File size: 88,537 Bytes
069adbe 1a21253 069adbe 9a3a501 40fb788 069adbe 7852820 069adbe 4f8d205 8e0da46 069adbe bbf380a 069adbe bbf380a cefd40a bbf380a cefd40a bbf380a 913cfd5 cefd40a bbf380a 255a179 069adbe bf8ac3e 069adbe 7852820 069adbe 28cb5be 7852820 28cb5be 7852820 28cb5be 069adbe 901bbc5 4af194e 901bbc5 5492e06 6ba45c0 604e91a f396a8d 94216f6 f396a8d 069adbe 730a147 8e0da46 f5bf222 a4e6673 95ffa8b 8e0da46 85f2b5f f5bf222 8e0da46 f5bf222 3c99d96 8e0da46 0dc28ee 14ac337 8e0da46 85f2b5f 8e0da46 f5bf222 730a147 f5bf222 3c99d96 730a147 f5bf222 14ac337 3c99d96 14ac337 730a147 f5bf222 1a21253 604e91a fc490c5 911d70e fc490c5 911d70e 40fb788 37c59a0 d5e058d 730a147 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe 255a179 069adbe cefd40a 7900a20 cefd40a a4e6673 cefd40a 4088be2 37c59a0 5581d98 28cb5be 37c59a0 b60e0d6 37c59a0 fdf735a 37c59a0 fdf735a 37c59a0 3212032 c1e2fb5 3212032 37c59a0 fdf735a e91c761 37c59a0 e91c761 37c59a0 fdf735a 37c59a0 fdf735a 37c59a0 fdf735a 37c59a0 fdf735a 37c59a0 911d70e 37c59a0 87e0fcb 37c59a0 fdf735a 87e0fcb 37c59a0 fdf735a 87e0fcb 37c59a0 fdf735a 87e0fcb fdf735a 20a3bf0 7a8cb18 e16cb76 74f9c17 0da9464 5404e76 0da9464 74f9c17 488d713 13857b8 74f9c17 13857b8 74f9c17 37c59a0 20a3bf0 13857b8 20a3bf0 37c59a0 20a3bf0 13857b8 20a3bf0 13857b8 20a3bf0 37c59a0 74f9c17 37c59a0 74f9c17 37c59a0 74f9c17 546a43f 69d924b 74f9c17 37c59a0 20a3bf0 37c59a0 20a3bf0 37c59a0 20a3bf0 37c59a0 20a3bf0 37c59a0 20a3bf0 37c59a0 20a3bf0 646dbbe 911d70e 646dbbe 20a3bf0 37c59a0 74f9c17 5404e76 74f9c17 5404e76 74f9c17 b3c964e 11b09bf b3c964e 37c59a0 51ae6c5 28cb5be e39607b 7a401f2 e39607b 7a401f2 e39607b 7a401f2 e39607b 97f24d7 e39607b 97f24d7 e39607b 7a401f2 e39607b 7a401f2 e39607b a4a0f62 e39607b a4a0f62 e39607b 5647915 e39607b 5647915 e39607b ab4dc7d 9fdec5f 0157a17 e39607b a4a0f62 e39607b ab4dc7d 069adbe ab4dc7d 069adbe ab4dc7d 069adbe ab4dc7d 069adbe ab4dc7d e631d8e 6c81ee5 069adbe 6c81ee5 069adbe 9fdec5f 069adbe 92ae630 5ed7a1c 9fdec5f 069adbe 9fdec5f 069adbe 92ae630 5ed7a1c 9fdec5f 069adbe 6c81ee5 92ae630 5ed7a1c 6c81ee5 92ae630 5ed7a1c 6c81ee5 92ae630 5ed7a1c 6c81ee5 0abd3e1 6c81ee5 76c1d87 6c81ee5 97529bc 6c81ee5 92ae630 5ed7a1c 6c81ee5 6a58427 6c81ee5 0abd3e1 6c81ee5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 |
import streamlit as st
st.set_page_config(layout="wide")
import numpy as np
import pandas as pd
import time
from rapidfuzz import process, fuzz
import random
import re
from collections import Counter
## import global functions
from global_func.clean_player_name import clean_player_name
from global_func.load_file import load_file
from global_func.load_ss_file import load_ss_file
from global_func.load_dk_fd_file import load_dk_fd_file
from global_func.find_name_mismatches import find_name_mismatches
from global_func.predict_dupes import predict_dupes
from global_func.highlight_rows import highlight_changes, highlight_changes_winners, highlight_changes_losers
from global_func.load_csv import load_csv
from global_func.find_csv_mismatches import find_csv_mismatches
from global_func.trim_portfolio import trim_portfolio
from global_func.get_portfolio_names import get_portfolio_names
freq_format = {'Finish_percentile': '{:.2%}', 'Lineup Edge': '{:.2%}', 'Win%': '{:.2%}'}
stacking_sports = ['MLB', 'NHL', 'NFL']
player_wrong_names_mlb = ['Enrique Hernandez']
player_right_names_mlb = ['Kike Hernandez']
with st.container():
col1, col2, col3, col4 = st.columns(4)
with col1:
if st.button('Clear data', key='reset3'):
st.session_state.clear()
with col2:
site_var = st.selectbox("Select Site", ['Draftkings', 'Fanduel'])
with col3:
sport_var = st.selectbox("Select Sport", ['NFL', 'MLB', 'NBA', 'NHL', 'MMA', 'CS2', 'TENNIS', 'GOLF', 'WNBA'])
with col4:
type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
tab1, tab2 = st.tabs(["Data Load", "Manage Portfolio"])
with tab1:
if st.button('Clear data', key='reset1'):
st.session_state.clear()
# Add file uploaders to your app
col1, col2, col3 = st.columns(3)
with col1:
st.subheader("Draftkings/Fanduel CSV")
st.info("Upload the player pricing CSV from the site you are playing on. This is used in late swap exporting and/or with SaberSim portfolios, but is not necessary for the portfolio management functions.")
upload_csv_col, csv_template_col = st.columns([3, 1])
with upload_csv_col:
csv_file = st.file_uploader("Upload CSV File", type=['csv'])
if 'csv_file' in st.session_state:
del st.session_state['csv_file']
with csv_template_col:
csv_template_df = pd.DataFrame(columns=['Name', 'ID', 'Roster Position', 'Salary'])
st.download_button(
label="CSV Template",
data=csv_template_df.to_csv(index=False),
file_name="csv_template.csv",
mime="text/csv"
)
st.session_state['csv_file'] = load_csv(csv_file)
try:
st.session_state['csv_file']['Salary'] = st.session_state['csv_file']['Salary'].astype(str).str.replace(',', '').astype(int)
except:
pass
if csv_file:
# st.session_state['csv_file'] = st.session_state['csv_file'].drop_duplicates(subset=['Name'])
st.success('Projections file loaded successfully!')
st.dataframe(st.session_state['csv_file'].head(10))
with col2:
st.subheader("Portfolio File")
st.info("Go ahead and upload a portfolio file here. Only include player columns and an optional 'Stack' column if you are playing MLB.")
upload_toggle = st.selectbox("What source are you uploading from?", options=['SaberSim (Just IDs)', 'Draftkings/Fanduel (Names + IDs)', 'Other (Just Names)'])
if upload_toggle == 'SaberSim (Just IDs)' or upload_toggle == 'Draftkings/Fanduel (Names + IDs)':
portfolio_file = st.file_uploader("Upload Portfolio File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
if 'portfolio' in st.session_state:
del st.session_state['portfolio']
if 'export_portfolio' in st.session_state:
del st.session_state['export_portfolio']
else:
portfolio_file = st.file_uploader("Upload Portfolio File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
if 'portfolio' in st.session_state:
del st.session_state['portfolio']
if 'export_portfolio' in st.session_state:
del st.session_state['export_portfolio']
if 'portfolio' not in st.session_state:
if portfolio_file:
if upload_toggle == 'SaberSim (Just IDs)':
st.session_state['export_portfolio'], st.session_state['portfolio'] = load_ss_file(portfolio_file, st.session_state['csv_file'])
st.session_state['export_portfolio'] = st.session_state['export_portfolio'].dropna(how='all')
st.session_state['export_portfolio'] = st.session_state['export_portfolio'].reset_index(drop=True)
st.session_state['portfolio'] = st.session_state['portfolio'].dropna(how='all')
st.session_state['portfolio'] = st.session_state['portfolio'].reset_index(drop=True)
elif upload_toggle == 'Draftkings/Fanduel (Names + IDs)':
st.session_state['export_portfolio'], st.session_state['portfolio'] = load_dk_fd_file(portfolio_file, st.session_state['csv_file'])
st.session_state['export_portfolio'] = st.session_state['export_portfolio'].dropna(how='all')
st.session_state['export_portfolio'] = st.session_state['export_portfolio'].reset_index(drop=True)
st.session_state['portfolio'] = st.session_state['portfolio'].dropna(how='all')
st.session_state['portfolio'] = st.session_state['portfolio'].reset_index(drop=True)
else:
st.session_state['export_portfolio'], st.session_state['portfolio'] = load_file(portfolio_file)
st.session_state['export_portfolio'] = st.session_state['export_portfolio'].dropna(how='all')
st.session_state['export_portfolio'] = st.session_state['export_portfolio'].reset_index(drop=True)
st.session_state['portfolio'] = st.session_state['portfolio'].dropna(how='all')
st.session_state['portfolio'] = st.session_state['portfolio'].reset_index(drop=True)
# Check if Stack column exists in the portfolio
if 'Stack' in st.session_state['portfolio'].columns:
# Create dictionary mapping index to Stack values
stack_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Stack']))
st.write(f"Found {len(stack_dict)} stack assignments")
st.session_state['portfolio'] = st.session_state['portfolio'].drop(columns=['Stack'])
else:
stack_dict = None
st.info("No Stack column found in portfolio")
if st.session_state['portfolio'] is not None:
st.success('Portfolio file loaded successfully!')
st.session_state['portfolio'] = st.session_state['portfolio'].apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb))
st.dataframe(st.session_state['portfolio'].head(10))
with col3:
st.subheader("Projections File")
st.info("upload a projections file that has 'player_names', 'salary', 'median', 'ownership', and 'captain ownership' (Needed for Showdown) columns. Note that the salary for showdown needs to be the FLEX salary, not the captain salary.")
# Create two columns for the uploader and template button
upload_col, template_col = st.columns([3, 1])
with upload_col:
projections_file = st.file_uploader("Upload Projections File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
if 'projections_df' in st.session_state:
del st.session_state['projections_df']
with template_col:
# Create empty DataFrame with required columns
template_df = pd.DataFrame(columns=['player_names', 'position', 'team', 'salary', 'median', 'ownership', 'captain ownership'])
# Add download button for template
st.download_button(
label="Template",
data=template_df.to_csv(index=False),
file_name="projections_template.csv",
mime="text/csv"
)
if projections_file:
export_projections, projections = load_file(projections_file)
if projections is not None:
st.success('Projections file loaded successfully!')
try:
projections['salary'] = projections['salary'].str.replace(',', '').str.replace('$', '').str.replace(' ', '')
st.write('replaced salary symbols')
except:
pass
try:
projections['ownership'] = projections['ownership'].str.replace('%', '').str.replace(' ', '')
st.write('replaced ownership symbols')
except:
pass
projections['salary'] = projections['salary'].dropna().astype(int)
projections['ownership'] = projections['ownership'].astype(float)
if type_var == 'Showdown':
if projections['captain ownership'].isna().all():
projections['CPT_Own_raw'] = (projections['ownership'] / 2) * ((100 - (100-projections['ownership']))/100)
cpt_own_var = 100 / projections['CPT_Own_raw'].sum()
projections['captain ownership'] = projections['CPT_Own_raw'] * cpt_own_var
projections = projections.drop(columns='CPT_Own_raw', axis=1)
projections = projections.apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb))
st.dataframe(projections.head(10))
if portfolio_file and projections_file:
if st.session_state['portfolio'] is not None and projections is not None:
st.subheader("Name Matching Analysis")
# Initialize projections_df in session state if it doesn't exist
# Get unique names from portfolio
portfolio_names = get_portfolio_names(st.session_state['portfolio'])
try:
csv_names = st.session_state['csv_file']['Name'].tolist()
except:
csv_names = st.session_state['csv_file']['Nickname'].tolist()
projection_names = projections['player_names'].tolist()
# Create match dictionary for portfolio names to projection names
portfolio_match_dict = {}
unmatched_names = []
for portfolio_name in portfolio_names:
match = process.extractOne(
portfolio_name,
csv_names,
score_cutoff=87
)
if match:
portfolio_match_dict[portfolio_name] = match[0]
if match[1] < 100:
st.write(f"{portfolio_name} matched from portfolio to site csv {match[0]} with a score of {match[1]}%")
else:
portfolio_match_dict[portfolio_name] = portfolio_name
unmatched_names.append(portfolio_name)
# Update portfolio with matched names
portfolio = st.session_state['portfolio'].copy()
player_columns = [col for col in portfolio.columns
if col not in ['salary', 'median', 'Own']]
# For each player column, update names using the match dictionary
for col in player_columns:
portfolio[col] = portfolio[col].map(lambda x: portfolio_match_dict.get(x, x))
st.session_state['portfolio'] = portfolio
# Create match dictionary for portfolio names to projection names
projections_match_dict = {}
unmatched_proj_names = []
for projections_name in projection_names:
match = process.extractOne(
projections_name,
csv_names,
score_cutoff=87
)
if match:
projections_match_dict[projections_name] = match[0]
if match[1] < 100:
st.write(f"{projections_name} matched from projections to site csv {match[0]} with a score of {match[1]}%")
else:
projections_match_dict[projections_name] = projections_name
unmatched_proj_names.append(projections_name)
# Update projections with matched names
projections['player_names'] = projections['player_names'].map(lambda x: projections_match_dict.get(x, x))
st.session_state['projections_df'] = projections
projections_names = st.session_state['projections_df']['player_names'].tolist()
portfolio_names = get_portfolio_names(st.session_state['portfolio'])
# Create match dictionary for portfolio names to projection names
projections_match_dict = {}
unmatched_proj_names = []
for projections_name in projection_names:
match = process.extractOne(
projections_name,
portfolio_names,
score_cutoff=87
)
if match:
projections_match_dict[projections_name] = match[0]
if match[1] < 100:
st.write(f"{projections_name} matched from portfolio to projections {match[0]} with a score of {match[1]}%")
else:
projections_match_dict[projections_name] = projections_name
unmatched_proj_names.append(projections_name)
# Update projections with matched names
projections['player_names'] = projections['player_names'].map(lambda x: projections_match_dict.get(x, x))
st.session_state['projections_df'] = projections
if sport_var in stacking_sports:
team_dict = dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team']))
st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].apply(
lambda row: Counter(
team_dict.get(player, '') for player in row[2:]
if team_dict.get(player, '') != ''
).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row[2:]) else '',
axis=1
)
st.session_state['portfolio']['Size'] = st.session_state['portfolio'].apply(
lambda row: Counter(
team_dict.get(player, '') for player in row[2:]
if team_dict.get(player, '') != ''
).most_common(1)[0][1] if any(team_dict.get(player, '') for player in row[2:]) else 0,
axis=1
)
stack_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Stack']))
size_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Size']))
working_frame = st.session_state['portfolio'].copy()
try:
st.session_state['export_dict'] = dict(zip(st.session_state['csv_file']['Name'], st.session_state['csv_file']['Name + ID']))
except:
st.session_state['export_dict'] = dict(zip(st.session_state['csv_file']['Nickname'], st.session_state['csv_file']['Id']))
st.session_state['origin_portfolio'] = st.session_state['portfolio'].copy()
# with tab2:
# if st.button('Clear data', key='reset2'):
# st.session_state.clear()
# if 'portfolio' in st.session_state and 'projections_df' in st.session_state:
# optimized_df = None
# map_dict = {
# 'pos_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['position'])),
# 'salary_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['salary'])),
# 'proj_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['median'])),
# 'own_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['ownership'])),
# 'team_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['team']))
# }
# # Calculate new stats for optimized lineups
# st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1
# )
# st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1
# )
# st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1
# )
# options_container = st.container()
# with options_container:
# col1, col2, col3, col4, col5, col6 = st.columns(6)
# with col1:
# curr_site_var = st.selectbox("Select your current site", options=['DraftKings', 'FanDuel'])
# with col2:
# curr_sport_var = st.selectbox("Select your current sport", options=['NBA', 'MLB', 'NFL', 'NHL', 'MMA'])
# with col3:
# swap_var = st.multiselect("Select late swap strategy", options=['Optimize', 'Increase volatility', 'Decrease volatility'])
# with col4:
# remove_teams_var = st.multiselect("What teams have already played?", options=st.session_state['projections_df']['team'].unique())
# with col5:
# winners_var = st.multiselect("Are there any players doing exceptionally well?", options=st.session_state['projections_df']['player_names'].unique(), max_selections=3)
# with col6:
# losers_var = st.multiselect("Are there any players doing exceptionally poorly?", options=st.session_state['projections_df']['player_names'].unique(), max_selections=3)
# if st.button('Clear Late Swap'):
# if 'optimized_df' in st.session_state:
# del st.session_state['optimized_df']
# map_dict = {
# 'pos_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['position'])),
# 'salary_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['salary'])),
# 'proj_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['median'])),
# 'own_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['ownership'])),
# 'team_map': dict(zip(st.session_state['projections_df']['player_names'],
# st.session_state['projections_df']['team']))
# }
# # Calculate new stats for optimized lineups
# st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1
# )
# st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1
# )
# st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
# lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1
# )
# if st.button('Run Late Swap'):
# st.session_state['portfolio'] = st.session_state['portfolio'].drop(columns=['salary', 'median', 'Own'])
# if curr_sport_var == 'NBA':
# if curr_site_var == 'DraftKings':
# st.session_state['portfolio'] = st.session_state['portfolio'].set_axis(['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'], axis=1)
# else:
# st.session_state['portfolio'] = st.session_state['portfolio'].set_axis(['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C'], axis=1)
# # Define roster position rules
# if curr_site_var == 'DraftKings':
# position_rules = {
# 'PG': ['PG'],
# 'SG': ['SG'],
# 'SF': ['SF'],
# 'PF': ['PF'],
# 'C': ['C'],
# 'G': ['PG', 'SG'],
# 'F': ['SF', 'PF'],
# 'UTIL': ['PG', 'SG', 'SF', 'PF', 'C']
# }
# else:
# position_rules = {
# 'PG': ['PG'],
# 'SG': ['SG'],
# 'SF': ['SF'],
# 'PF': ['PF'],
# 'C': ['C'],
# }
# # Create position groups from projections data
# position_groups = {}
# for _, player in st.session_state['projections_df'].iterrows():
# positions = player['position'].split('/')
# for pos in positions:
# if pos not in position_groups:
# position_groups[pos] = []
# position_groups[pos].append({
# 'player_names': player['player_names'],
# 'salary': player['salary'],
# 'median': player['median'],
# 'ownership': player['ownership'],
# 'positions': positions # Store all eligible positions
# })
# def optimize_lineup(row):
# current_lineup = []
# total_salary = 0
# if curr_site_var == 'DraftKings':
# salary_cap = 50000
# else:
# salary_cap = 60000
# used_players = set()
# # Convert row to dictionary with roster positions
# roster = {}
# for col, player in zip(row.index, row):
# if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']:
# roster[col] = {
# 'name': player,
# 'position': map_dict['pos_map'].get(player, '').split('/'),
# 'team': map_dict['team_map'].get(player, ''),
# 'salary': map_dict['salary_map'].get(player, 0),
# 'median': map_dict['proj_map'].get(player, 0),
# 'ownership': map_dict['own_map'].get(player, 0)
# }
# total_salary += roster[col]['salary']
# used_players.add(player)
# # Optimize each roster position in random order
# roster_positions = list(roster.items())
# random.shuffle(roster_positions)
# for roster_pos, current in roster_positions:
# # Skip optimization for players from removed teams
# if current['team'] in remove_teams_var:
# continue
# valid_positions = position_rules[roster_pos]
# better_options = []
# # Find valid replacements for this roster position
# for pos in valid_positions:
# if pos in position_groups:
# pos_options = [
# p for p in position_groups[pos]
# if p['median'] > current['median']
# and (total_salary - current['salary'] + p['salary']) <= salary_cap
# and p['player_names'] not in used_players
# and any(valid_pos in p['positions'] for valid_pos in valid_positions)
# and map_dict['team_map'].get(p['player_names']) not in remove_teams_var # Check team restriction
# ]
# better_options.extend(pos_options)
# if better_options:
# # Remove duplicates
# better_options = {opt['player_names']: opt for opt in better_options}.values()
# # Sort by median projection and take the best one
# best_replacement = max(better_options, key=lambda x: x['median'])
# # Update the lineup and tracking variables
# used_players.remove(current['name'])
# used_players.add(best_replacement['player_names'])
# total_salary = total_salary - current['salary'] + best_replacement['salary']
# roster[roster_pos] = {
# 'name': best_replacement['player_names'],
# 'position': map_dict['pos_map'][best_replacement['player_names']].split('/'),
# 'team': map_dict['team_map'][best_replacement['player_names']],
# 'salary': best_replacement['salary'],
# 'median': best_replacement['median'],
# 'ownership': best_replacement['ownership']
# }
# # Return optimized lineup maintaining original column order
# return [roster[pos]['name'] for pos in row.index if pos in roster]
# def optimize_lineup_winners(row):
# current_lineup = []
# total_salary = 0
# if curr_site_var == 'DraftKings':
# salary_cap = 50000
# else:
# salary_cap = 60000
# used_players = set()
# # Check if any winners are in the lineup and count them
# winners_in_lineup = sum(1 for player in row if player in winners_var)
# changes_needed = min(winners_in_lineup, 3) if winners_in_lineup > 0 else 0
# changes_made = 0
# # Convert row to dictionary with roster positions
# roster = {}
# for col, player in zip(row.index, row):
# if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']:
# roster[col] = {
# 'name': player,
# 'position': map_dict['pos_map'].get(player, '').split('/'),
# 'team': map_dict['team_map'].get(player, ''),
# 'salary': map_dict['salary_map'].get(player, 0),
# 'median': map_dict['proj_map'].get(player, 0),
# 'ownership': map_dict['own_map'].get(player, 0)
# }
# total_salary += roster[col]['salary']
# used_players.add(player)
# # Only proceed with ownership-based optimization if we have winners in the lineup
# if changes_needed > 0:
# # Randomize the order of positions to optimize
# roster_positions = list(roster.items())
# random.shuffle(roster_positions)
# for roster_pos, current in roster_positions:
# # Stop if we've made enough changes
# if changes_made >= changes_needed:
# break
# # Skip optimization for players from removed teams or if the current player is a winner
# if current['team'] in remove_teams_var or current['name'] in winners_var:
# continue
# valid_positions = list(position_rules[roster_pos])
# random.shuffle(valid_positions)
# better_options = []
# # Find valid replacements with higher ownership
# for pos in valid_positions:
# if pos in position_groups:
# pos_options = [
# p for p in position_groups[pos]
# if p['ownership'] > current['ownership']
# and p['median'] >= current['median'] - 3
# and (total_salary - current['salary'] + p['salary']) <= salary_cap
# and (total_salary - current['salary'] + p['salary']) >= salary_cap - 1000
# and p['player_names'] not in used_players
# and any(valid_pos in p['positions'] for valid_pos in valid_positions)
# and map_dict['team_map'].get(p['player_names']) not in remove_teams_var
# ]
# better_options.extend(pos_options)
# if better_options:
# # Remove duplicates
# better_options = {opt['player_names']: opt for opt in better_options}.values()
# # Sort by ownership and take the highest owned option
# best_replacement = max(better_options, key=lambda x: x['ownership'])
# # Update the lineup and tracking variables
# used_players.remove(current['name'])
# used_players.add(best_replacement['player_names'])
# total_salary = total_salary - current['salary'] + best_replacement['salary']
# roster[roster_pos] = {
# 'name': best_replacement['player_names'],
# 'position': map_dict['pos_map'][best_replacement['player_names']].split('/'),
# 'team': map_dict['team_map'][best_replacement['player_names']],
# 'salary': best_replacement['salary'],
# 'median': best_replacement['median'],
# 'ownership': best_replacement['ownership']
# }
# changes_made += 1
# # Return optimized lineup maintaining original column order
# return [roster[pos]['name'] for pos in row.index if pos in roster]
# def optimize_lineup_losers(row):
# current_lineup = []
# total_salary = 0
# if curr_site_var == 'DraftKings':
# salary_cap = 50000
# else:
# salary_cap = 60000
# used_players = set()
# # Check if any winners are in the lineup and count them
# losers_in_lineup = sum(1 for player in row if player in losers_var)
# changes_needed = min(losers_in_lineup, 3) if losers_in_lineup > 0 else 0
# changes_made = 0
# # Convert row to dictionary with roster positions
# roster = {}
# for col, player in zip(row.index, row):
# if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']:
# roster[col] = {
# 'name': player,
# 'position': map_dict['pos_map'].get(player, '').split('/'),
# 'team': map_dict['team_map'].get(player, ''),
# 'salary': map_dict['salary_map'].get(player, 0),
# 'median': map_dict['proj_map'].get(player, 0),
# 'ownership': map_dict['own_map'].get(player, 0)
# }
# total_salary += roster[col]['salary']
# used_players.add(player)
# # Only proceed with ownership-based optimization if we have winners in the lineup
# if changes_needed > 0:
# # Randomize the order of positions to optimize
# roster_positions = list(roster.items())
# random.shuffle(roster_positions)
# for roster_pos, current in roster_positions:
# # Stop if we've made enough changes
# if changes_made >= changes_needed:
# break
# # Skip optimization for players from removed teams or if the current player is a winner
# if current['team'] in remove_teams_var or current['name'] in losers_var:
# continue
# valid_positions = list(position_rules[roster_pos])
# random.shuffle(valid_positions)
# better_options = []
# # Find valid replacements with higher ownership
# for pos in valid_positions:
# if pos in position_groups:
# pos_options = [
# p for p in position_groups[pos]
# if p['ownership'] < current['ownership']
# and p['median'] >= current['median'] - 3
# and (total_salary - current['salary'] + p['salary']) <= salary_cap
# and (total_salary - current['salary'] + p['salary']) >= salary_cap - 1000
# and p['player_names'] not in used_players
# and any(valid_pos in p['positions'] for valid_pos in valid_positions)
# and map_dict['team_map'].get(p['player_names']) not in remove_teams_var
# ]
# better_options.extend(pos_options)
# if better_options:
# # Remove duplicates
# better_options = {opt['player_names']: opt for opt in better_options}.values()
# # Sort by ownership and take the highest owned option
# best_replacement = max(better_options, key=lambda x: x['ownership'])
# # Update the lineup and tracking variables
# used_players.remove(current['name'])
# used_players.add(best_replacement['player_names'])
# total_salary = total_salary - current['salary'] + best_replacement['salary']
# roster[roster_pos] = {
# 'name': best_replacement['player_names'],
# 'position': map_dict['pos_map'][best_replacement['player_names']].split('/'),
# 'team': map_dict['team_map'][best_replacement['player_names']],
# 'salary': best_replacement['salary'],
# 'median': best_replacement['median'],
# 'ownership': best_replacement['ownership']
# }
# changes_made += 1
# # Return optimized lineup maintaining original column order
# return [roster[pos]['name'] for pos in row.index if pos in roster]
# # Create a progress bar
# progress_bar = st.progress(0)
# status_text = st.empty()
# # Process each lineup
# optimized_lineups = []
# total_lineups = len(st.session_state['portfolio'])
# for idx, row in st.session_state['portfolio'].iterrows():
# # First optimization pass
# first_pass = optimize_lineup(row)
# first_pass_series = pd.Series(first_pass, index=row.index)
# second_pass = optimize_lineup(first_pass_series)
# second_pass_series = pd.Series(second_pass, index=row.index)
# third_pass = optimize_lineup(second_pass_series)
# third_pass_series = pd.Series(third_pass, index=row.index)
# fourth_pass = optimize_lineup(third_pass_series)
# fourth_pass_series = pd.Series(fourth_pass, index=row.index)
# fifth_pass = optimize_lineup(fourth_pass_series)
# fifth_pass_series = pd.Series(fifth_pass, index=row.index)
# # Second optimization pass
# final_lineup = optimize_lineup(fifth_pass_series)
# optimized_lineups.append(final_lineup)
# if 'Optimize' in swap_var:
# progress = (idx + 1) / total_lineups
# progress_bar.progress(progress)
# status_text.text(f'Optimizing Lineups {idx + 1} of {total_lineups}')
# else:
# pass
# # Create new dataframe with optimized lineups
# if 'Optimize' in swap_var:
# st.session_state['optimized_df_medians'] = pd.DataFrame(optimized_lineups, columns=st.session_state['portfolio'].columns)
# else:
# st.session_state['optimized_df_medians'] = st.session_state['portfolio']
# # Create a progress bar
# progress_bar_winners = st.progress(0)
# status_text_winners = st.empty()
# # Process each lineup
# optimized_lineups_winners = []
# total_lineups = len(st.session_state['optimized_df_medians'])
# for idx, row in st.session_state['optimized_df_medians'].iterrows():
# final_lineup = optimize_lineup_winners(row)
# optimized_lineups_winners.append(final_lineup)
# if 'Decrease volatility' in swap_var:
# progress_winners = (idx + 1) / total_lineups
# progress_bar_winners.progress(progress_winners)
# status_text_winners.text(f'Lowering Volatility around Winners {idx + 1} of {total_lineups}')
# else:
# pass
# # Create new dataframe with optimized lineups
# if 'Decrease volatility' in swap_var:
# st.session_state['optimized_df_winners'] = pd.DataFrame(optimized_lineups_winners, columns=st.session_state['optimized_df_medians'].columns)
# else:
# st.session_state['optimized_df_winners'] = st.session_state['optimized_df_medians']
# # Create a progress bar
# progress_bar_losers = st.progress(0)
# status_text_losers = st.empty()
# # Process each lineup
# optimized_lineups_losers = []
# total_lineups = len(st.session_state['optimized_df_winners'])
# for idx, row in st.session_state['optimized_df_winners'].iterrows():
# final_lineup = optimize_lineup_losers(row)
# optimized_lineups_losers.append(final_lineup)
# if 'Increase volatility' in swap_var:
# progress_losers = (idx + 1) / total_lineups
# progress_bar_losers.progress(progress_losers)
# status_text_losers.text(f'Increasing Volatility around Losers {idx + 1} of {total_lineups}')
# else:
# pass
# # Create new dataframe with optimized lineups
# if 'Increase volatility' in swap_var:
# st.session_state['optimized_df'] = pd.DataFrame(optimized_lineups_losers, columns=st.session_state['optimized_df_winners'].columns)
# else:
# st.session_state['optimized_df'] = st.session_state['optimized_df_winners']
# # Calculate new stats for optimized lineups
# st.session_state['optimized_df']['salary'] = st.session_state['optimized_df'].apply(
# lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1
# )
# st.session_state['optimized_df']['median'] = st.session_state['optimized_df'].apply(
# lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1
# )
# st.session_state['optimized_df']['Own'] = st.session_state['optimized_df'].apply(
# lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1
# )
# # Display results
# st.success('Optimization complete!')
# if 'optimized_df' in st.session_state:
# st.write("Increase in median highlighted in yellow, descrease in volatility highlighted in blue, increase in volatility highlighted in red:")
# st.dataframe(
# st.session_state['optimized_df'].style
# .apply(highlight_changes, axis=1)
# .apply(highlight_changes_winners, axis=1)
# .apply(highlight_changes_losers, axis=1)
# .background_gradient(axis=0)
# .background_gradient(cmap='RdYlGn')
# .format(precision=2),
# height=1000,
# use_container_width=True
# )
# # Option to download optimized lineups
# if st.button('Prepare Late Swap Export'):
# export_df = st.session_state['optimized_df'].copy()
# # Map player names to their export IDs for all player columns
# for col in export_df.columns:
# if col not in ['salary', 'median', 'Own']:
# export_df[col] = export_df[col].map(st.session_state['export_dict'])
# csv = export_df.to_csv(index=False)
# st.download_button(
# label="Download CSV",
# data=csv,
# file_name="optimized_lineups.csv",
# mime="text/csv"
# )
# else:
# st.write("Current Portfolio")
# st.dataframe(
# st.session_state['portfolio'].style
# .background_gradient(axis=0)
# .background_gradient(cmap='RdYlGn')
# .format(precision=2),
# height=1000,
# use_container_width=True
# )
with tab2:
if 'portfolio' in st.session_state and 'projections_df' in st.session_state:
with st.container():
col1, col2 = st.columns(2)
with col1:
if st.button('Reset Portfolio', key='reset_port'):
del st.session_state['working_frame']
with col2:
with st.form(key='contest_size_form'):
size_col, strength_col, submit_col = st.columns(3)
with size_col:
Contest_Size = st.number_input("Enter Contest Size", value=25000, min_value=1, step=1)
with strength_col:
strength_var = st.selectbox("Select field strength", ['Average', 'Sharp', 'Weak'])
with submit_col:
submitted = st.form_submit_button("Submit Size/Strength")
if submitted:
del st.session_state['working_frame']
excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean']
if 'working_frame' not in st.session_state:
st.session_state['working_frame'] = st.session_state['origin_portfolio'].copy()
if site_var == 'Draftkings':
if type_var == 'Classic':
if sport_var == 'CS2':
st.session_state['map_dict'] = {
'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'] * 1.5)),
'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
}
elif sport_var != 'CS2':
st.session_state['map_dict'] = {
'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
}
elif type_var == 'Showdown':
if sport_var == 'GOLF':
st.session_state['map_dict'] = {
'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership']))
}
if sport_var != 'GOLF':
st.session_state['map_dict'] = {
'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'] * 1.5)),
'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
}
elif site_var == 'Fanduel':
st.session_state['map_dict'] = {
'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
}
if type_var == 'Classic':
if sport_var == 'CS2':
# Calculate salary (CPT uses cpt_salary_map, others use salary_map)
st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(
lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) +
sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
# Calculate median (CPT uses cpt_proj_map, others use proj_map)
st.session_state['working_frame']['median'] = st.session_state['working_frame'].apply(
lambda row: st.session_state['map_dict']['cpt_proj_map'].get(row.iloc[0], 0) +
sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
# Calculate ownership (CPT uses cpt_own_map, others use own_map)
st.session_state['working_frame']['Own'] = st.session_state['working_frame'].apply(
lambda row: st.session_state['map_dict']['cpt_own_map'].get(row.iloc[0], 0) +
sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
elif sport_var != 'CS2':
st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row), axis=1)
st.session_state['working_frame']['median'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row), axis=1)
st.session_state['working_frame']['Own'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row), axis=1)
if stack_dict is not None:
st.session_state['working_frame']['Stack'] = st.session_state['working_frame'].index.map(stack_dict)
st.session_state['working_frame']['Size'] = st.session_state['working_frame'].index.map(size_dict)
elif type_var == 'Showdown':
# Calculate salary (CPT uses cpt_salary_map, others use salary_map)
st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(
lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) +
sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
# Calculate median (CPT uses cpt_proj_map, others use proj_map)
st.session_state['working_frame']['median'] = st.session_state['working_frame'].apply(
lambda row: st.session_state['map_dict']['cpt_proj_map'].get(row.iloc[0], 0) +
sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
# Calculate ownership (CPT uses cpt_own_map, others use own_map)
st.session_state['working_frame']['Own'] = st.session_state['working_frame'].apply(
lambda row: st.session_state['map_dict']['cpt_own_map'].get(row.iloc[0], 0) +
sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
axis=1
)
st.session_state['working_frame'] = predict_dupes(st.session_state['working_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
if 'info_columns_dict' not in st.session_state:
st.session_state['info_columns_dict'] = {
'Dupes': st.session_state['working_frame']['Dupes'],
'Finish_percentile': st.session_state['working_frame']['Finish_percentile'],
'Win%': st.session_state['working_frame']['Win%'],
'Lineup Edge': st.session_state['working_frame']['Lineup Edge'],
'Weighted Own': st.session_state['working_frame']['Weighted Own'],
'Geomean': st.session_state['working_frame']['Geomean'],
}
if 'trimming_dict_maxes' not in st.session_state:
st.session_state['trimming_dict_maxes'] = {
'Own': st.session_state['working_frame']['Own'].max(),
'Geomean': st.session_state['working_frame']['Geomean'].max(),
'Weighted Own': st.session_state['working_frame']['Weighted Own'].max(),
'median': st.session_state['working_frame']['median'].max(),
'Finish_percentile': st.session_state['working_frame']['Finish_percentile'].max()
}
col1, col2 = st.columns([2, 8])
with col1:
if 'trimming_dict_maxes' not in st.session_state:
st.session_state['trimming_dict_maxes'] = {
'Own': 500.0,
'Geomean': 500.0,
'Weighted Own': 500.0,
'median': 500.0,
'Finish_percentile': 1.0
}
with st.expander('Macro Filter Options'):
with st.form(key='macro_filter_form'):
max_dupes = st.number_input("Max acceptable dupes?", value=1000, min_value=1, step=1)
min_salary = st.number_input("Min acceptable salary?", value=1000, min_value=1000, step=100)
max_salary = st.number_input("Max acceptable salary?", value=100000, min_value=1000, step=100)
max_finish_percentile = st.number_input("Max acceptable finish percentile?", value=.50, min_value=0.005, step=.001)
min_lineup_edge = st.number_input("Min acceptable Lineup Edge?", value=-.5, min_value=-1.00, step=.001)
if sport_var in ['NFL', 'MLB', 'NHL']:
stack_include_toggle = st.selectbox("Include specific stacks?", options=['All Stacks', 'Specific Stacks'], index=0)
stack_selections = st.multiselect("If Specific Stacks, Which to include?", options=sorted(list(set(stack_dict.values()))), default=[])
stack_remove_toggle = st.selectbox("Remove specific stacks?", options=['No', 'Yes'], index=0)
stack_remove = st.multiselect("If Specific Stacks, Which to remove?", options=sorted(list(set(stack_dict.values()))), default=[])
submitted = st.form_submit_button("Submit")
if submitted:
parsed_frame = st.session_state['working_frame'].copy()
parsed_frame = parsed_frame[parsed_frame['Dupes'] <= max_dupes]
parsed_frame = parsed_frame[parsed_frame['salary'] >= min_salary]
parsed_frame = parsed_frame[parsed_frame['salary'] <= max_salary]
parsed_frame = parsed_frame[parsed_frame['Finish_percentile'] <= max_finish_percentile]
parsed_frame = parsed_frame[parsed_frame['Lineup Edge'] >= min_lineup_edge]
if 'Stack' in parsed_frame.columns:
if stack_include_toggle == 'All Stacks':
parsed_frame = parsed_frame
else:
parsed_frame = parsed_frame[parsed_frame['Stack'].isin(stack_selections)]
if stack_remove_toggle == 'Yes':
parsed_frame = parsed_frame[~parsed_frame['Stack'].isin(stack_remove)]
else:
parsed_frame = parsed_frame
st.session_state['working_frame'] = parsed_frame.sort_values(by='median', ascending=False)
st.session_state['export_merge'] = st.session_state['working_frame'].copy()
with st.expander('Micro Filter Options'):
with st.form(key='micro_filter_form'):
player_names = set()
for col in st.session_state['working_frame'].columns:
if col not in excluded_cols:
player_names.update(st.session_state['working_frame'][col].unique())
player_lock = st.multiselect("Lock players?", options=sorted(list(player_names)), default=[])
player_remove = st.multiselect("Remove players?", options=sorted(list(player_names)), default=[])
team_include = st.multiselect("Include teams?", options=sorted(list(set(st.session_state['projections_df']['team'].unique()))), default=[])
team_remove = st.multiselect("Remove teams?", options=sorted(list(set(st.session_state['projections_df']['team'].unique()))), default=[])
if sport_var in ['NFL', 'MLB', 'NHL']:
size_include = st.multiselect("Include sizes?", options=sorted(list(set(st.session_state['working_frame']['Size'].unique()))), default=[])
else:
size_include = []
submitted = st.form_submit_button("Submit")
if submitted:
parsed_frame = st.session_state['working_frame'].copy()
if player_remove:
# Create mask for lineups that contain any of the removed players
player_columns = [col for col in parsed_frame.columns if col not in excluded_cols]
remove_mask = parsed_frame[player_columns].apply(
lambda row: not any(player in list(row) for player in player_remove), axis=1
)
parsed_frame = parsed_frame[remove_mask]
if player_lock:
# Create mask for lineups that contain all locked players
player_columns = [col for col in parsed_frame.columns if col not in excluded_cols]
lock_mask = parsed_frame[player_columns].apply(
lambda row: all(player in list(row) for player in player_lock), axis=1
)
parsed_frame = parsed_frame[lock_mask]
if team_include:
# Create a copy of the frame with player names replaced by teams, excluding SP1 and SP2
filtered_player_columns = [col for col in player_columns if col not in ['SP1', 'SP2']]
team_frame = parsed_frame[filtered_player_columns].apply(
lambda x: x.map(st.session_state['map_dict']['team_map'])
)
# Create mask for lineups that contain any of the included teams
include_mask = team_frame.apply(
lambda row: any(team in list(row) for team in team_include), axis=1
)
parsed_frame = parsed_frame[include_mask]
if team_remove:
# Create a copy of the frame with player names replaced by teams, excluding SP1 and SP2
filtered_player_columns = [col for col in player_columns if col not in ['SP1', 'SP2']]
team_frame = parsed_frame[filtered_player_columns].apply(
lambda x: x.map(st.session_state['map_dict']['team_map'])
)
# Create mask for lineups that don't contain any of the removed teams
remove_mask = team_frame.apply(
lambda row: not any(team in list(row) for team in team_remove), axis=1
)
parsed_frame = parsed_frame[remove_mask]
if size_include:
parsed_frame = parsed_frame[parsed_frame['Size'].isin(size_include)]
st.session_state['working_frame'] = parsed_frame.sort_values(by='median', ascending=False)
st.session_state['export_merge'] = st.session_state['working_frame'].copy()
with st.expander('Trimming Options'):
st.info("Make sure you filter before trimming if you want to filter, trimming before a filter will reset your portfolio")
with st.form(key='trim_form'):
st.write("Sorting and trimming variables:")
perf_var, own_var = st.columns(2)
with perf_var:
performance_type = st.selectbox("Sorting variable", ['median', 'Finish_percentile'], key='sort_var')
with own_var:
own_type = st.selectbox("Trimming variable", ['Own', 'Geomean', 'Weighted Own'], key='trim_var')
trim_slack_var = st.number_input("Trim slack (percentile addition to trimming variable ceiling)", value=0.0, min_value=0.0, max_value=1.0, step=0.1, key='trim_slack')
st.write("Sorting threshold range:")
min_sort, max_sort = st.columns(2)
with min_sort:
performance_threshold_low = st.number_input("Min", value=0.0, min_value=0.0, step=1.0, key='min_sort')
with max_sort:
performance_threshold_high = st.number_input("Max", value=st.session_state['trimming_dict_maxes'][performance_type], min_value=0.0, step=1.0, key='max_sort')
st.write("Trimming threshold range:")
min_trim, max_trim = st.columns(2)
with min_trim:
own_threshold_low = st.number_input("Min", value=0.0, min_value=0.0, step=1.0, key='min_trim')
with max_trim:
own_threshold_high = st.number_input("Max", value=st.session_state['trimming_dict_maxes'][own_type], min_value=0.0, step=1.0, key='max_trim')
submitted = st.form_submit_button("Trim")
if submitted:
st.write('initiated')
parsed_frame = st.session_state['working_frame'].copy()
st.session_state['working_frame'] = trim_portfolio(parsed_frame, trim_slack_var, performance_type, own_type, performance_threshold_high, performance_threshold_low, own_threshold_high, own_threshold_low)
st.session_state['working_frame'] = st.session_state['working_frame'].sort_values(by='median', ascending=False)
st.session_state['export_merge'] = st.session_state['working_frame'].copy()
with col2:
# with st.expander("Download options"):
# if stack_dict is not None:
# download_type = st.selectbox("Simple or Advanced Download?", options=['Simple', 'Advanced'], key='download_choice')
# if download_type == 'Simple':
# st.session_state['export_file'] = st.session_state['working_frame'].copy()
# for col in st.session_state['export_file'].columns:
# if col not in excluded_cols:
# st.session_state['export_file'][col] = st.session_state['export_file'][col].map(st.session_state['export_dict'])
# else:
# with st.form(key='stack_form'):
# st.subheader("Stack Count Adjustments")
# st.info("This allows you to fine tune the stacks that you wish to export. If you want to make sure you don't export any of a specific stack you can 0 it out.")
# # Create a container for stack value inputs
# sort_container = st.container()
# with sort_container:
# sort_var = st.selectbox("Sort export portfolio by:", options=['median', 'Lineup Edge', 'Own'])
# # Get unique stack values
# unique_stacks = sorted(list(set(stack_dict.values())))
# # Create a dictionary to store stack multipliers
# if 'stack_multipliers' not in st.session_state:
# st.session_state.stack_multipliers = {stack: 0.0 for stack in unique_stacks}
# # Create columns for the stack inputs
# num_cols = 6 # Number of columns to display
# for i in range(0, len(unique_stacks), num_cols):
# cols = st.columns(num_cols)
# for j, stack in enumerate(unique_stacks[i:i+num_cols]):
# with cols[j]:
# # Create a unique key for each number input
# key = f"stack_count_{stack}"
# # Get the current count of this stack in the portfolio
# current_stack_count = len(st.session_state['working_frame'][st.session_state['working_frame']['Stack'] == stack])
# # Create number input with current value and max value based on actual count
# st.session_state.stack_multipliers[stack] = st.number_input(
# f"{stack} count",
# min_value=0.0,
# max_value=float(current_stack_count),
# value=0.0,
# step=1.0,
# key=key
# )
# portfolio_copy = st.session_state['working_frame'].copy()
# submitted = st.form_submit_button("Submit")
# if submitted:
# # Create a list to store selected rows
# selected_rows = []
# # For each stack, select the top N rows based on the count value
# for stack in unique_stacks:
# if stack in st.session_state.stack_multipliers:
# count = int(st.session_state.stack_multipliers[stack])
# # Get rows for this stack
# stack_rows = portfolio_copy[portfolio_copy['Stack'] == stack]
# # Sort by median and take top N rows
# top_rows = stack_rows.nlargest(count, sort_var)
# selected_rows.append(top_rows)
# # Combine all selected rows
# portfolio_concat = pd.concat(selected_rows)
# # Update export_file with filtered data
# st.session_state['export_file'] = portfolio_concat.copy()
# for col in st.session_state['export_file'].columns:
# if col not in excluded_cols:
# st.session_state['export_file'][col] = st.session_state['export_file'][col].map(st.session_state['export_dict'])
# st.write('Export portfolio updated!')
# else:
# st.session_state['export_file'] = st.session_state['working_frame'].copy()
# if 'export_base' not in st.session_state:
# st.session_state['export_base'] = pd.DataFrame(columns=st.session_state['working_frame'].columns)
# for col in st.session_state['export_file'].columns:
# if col not in excluded_cols:
# st.session_state['export_file'][col] = st.session_state['export_file'][col].map(st.session_state['export_dict'])
if 'export_base' not in st.session_state:
st.session_state['export_base'] = pd.DataFrame(columns=st.session_state['working_frame'].columns)
display_frame_source = st.selectbox("Display:", options=['Portfolio', 'Export Base'], key='display_frame_source')
if display_frame_source == 'Portfolio':
display_frame = st.session_state['working_frame']
st.session_state['export_file'] = display_frame.copy()
for col in st.session_state['export_file'].columns:
if col not in excluded_cols:
st.session_state['export_file'][col] = st.session_state['export_file'][col].map(st.session_state['export_dict'])
elif display_frame_source == 'Export Base':
display_frame = st.session_state['export_base']
st.session_state['export_file'] = display_frame.copy()
for col in st.session_state['export_file'].columns:
if col not in excluded_cols:
st.session_state['export_file'][col] = st.session_state['export_file'][col].map(st.session_state['export_dict'])
if 'export_file' in st.session_state:
download_port, merge_port, clear_export, blank_export_col = st.columns([1, 1, 1, 8])
with download_port:
st.download_button(label="Download Portfolio", data=st.session_state['export_file'].to_csv(index=False), file_name="portfolio.csv", mime="text/csv")
with merge_port:
if st.button("Add to Custom Export"):
st.session_state['export_base'] = pd.concat([st.session_state['export_base'], st.session_state['export_merge']])
st.session_state['export_base'] = st.session_state['export_base'].drop_duplicates()
st.session_state['export_base'] = st.session_state['export_base'].reset_index(drop=True)
with clear_export:
if st.button("Clear Custom Export"):
st.session_state['export_base'] = pd.DataFrame(columns=st.session_state['working_frame'].columns)
if display_frame_source == 'Portfolio':
display_frame = st.session_state['working_frame']
elif display_frame_source == 'Export Base':
display_frame = st.session_state['export_base']
total_rows = len(display_frame)
rows_per_page = 500
total_pages = (total_rows + rows_per_page - 1) // rows_per_page # Ceiling division
# Initialize page number in session state if not exists
if 'current_page' not in st.session_state:
st.session_state.current_page = 1
# Display current page range info and pagination control in a single line
st.write(
f"Showing rows {(st.session_state.current_page - 1) * rows_per_page + 1} "
f"to {min(st.session_state.current_page * rows_per_page, total_rows)} of {total_rows}"
)
# Add page number input
st.session_state.current_page = st.number_input(
f"Page (1-{total_pages})",
min_value=1,
max_value=total_pages,
value=st.session_state.current_page
)
# Calculate start and end indices for current page
start_idx = (st.session_state.current_page - 1) * rows_per_page
end_idx = min(start_idx + rows_per_page, total_rows)
# Get the subset of data for the current page
current_page_data = display_frame.iloc[start_idx:end_idx]
# Display the paginated dataframe first
st.dataframe(
current_page_data.style
.background_gradient(axis=0)
.background_gradient(cmap='RdYlGn')
.background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes'])
.format(freq_format, precision=2),
height=1000,
use_container_width=True
)
player_stats_col, stack_stats_col = st.tabs(['Player Stats', 'Stack Stats'])
with player_stats_col:
player_stats = []
player_columns = [col for col in display_frame.columns if col not in excluded_cols]
if type_var == 'Showdown':
for player in player_names:
# Create mask for lineups where this player is Captain (first column)
cpt_mask = display_frame[player_columns[0]] == player
if cpt_mask.any():
player_stats.append({
'Player': f"{player} (CPT)",
'Lineup Count': cpt_mask.sum(),
'Exposure': cpt_mask.sum() / len(display_frame),
'Avg Median': display_frame[cpt_mask]['median'].mean(),
'Avg Own': display_frame[cpt_mask]['Own'].mean(),
'Avg Dupes': display_frame[cpt_mask]['Dupes'].mean(),
'Avg Finish %': display_frame[cpt_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': display_frame[cpt_mask]['Lineup Edge'].mean(),
})
# Create mask for lineups where this player is FLEX (other columns)
flex_mask = display_frame[player_columns[1:]].apply(
lambda row: player in list(row), axis=1
)
if flex_mask.any():
player_stats.append({
'Player': f"{player} (FLEX)",
'Lineup Count': flex_mask.sum(),
'Exposure': flex_mask.sum() / len(display_frame),
'Avg Median': display_frame[flex_mask]['median'].mean(),
'Avg Own': display_frame[flex_mask]['Own'].mean(),
'Avg Dupes': display_frame[flex_mask]['Dupes'].mean(),
'Avg Finish %': display_frame[flex_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': display_frame[flex_mask]['Lineup Edge'].mean(),
})
else:
if sport_var == 'CS2':
# Handle Captain positions
for player in player_names:
# Create mask for lineups where this player is Captain (first column)
cpt_mask = display_frame[player_columns[0]] == player
if cpt_mask.any():
player_stats.append({
'Player': f"{player} (CPT)",
'Lineup Count': cpt_mask.sum(),
'Exposure': cpt_mask.sum() / len(display_frame),
'Avg Median': display_frame[cpt_mask]['median'].mean(),
'Avg Own': display_frame[cpt_mask]['Own'].mean(),
'Avg Dupes': display_frame[cpt_mask]['Dupes'].mean(),
'Avg Finish %': display_frame[cpt_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': display_frame[cpt_mask]['Lineup Edge'].mean(),
})
# Create mask for lineups where this player is FLEX (other columns)
flex_mask = display_frame[player_columns[1:]].apply(
lambda row: player in list(row), axis=1
)
if flex_mask.any():
player_stats.append({
'Player': f"{player} (FLEX)",
'Lineup Count': flex_mask.sum(),
'Exposure': flex_mask.sum() / len(display_frame),
'Avg Median': display_frame[flex_mask]['median'].mean(),
'Avg Own': display_frame[flex_mask]['Own'].mean(),
'Avg Dupes': display_frame[flex_mask]['Dupes'].mean(),
'Avg Finish %': display_frame[flex_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': display_frame[flex_mask]['Lineup Edge'].mean(),
})
elif sport_var != 'CS2':
# Original Classic format processing
for player in player_names:
player_mask = display_frame[player_columns].apply(
lambda row: player in list(row), axis=1
)
if player_mask.any():
player_stats.append({
'Player': player,
'Lineup Count': player_mask.sum(),
'Exposure': player_mask.sum() / len(display_frame),
'Avg Median': display_frame[player_mask]['median'].mean(),
'Avg Own': display_frame[player_mask]['Own'].mean(),
'Avg Dupes': display_frame[player_mask]['Dupes'].mean(),
'Avg Finish %': display_frame[player_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': display_frame[player_mask]['Lineup Edge'].mean(),
})
player_summary = pd.DataFrame(player_stats)
player_summary = player_summary.sort_values('Lineup Count', ascending=False)
st.subheader("Player Summary")
st.dataframe(
player_summary.style
.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes'])
.format({
'Avg Median': '{:.2f}',
'Avg Own': '{:.2f}',
'Avg Dupes': '{:.2f}',
'Avg Finish %': '{:.2%}',
'Avg Lineup Edge': '{:.2%}',
'Exposure': '{:.2%}'
}),
height=400,
use_container_width=True
)
with stack_stats_col:
if 'Stack' in display_frame.columns:
stack_stats = []
stack_columns = [col for col in display_frame.columns if col.startswith('Stack')]
for stack in stack_dict.values():
stack_mask = display_frame['Stack'] == stack
if stack_mask.any():
stack_stats.append({
'Stack': stack,
'Lineup Count': stack_mask.sum(),
'Exposure': stack_mask.sum() / len(display_frame),
'Avg Median': display_frame[stack_mask]['median'].mean(),
'Avg Own': display_frame[stack_mask]['Own'].mean(),
'Avg Dupes': display_frame[stack_mask]['Dupes'].mean(),
'Avg Finish %': display_frame[stack_mask]['Finish_percentile'].mean(),
'Avg Lineup Edge': display_frame[stack_mask]['Lineup Edge'].mean(),
})
stack_summary = pd.DataFrame(stack_stats)
stack_summary = stack_summary.sort_values('Lineup Count', ascending=False).drop_duplicates()
st.subheader("Stack Summary")
st.dataframe(
stack_summary.style
.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes'])
.format({
'Avg Median': '{:.2f}',
'Avg Own': '{:.2f}',
'Avg Dupes': '{:.2f}',
'Avg Finish %': '{:.2%}',
'Avg Lineup Edge': '{:.2%}',
'Exposure': '{:.2%}'
}),
height=400,
use_container_width=True
)
else:
stack_summary = pd.DataFrame(columns=['Stack', 'Lineup Count', 'Avg Median', 'Avg Own', 'Avg Dupes', 'Avg Finish %', 'Avg Lineup Edge']) |