUBDW+FHbvZSp\nd1m1UQuXyerqOTA9smFaM8sr/UraeH85DJPEIEk8qsntMBVMhvD3Pw8uIUeFNMYj\naLmZFObsL+WctepXrVo5NB6RtLB/jZYxiKMatMLUJIYtcKIp+2z/YtKiWcLnwotB\nWdCjVnPTxpkurmF2fWP/eewZ+wKBgQDRMtJg7etjvKyjYNQ5fARnCc+XsI3gkBe1\nX9oeXfhyfZFeBXWnZzN1ITgFHplDznmBdxAyYGiQdbbkdKQSghviUQ0igBvoDMYy\n1rWcy+a17Mj98uyNEfmb3X2cC6WpvOZaGHwg9+GY67BThwI3FqHIbyk6Ko09WlTX\nQpRQjMzU7QKBgAfi1iflu+q0LR+3a3vvFCiaToskmZiD7latd9AKk2ocsBd3Woy9\n+hXXecJHPOKV4oUJlJgvAZqe5HGBqEoTEK0wyPNLSQlO/9ypd+0fEnArwFHO7CMF\nycQprAKHJXM1eOOFFuZeQCaInqdPZy1UcV5Szla4UmUZWkk1m24blHzXAoGBAMcA\nyH4qdbxX9AYrC1dvsSRvgcnzytMvX05LU0uF6tzGtG0zVlub4ahvpEHCfNuy44UT\nxRWW/oFFaWjjyFxO5sWggpUqNuHEnRopg3QXx22SRRTGbN45li/+QAocTkgsiRh1\nqEcYZsO4mPCsQqAy6E2p6RcK+Xa+omxvSnVhq0x1AoGAKr8GdkCl4CF6rieLMAQ7\nLNBuuoYGaHoh8l5E2uOQpzwxVy/nMBcAv+2+KqHEzHryUv1owOi6pMLv7A9mTFoS\n18B0QRLuz5fSOsVnmldfC9fpUc6H8cH1SINZpzajqQA74bPwELJjnzrCnH79TnHG\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
23
+ "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
24
+ "client_id": "106625872877651920064",
25
+ "auth_uri": "https://accounts.google.com/o/oauth2/auth",
26
+ "token_uri": "https://oauth2.googleapis.com/token",
27
+ "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
28
+ "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
29
+ }
30
+
31
+ gc = gspread.service_account_from_dict(credentials)
32
+ return gc
33
+
34
+ gc = init_conn()
35
+
36
+ game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
37
+ 'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
38
+
39
+ player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
40
+ '4x%': '{:.2%}','GPP%': '{:.2%}'}
41
+
42
+ freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
43
+
44
+ @st.cache_resource(ttl=600)
45
+ def load_dk_player_projections():
46
+ sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348')
47
+ worksheet = sh.worksheet('SD_Projections')
48
+ load_display = pd.DataFrame(worksheet.get_all_records())
49
+ load_display.rename(columns={"PPR": "Median", "name": "Player"}, inplace = True)
50
+ load_display['Floor'] = load_display['Median'] * .25
51
+ load_display['Ceiling'] = load_display['Median'] + (load_display['Median'] * .75)
52
+ load_display.replace('', np.nan, inplace=True)
53
+ raw_display = load_display.dropna(subset=['Median'])
54
+ del load_display
55
+
56
+ return raw_display
57
+
58
+ @st.cache_resource(ttl=600)
59
+ def load_fd_player_projections():
60
+ sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348')
61
+ worksheet = sh.worksheet('FD_SD_Projections')
62
+ load_display = pd.DataFrame(worksheet.get_all_records())
63
+ load_display.rename(columns={"Half_PPR": "Median", "name": "Player"}, inplace = True)
64
+ load_display['Floor'] = load_display['Median'] * .25
65
+ load_display['Ceiling'] = load_display['Median'] + (load_display['Median'] * .75)
66
+ load_display.replace('', np.nan, inplace=True)
67
+ raw_display = load_display.dropna(subset=['Median'])
68
+ del load_display
69
+
70
+ return raw_display
71
+
72
+ @st.cache_resource(ttl=600)
73
+ def load_dk_player_projections_2():
74
+ sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348')
75
+ worksheet = sh.worksheet('SD_Projections_2')
76
+ load_display = pd.DataFrame(worksheet.get_all_records())
77
+ load_display.rename(columns={"PPR": "Median", "name": "Player"}, inplace = True)
78
+ load_display['Floor'] = load_display['Median'] * .25
79
+ load_display['Ceiling'] = load_display['Median'] + (load_display['Median'] * .75)
80
+ load_display.replace('', np.nan, inplace=True)
81
+ raw_display = load_display.dropna(subset=['Median'])
82
+ del load_display
83
+
84
+ return raw_display
85
+
86
+ @st.cache_resource(ttl=600)
87
+ def load_fd_player_projections_2():
88
+ sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348')
89
+ worksheet = sh.worksheet('FD_SD_Projections_2')
90
+ load_display = pd.DataFrame(worksheet.get_all_records())
91
+ load_display.rename(columns={"Half_PPR": "Median", "name": "Player"}, inplace = True)
92
+ load_display['Floor'] = load_display['Median'] * .25
93
+ load_display['Ceiling'] = load_display['Median'] + (load_display['Median'] * .75)
94
+ load_display.replace('', np.nan, inplace=True)
95
+ raw_display = load_display.dropna(subset=['Median'])
96
+ del load_display
97
+
98
+ return raw_display
99
+
100
+ @st.cache_data
101
+ def convert_df_to_csv(df):
102
+ return df.to_csv().encode('utf-8')
103
+
104
+ def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs):
105
+ RunsVar = 1
106
+ seed_depth_def = seed_depth1
107
+ Strength_var_def = Strength_var
108
+ strength_grow_def = strength_grow
109
+ Teams_used_def = Teams_used
110
+ Total_Runs_def = Total_Runs
111
+ while RunsVar <= seed_depth_def:
112
+ if RunsVar <= 3:
113
+ FieldStrength = Strength_var_def
114
+ RandomPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
115
+ FinalPortfolio = RandomPortfolio
116
+ FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
117
+ FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
118
+ maps_dict.update(maps_dict2)
119
+ del FinalPortfolio2
120
+ del maps_dict2
121
+ elif RunsVar > 3 and RunsVar <= 4:
122
+ FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001))
123
+ FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
124
+ FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
125
+ FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
126
+ FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
127
+ FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
128
+ maps_dict.update(maps_dict3)
129
+ maps_dict.update(maps_dict4)
130
+ del FinalPortfolio3
131
+ del maps_dict3
132
+ del FinalPortfolio4
133
+ del maps_dict4
134
+ elif RunsVar > 4:
135
+ FieldStrength = 1
136
+ FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
137
+ FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
138
+ FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
139
+ FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
140
+ FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
141
+ maps_dict.update(maps_dict3)
142
+ maps_dict.update(maps_dict4)
143
+ del FinalPortfolio3
144
+ del maps_dict3
145
+ del FinalPortfolio4
146
+ del maps_dict4
147
+ RunsVar += 1
148
+
149
+ return FinalPortfolio, maps_dict
150
+
151
+ def create_overall_dfs(pos_players, table_name, dict_name, pos):
152
+ pos_players = pos_players.sort_values(by='Value', ascending=False)
153
+ table_name_raw = pos_players.reset_index(drop=True)
154
+ overall_table_name = table_name_raw.head(round(len(table_name_raw)))
155
+ overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name)))
156
+ overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict()
157
+
158
+ del pos_players
159
+ del table_name_raw
160
+
161
+ return overall_table_name, overall_dict_name
162
+
163
+
164
+ def get_overall_merged_df():
165
+ ref_dict = {
166
+ 'pos':['FLEX'],
167
+ 'pos_dfs':['FLEX_Table'],
168
+ 'pos_dicts':['flex_dict']
169
+ }
170
+
171
+ for i in range(0,1):
172
+ ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] =\
173
+ create_overall_dfs(pos_players, ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i], ref_dict['pos'][i])
174
+
175
+ df_out = pd.concat(ref_dict['pos_dfs'], ignore_index=True)
176
+
177
+ return df_out, ref_dict
178
+
179
+ def create_random_portfolio(Total_Sample_Size):
180
+
181
+ O_merge, full_pos_player_dict = get_overall_merged_df()
182
+ Overall_Merge = O_merge[['Var', 'Player', 'Team', 'Salary', 'Median', 'Own']].copy()
183
+
184
+ # Calculate Floor, Ceiling, and STDev directly
185
+ Overall_Merge['Floor'] = Overall_Merge['Median'] * .25
186
+ Overall_Merge['Ceiling'] = Overall_Merge['Median'] + Overall_Merge['Floor']
187
+ Overall_Merge['STDev'] = Overall_Merge['Median'] / 4
188
+
189
+ # Calculate the flex range and generate unique range list
190
+ flex_range_var = len(Overall_Merge)
191
+ ranges_dict = {'flex_range': flex_range_var}
192
+ ranges_dict['flex_Uniques'] = list(range(0, flex_range_var))
193
+
194
+ # Generate random portfolios
195
+ rng = np.random.default_rng()
196
+ all_choices = rng.choice(flex_range_var, size=(Total_Sample_Size, 6))
197
+
198
+ # Create RandomPortfolio DataFrame
199
+ RandomPortfolio = pd.DataFrame(all_choices, columns=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
200
+ RandomPortfolio['User/Field'] = 0
201
+
202
+ return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict
203
+
204
+ def get_correlated_portfolio_for_sim(Total_Sample_Size):
205
+
206
+ sizesplit = round(Total_Sample_Size * .50)
207
+
208
+ RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit)
209
+
210
+ RandomPortfolio['CPT'] = pd.Series(list(RandomPortfolio['CPT'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
211
+ RandomPortfolio['FLEX1'] = pd.Series(list(RandomPortfolio['FLEX1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
212
+ RandomPortfolio['FLEX2'] = pd.Series(list(RandomPortfolio['FLEX2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
213
+ RandomPortfolio['FLEX3'] = pd.Series(list(RandomPortfolio['FLEX3'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
214
+ RandomPortfolio['FLEX4'] = pd.Series(list(RandomPortfolio['FLEX4'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
215
+ RandomPortfolio['FLEX5'] = pd.Series(list(RandomPortfolio['FLEX5'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
216
+ RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
217
+ RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
218
+ RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 7].drop(columns=['plyr_list','plyr_count']).\
219
+ reset_index(drop=True)
220
+
221
+ del sizesplit
222
+ del full_pos_player_dict
223
+ del ranges_dict
224
+
225
+ RandomPortfolio['CPTs'] = RandomPortfolio['CPT'].map(maps_dict['Salary_map']).astype(np.int32) * 1.5
226
+ RandomPortfolio['FLEX1s'] = RandomPortfolio['FLEX1'].map(maps_dict['Salary_map']).astype(np.int32)
227
+ RandomPortfolio['FLEX2s'] = RandomPortfolio['FLEX2'].map(maps_dict['Salary_map']).astype(np.int32)
228
+ RandomPortfolio['FLEX3s'] = RandomPortfolio['FLEX3'].map(maps_dict['Salary_map']).astype(np.int32)
229
+ RandomPortfolio['FLEX4s'] = RandomPortfolio['FLEX4'].map(maps_dict['Salary_map']).astype(np.int32)
230
+ RandomPortfolio['FLEX5s'] = RandomPortfolio['FLEX5'].map(maps_dict['Salary_map']).astype(np.int32)
231
+
232
+ RandomPortfolio['CPTp'] = RandomPortfolio['CPT'].map(maps_dict['Projection_map']).astype(np.float16) * 1.5
233
+ RandomPortfolio['FLEX1p'] = RandomPortfolio['FLEX1'].map(maps_dict['Projection_map']).astype(np.float16)
234
+ RandomPortfolio['FLEX2p'] = RandomPortfolio['FLEX2'].map(maps_dict['Projection_map']).astype(np.float16)
235
+ RandomPortfolio['FLEX3p'] = RandomPortfolio['FLEX3'].map(maps_dict['Projection_map']).astype(np.float16)
236
+ RandomPortfolio['FLEX4p'] = RandomPortfolio['FLEX4'].map(maps_dict['Projection_map']).astype(np.float16)
237
+ RandomPortfolio['FLEX5p'] = RandomPortfolio['FLEX5'].map(maps_dict['Projection_map']).astype(np.float16)
238
+
239
+ RandomPortfolio['CPTo'] = RandomPortfolio['CPT'].map(maps_dict['Own_map']).astype(np.float16) / 4
240
+ RandomPortfolio['FLEX1o'] = RandomPortfolio['FLEX1'].map(maps_dict['Own_map']).astype(np.float16)
241
+ RandomPortfolio['FLEX2o'] = RandomPortfolio['FLEX2'].map(maps_dict['Own_map']).astype(np.float16)
242
+ RandomPortfolio['FLEX3o'] = RandomPortfolio['FLEX3'].map(maps_dict['Own_map']).astype(np.float16)
243
+ RandomPortfolio['FLEX4o'] = RandomPortfolio['FLEX4'].map(maps_dict['Own_map']).astype(np.float16)
244
+ RandomPortfolio['FLEX5o'] = RandomPortfolio['FLEX5'].map(maps_dict['Own_map']).astype(np.float16)
245
+
246
+ portHeaderList = RandomPortfolio.columns.values.tolist()
247
+ portHeaderList.append('Salary')
248
+ portHeaderList.append('Projection')
249
+ portHeaderList.append('Own')
250
+
251
+ RandomPortArray = RandomPortfolio.to_numpy()
252
+ del RandomPortfolio
253
+
254
+ RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,7:13].astype(int))]
255
+ RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,13:19].astype(np.double))]
256
+ RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:25].astype(np.double))]
257
+
258
+ RandomPortArrayOut = np.delete(RandomPortArray, np.s_[7:25], axis=1)
259
+ RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'User/Field', 'Salary', 'Projection', 'Own'])
260
+ RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
261
+ del RandomPortArray
262
+ del RandomPortArrayOut
263
+ # st.table(RandomPortfolioDF.head(50))
264
+
265
+ if insert_port == 1:
266
+ CleanPortfolio['Salary'] = sum([CleanPortfolio['CPT'].map(maps_dict['Salary_map']) * 1.5,
267
+ CleanPortfolio['FLEX1'].map(maps_dict['Salary_map']),
268
+ CleanPortfolio['FLEX2'].map(maps_dict['Salary_map']),
269
+ CleanPortfolio['FLEX3'].map(maps_dict['Salary_map']),
270
+ CleanPortfolio['FLEX4'].map(maps_dict['Salary_map']),
271
+ CleanPortfolio['FLEX5'].map(maps_dict['Salary_map'])
272
+ ]).astype(np.int16)
273
+ if insert_port == 1:
274
+ CleanPortfolio['Projection'] = sum([CleanPortfolio['CPT'].map(maps_dict['Projection_map']) * 1.5,
275
+ CleanPortfolio['FLEX1'].map(maps_dict['Projection_map']),
276
+ CleanPortfolio['FLEX2'].map(maps_dict['Projection_map']),
277
+ CleanPortfolio['FLEX3'].map(maps_dict['Projection_map']),
278
+ CleanPortfolio['FLEX4'].map(maps_dict['Projection_map']),
279
+ CleanPortfolio['FLEX5'].map(maps_dict['Projection_map'])
280
+ ]).astype(np.float16)
281
+ if insert_port == 1:
282
+ CleanPortfolio['Own'] = sum([CleanPortfolio['CPT'].map(maps_dict['own_map']) / 4,
283
+ CleanPortfolio['FLEX1'].map(maps_dict['own_map']),
284
+ CleanPortfolio['FLEX2'].map(maps_dict['own_map']),
285
+ CleanPortfolio['FLEX3'].map(maps_dict['own_map']),
286
+ CleanPortfolio['FLEX4'].map(maps_dict['own_map']),
287
+ CleanPortfolio['FLEX5'].map(maps_dict['own_map'])
288
+ ]).astype(np.float16)
289
+
290
+ if site_var1 == 'Draftkings':
291
+ RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True)
292
+ RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= 49500 - (FieldStrength * 1000)].reset_index(drop=True)
293
+ elif site_var1 == 'Fanduel':
294
+ RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True)
295
+ RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= 59500 - (FieldStrength * 1000)].reset_index(drop=True)
296
+
297
+ RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
298
+
299
+ RandomPortfolio = RandomPortfolio[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'User/Field', 'Salary', 'Projection', 'Own']]
300
+
301
+ return RandomPortfolio, maps_dict
302
+
303
+ def get_uncorrelated_portfolio_for_sim(Total_Sample_Size):
304
+
305
+ sizesplit = round(Total_Sample_Size * .50)
306
+
307
+ RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit)
308
+
309
+ RandomPortfolio['CPT'] = pd.Series(list(RandomPortfolio['CPT'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
310
+ RandomPortfolio['FLEX1'] = pd.Series(list(RandomPortfolio['FLEX1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
311
+ RandomPortfolio['FLEX2'] = pd.Series(list(RandomPortfolio['FLEX2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
312
+ RandomPortfolio['FLEX3'] = pd.Series(list(RandomPortfolio['FLEX3'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
313
+ RandomPortfolio['FLEX4'] = pd.Series(list(RandomPortfolio['FLEX4'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
314
+ RandomPortfolio['FLEX5'] = pd.Series(list(RandomPortfolio['FLEX5'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
315
+ RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
316
+ RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
317
+ RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 7].drop(columns=['plyr_list','plyr_count']).\
318
+ reset_index(drop=True)
319
+
320
+ del sizesplit
321
+ del full_pos_player_dict
322
+ del ranges_dict
323
+
324
+ RandomPortfolio['CPTs'] = RandomPortfolio['CPT'].map(maps_dict['Salary_map']).astype(np.int32) * 1.5
325
+ RandomPortfolio['FLEX1s'] = RandomPortfolio['FLEX1'].map(maps_dict['Salary_map']).astype(np.int32)
326
+ RandomPortfolio['FLEX2s'] = RandomPortfolio['FLEX2'].map(maps_dict['Salary_map']).astype(np.int32)
327
+ RandomPortfolio['FLEX3s'] = RandomPortfolio['FLEX3'].map(maps_dict['Salary_map']).astype(np.int32)
328
+ RandomPortfolio['FLEX4s'] = RandomPortfolio['FLEX4'].map(maps_dict['Salary_map']).astype(np.int32)
329
+ RandomPortfolio['FLEX5s'] = RandomPortfolio['FLEX5'].map(maps_dict['Salary_map']).astype(np.int32)
330
+
331
+ RandomPortfolio['CPTp'] = RandomPortfolio['CPT'].map(maps_dict['Projection_map']).astype(np.float16) * 1.5
332
+ RandomPortfolio['FLEX1p'] = RandomPortfolio['FLEX1'].map(maps_dict['Projection_map']).astype(np.float16)
333
+ RandomPortfolio['FLEX2p'] = RandomPortfolio['FLEX2'].map(maps_dict['Projection_map']).astype(np.float16)
334
+ RandomPortfolio['FLEX3p'] = RandomPortfolio['FLEX3'].map(maps_dict['Projection_map']).astype(np.float16)
335
+ RandomPortfolio['FLEX4p'] = RandomPortfolio['FLEX4'].map(maps_dict['Projection_map']).astype(np.float16)
336
+ RandomPortfolio['FLEX5p'] = RandomPortfolio['FLEX5'].map(maps_dict['Projection_map']).astype(np.float16)
337
+
338
+ RandomPortfolio['CPTo'] = RandomPortfolio['CPT'].map(maps_dict['Own_map']).astype(np.float16) / 4
339
+ RandomPortfolio['FLEX1o'] = RandomPortfolio['FLEX1'].map(maps_dict['Own_map']).astype(np.float16)
340
+ RandomPortfolio['FLEX2o'] = RandomPortfolio['FLEX2'].map(maps_dict['Own_map']).astype(np.float16)
341
+ RandomPortfolio['FLEX3o'] = RandomPortfolio['FLEX3'].map(maps_dict['Own_map']).astype(np.float16)
342
+ RandomPortfolio['FLEX4o'] = RandomPortfolio['FLEX4'].map(maps_dict['Own_map']).astype(np.float16)
343
+ RandomPortfolio['FLEX5o'] = RandomPortfolio['FLEX5'].map(maps_dict['Own_map']).astype(np.float16)
344
+
345
+ portHeaderList = RandomPortfolio.columns.values.tolist()
346
+ portHeaderList.append('Salary')
347
+ portHeaderList.append('Projection')
348
+ portHeaderList.append('Own')
349
+
350
+ RandomPortArray = RandomPortfolio.to_numpy()
351
+ del RandomPortfolio
352
+
353
+ RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,7:13].astype(int))]
354
+ RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,13:19].astype(np.double))]
355
+ RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:25].astype(np.double))]
356
+
357
+ RandomPortArrayOut = np.delete(RandomPortArray, np.s_[7:25], axis=1)
358
+ RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'User/Field', 'Salary', 'Projection', 'Own'])
359
+ RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
360
+ del RandomPortArray
361
+ del RandomPortArrayOut
362
+ # st.table(RandomPortfolioDF.head(50))
363
+
364
+ if insert_port == 1:
365
+ CleanPortfolio['Salary'] = sum([CleanPortfolio['CPT'].map(maps_dict['Salary_map']) * 1.5,
366
+ CleanPortfolio['FLEX1'].map(maps_dict['Salary_map']),
367
+ CleanPortfolio['FLEX2'].map(maps_dict['Salary_map']),
368
+ CleanPortfolio['FLEX3'].map(maps_dict['Salary_map']),
369
+ CleanPortfolio['FLEX4'].map(maps_dict['Salary_map']),
370
+ CleanPortfolio['FLEX5'].map(maps_dict['Salary_map'])
371
+ ]).astype(np.int16)
372
+ if insert_port == 1:
373
+ CleanPortfolio['Projection'] = sum([CleanPortfolio['CPT'].map(maps_dict['Projection_map']) * 1.5,
374
+ CleanPortfolio['FLEX1'].map(maps_dict['Projection_map']),
375
+ CleanPortfolio['FLEX2'].map(maps_dict['Projection_map']),
376
+ CleanPortfolio['FLEX3'].map(maps_dict['Projection_map']),
377
+ CleanPortfolio['FLEX4'].map(maps_dict['Projection_map']),
378
+ CleanPortfolio['FLEX5'].map(maps_dict['Projection_map'])
379
+ ]).astype(np.float16)
380
+ if insert_port == 1:
381
+ CleanPortfolio['Own'] = sum([CleanPortfolio['CPT'].map(maps_dict['own_map']) / 4,
382
+ CleanPortfolio['FLEX1'].map(maps_dict['own_map']),
383
+ CleanPortfolio['FLEX2'].map(maps_dict['own_map']),
384
+ CleanPortfolio['FLEX3'].map(maps_dict['own_map']),
385
+ CleanPortfolio['FLEX4'].map(maps_dict['own_map']),
386
+ CleanPortfolio['FLEX5'].map(maps_dict['own_map'])
387
+ ]).astype(np.float16)
388
+
389
+ if site_var1 == 'Draftkings':
390
+ RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True)
391
+ RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= 49500 - (FieldStrength * 1000)].reset_index(drop=True)
392
+ elif site_var1 == 'Fanduel':
393
+ RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True)
394
+ RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= 59500 - (FieldStrength * 1000)].reset_index(drop=True)
395
+
396
+ RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
397
+
398
+ RandomPortfolio = RandomPortfolio[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'User/Field', 'Salary', 'Projection', 'Own']]
399
+
400
+ return RandomPortfolio, maps_dict
401
+
402
+ dk_roo_raw = load_dk_player_projections()
403
+ dk_roo_raw_2 = load_dk_player_projections_2()
404
+ fd_roo_raw = load_fd_player_projections()
405
+ fd_roo_raw_2 = load_fd_player_projections_2()
406
+
407
+ static_exposure = pd.DataFrame(columns=['Player', 'count'])
408
+ overall_exposure = pd.DataFrame(columns=['Player', 'count'])
409
+
410
+ tab1, tab2 = st.tabs(['Uploads', 'Contest Sim'])
411
+
412
+ with tab1:
413
+ with st.container():
414
+ st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'. Upload your projections first to avoid an error message.")
415
+ col1, col2 = st.columns([3, 3])
416
+
417
+ with col1:
418
+ proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
419
+
420
+ if proj_file is not None:
421
+ try:
422
+ proj_dataframe = pd.read_csv(proj_file)
423
+ proj_dataframe = proj_dataframe.dropna(subset='Median')
424
+ except:
425
+ proj_dataframe = pd.read_excel(proj_file)
426
+ proj_dataframe = proj_dataframe.dropna(subset='Median')
427
+
428
+ player_salary_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Salary))
429
+ player_proj_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Median))
430
+ player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own))
431
+ player_team_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Team))
432
+
433
+ with col2:
434
+ portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader')
435
+
436
+ if portfolio_file is not None:
437
+ try:
438
+ portfolio_dataframe = pd.read_csv(portfolio_file)
439
+ except:
440
+ portfolio_dataframe = pd.read_excel(portfolio_file)
441
+ try:
442
+ try:
443
+ portfolio_dataframe.columns=["CPT", "FLEX1", "FLEX2", "FLEX3", "FLEX4", "FLEX5"]
444
+ split_portfolio = portfolio_dataframe
445
+ split_portfolio[['CPT', 'CPT_ID']] = split_portfolio.CPT.str.split("(", n=1, expand = True)
446
+ split_portfolio[['FLEX1', 'FLEX1_ID']] = split_portfolio.FLEX1.str.split("(", n=1, expand = True)
447
+ split_portfolio[['FLEX2', 'FLEX2_ID']] = split_portfolio.FLEX2.str.split("(", n=1, expand = True)
448
+ split_portfolio[['FLEX3', 'FLEX3_ID']] = split_portfolio.FLEX3.str.split("(", n=1, expand = True)
449
+ split_portfolio[['FLEX4', 'FLEX4_ID']] = split_portfolio.FLEX4.str.split("(", n=1, expand = True)
450
+ split_portfolio[['FLEX5', 'FLEX5_ID']] = split_portfolio.FLEX5.str.split("(", n=1, expand = True)
451
+
452
+ split_portfolio['CPT'] = split_portfolio['CPT'].str.strip()
453
+ split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
454
+ split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
455
+ split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
456
+ split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
457
+ split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip()
458
+
459
+ CPT_dict = dict(zip(split_portfolio.CPT, split_portfolio.CPT_ID))
460
+ FLEX1_dict = dict(zip(split_portfolio.FLEX1, split_portfolio.FLEX1_ID))
461
+ FLEX2_dict = dict(zip(split_portfolio.FLEX2, split_portfolio.FLEX2_ID))
462
+ FLEX3_dict = dict(zip(split_portfolio.FLEX3, split_portfolio.FLEX3_ID))
463
+ FLEX4_dict = dict(zip(split_portfolio.FLEX4, split_portfolio.FLEX4_ID))
464
+ FLEX5_dict = dict(zip(split_portfolio.FLEX5, split_portfolio.FLEX5_ID))
465
+
466
+ split_portfolio['Salary'] = sum([split_portfolio['CPT'].map(player_salary_dict) * 1.5,
467
+ split_portfolio['FLEX1'].map(player_salary_dict),
468
+ split_portfolio['FLEX2'].map(player_salary_dict),
469
+ split_portfolio['FLEX3'].map(player_salary_dict),
470
+ split_portfolio['FLEX4'].map(player_salary_dict),
471
+ split_portfolio['FLEX5'].map(player_salary_dict)])
472
+
473
+ del player_salary_dict
474
+
475
+ split_portfolio['Projection'] = sum([split_portfolio['CPT'].map(player_proj_dict) * 1.5,
476
+ split_portfolio['FLEX1'].map(player_proj_dict),
477
+ split_portfolio['FLEX2'].map(player_proj_dict),
478
+ split_portfolio['FLEX3'].map(player_proj_dict),
479
+ split_portfolio['FLEX4'].map(player_proj_dict),
480
+ split_portfolio['FLEX5'].map(player_proj_dict)])
481
+
482
+ del player_proj_dict
483
+
484
+ split_portfolio['Ownership'] = sum([split_portfolio['CPT'].map(player_own_dict) / 4,
485
+ split_portfolio['FLEX1'].map(player_own_dict),
486
+ split_portfolio['FLEX2'].map(player_own_dict),
487
+ split_portfolio['FLEX3'].map(player_own_dict),
488
+ split_portfolio['FLEX4'].map(player_own_dict),
489
+ split_portfolio['FLEX5'].map(player_own_dict)])
490
+
491
+ del player_own_dict
492
+
493
+ split_portfolio['CPT_team'] = split_portfolio['CPT'].map(player_team_dict)
494
+ split_portfolio['FLEX1_team'] = split_portfolio['FLEX1'].map(player_team_dict)
495
+ split_portfolio['FLEX2_team'] = split_portfolio['FLEX2'].map(player_team_dict)
496
+ split_portfolio['FLEX3_team'] = split_portfolio['FLEX3'].map(player_team_dict)
497
+ split_portfolio['FLEX4_team'] = split_portfolio['FLEX4'].map(player_team_dict)
498
+ split_portfolio['FLEX5_team'] = split_portfolio['FLEX5'].map(player_team_dict)
499
+
500
+ split_portfolio = split_portfolio[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'Salary', 'Projection', 'Ownership', 'CPT_team',
501
+ 'FLEX1_team', 'FLEX2_team', 'FLEX3_team', 'FLEX4_team', 'FLEX5_team']]
502
+
503
+ split_portfolio['Main_Stack'] = 0
504
+ split_portfolio['Main_Stack_Size'] = 0
505
+ split_portfolio['Main_Stack_Size'] = 0
506
+ except:
507
+ portfolio_dataframe.columns=["CPT", "FLEX1", "FLEX2", "FLEX3", "FLEX4", "FLEX5"]
508
+ split_portfolio = portfolio_dataframe
509
+ split_portfolio[['CPT_ID', 'CPT']] = split_portfolio.CPT.str.split(":", n=1, expand = True)
510
+ split_portfolio[['FLEX1_ID', 'FLEX1']] = split_portfolio.FLEX1.str.split(":", n=1, expand = True)
511
+ split_portfolio[['FLEX2_ID', 'FLEX2']] = split_portfolio.FLEX2.str.split(":", n=1, expand = True)
512
+ split_portfolio[['FLEX3_ID', 'FLEX3']] = split_portfolio.FLEX3.str.split(":", n=1, expand = True)
513
+ split_portfolio[['FLEX4_ID', 'FLEX4']] = split_portfolio.FLEX4.str.split(":", n=1, expand = True)
514
+ split_portfolio[['FLEX5_ID', 'FLEX5']] = split_portfolio.FLEX5.str.split(":", n=1, expand = True)
515
+
516
+ split_portfolio['CPT'] = split_portfolio['CPT'].str.strip()
517
+ split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
518
+ split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
519
+ split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
520
+ split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
521
+ split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip()
522
+
523
+ CPT_dict = dict(zip(split_portfolio.CPT, split_portfolio.CPT_ID))
524
+ FLEX1_dict = dict(zip(split_portfolio.FLEX1, split_portfolio.FLEX1_ID))
525
+ FLEX2_dict = dict(zip(split_portfolio.FLEX2, split_portfolio.FLEX2_ID))
526
+ FLEX3_dict = dict(zip(split_portfolio.FLEX3, split_portfolio.FLEX3_ID))
527
+ FLEX4_dict = dict(zip(split_portfolio.FLEX4, split_portfolio.FLEX4_ID))
528
+ FLEX5_dict = dict(zip(split_portfolio.FLEX5, split_portfolio.FLEX5_ID))
529
+
530
+ split_portfolio['Salary'] = sum([split_portfolio['CPT'].map(player_salary_dict),
531
+ split_portfolio['FLEX1'].map(player_salary_dict),
532
+ split_portfolio['FLEX2'].map(player_salary_dict),
533
+ split_portfolio['FLEX3'].map(player_salary_dict),
534
+ split_portfolio['FLEX4'].map(player_salary_dict),
535
+ split_portfolio['FLEX5'].map(player_salary_dict)])
536
+
537
+ del player_salary_dict
538
+
539
+ split_portfolio['Projection'] = sum([split_portfolio['CPT'].map(player_proj_dict) * 1.5,
540
+ split_portfolio['FLEX1'].map(player_proj_dict),
541
+ split_portfolio['FLEX2'].map(player_proj_dict),
542
+ split_portfolio['FLEX3'].map(player_proj_dict),
543
+ split_portfolio['FLEX4'].map(player_proj_dict),
544
+ split_portfolio['FLEX5'].map(player_proj_dict)])
545
+
546
+ del player_proj_dict
547
+
548
+ split_portfolio['Ownership'] = sum([split_portfolio['CPT'].map(player_own_dict) / 4,
549
+ split_portfolio['FLEX1'].map(player_own_dict),
550
+ split_portfolio['FLEX2'].map(player_own_dict),
551
+ split_portfolio['FLEX3'].map(player_own_dict),
552
+ split_portfolio['FLEX4'].map(player_own_dict),
553
+ split_portfolio['FLEX5'].map(player_own_dict)])
554
+
555
+ del player_own_dict
556
+
557
+ split_portfolio['CPT_team'] = split_portfolio['CPT'].map(player_team_dict)
558
+ split_portfolio['FLEX1_team'] = split_portfolio['FLEX1'].map(player_team_dict)
559
+ split_portfolio['FLEX2_team'] = split_portfolio['FLEX2'].map(player_team_dict)
560
+ split_portfolio['FLEX3_team'] = split_portfolio['FLEX3'].map(player_team_dict)
561
+ split_portfolio['FLEX4_team'] = split_portfolio['FLEX4'].map(player_team_dict)
562
+ split_portfolio['FLEX5_team'] = split_portfolio['FLEX5'].map(player_team_dict)
563
+
564
+ split_portfolio = split_portfolio[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'Salary', 'Projection', 'Ownership', 'CPT_team',
565
+ 'FLEX1_team', 'FLEX2_team', 'FLEX3_team', 'FLEX4_team', 'FLEX5_team']]
566
+
567
+ split_portfolio['Main_Stack'] = 0
568
+ split_portfolio['Main_Stack_Size'] = 0
569
+ split_portfolio['Main_Stack_Size'] = 0
570
+ except:
571
+ split_portfolio = portfolio_dataframe
572
+
573
+ split_portfolio['CPT'] = split_portfolio['CPT'].str[:-6]
574
+ split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str[:-6]
575
+ split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str[:-6]
576
+ split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str[:-6]
577
+ split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str[:-6]
578
+ split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str[:-6]
579
+
580
+ split_portfolio['CPT'] = split_portfolio['CPT'].str.strip()
581
+ split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
582
+ split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
583
+ split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
584
+ split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
585
+ split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip()
586
+
587
+ split_portfolio['Salary'] = sum([split_portfolio['CPT'].map(player_salary_dict) * 1.5,
588
+ split_portfolio['FLEX1'].map(player_salary_dict),
589
+ split_portfolio['FLEX2'].map(player_salary_dict),
590
+ split_portfolio['FLEX3'].map(player_salary_dict),
591
+ split_portfolio['FLEX4'].map(player_salary_dict),
592
+ split_portfolio['FLEX5'].map(player_salary_dict)])
593
+
594
+ del player_salary_dict
595
+
596
+ split_portfolio['Projection'] = sum([split_portfolio['CPT'].map(player_proj_dict) * 1.5,
597
+ split_portfolio['FLEX1'].map(player_proj_dict),
598
+ split_portfolio['FLEX2'].map(player_proj_dict),
599
+ split_portfolio['FLEX3'].map(player_proj_dict),
600
+ split_portfolio['FLEX4'].map(player_proj_dict),
601
+ split_portfolio['FLEX5'].map(player_proj_dict)])
602
+
603
+ del player_proj_dict
604
+
605
+ split_portfolio['Ownership'] = sum([split_portfolio['CPT'].map(player_own_dict) / 4,
606
+ split_portfolio['FLEX1'].map(player_own_dict),
607
+ split_portfolio['FLEX2'].map(player_own_dict),
608
+ split_portfolio['FLEX3'].map(player_own_dict),
609
+ split_portfolio['FLEX4'].map(player_own_dict),
610
+ split_portfolio['FLEX5'].map(player_own_dict)])
611
+
612
+ del player_own_dict
613
+
614
+ split_portfolio['CPT_team'] = split_portfolio['CPT'].map(player_team_dict)
615
+ split_portfolio['FLEX1_team'] = split_portfolio['FLEX1'].map(player_team_dict)
616
+ split_portfolio['FLEX2_team'] = split_portfolio['FLEX2'].map(player_team_dict)
617
+ split_portfolio['FLEX3_team'] = split_portfolio['FLEX3'].map(player_team_dict)
618
+ split_portfolio['FLEX4_team'] = split_portfolio['FLEX4'].map(player_team_dict)
619
+ split_portfolio['FLEX5_team'] = split_portfolio['FLEX5'].map(player_team_dict)
620
+
621
+ split_portfolio = split_portfolio[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'Salary', 'Projection', 'Ownership', 'CPT_team',
622
+ 'FLEX1_team', 'FLEX2_team', 'FLEX3_team', 'FLEX4_team', 'FLEX5_team']]
623
+
624
+ split_portfolio['Main_Stack'] = 0
625
+ split_portfolio['Main_Stack_Size'] = 0
626
+ split_portfolio['Main_Stack_Size'] = 0
627
+
628
+ for player_cols in split_portfolio.iloc[:, 0:6]:
629
+ static_col_raw = split_portfolio[player_cols].value_counts()
630
+ static_col = static_col_raw.to_frame()
631
+ static_col.reset_index(inplace=True)
632
+ static_col.columns = ['Player', 'count']
633
+ static_exposure = pd.concat([static_exposure, static_col], ignore_index=True)
634
+ static_exposure['Exposure'] = static_exposure['count'] / len(split_portfolio)
635
+ static_exposure = static_exposure[['Player', 'Exposure']]
636
+
637
+ del static_col_raw
638
+ del static_col
639
+ with st.container():
640
+ col1, col2 = st.columns([3, 3])
641
+
642
+ if portfolio_file is not None:
643
+ with col1:
644
+ st.write(len(portfolio_dataframe))
645
+ team_split_var1 = st.radio("Are you wanting to isolate any lineups with specific main stacks?", ('Full Portfolio', 'Specific Stacks'))
646
+ if team_split_var1 == 'Specific Stacks':
647
+ team_var1 = st.multiselect('Which main stacks would you like to include in the Portfolio?', options = split_portfolio['Main_Stack'].unique())
648
+ elif team_split_var1 == 'Full Portfolio':
649
+ team_var1 = split_portfolio.Main_Stack.values.tolist()
650
+ with col2:
651
+ player_split_var1 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'))
652
+ if player_split_var1 == 'Specific Players':
653
+ find_var1 = st.multiselect('Which players must be included in the lineups?', options = static_exposure['Player'].unique())
654
+ elif player_split_var1 == 'Full Players':
655
+ find_var1 = static_exposure.Player.values.tolist()
656
+
657
+ split_portfolio = split_portfolio[split_portfolio['Main_Stack'].isin(team_var1)]
658
+ if player_split_var1 == 'Specific Players':
659
+ split_portfolio = split_portfolio[np.equal.outer(split_portfolio.to_numpy(copy=False), find_var1).any(axis=1).all(axis=1)]
660
+ elif player_split_var1 == 'Full Players':
661
+ split_portfolio = split_portfolio
662
+
663
+ for player_cols in split_portfolio.iloc[:, 0:6]:
664
+ exposure_col_raw = split_portfolio[player_cols].value_counts()
665
+ exposure_col = exposure_col_raw.to_frame()
666
+ exposure_col.reset_index(inplace=True)
667
+ exposure_col.columns = ['Player', 'count']
668
+ overall_exposure = pd.concat([overall_exposure, exposure_col], ignore_index=True)
669
+ overall_exposure['Exposure'] = overall_exposure['count'] / len(split_portfolio)
670
+ overall_exposure = overall_exposure.groupby('Player').sum()
671
+ overall_exposure.reset_index(inplace=True)
672
+ overall_exposure = overall_exposure[['Player', 'Exposure']]
673
+ overall_exposure = overall_exposure.set_index('Player')
674
+ overall_exposure = overall_exposure.sort_values(by='Exposure', ascending=False)
675
+ overall_exposure['Exposure'] = overall_exposure['Exposure'].astype(float).map(lambda n: '{:.2%}'.format(n))
676
+
677
+ with st.container():
678
+ col1, col2 = st.columns([1, 6])
679
+
680
+ with col1:
681
+ if portfolio_file is not None:
682
+ st.header('Exposure View')
683
+ st.dataframe(overall_exposure)
684
+
685
+ with col2:
686
+ if portfolio_file is not None:
687
+ st.header('Portfolio View')
688
+ split_portfolio = split_portfolio.reset_index()
689
+ split_portfolio['Lineup'] = split_portfolio['index'] + 1
690
+ display_portfolio = split_portfolio[['Lineup', 'CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'Salary', 'Main_Stack', 'Main_Stack_Size', 'Projection', 'Ownership']]
691
+ hold_display = display_portfolio
692
+ display_portfolio = display_portfolio.set_index('Lineup')
693
+ st.dataframe(display_portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Ownership']).format(precision=2))
694
+ del split_portfolio
695
+ del exposure_col_raw
696
+ del exposure_col
697
+ with tab2:
698
+ col1, col2 = st.columns([1, 5])
699
+ with col1:
700
+ if st.button("Load/Reset Data", key='reset1'):
701
+ st.cache_data.clear()
702
+ dk_roo_raw = load_dk_player_projections()
703
+ dk_roo_raw_2 = load_dk_player_projections_2()
704
+ fd_roo_raw = load_fd_player_projections()
705
+ fd_roo_raw_2 = load_fd_player_projections_2()
706
+
707
+ slate_var1 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'User'))
708
+ site_var1 = 'Draftkings'
709
+ if site_var1 == 'Draftkings':
710
+ if slate_var1 == 'User':
711
+ raw_baselines = proj_dataframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
712
+ elif slate_var1 == 'Paydirt (Main)':
713
+ raw_baselines = dk_roo_raw
714
+ elif slate_var1 == 'Paydirt (Secondary)':
715
+ raw_baselines = dk_roo_raw_2
716
+ elif site_var1 == 'Fanduel':
717
+ if slate_var1 == 'User':
718
+ raw_baselines = proj_dataframe
719
+ elif slate_var1 == 'Paydirt (Main)':
720
+ raw_baselines = dk_roo_raw
721
+ elif slate_var1 == 'Paydirt (Secondary)':
722
+ raw_baselines = dk_roo_raw_2
723
+ st.info("If you are uploading a portfolio, note that there is an adjustments to projections and deviation mapping to prevent 'Projection Bias' and create a fair simulation")
724
+ insert_port1 = st.selectbox("Are you uploading a portfolio?", ('No', 'Yes'))
725
+ if insert_port1 == 'Yes':
726
+ insert_port = 1
727
+ elif insert_port1 == 'No':
728
+ insert_port = 0
729
+ contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
730
+ if contest_var1 == 'Small':
731
+ Contest_Size = 500
732
+ elif contest_var1 == 'Medium':
733
+ Contest_Size = 2500
734
+ elif contest_var1 == 'Large':
735
+ Contest_Size = 10000
736
+ linenum_var1 = 1000
737
+ strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very'))
738
+ if strength_var1 == 'Not Very':
739
+ Strength_var = 1
740
+ scaling_var = 5
741
+ elif strength_var1 == 'Average':
742
+ Strength_var = .75
743
+ scaling_var = 10
744
+ elif strength_var1 == 'Very':
745
+ Strength_var = .5
746
+ scaling_var = 15
747
+
748
+ with col2:
749
+ if st.button("Simulate Contest", key='sim1'):
750
+ try:
751
+ del dst_freq
752
+ del flex_freq
753
+ del te_freq
754
+ del wr_freq
755
+ del rb_freq
756
+ del qb_freq
757
+ del player_freq
758
+ del Sim_Winner_Export
759
+ del Sim_Winner_Frame
760
+ except:
761
+ pass
762
+ with st.container():
763
+ st.write('Contest Simulation Starting')
764
+ Total_Runs = 1000000
765
+ seed_depth1 = 5
766
+ Total_Runs = 2500000
767
+ if Contest_Size <= 1000:
768
+ strength_grow = .01
769
+ elif Contest_Size > 1000 and Contest_Size <= 2500:
770
+ strength_grow = .025
771
+ elif Contest_Size > 2500 and Contest_Size <= 5000:
772
+ strength_grow = .05
773
+ elif Contest_Size > 5000 and Contest_Size <= 20000:
774
+ strength_grow = .075
775
+ elif Contest_Size > 20000:
776
+ strength_grow = .1
777
+
778
+ field_growth = 100 * strength_grow
779
+
780
+ Sort_function = 'Median'
781
+ if Sort_function == 'Median':
782
+ Sim_function = 'Projection'
783
+ elif Sort_function == 'Own':
784
+ Sim_function = 'Own'
785
+
786
+ if slate_var1 == 'User':
787
+ OwnFrame = proj_dataframe
788
+ if contest_var1 == 'Large':
789
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
790
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
791
+ OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
792
+ OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
793
+ if contest_var1 == 'Medium':
794
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
795
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
796
+ OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
797
+ OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
798
+ if contest_var1 == 'Small':
799
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
800
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
801
+ OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
802
+ OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
803
+ Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
804
+
805
+ del OwnFrame
806
+
807
+ elif slate_var1 != 'User':
808
+ initial_proj = raw_baselines
809
+ drop_frame = initial_proj.drop_duplicates(subset = 'Player',keep = 'first')
810
+ OwnFrame = drop_frame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
811
+ if contest_var1 == 'Large':
812
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
813
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
814
+ OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
815
+ OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
816
+ if contest_var1 == 'Medium':
817
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
818
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
819
+ OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
820
+ OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
821
+ if contest_var1 == 'Small':
822
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
823
+ OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
824
+ OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
825
+ OwnFrame['Own'] = OwnFrame['Own%'] * (500 / OwnFrame['Own%'].sum())
826
+ Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
827
+
828
+ del initial_proj
829
+ del drop_frame
830
+ del OwnFrame
831
+
832
+ if insert_port == 1:
833
+ UserPortfolio = portfolio_dataframe[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']]
834
+ elif insert_port == 0:
835
+ UserPortfolio = pd.DataFrame(columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
836
+
837
+ Overall_Proj.replace('', np.nan, inplace=True)
838
+ Overall_Proj = Overall_Proj.dropna(subset=['Median'])
839
+ Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000)))
840
+ Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2
841
+ Overall_Proj = Overall_Proj.sort_values(by='Sort_var', ascending=False)
842
+ Overall_Proj['Own'] = np.where((Overall_Proj['Median'] > 0) & (Overall_Proj['Own'] == 0), 1, Overall_Proj['Own'])
843
+ Overall_Proj = Overall_Proj.loc[Overall_Proj['Own'] > 0]
844
+
845
+ Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'QB', Overall_Proj['Median'] * .5, Overall_Proj['Median'] * .25)
846
+ Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'WR', Overall_Proj['Median'] + Overall_Proj['Median'], Overall_Proj['Median'] + Overall_Proj['Floor'])
847
+ Overall_Proj['STDev'] = Overall_Proj['Median'] / 4
848
+
849
+ Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True)
850
+ Teams_used = Teams_used.reset_index()
851
+ Teams_used['team_item'] = Teams_used['index'] + 1
852
+ Teams_used = Teams_used.drop(columns=['index'])
853
+ Teams_used_dictraw = Teams_used.drop(columns=['team_item'])
854
+ Teams_used_dict = Teams_used_dictraw.to_dict()
855
+
856
+ del Teams_used_dictraw
857
+
858
+ team_list = Teams_used['Team'].to_list()
859
+ item_list = Teams_used['team_item'].to_list()
860
+
861
+ FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01)
862
+ FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size))
863
+
864
+ del FieldStrength_raw
865
+
866
+ if FieldStrength < 0:
867
+ FieldStrength = Strength_var
868
+ field_split = Strength_var
869
+
870
+ for checkVar in range(len(team_list)):
871
+ Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list)
872
+
873
+ flex_raw = Overall_Proj
874
+ flex_raw.dropna(subset=['Median']).reset_index(drop=True)
875
+ flex_raw = flex_raw.reset_index(drop=True)
876
+ flex_raw = flex_raw.sort_values(by='Own', ascending=False)
877
+
878
+ pos_players = flex_raw
879
+ pos_players.dropna(subset=['Median']).reset_index(drop=True)
880
+ pos_players = pos_players.reset_index(drop=True)
881
+
882
+ del flex_raw
883
+
884
+ if insert_port == 1:
885
+ try:
886
+ # Initialize an empty DataFrame to store raw portfolio data
887
+ Raw_Portfolio = pd.DataFrame()
888
+
889
+ # Split each portfolio column and concatenate to Raw_Portfolio
890
+ columns_to_process = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
891
+ for col in columns_to_process:
892
+ temp_df = UserPortfolio[col].str.split("(", n=1, expand=True)
893
+ temp_df.columns = [col, 'Drop']
894
+ Raw_Portfolio = pd.concat([Raw_Portfolio, temp_df], axis=1)
895
+
896
+ # Keep only required variables and remove whitespace
897
+ keep_vars = columns_to_process
898
+ CleanPortfolio = Raw_Portfolio[keep_vars]
899
+ CleanPortfolio = CleanPortfolio.apply(lambda x: x.str.strip())
900
+
901
+ # Reset index and clean up the DataFrame
902
+ CleanPortfolio.reset_index(inplace=True)
903
+ CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
904
+ CleanPortfolio.drop(columns=['index'], inplace=True)
905
+ CleanPortfolio.replace('', np.nan, inplace=True)
906
+ CleanPortfolio.dropna(subset=['QB'], inplace=True)
907
+
908
+ # Create cleaport_players DataFrame
909
+ unique_vals, counts = np.unique(CleanPortfolio.iloc[:, 0:6].values, return_counts=True)
910
+ cleaport_players = pd.DataFrame(np.column_stack([unique_vals, counts]), columns=['Player', 'Freq']).astype({'Freq': int}).sort_values('Freq', ascending=False).reset_index(drop=True)
911
+
912
+ # Merge and update nerf_frame DataFrame
913
+ nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
914
+ nerf_frame[['Median', 'Floor', 'Ceiling', 'STDev']] *= 0.9
915
+ del Raw_Portfolio
916
+ except:
917
+ # Reset index and perform column-wise operations
918
+ CleanPortfolio = UserPortfolio.reset_index(drop=True)
919
+ CleanPortfolio['User/Field'] = CleanPortfolio.index + 1
920
+ CleanPortfolio.replace('', np.nan, inplace=True)
921
+ CleanPortfolio.dropna(subset=['QB'], inplace=True)
922
+
923
+ # Create cleaport_players DataFrame
924
+ unique_vals, counts = np.unique(CleanPortfolio.iloc[:, 0:6].values, return_counts=True)
925
+ cleaport_players = pd.DataFrame({'Player': unique_vals, 'Freq': counts}).sort_values('Freq', ascending=False).reset_index(drop=True).astype({'Freq': int})
926
+
927
+ # Merge and update nerf_frame DataFrame
928
+ nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
929
+ nerf_frame[['Median', 'Floor', 'Ceiling', 'STDev']] *= 0.9
930
+
931
+ elif insert_port == 0:
932
+ CleanPortfolio = UserPortfolio
933
+ cleaport_players = pd.DataFrame(np.column_stack(np.unique(CleanPortfolio.iloc[:,0:6].values, return_counts=True)),
934
+ columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
935
+ cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
936
+ nerf_frame = Overall_Proj
937
+
938
+ ref_dict = {
939
+ 'pos':['FLEX'],
940
+ 'pos_dfs':['FLEX_Table'],
941
+ 'pos_dicts':['flex_dict']
942
+ }
943
+
944
+ maps_dict = {
945
+ 'Floor_map':dict(zip(Overall_Proj.Player,Overall_Proj.Floor)),
946
+ 'Projection_map':dict(zip(Overall_Proj.Player,Overall_Proj.Median)),
947
+ 'Ceiling_map':dict(zip(Overall_Proj.Player,Overall_Proj.Ceiling)),
948
+ 'Salary_map':dict(zip(Overall_Proj.Player,Overall_Proj.Salary)),
949
+ 'Pos_map':dict(zip(Overall_Proj.Player,Overall_Proj.Position)),
950
+ 'Own_map':dict(zip(Overall_Proj.Player,Overall_Proj.Own)),
951
+ 'Team_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)),
952
+ 'STDev_map':dict(zip(Overall_Proj.Player,Overall_Proj.STDev)),
953
+ 'team_check_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team))
954
+ }
955
+
956
+ up_dict = {
957
+ 'Floor_map':dict(zip(cleaport_players.Player,nerf_frame.Floor)),
958
+ 'Projection_map':dict(zip(cleaport_players.Player,nerf_frame.Median)),
959
+ 'Ceiling_map':dict(zip(cleaport_players.Player,nerf_frame.Ceiling)),
960
+ 'Salary_map':dict(zip(cleaport_players.Player,nerf_frame.Salary)),
961
+ 'Pos_map':dict(zip(cleaport_players.Player,nerf_frame.Position)),
962
+ 'Own_map':dict(zip(cleaport_players.Player,nerf_frame.Own)),
963
+ 'Team_map':dict(zip(cleaport_players.Player,nerf_frame.Team)),
964
+ 'STDev_map':dict(zip(cleaport_players.Player,nerf_frame.STDev)),
965
+ 'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team))
966
+ }
967
+
968
+ del Overall_Proj
969
+ del nerf_frame
970
+
971
+ RunsVar = 1
972
+ st.write('Seed frame creation')
973
+ FinalPortfolio, maps_dict = run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs)
974
+
975
+ Sim_size = linenum_var1
976
+ SimVar = 1
977
+ Sim_Winners = []
978
+ fp_array = FinalPortfolio.values
979
+ if insert_port == 1:
980
+ up_array = CleanPortfolio.values
981
+ st.write('Simulating contest on frames')
982
+ while SimVar <= Sim_size:
983
+ try:
984
+ fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio), replace=False)]
985
+
986
+ smple_arrays1 = np.c_[fp_random,
987
+ np.sum(np.random.normal(
988
+ loc = np.vectorize(maps_dict['Projection_map'].__getitem__)(fp_random[:,:-5]),
989
+ scale = np.vectorize(maps_dict['STDev_map'].__getitem__)(fp_random[:,:-5])),
990
+ axis=1)]
991
+ try:
992
+ smple_arrays2 = np.c_[up_array,
993
+ np.sum(np.random.normal(
994
+ loc = np.vectorize(up_dict['Projection_map'].__getitem__)(up_array[:,:-5]),
995
+ scale = np.vectorize(up_dict['STDev_map'].__getitem__)(up_array[:,:-5])),
996
+ axis=1)]
997
+ except:
998
+ pass
999
+ try:
1000
+ smple_arrays = np.vstack((smple_arrays1, smple_arrays2))
1001
+ except:
1002
+ smple_arrays = smple_arrays1
1003
+ final_array = smple_arrays[smple_arrays[:, 7].argsort()[::-1]]
1004
+ best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
1005
+ Sim_Winners.append(best_lineup)
1006
+ SimVar += 1
1007
+
1008
+ except:
1009
+ FieldStrength += (strength_grow + ((30 - len(Teams_used)) * .001))
1010
+ FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs * field_split)
1011
+ FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs * field_split)
1012
+ FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
1013
+ FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
1014
+ try:
1015
+ FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Ownership'],keep = 'last').reset_index(drop = True)
1016
+ except:
1017
+ FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
1018
+ maps_dict.update(maps_dict3)
1019
+ maps_dict.update(maps_dict4)
1020
+ del FinalPortfolio3
1021
+ del maps_dict3
1022
+ del FinalPortfolio4
1023
+ del maps_dict4
1024
+ fp_array = FinalPortfolio.values
1025
+ if insert_port == 1:
1026
+ up_array = CleanPortfolio.values
1027
+ SimVar = SimVar
1028
+ st.write('Contest simulation complete')
1029
+
1030
+ Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
1031
+ Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
1032
+ Sim_Winner_Frame['Salary'] = Sim_Winner_Frame['Salary'].astype(int)
1033
+ Sim_Winner_Frame['Projection'] = Sim_Winner_Frame['Projection'].astype(np.float16)
1034
+ Sim_Winner_Frame['Fantasy'] = Sim_Winner_Frame['Fantasy'].astype(np.float16)
1035
+ Sim_Winner_Frame['GPP_Proj'] = Sim_Winner_Frame['GPP_Proj'].astype(np.float16)
1036
+ Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False)
1037
+
1038
+ player_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:6].values, return_counts=True)),
1039
+ columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
1040
+ player_freq['Freq'] = player_freq['Freq'].astype(int)
1041
+ player_freq['Position'] = player_freq['Player'].map(maps_dict['Pos_map'])
1042
+ player_freq['Salary'] = player_freq['Player'].map(maps_dict['Salary_map'])
1043
+ player_freq['Proj Own'] = (player_freq['Player'].map(maps_dict['Own_map']) / 100)
1044
+ player_freq['Exposure'] = player_freq['Freq']/(Sim_size)
1045
+ player_freq['Edge'] = player_freq['Exposure'] - player_freq['Proj Own']
1046
+ player_freq['Team'] = player_freq['Player'].map(maps_dict['Team_map'])
1047
+ for checkVar in range(len(team_list)):
1048
+ player_freq['Team'] = player_freq['Team'].replace(item_list, team_list)
1049
+
1050
+ player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
1051
+
1052
+ cpt_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
1053
+ columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
1054
+ cpt_freq['Freq'] = cpt_freq['Freq'].astype(int)
1055
+ cpt_freq['Position'] = cpt_freq['Player'].map(maps_dict['Pos_map'])
1056
+ cpt_freq['Salary'] = cpt_freq['Player'].map(maps_dict['Salary_map'])
1057
+ cpt_freq['Proj Own'] = (cpt_freq['Player'].map(maps_dict['Own_map']) / 4) / 100
1058
+ cpt_freq['Exposure'] = cpt_freq['Freq']/(Sim_size)
1059
+ cpt_freq['Edge'] = cpt_freq['Exposure'] - cpt_freq['Proj Own']
1060
+ cpt_freq['Team'] = cpt_freq['Player'].map(maps_dict['Team_map'])
1061
+ for checkVar in range(len(team_list)):
1062
+ cpt_freq['Team'] = cpt_freq['Team'].replace(item_list, team_list)
1063
+
1064
+ cpt_freq = cpt_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
1065
+
1066
+ flex_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[1, 2, 3, 4, 5]].values, return_counts=True)),
1067
+ columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
1068
+ flex_freq['Freq'] = flex_freq['Freq'].astype(int)
1069
+ flex_freq['Position'] = flex_freq['Player'].map(maps_dict['Pos_map'])
1070
+ flex_freq['Salary'] = flex_freq['Player'].map(maps_dict['Salary_map'])
1071
+ flex_freq['Proj Own'] = (flex_freq['Player'].map(maps_dict['Own_map']) / 100) - ((flex_freq['Player'].map(maps_dict['Own_map']) / 4) / 100)
1072
+ flex_freq['Exposure'] = flex_freq['Freq']/(Sim_size)
1073
+ flex_freq['Edge'] = flex_freq['Exposure'] - flex_freq['Proj Own']
1074
+ flex_freq['Team'] = flex_freq['Player'].map(maps_dict['Team_map'])
1075
+ for checkVar in range(len(team_list)):
1076
+ flex_freq['Team'] = flex_freq['Team'].replace(item_list, team_list)
1077
+
1078
+ flex_freq = flex_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
1079
+
1080
+ del fp_random
1081
+ del smple_arrays
1082
+ del final_array
1083
+ del fp_array
1084
+ try:
1085
+ del up_array
1086
+ except:
1087
+ pass
1088
+ del best_lineup
1089
+ del CleanPortfolio
1090
+ del FinalPortfolio
1091
+ del maps_dict
1092
+ del team_list
1093
+ del item_list
1094
+ del Sim_size
1095
+
1096
+ with st.container():
1097
+ st.dataframe(Sim_Winner_Frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
1098
+
1099
+ with st.container():
1100
+ tab1, tab2, tab3 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures'])
1101
+ with tab1:
1102
+ st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
1103
+ st.download_button(
1104
+ label="Export Exposures",
1105
+ data=convert_df_to_csv(player_freq),
1106
+ file_name='player_freq_export.csv',
1107
+ mime='text/csv',
1108
+ )
1109
+ with tab2:
1110
+ st.dataframe(cpt_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
1111
+ st.download_button(
1112
+ label="Export Exposures",
1113
+ data=convert_df_to_csv(cpt_freq),
1114
+ file_name='cpt_freq_export.csv',
1115
+ mime='text/csv',
1116
+ )
1117
+ with tab3:
1118
+ st.dataframe(flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
1119
+ st.download_button(
1120
+ label="Export Exposures",
1121
+ data=convert_df_to_csv(flex_freq),
1122
+ file_name='flex_freq_export.csv',
1123
+ mime='text/csv',
1124
+ )
1125
+
1126
+ st.download_button(
1127
+ label="Export Tables",
1128
+ data=convert_df_to_csv(Sim_Winner_Frame),
1129
+ file_name='NFL_consim_export.csv',
1130
+ mime='text/csv',
1131
+ )