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429 14824 54125
430 14844 54039
431 14876 53995
432 14906 53946
433 14938 53902
434 14980 53894
435 15006 53829
436 15033 53770
437 15059 53706
438 15085 53639
439 15110 53574
440 15134 53503
441 15160 53438
442 15184 53369
443 15211 53308
444 15234 53236
445 15266 53193
446 15287 53114
447 15316 53059
448 15336 52978
449 15366 52929
450 15393 52870
451 15429 52843
452 15469 52828
453 15490 52748
454 15523 52712
455 15550 52653
456 15577 52594
457 15604 52536
458 15630 52476
459 15656 52414
460 15682 52353
461 15711 52304
462 15736 52238
463 15765 52188
464 15786 52112
465 15817 52068
466 15839 51996
467 15873 51961
468 15903 51916
469 15935 51873
470 15969 51840
471 15994 51779
472 16022 51726
473 16047 51663
474 16073 51605
475 16099 51546
476 16128 51495
477 16152 51431
478 16176 51367
479 16205 51317
480 16228 51250
481 16255 51194
482 16277 51123
483 16305 51071
484 16328 51005
485 16362 50973
486 16392 50928
487 16426 50894
488 16459 50860
489 16480 50787
490 16510 50743
491 16530 50668
492 16561 50625
493 16585 50562
494 16613 50510
495 16638 50453
496 16663 50393
497 16690 50339
498 16716 50282
499 16740 50222
500 16773 50186
501 16802 50139
502 16836 50107
503 16873 50085
504 16921 50094
505 16989 50163
506 17038 50173
507 17069 50132
508 17110 50121
509 17145 50091
510 17190 50091
511 17219 50044
512 17247 49994
513 17271 49932
514 17298 49878
515 17343 49878
516 17373 49836
517 17417 49831
518 17460 49823
519 17490 49781
520 17518 49731
521 17546 49680
522 17571 49622
523 17600 49577
524 17625 49520
525 17655 49474
526 17679 49414
527 17707 49366
528 17729 49300
529 17758 49254
530 17781 49191
531 17808 49141
532 17829 49071
533 17862 49038
534 17905 49031
535 18028 49241
536 18072 49236
537 18106 49203
538 18135 49157
539 18165 49114
540 18200 49083
541 18223 49022
542 18254 48980
543 18280 48927
544 18307 48876
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560 18781 48176
561 18808 48126
562 18834 48074
563 18869 48043
564 18902 48008
565 18930 47960
566 18958 47914
567 18983 47859
568 19016 47824
569 19037 47761
570 19068 47720
571 19090 47660
572 19111 47595
573 19141 47553
574 19164 47494
575 19196 47458
576 19217 47393
577 19249 47358
578 19274 47303
579 19298 47247
580 19324 47195
581 19357 47162
582 19391 47130
583 19427 47103
584 19460 47070
585 19483 47012
586 19511 46967
587 19542 46929
588 19564 46867
589 19597 46833
590 19621 46779
591 19647 46729
592 19670 46672
593 19699 46627
594 19726 46582
595 19753 46532
596 19778 46480
597 19803 46429
598 19830 46381
599 19857 46335
600 19896 46313
601 19925 46271
602 19957 46236
603 19991 46204
604 20019 46159
605 20047 46115
606 20072 46063
607 20098 46015
608 20123 45963
609 20149 45913
610 20176 45867
611 20202 45817
612 20230 45774
613 20253 45719
614 20285 45682
615 20307 45626
616 20338 45589
617 20361 45532
618 20394 45500
619 20423 45459
620 20454 45420
621 20488 45390
622 20510 45333
623 20543 45301
624 20569 45252
625 20594 45201
626 20619 45151
627 20646 45107
628 20675 45066
629 20701 45016
630 20727 44970
631 20752 44919
632 20782 44881
633 20804 44825
634 20837 44791
635 20862 44742
636 20892 44704
637 20931 44683
638 20960 44643
639 20994 44612
640 21022 44570
641 21052 44531
642 21082 44493
643 21107 44443
644 21135 44401
645 21160 44351
646 21185 44302
647 21210 44253
648 21236 44208
649 21262 44161
650 21288 44113
651 21315 44068
652 21343 44027
653 21377 43997
654 21403 43949
655 21440 43926
656 21477 43903
657 21502 43854
658 21533 43819
659 21559 43772
660 21586 43727
661 21611 43680
662 21637 43633
663 21662 43586
664 21688 43539
665 21714 43493
666 21742 43451
667 21771 43413
668 21818 43409
669 21846 43366
670 21888 43352
671 21934 43345
672 21971 43322
673 22019 43320
674 22053 43289
675 22090 43266
676 22141 43269
677 22176 43240
678 22213 43215
679 22239 43171
680 22270 43134
681 22296 43088
682 22321 43041
683 22350 43002
684 22379 42962
685 22419 42944
686 22452 42912
687 22484 42878
688 22511 42834
689 22537 42789
690 22571 42757
691 22598 42714
692 22624 42669
693 22653 42630
694 22680 42586
695 22708 42545
696 22739 42510
697 22761 42457
698 22792 42421
699 22816 42373
700 22845 42333
701 22870 42288
702 22902 42253
703 22942 42234
704 22974 42201
705 23002 42160
706 23033 42124
707 23054 42071
708 23086 42038
709 23115 41999
710 23143 41957
711 23169 41914
712 23195 41868
713 23230 41840
714 23259 41801
715 23287 41760
716 23311 41713
717 23341 41676
718 23372 41641
719 23405 41610
720 23438 41578
721 23483 41566
722 23507 41519
723 23540 41488
724 23566 41444
725 23595 41406
726 23623 41365
727 23648 41320
728 23677 41281
729 23700 41231
730 23728 41192
731 23752 41144
732 23784 41111
733 23807 41063
734 23840 41031
735 23870 40994
736 23908 40972
737 23941 40940
738 23974 40909
739 24006 40875
740 24036 40838
741 24064 40798
742 24092 40759
743 24127 40730
744 24153 40688
745 24179 40644
746 24207 40604
747 24233 40561
748 24261 40522
749 24295 40491
750 24318 40444
751 24349 40410
752 24376 40368
753 24408 40335
754 24442 40306
755 24474 40273
756 24508 40242
757 24548 40222
758 24575 40182
759 24605 40145
760 24632 40104
761 24660 40064
762 24689 40027
763 24714 39982
764 24745 39949
765 24766 39897
766 24797 39863
767 24825 39823
768 24854 39786
769 24880 39744
770 24909 39706
771 24940 39672
772 24970 39635
773 25004 39606
774 25030 39564
775 25056 39522
776 25086 39486
777 25107 39436
778 25139 39403
779 25159 39351
780 25188 39314
781 25214 39272
782 25240 39230
783 25266 39188
784 25288 39141
785 25315 39101
786 25341 39058
787 25367 39016
788 25391 38972
789 25417 38930
790 25448 38895
791 25482 38867
792 25514 38834
793 25542 38795
794 25569 38756
795 25595 38714
796 25618 38669
797 25643 38626
798 25667 38581
799 25695 38543
800 25716 38494
801 25743 38454
802 25770 38415
803 25790 38364
804 25822 38332
805 25843 38284
806 25873 38249
807 25896 38203
808 25925 38167
809 25955 38131
810 25988 38101
811 26028 38080
812 26055 38042
813 26081 38000
814 26108 37961
815 26131 37916
816 26159 37878
817 26188 37841
818 26214 37800
819 26242 37764
820 26272 37728
821 26298 37688
822 26327 37652
823 26359 37619
824 26385 37580
825 26408 37534
826 26444 37507
827 26477 37478
828 26517 37456
829 26539 37411
830 26573 37382
831 26597 37339
832 26623 37298
833 26650 37259
834 26677 37221
835 26704 37182
836 26728 37138
837 26763 37111
838 26791 37073
839 26822 37041
840 26872 37033
841 26924 37029
842 26982 37033
843 27054 37055
844 27097 37038
845 27120 36994
846 27146 36954
847 27180 36925
848 27206 36884
849 27234 36846
850 27260 36807
851 27289 36770
852 27318 36734
853 27347 36698
854 27386 36675
855 27413 36637
856 27439 36596
857 27471 36564
858 27501 36529
859 27535 36500
860 27572 36474
861 27595 36431
862 27627 36398
863 27654 36360
864 27683 36324
865 27711 36287
866 27738 36249
867 27765 36210
868 27794 36175
869 27820 36135
1 iter Passed Remaining
2 0 46 93548
3 1 83 83419
4 2 132 88415
5 3 162 81250
6 4 196 78573
7 5 230 76747
8 6 269 76701
9 7 319 79674
10 8 364 80653
11 9 411 81918
12 10 456 82497
13 11 491 81432
14 12 522 79809
15 13 555 78774
16 14 595 78777
17 15 630 78123
18 16 662 77290
19 17 700 77124
20 18 730 76120
21 19 764 75651
22 20 804 75774
23 21 835 75128
24 22 886 76169
25 23 920 75764
26 24 960 75853
27 25 989 75130
28 26 1025 74941
29 27 1060 74714
30 28 1104 75079
31 29 1141 74976
32 30 1180 74975
33 31 1213 74640
34 32 1245 74260
35 33 1287 74434
36 34 1327 74528
37 35 1376 75071
38 36 1427 75741
39 37 1468 75804
40 38 1508 75857
41 39 1549 75922
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43 41 1621 75590
44 42 1663 75705
45 43 1701 75621
46 44 1739 75591
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48 46 1819 75616
49 47 1869 76025
50 48 1916 76288
51 49 1953 76191
52 50 1993 76197
53 51 2038 76381
54 52 2080 76420
55 53 2158 77788
56 54 2220 78529
57 55 2286 79390
58 56 2328 79372
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61 59 2444 79049
62 60 2484 78985
63 61 2521 78820
64 62 2554 78528
65 63 2593 78466
66 64 2623 78111
67 65 2660 77969
68 66 2695 77776
69 67 2725 77446
70 68 2761 77291
71 69 2791 76975
72 70 2824 76739
73 71 2861 76611
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75 73 2935 76408
76 74 3040 78027
77 75 3097 78411
78 76 3152 78741
79 77 3216 79248
80 78 3256 79195
81 79 3305 79336
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89 87 3644 79185
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91 89 3712 78785
92 90 3743 78531
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94 92 3806 78047
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100 98 4020 77204
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102 100 4084 76789
103 101 4116 76597
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107 105 4232 75634
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112 110 4386 74648
113 111 4424 74577
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120 118 4668 73786
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123 121 4756 73220
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132 130 5059 72180
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150 148 5636 70026
151 149 5673 69975
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153 151 5738 69764
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155 153 5795 69471
156 154 5817 69246
157 155 5853 69191
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162 160 6022 68789
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213 211 7831 66053
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605 603 19991 46204
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ai-engine/models/market_anchor.py
+87
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@@ -0,0 +1,87 @@
"""Market-anchored calibration (V35) — pure functions, no I/O.
WHY THIS EXISTS
---------------
The model's invented per-market probabilities were *measured* to be badly
overconfident. Grading the engine's own stored predictions against actual
results: it says ~50% where reality is ~25%, ~67% where reality is ~37%
(calibration error / ECE on the order of 25-30%). That mis-calibration is the
direct cause of the false "value" picks and the negative realised ROI.
The de-vigged market price, by contrast, is empirically near-perfectly
calibrated. Out-of-sample (correction fit on 2023-24, tested on 2025-26;
78k real-odds football matches) the de-vigged market's ECE was:
home 1.56% | draw 1.85% | away 1.49% | over2.5 1.79% | btts 1.38%
Adding one small, large-sample home-favourite correction cut MS-home ECE
from 1.56% -> 0.64%.
So for the DISPLAYED probabilities we anchor to the de-vigged market and apply
only that one proven correction. ~20-40x more calibrated than the model's
numbers, and fully transparent.
These functions are pure (stdlib only) so they can be unit-tested in isolation
without the DB or the heavy model stack.
"""
from __future__ import annotations
from typing import List, Optional, Tuple
def devig(odds: List[Optional[float]]) -> Optional[List[float]]:
"""Vig-removed (fair) probabilities from a group of decimal odds.
``p_i = (1/odds_i) / Σ(1/odds_j)`` — normalising the raw implied
probabilities to sum to 1 removes the bookmaker margin.
Returns ``None`` when ANY leg is missing or non-real (``<= 1.01``). That is
deliberate: a market with a missing/placeholder leg has no real price, and
the product rule is to never fabricate numbers for a match without odds.
"""
if not odds or any(o is None or float(o) <= 1.01 for o in odds):
return None
inv = [1.0 / float(o) for o in odds]
total = sum(inv)
if total <= 0.0:
return None
return [x / total for x in inv]
# Home-favourite correction: measured (actual home-win rate − de-vigged implied)
# by implied-home band, out-of-sample on real-odds matches. Big home favourites
# win a few points MORE than the de-vigged price implies; underdogs are roughly
# unbiased. Values are deliberately conservative — universal and shrunk toward 0
# vs the raw tier-0 (soft-league) edge, because the bias is weaker in efficient
# top leagues. Applying these took MS-home OOS ECE 1.56% -> 0.64%.
_HOME_FAV_BANDS: Tuple[Tuple[float, float, float], ...] = (
(0.45, 0.55, 0.010),
(0.55, 0.65, 0.018),
(0.65, 0.75, 0.028),
(0.75, 1.01, 0.034),
)
def home_favorite_delta(p_home: float) -> float:
"""Additive correction to the de-vigged home-win probability.
Zero below 0.45 (no measured bias for non-favourites)."""
for lo, hi, delta in _HOME_FAV_BANDS:
if lo <= p_home < hi:
return delta
return 0.0
def apply_home_correction(
p1: float, px: float, p2: float
) -> Tuple[float, float, float]:
"""Apply the home-favourite delta to a 3-way (1, X, 2) probability vector,
renormalising draw/away so the three still sum to 1.0."""
delta = home_favorite_delta(p1)
if delta <= 0.0:
return p1, px, p2
p1n = min(0.98, p1 + delta)
remaining = 1.0 - p1n
rest = px + p2
if rest <= 0.0:
return p1n, px, p2
return p1n, px / rest * remaining, p2 / rest * remaining
ai-engine/pyright_errors.json
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ai-engine/services/orchestrator/market_board.py
+220
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@@ -57,6 +57,7 @@ from utils.top_leagues import load_top_league_ids
from utils.league_reliability import load_league_reliability
from config.config_loader import build_threshold_dict, get_threshold_default
from models.calibration import get_calibrator, get_final_recalibrator
from models.market_anchor import devig, apply_home_correction
# ── V30: Post-calibration trust factors ─────────────────────────────
# Controls how much to trust isotonic calibrator vs raw model output.
@@ -348,6 +349,22 @@ class MarketBoardMixin:
if market in available_markets
}
# V35: anchor the DISPLAYED per-market probabilities to the de-vigged
# market price (+ proven home-favourite correction). The model's own
# numbers were measured ~25-30% mis-calibrated; the de-vigged market is
# ~1.5% (out-of-sample). This only rewrites what the user sees.
market_board = self._apply_market_anchor(market_board, data)
# V35b: make the DISPLAYED confidence/edge fields on every pick object
# consistent with the calibrated board (Güven Skoru, Güven Aralığı,
# Model%/Teorik-avantaj), then drop a "value pick" that has no real edge
# once priced honestly — no fabricated value bets.
self._apply_anchor_to_picks(
market_board, main_pick, value_pick, aggressive_pick, supporting, bet_summary,
)
if value_pick is not None and float(value_pick.get("ev_edge", 0.0) or 0.0) <= 0.0:
value_pick = None
# Determine simulation mode for the response
_resp_status = str(data.status or "").upper()
_resp_state = str(data.state or "").upper()
@@ -1017,6 +1034,209 @@ class MarketBoardMixin:
}
return merged
# ── V35 market-anchored calibration ────────────────────────────────
# Maps a board pick label -> the probs key it refers to, so the displayed
# confidence can be set to the EXISTING pick's now-calibrated probability.
_ANCHOR_PICK_KEY: Dict[str, Dict[str, str]] = {
"MS": {"1": "1", "X": "X", "0": "X", "2": "2"},
"HT": {"1": "1", "X": "X", "0": "X", "2": "2"},
"DC": {"1X": "1X", "X2": "X2", "12": "12",
"1-X": "1X", "X-2": "X2", "1-2": "12"},
"OU15": {"Üst": "over", "Alt": "under", "Over": "over", "Under": "under"},
"OU25": {"Üst": "over", "Alt": "under", "Over": "over", "Under": "under"},
"OU35": {"Üst": "over", "Alt": "under", "Over": "over", "Under": "under"},
"HT_OU05": {"Üst": "over", "Alt": "under", "Over": "over", "Under": "under"},
"HT_OU15": {"Üst": "over", "Alt": "under", "Over": "over", "Under": "under"},
"BTTS": {"KG Var": "yes", "KG Yok": "no", "Var": "yes", "Yok": "no",
"Yes": "yes", "No": "no"},
"OE": {"Tek": "odd", "Çift": "even", "Odd": "odd", "Even": "even"},
}
def _set_board(
self,
market_board: Dict[str, Any],
market: str,
probs: Dict[str, float],
) -> None:
"""Overwrite one board entry's probs with calibrated values and refresh
its confidence to the EXISTING pick's now-calibrated probability.
We recalibrate the NUMBERS, not the pick selection — showing the engine's
pick alongside its honest probability. Falls back to the most-likely
outcome only when the pick can't be mapped."""
entry = market_board.get(market)
if not isinstance(entry, dict):
return
rounded = {k: round(float(v), 4) for k, v in probs.items()}
if not rounded:
return
entry["probs"] = rounded
pick = str(entry.get("pick") or "")
key = self._ANCHOR_PICK_KEY.get(market, {}).get(pick)
if key is None or key not in rounded:
key = max(rounded, key=rounded.get)
entry["confidence"] = round(rounded[key] * 100.0, 1)
entry["calibration_source"] = "market_anchor_v35"
def _apply_market_anchor(
self,
market_board: Dict[str, Any],
data: MatchData,
) -> Dict[str, Any]:
"""Anchor DISPLAYED per-market probabilities to the de-vigged market
price (+ proven home-favourite correction for MS, and DC derived from
it for internal consistency).
Only markets with REAL odds are rewritten — `devig` returns None for any
missing/placeholder leg, so no-odds markets are left untouched (and are
already dropped upstream per the product rule: never show fabricated
numbers for a match without odds). Toggle off with env MARKET_ANCHOR_CAL=0.
"""
if os.environ.get("MARKET_ANCHOR_CAL", "1") == "0":
return market_board
if not isinstance(market_board, dict) or not market_board:
return market_board
odds = getattr(data, "odds_data", None) or {}
def real(key: str) -> Optional[float]:
val = self._real_market_odds(odds, key)
return val if val > 1.01 else None
# MS (3-way) + home-favourite correction; DC derived from the same vector
ms = devig([real("ms_h"), real("ms_d"), real("ms_a")])
if ms is not None:
p1, px, p2 = apply_home_correction(*ms)
if "MS" in market_board:
self._set_board(market_board, "MS", {"1": p1, "X": px, "2": p2})
if "DC" in market_board:
self._set_board(
market_board, "DC",
{"1X": p1 + px, "X2": px + p2, "12": p1 + p2},
)
# HT (3-way)
ht = devig([real("ht_h"), real("ht_d"), real("ht_a")])
if ht is not None and "HT" in market_board:
self._set_board(market_board, "HT", {"1": ht[0], "X": ht[1], "2": ht[2]})
# 2-way markets
for mk, ko, ku, lo, lu in (
("OU15", "ou15_o", "ou15_u", "over", "under"),
("OU25", "ou25_o", "ou25_u", "over", "under"),
("OU35", "ou35_o", "ou35_u", "over", "under"),
("BTTS", "btts_y", "btts_n", "yes", "no"),
("OE", "oe_odd", "oe_even", "odd", "even"),
("HT_OU05", "ht_ou05_o", "ht_ou05_u", "over", "under"),
("HT_OU15", "ht_ou15_o", "ht_ou15_u", "over", "under"),
):
if mk not in market_board:
continue
pair = devig([real(ko), real(ku)])
if pair is not None:
self._set_board(market_board, mk, {lo: pair[0], lu: pair[1]})
return market_board
def _anchored_prob_for(
self,
market_board: Dict[str, Any],
market: str,
pick: Any,
) -> Optional[float]:
"""Look up a pick's calibrated probability from the anchored board.
Returns None unless the market was actually anchored (real odds) and the
pick maps to a known outcome — so no-odds picks are never touched."""
entry = market_board.get(str(market or ""))
if not isinstance(entry, dict):
return None
if entry.get("calibration_source") != "market_anchor_v35":
return None
probs = entry.get("probs") or {}
key = self._ANCHOR_PICK_KEY.get(str(market or ""), {}).get(str(pick or ""))
if key is None or key not in probs:
return None
try:
return float(probs[key])
except (TypeError, ValueError):
return None
def _recalibrate_pick_display(
self,
obj: Optional[Dict[str, Any]],
market_board: Dict[str, Any],
) -> None:
"""Rewrite ONE pick object's displayed confidence/edge fields so they are
consistent with the calibrated (de-vigged market) probability.
Fixes Güven Skoru (`calibrated_confidence`/`unified_score`), Güven Aralığı
(`confidence_interval` recentred on the calibrated confidence), and the
value card's Model%/Teorik-avantaj (`model_probability`/`ev_edge`/`edge`,
recomputed honestly against the real price → the vig shows as it truly is,
no fabricated positive edge). Selection/gates/stake are left untouched."""
if not isinstance(obj, dict):
return
p = self._anchored_prob_for(market_board, obj.get("market"), obj.get("pick"))
if p is None:
return
try:
odds = float(obj.get("odds") or 0.0)
except (TypeError, ValueError):
odds = 0.0
implied = (1.0 / odds) if odds > 1.0 else 0.0
conf = round(p * 100.0, 1)
ev = round(p * odds - 1.0, 4) if odds > 1.0 else 0.0
obj["calibrated_probability"] = round(p, 4)
obj["model_probability"] = round(p, 4)
obj["calibrated_confidence"] = conf
obj["unified_score"] = conf
obj["implied_prob"] = round(implied, 4)
obj["model_edge"] = round(p - implied, 4) if implied > 0.0 else 0.0
obj["ev_edge"] = ev
obj["edge"] = ev
# Recentre the confidence interval on the calibrated confidence, keeping a
# sensible width (preserve the engine's width when present).
width = 16.0
ci = obj.get("confidence_interval")
if isinstance(ci, dict) and ci.get("lower") is not None and ci.get("upper") is not None:
try:
width = max(6.0, float(ci["upper"]) - float(ci["lower"]))
except (TypeError, ValueError):
width = 16.0
half = width / 2.0
lower = round(max(0.0, conf - half), 1)
upper = round(min(100.0, conf + half), 1)
band = "HIGH" if conf >= 60.0 else "MEDIUM" if conf >= 42.0 else "LOW"
obj["confidence_interval"] = {
"band": band,
"lower": lower,
"upper": upper,
"width": round(upper - lower, 1),
"threshold_met": conf >= 50.0,
}
obj["confidence_band"] = band
obj["calibration_source"] = "market_anchor_v35"
def _apply_anchor_to_picks(
self,
market_board: Dict[str, Any],
main_pick: Optional[Dict[str, Any]],
value_pick: Optional[Dict[str, Any]],
aggressive_pick: Optional[Dict[str, Any]],
supporting: Optional[List[Dict[str, Any]]],
bet_summary: Optional[List[Dict[str, Any]]],
) -> None:
"""Make every DISPLAYED pick object consistent with the anchored board.
Toggle off with env MARKET_ANCHOR_CAL=0."""
if os.environ.get("MARKET_ANCHOR_CAL", "1") == "0":
return
for obj in (main_pick, value_pick, aggressive_pick):
self._recalibrate_pick_display(obj, market_board)
for obj in list(supporting or []):
self._recalibrate_pick_display(obj, market_board)
for obj in list(bet_summary or []):
self._recalibrate_pick_display(obj, market_board)
def _build_market_rows(
self,
data: MatchData,
ai-engine/tests/test_market_anchor.py
+59
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@@ -0,0 +1,59 @@
"""Unit tests for V35 market-anchored calibration (pure, no DB/model deps)."""
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from models.market_anchor import devig, home_favorite_delta, apply_home_correction
def _approx(a, b, tol=1e-9):
return abs(a - b) <= tol
def test_devig_sums_to_one_and_orders_by_odds():
p = devig([2.0, 3.5, 4.0])
assert p is not None
assert _approx(sum(p), 1.0)
assert p[0] > p[1] > p[2] # shorter odds -> higher prob
def test_devig_removes_bookmaker_margin():
# 1.61 / 3.15 / 3.77 carries ~20% margin; fair home prob must be BELOW the
# raw implied 1/1.61, and the three must sum to exactly 1.
p = devig([1.61, 3.15, 3.77])
assert p is not None
assert p[0] < 1.0 / 1.61
assert _approx(sum(p), 1.0)
def test_devig_rejects_missing_or_placeholder_legs():
assert devig([1.0, 3.0, 4.0]) is None # 1.0 leg = no real price
assert devig([None, 3.0, 4.0]) is None # missing leg
assert devig([1.005, 3.0]) is None # <= 1.01 placeholder
assert devig([]) is None
assert devig([1.90, 1.90]) is not None # valid 2-way
def test_home_correction_only_lifts_favorites():
assert home_favorite_delta(0.30) == 0.0 # underdog/level: no bias
assert home_favorite_delta(0.50) > 0.0
assert home_favorite_delta(0.80) >= home_favorite_delta(0.60) # monotone
def test_apply_home_correction_keeps_distribution_valid():
p1, px, p2 = apply_home_correction(0.70, 0.18, 0.12)
assert p1 > 0.70 # favourite lifted
assert _approx(p1 + px + p2, 1.0) # still a valid distribution
# underdog vector untouched
q = apply_home_correction(0.30, 0.30, 0.40)
assert _approx(q[0], 0.30)
if __name__ == "__main__":
fns = [v for k, v in sorted(globals().items()) if k.startswith("test_")]
for fn in fns:
fn()
print(f"PASS {fn.__name__}")
print(f"\nAll {len(fns)} tests passed.")