File size: 39,762 Bytes
ac9a5f3 2805ddf efc18fb ac9a5f3 2805ddf efc18fb 2805ddf efc18fb ac9a5f3 efc18fb ac9a5f3 2805ddf ac9a5f3 5345f4f ac9a5f3 2805ddf da9634d ac9a5f3 da9634d ac9a5f3 cace125 ac9a5f3 efc18fb a4b28d5 efc18fb 63731a1 efc18fb a4b28d5 efc18fb 6446a66 efc18fb 5345f4f efc18fb 5345f4f efc18fb 5345f4f efc18fb 2805ddf ac9a5f3 373e2da efc18fb 373e2da b8ba443 ac9a5f3 efc18fb ac9a5f3 efc18fb ac9a5f3 b8ba443 efc18fb b8ba443 da9634d b8ba443 ac9a5f3 b8ba443 ac9a5f3 2805ddf efc18fb ffa148f efc18fb ac9a5f3 2805ddf efc18fb 2805ddf efc18fb b8ba443 ac9a5f3 efc18fb ac9a5f3 efc18fb ac9a5f3 efc18fb ac9a5f3 efc18fb ac9a5f3 b849f0e efc18fb ac9a5f3 2805ddf ac9a5f3 2805ddf ac9a5f3 b849f0e ac9a5f3 da9634d ac9a5f3 2805ddf ac9a5f3 b849f0e ac9a5f3 6037d47 ac9a5f3 2805ddf ac9a5f3 0cf815a ac9a5f3 5345f4f ec277ee ac9a5f3 5345f4f ac9a5f3 373e2da ac9a5f3 d0ce248 ac9a5f3 d0ce248 ac9a5f3 5345f4f efc18fb ac9a5f3 d0ce248 ac9a5f3 3285510 ac9a5f3 d0ce248 ac9a5f3 d0ce248 ac9a5f3 5345f4f efc18fb ac9a5f3 3285510 ac9a5f3 d6075ef ac9a5f3 d6075ef ac9a5f3 d6075ef ac9a5f3 1ef4e2b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 |
import pulp
import numpy as np
import pandas as pd
import streamlit as st
import pymongo
from itertools import combinations
import time
@st.cache_resource
def init_conn():
uri = st.secrets['mongo_uri']
client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
nba_db = client["NBA_DFS"]
nfl_db = client["NFL_Database"]
return nba_db, nfl_db
st.set_page_config(layout="wide")
nba_db, nfl_db = init_conn()
wrong_acro = ['WSH', 'AZ']
right_acro = ['WAS', 'ARI']
game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}',
'5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'}
nfl_player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
'4x%': '{:.2%}','GPP%': '{:.2%}'}
nba_player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}',
'6x%': '{:.2%}','GPP%': '{:.2%}'}
expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'}
all_dk_player_projections = st.secrets["NFL_data"]
@st.cache_resource(ttl=60)
def init_baselines():
collection = nba_db["Player_SD_Range_Of_Outcomes"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
raw_display = raw_display.loc[raw_display['Median'] > 0]
raw_display = raw_display.sort_values(by='Median', ascending=False)
nba_dk_sd_raw = raw_display[raw_display['site'] == 'Draftkings']
nba_fd_sd_raw = raw_display[raw_display['site'] == 'Fanduel']
collection = nfl_db["DK_SD_NFL_ROO"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
raw_display = raw_display.loc[raw_display['Median'] > 0]
raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
nfl_dk_sd_raw = raw_display.sort_values(by='Median', ascending=False)
collection = nfl_db["FD_SD_NFL_ROO"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
raw_display = raw_display.loc[raw_display['Median'] > 0]
raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
nfl_fd_sd_raw = raw_display.sort_values(by='Median', ascending=False)
nba_timestamp = nba_dk_sd_raw['timestamp'].values[0]
nfl_dk_timestamp = nfl_dk_sd_raw['timestamp'].values[0]
nba_dk_id_dict = dict(zip(nba_dk_sd_raw['player_id'], nba_dk_sd_raw['player_id']))
nfl_dk_id_dict = dict(zip(nfl_dk_sd_raw['player_id'], nfl_dk_sd_raw['player_id']))
nba_fd_id_dict = dict(zip(nba_fd_sd_raw['player_id'], nba_fd_sd_raw['player_id']))
nfl_fd_id_dict = dict(zip(nfl_fd_sd_raw['player_id'], nfl_fd_sd_raw['player_id']))
return nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict
nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines()
@st.cache_data
def convert_df_to_csv(df):
return df.to_csv().encode('utf-8')
tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimizer'])
with tab1:
col1, col2 = st.columns([1, 5])
with col1:
if st.button("Load/Reset Data", key='reset2'):
st.cache_data.clear()
nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines()
sport_var2 = st.radio("What sport are you loading?", ('NBA', 'NFL'), key='sport_var2')
if sport_var2 == 'NBA':
dk_roo_raw = nba_dk_sd_raw
fd_roo_raw = nba_fd_sd_raw
elif sport_var2 == 'NFL':
dk_roo_raw = nfl_dk_sd_raw
fd_roo_raw = nfl_fd_sd_raw
slate_var2 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Auxiliary)'), key='slate_var2')
site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
if site_var2 == 'Draftkings':
if slate_var2 == 'Paydirt (Main)':
raw_baselines = dk_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
elif slate_var2 == 'Paydirt (Secondary)':
raw_baselines = dk_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
elif slate_var2 == 'Paydirt (Auxiliary)':
raw_baselines = dk_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
elif site_var2 == 'Fanduel':
if slate_var2 == 'Paydirt (Main)':
raw_baselines = fd_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
elif slate_var2 == 'Paydirt (Secondary)':
raw_baselines = fd_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
elif slate_var2 == 'Paydirt (Auxiliary)':
raw_baselines = fd_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
with col2:
hold_container = st.empty()
if sport_var2 == 'NBA':
display_Proj = raw_baselines[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX']]
elif sport_var2 == 'NFL':
display_Proj = raw_baselines[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
display_Proj = display_Proj.set_index('Player')
display_Proj = display_Proj.sort_values(by='Median', ascending=False)
with hold_container:
hold_container = st.empty()
display_Proj = display_Proj
if sport_var2 == 'NBA':
st.dataframe(display_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(nba_player_roo_format, precision=2), height=1000, use_container_width = True)
elif sport_var2 == 'NFL':
st.dataframe(display_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(nfl_player_roo_format, precision=2), height=1000, use_container_width = True)
st.download_button(
label="Export Tables",
data=convert_df_to_csv(raw_baselines),
file_name='NFL_SD_export.csv',
mime='text/csv',
)
with tab2:
col1, col2 = st.columns([1, 5])
with col1:
if st.button("Load/Reset Data", key='reset1'):
st.cache_data.clear()
nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines()
for key in st.session_state.keys():
del st.session_state[key]
sport_var1 = st.radio("What sport are you optimizing?", ('NBA', 'NFL'), key='sport_var1')
if sport_var1 == 'NBA':
dk_roo_raw = nba_dk_sd_raw
fd_roo_raw = nba_fd_sd_raw
elif sport_var1 == 'NFL':
dk_roo_raw = nfl_dk_sd_raw
fd_roo_raw = nfl_fd_sd_raw
slate_var1 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Auxiliary)'), key='slate_var1')
site_var1 = st.selectbox("What site is the showdown on?", ('Draftkings', 'Fanduel'), key='site_var1')
if site_var1 == 'Draftkings':
if slate_var1 == 'Paydirt (Main)':
raw_baselines = dk_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
elif slate_var1 == 'Paydirt (Secondary)':
raw_baselines = dk_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
elif slate_var1 == 'Paydirt (Auxiliary)':
raw_baselines = dk_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
elif site_var1 == 'Fanduel':
if slate_var1 == 'Paydirt (Main)':
st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
raw_baselines = fd_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
elif slate_var1 == 'Paydirt (Secondary)':
st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
raw_baselines = fd_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
elif slate_var1 == 'Paydirt (Auxiliary)':
st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
raw_baselines = fd_roo_raw
raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var1')
lock_var1 = st.multiselect("Are there any players you want to use in all lineups in the CAPTAIN (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var1')
lock_var2 = st.multiselect("Are there any players you want to use in all lineups in the FLEX (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var2')
avoid_var1 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = raw_baselines['Player'].unique(), key='avoid_var1')
trim_choice1 = st.selectbox("Allow overowned lineups?", options = ['Yes', 'No'])
linenum_var1 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 20, step = 1, key='linenum_var1')
if trim_choice1 == 'Yes':
trim_var1 = 0
elif trim_choice1 == 'No':
trim_var1 = 1
if site_var1 == 'Draftkings':
min_sal1 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal1')
max_sal1 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal1')
elif site_var1 == 'Fanduel':
min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 59900, value = 59000, step = 100, key='min_sal1')
max_sal1 = st.number_input('Max Salary', min_value = 45000, max_value = 60000, value = 60000, step = 100, key='max_sal1')
if contest_var1 == 'Small Field GPP':
ownframe = raw_baselines.copy()
if sport_var1 == 'NBA':
ownframe['Own'] = ownframe['Small_Own']
elif sport_var1 == 'NFL':
ownframe['Own'] = ownframe['Small_Field_Own']
elif contest_var1 == 'Large Field GPP':
ownframe = raw_baselines.copy()
if sport_var1 == 'NBA':
ownframe['Own'] = ownframe['Large_Own']
elif sport_var1 == 'NFL':
ownframe['Own'] = ownframe['Large_Field_Own']
elif contest_var1 == 'Cash':
ownframe = raw_baselines.copy()
if sport_var1 == 'NBA':
ownframe['Own'] = ownframe['Cash_Own']
elif sport_var1 == 'NFL':
ownframe['Own'] = ownframe['Cash_Field_Own']
export_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'CPT_Own', 'player_id']]
export_baselines['CPT_Proj'] = export_baselines['Median'] * 1.5
if sport_var1 == 'NBA':
export_baselines['CPT_Salary'] = export_baselines['Salary'] * 1.5
elif sport_var1 == 'NFL':
export_baselines['CPT_Salary'] = export_baselines['Salary']
export_baselines['ID'] = export_baselines['player_id']
display_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'CPT_Own']]
display_baselines = display_baselines.sort_values(by='Median', ascending=False)
display_baselines['cpt_lock'] = np.where(display_baselines['Player'].isin(lock_var1), 1, 0)
display_baselines['lock'] = np.where(display_baselines['Player'].isin(lock_var2), 1, 0)
st.session_state.display_baselines = display_baselines.copy()
st.session_state.export_baselines = export_baselines.copy()
index_check = pd.DataFrame()
flex_proj = pd.DataFrame()
cpt_proj = pd.DataFrame()
if site_var1 == 'Draftkings':
cpt_proj['Player'] = display_baselines['Player']
if sport_var1 == 'NBA':
cpt_proj['Salary'] = display_baselines['Salary'] * 1.5
elif sport_var1 == 'NFL':
cpt_proj['Salary'] = display_baselines['Salary']
cpt_proj['Position'] = display_baselines['Position']
cpt_proj['Team'] = display_baselines['Team']
cpt_proj['Opp'] = display_baselines['Opp']
cpt_proj['Median'] = display_baselines['Median'] * 1.5
cpt_proj['Own'] = display_baselines['CPT_Own']
cpt_proj['lock'] = display_baselines['cpt_lock']
cpt_proj['roster'] = 'CPT'
if len(lock_var1) > 0:
cpt_proj = cpt_proj[cpt_proj['lock'] == 1]
if len(lock_var2) > 0:
cpt_proj = cpt_proj[~cpt_proj['Player'].isin(lock_var2)]
flex_proj['Player'] = display_baselines['Player']
if sport_var1 == 'NBA':
flex_proj['Salary'] = display_baselines['Salary']
elif sport_var1 == 'NFL':
flex_proj['Salary'] = display_baselines['Salary'] / 1.5
flex_proj['Position'] = display_baselines['Position']
flex_proj['Team'] = display_baselines['Team']
flex_proj['Opp'] = display_baselines['Opp']
flex_proj['Median'] = display_baselines['Median']
flex_proj['Own'] = display_baselines['Own']
flex_proj['lock'] = display_baselines['lock']
flex_proj['roster'] = 'FLEX'
elif site_var1 == 'Fanduel':
cpt_proj['Player'] = display_baselines['Player']
cpt_proj['Salary'] = display_baselines['Salary']
cpt_proj['Position'] = display_baselines['Position']
cpt_proj['Team'] = display_baselines['Team']
cpt_proj['Opp'] = display_baselines['Opp']
cpt_proj['Median'] = display_baselines['Median'] * 1.5
cpt_proj['Own'] = display_baselines['CPT Own'] * .75
cpt_proj['lock'] = display_baselines['cpt_lock']
cpt_proj['roster'] = 'CPT'
flex_proj['Player'] = display_baselines['Player']
flex_proj['Salary'] = display_baselines['Salary']
flex_proj['Position'] = display_baselines['Position']
flex_proj['Team'] = display_baselines['Team']
flex_proj['Opp'] = display_baselines['Opp']
flex_proj['Median'] = display_baselines['Median']
flex_proj['Own'] = display_baselines['Own']
flex_proj['lock'] = display_baselines['lock']
flex_proj['roster'] = 'FLEX'
combo_file = pd.concat([cpt_proj, flex_proj], ignore_index=True)
with col2:
display_container = st.empty()
display_dl_container = st.empty()
optimize_container = st.empty()
download_container = st.empty()
freq_container = st.empty()
if st.button('Optimize'):
for key in st.session_state.keys():
del st.session_state[key]
max_proj = 1000
max_own = 1000
total_proj = 0
total_own = 0
display_container = st.empty()
display_dl_container = st.empty()
optimize_container = st.empty()
download_container = st.empty()
freq_container = st.empty()
lineup_display = []
check_list = []
lineups = []
portfolio = pd.DataFrame()
x = 1
with st.spinner('Wait for it...'):
with optimize_container:
while x <= linenum_var1:
sorted_lineup = []
p_used = []
raw_proj_file = combo_file
raw_flex_file = raw_proj_file.dropna(how='all')
raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0]
flex_file = raw_flex_file
flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True)
flex_file['name_var'] = flex_file['Player']
flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var2), 1, 0)
flex_file = flex_file[~flex_file['Player'].isin(avoid_var1)]
flex_file['Player'] = np.where(flex_file['roster'] == 'CPT', flex_file['Player'] + ' - CPT', flex_file['Player'] + ' - FLEX')
player_ids = flex_file.index
overall_players = flex_file[['Player']]
overall_players['player_var_add'] = flex_file.index
overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str)
player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger)
total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize)
player_match = dict(zip(overall_players['player_var'], overall_players['Player']))
player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add']))
player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%']))
player_team = dict(zip(flex_file['Player'], flex_file['Team']))
player_pos = dict(zip(flex_file['Player'], flex_file['Position']))
player_sal = dict(zip(flex_file['Player'], flex_file['Salary']))
player_proj = dict(zip(flex_file['Player'], flex_file['Median']))
obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
obj_points_max = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
obj_own_max = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index}
total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1
total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1
if site_var1 == 'Draftkings':
for flex in flex_file['lock'].unique():
sub_idx = flex_file[flex_file['lock'] == 1].index
total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var2)
for flex in flex_file['roster'].unique():
sub_idx = flex_file[flex_file['roster'] == "CPT"].index
total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
for flex in flex_file['roster'].unique():
sub_idx = flex_file[flex_file['roster'] == "FLEX"].index
total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5
for playerid in player_ids:
total_score += pulp.lpSum([player_vars[i] for i in player_ids if
(flex_file['name_var'][i] == flex_file['name_var'][playerid])]) <= 1
elif site_var1 == 'Fanduel':
for flex in flex_file['lock'].unique():
sub_idx = flex_file[flex_file['lock'] == 1].index
total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var2)
for flex in flex_file['Position'].unique():
sub_idx = flex_file[flex_file['Position'] != "Var"].index
total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5
for flex in flex_file['roster'].unique():
sub_idx = flex_file[flex_file['roster'] == "CPT"].index
total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
for playerid in player_ids:
total_score += pulp.lpSum([player_vars[i] for i in player_ids if
(flex_file['name_var'][i] == flex_file['name_var'][playerid])]) <= 1
player_count = []
player_trim = []
lineup_list = []
if contest_var1 == 'Cash':
obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_own - .001
elif contest_var1 != 'Cash':
obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_proj - .01
if trim_var1 == 1:
total_score += pulp.lpSum([player_vars[idx]*obj_own_max[idx] for idx in flex_file.index]) <= max_own - .001
total_score.solve()
for v in total_score.variables():
if v.varValue > 0:
lineup_list.append(v.name)
df = pd.DataFrame(lineup_list)
df['Names'] = df[0].map(player_match)
df['Cost'] = df['Names'].map(player_sal)
df['Proj'] = df['Names'].map(player_proj)
df['Own'] = df['Names'].map(player_own)
total_cost = sum(df['Cost'])
total_own = sum(df['Own'])
total_proj = sum(df['Proj'])
lineup_raw = pd.DataFrame(lineup_list)
lineup_raw['Names'] = lineup_raw[0].map(player_match)
lineup_raw['value'] = lineup_raw[0].map(player_index_match)
lineup_final = lineup_raw.sort_values(by=['value'])
del lineup_final[lineup_final.columns[0]]
del lineup_final[lineup_final.columns[1]]
lineup_final['Team'] = lineup_final['Names'].map(player_team)
lineup_final['Position'] = lineup_final['Names'].map(player_pos)
lineup_final['Salary'] = lineup_final['Names'].map(player_sal)
lineup_final['Proj'] = lineup_final['Names'].map(player_proj)
lineup_final['Own'] = lineup_final['Names'].map(player_own)
lineup_final.loc['Column_Total'] = lineup_final.sum(numeric_only=True, axis=0)
lineup_final = lineup_final.reset_index(drop=True)
max_proj = total_proj
max_own = total_own
if site_var1 == 'Draftkings':
if len(lineup_final) == 7:
port_display = pd.DataFrame(lineup_final['Names'][:-1].values.reshape(1, -1))
port_display['Cost'] = total_cost
port_display['Proj'] = total_proj
port_display['Own'] = total_own
st.table(port_display)
portfolio = pd.concat([portfolio, port_display], ignore_index = True)
elif site_var1 == 'Fanduel':
if len(lineup_final) == 6:
port_display = pd.DataFrame(lineup_final['Names'][:-1].values.reshape(1, -1))
port_display['Cost'] = total_cost
port_display['Proj'] = total_proj
port_display['Own'] = total_own
st.table(port_display)
portfolio = pd.concat([portfolio, port_display], ignore_index = True)
x += 1
if site_var1 == 'Draftkings':
portfolio.rename(columns={0: "CPT", 1: "FLEX1", 2: "FLEX2", 3: "FLEX3", 4: "FLEX4", 5: "FLEX5"}, inplace = True)
elif site_var1 == 'Fanduel':
portfolio.rename(columns={0: "MVP", 1: "FLEX1", 2: "FLEX2", 3: "FLEX3", 4: "FLEX4"}, inplace = True)
portfolio = portfolio.dropna()
portfolio = portfolio.reset_index()
portfolio['Lineup_num'] = portfolio['index'] + 1
portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True)
portfolio = portfolio.set_index('Lineup')
portfolio = portfolio.drop(columns=['index'])
st.session_state.portfolio = portfolio.drop_duplicates()
final_outcomes = portfolio
st.session_state.final_outcomes = portfolio
player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.portfolio.iloc[:,0:6].values, return_counts=True)),
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
player_freq['Freq'] = player_freq['Freq'].astype(int)
player_freq['Position'] = player_freq['Player'].map(player_pos)
player_freq['Salary'] = player_freq['Player'].map(player_sal)
player_freq['Proj Own'] = player_freq['Player'].map(player_own) / 100
player_freq['Exposure'] = player_freq['Freq']/(linenum_var1)
player_freq['Team'] = player_freq['Player'].map(player_team)
final_outcomes_export = pd.DataFrame()
split_portfolio = pd.DataFrame()
if site_var1 == 'Draftkings':
split_portfolio[['CPT', 'CPT_ID']] = final_outcomes.CPT.str.split("-", n=1, expand = True)
split_portfolio[['FLEX1', 'FLEX1_ID']] = final_outcomes.FLEX1.str.split("-", n=1, expand = True)
split_portfolio[['FLEX2', 'FLEX2_ID']] = final_outcomes.FLEX2.str.split("-", n=1, expand = True)
split_portfolio[['FLEX3', 'FLEX3_ID']] = final_outcomes.FLEX3.str.split("-", n=1, expand = True)
split_portfolio[['FLEX4', 'FLEX4_ID']] = final_outcomes.FLEX4.str.split("-", n=1, expand = True)
split_portfolio[['FLEX5', 'FLEX5_ID']] = final_outcomes.FLEX5.str.split("-", n=1, expand = True)
split_portfolio['CPT'] = split_portfolio['CPT'].str.strip()
split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip()
final_outcomes_export['CPT'] = split_portfolio['CPT']
final_outcomes_export['FLEX1'] = split_portfolio['FLEX1']
final_outcomes_export['FLEX2'] = split_portfolio['FLEX2']
final_outcomes_export['FLEX3'] = split_portfolio['FLEX3']
final_outcomes_export['FLEX4'] = split_portfolio['FLEX4']
final_outcomes_export['FLEX5'] = split_portfolio['FLEX5']
if sport_var1 == 'NFL':
final_outcomes_export['CPT'].replace(nfl_dk_id_dict, inplace=True)
final_outcomes_export['FLEX1'].replace(nfl_dk_id_dict, inplace=True)
final_outcomes_export['FLEX2'].replace(nfl_dk_id_dict, inplace=True)
final_outcomes_export['FLEX3'].replace(nfl_dk_id_dict, inplace=True)
final_outcomes_export['FLEX4'].replace(nfl_dk_id_dict, inplace=True)
final_outcomes_export['FLEX5'].replace(nfl_dk_id_dict, inplace=True)
elif sport_var1 == 'NBA':
final_outcomes_export['CPT'].replace(nba_dk_id_dict, inplace=True)
final_outcomes_export['FLEX1'].replace(nba_dk_id_dict, inplace=True)
final_outcomes_export['FLEX2'].replace(nba_dk_id_dict, inplace=True)
final_outcomes_export['FLEX3'].replace(nba_dk_id_dict, inplace=True)
final_outcomes_export['FLEX4'].replace(nba_dk_id_dict, inplace=True)
final_outcomes_export['FLEX5'].replace(nba_dk_id_dict, inplace=True)
final_outcomes_export['Salary'] = final_outcomes['Cost']
final_outcomes_export['Own'] = final_outcomes['Own']
final_outcomes_export['Proj'] = final_outcomes['Proj']
st.session_state.final_outcomes_export = final_outcomes_export.copy()
elif site_var1 == 'Fanduel':
split_portfolio[['MVP', 'CPT_ID']] = final_outcomes.MVP.str.split("-", n=1, expand = True)
split_portfolio[['FLEX1', 'FLEX1_ID']] = final_outcomes.FLEX1.str.split("-", n=1, expand = True)
split_portfolio[['FLEX2', 'FLEX2_ID']] = final_outcomes.FLEX2.str.split("-", n=1, expand = True)
split_portfolio[['FLEX3', 'FLEX3_ID']] = final_outcomes.FLEX3.str.split("-", n=1, expand = True)
split_portfolio[['FLEX4', 'FLEX4_ID']] = final_outcomes.FLEX4.str.split("-", n=1, expand = True)
split_portfolio['MVP'] = split_portfolio['MVP'].str.strip()
split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
final_outcomes_export['MVP'] = split_portfolio['MVP']
final_outcomes_export['FLEX1'] = split_portfolio['FLEX1']
final_outcomes_export['FLEX2'] = split_portfolio['FLEX2']
final_outcomes_export['FLEX3'] = split_portfolio['FLEX3']
final_outcomes_export['FLEX4'] = split_portfolio['FLEX4']
if sport_var1 == 'NFL':
final_outcomes_export['MVP'].replace(nfl_fd_id_dict, inplace=True)
final_outcomes_export['FLEX1'].replace(nfl_fd_id_dict, inplace=True)
final_outcomes_export['FLEX2'].replace(nfl_fd_id_dict, inplace=True)
final_outcomes_export['FLEX3'].replace(nfl_fd_id_dict, inplace=True)
final_outcomes_export['FLEX4'].replace(nfl_fd_id_dict, inplace=True)
elif sport_var1 == 'NBA':
final_outcomes_export['MVP'].replace(nba_fd_id_dict, inplace=True)
final_outcomes_export['FLEX1'].replace(nba_fd_id_dict, inplace=True)
final_outcomes_export['FLEX2'].replace(nba_fd_id_dict, inplace=True)
final_outcomes_export['FLEX3'].replace(nba_fd_id_dict, inplace=True)
final_outcomes_export['FLEX4'].replace(nba_fd_id_dict, inplace=True)
final_outcomes_export['Salary'] = final_outcomes['Cost']
final_outcomes_export['Own'] = final_outcomes['Own']
final_outcomes_export['Proj'] = final_outcomes['Proj']
st.session_state.FD_final_outcomes_export = final_outcomes_export.copy()
st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
with display_container:
display_container = st.empty()
if 'display_baselines' in st.session_state:
st.dataframe(st.session_state.display_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
with display_dl_container:
display_dl_container = st.empty()
if 'export_baselines' in st.session_state:
st.download_button(
label="Export Projections",
data=convert_df_to_csv(st.session_state.export_baselines),
file_name='showdown_proj_export.csv',
mime='text/csv',
)
with optimize_container:
optimize_container = st.empty()
if 'final_outcomes' in st.session_state:
st.dataframe(st.session_state.final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
with download_container:
download_container = st.empty()
if site_var1 == 'Draftkings':
if 'final_outcomes_export' in st.session_state:
st.download_button(
label="Export Optimals",
data=convert_df_to_csv(st.session_state.final_outcomes_export),
file_name='NFL_optimals_export.csv',
mime='text/csv',
)
elif site_var1 == 'Fanduel':
if 'FD_final_outcomes_export' in st.session_state:
st.download_button(
label="Export Optimals",
data=convert_df_to_csv(st.session_state.FD_final_outcomes_export),
file_name='FD_NFL_optimals_export.csv',
mime='text/csv',
)
with freq_container:
freq_container = st.empty()
if 'player_freq' in st.session_state:
st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)
|