Upload app.py
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app.py
ADDED
@@ -0,0 +1,1131 @@
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1 |
+
import streamlit as st
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2 |
+
st.set_page_config(layout="wide")
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3 |
+
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4 |
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for name in dir():
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5 |
+
if not name.startswith('_'):
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6 |
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del globals()[name]
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7 |
+
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8 |
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import numpy as np
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9 |
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import pandas as pd
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import streamlit as st
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11 |
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import gspread
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12 |
+
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+
@st.cache_resource
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def init_conn():
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scope = ['https://www.googleapis.com/auth/spreadsheets',
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"https://www.googleapis.com/auth/drive"]
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+
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credentials = {
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"type": "service_account",
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"project_id": "sheets-api-connect-378620",
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"private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
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22 |
+
"private_key": "-----BEGIN PRIVATE KEY-----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\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 |
+
)
|