Per-sport CLV review

A descriptive view of terminal in-play CLV by sport. Runtime live/paused state is deliberately omitted because this analytics view is not its source of truth. These observations do not authorize scaling, restriction, reactivation, or a claim of model skill.

Updated 2026-10-31 · runtime state intentionally not inferred here

Descriptive bands — not trading actions
Positive observation
Mean CLV ≥ +1c; investigate persistence and execution
Near zero
Between −1c and +1c; descriptive only
Negative observation
Between −3c and −1c; inspect measurement and fills
Low observation
Below −3c; still not a causal or money gate

NCAAMB

CLV shown descriptively; runtime state is out of scope
LOW N
descriptive only · fewer than 10 measured fills
Mean CLV
+19.25c
n=4
30d
—
n=0
Overall WR
100%
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

NCAAWB

CLV shown descriptively; runtime state is out of scope
LOW N
descriptive only · fewer than 10 measured fills
Mean CLV
+6.82c
n=4
30d
—
n=0
Overall WR
75%
Failure-mode breakdown (1 negative-CLV trades)
bad_model1
bad_blend0
bad_timing0
bad_clv_label0
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

WNBA

CLV shown descriptively; runtime state is out of scope
POSITIVE OBS.
descriptive +CLV · not authority to scale
Mean CLV
+3.48c
n=154
30d
-3.08c
n=12
Overall WR
38%
Failure-mode breakdown (84 negative-CLV trades)
bad_model81
bad_blend3
bad_timing0
bad_clv_label0
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

LOL

CLV shown descriptively; runtime state is out of scope
POSITIVE OBS.
descriptive +CLV · not authority to scale
Mean CLV
+2.85c
n=182
30d
—
n=0
Overall WR
46%
Failure-mode breakdown (91 negative-CLV trades)
bad_model59
bad_blend23
bad_timing2
bad_clv_label7
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

NFL

CLV shown descriptively; runtime state is out of scope
POSITIVE OBS.
descriptive +CLV · not authority to scale
Mean CLV
+2.14c
n=27
30d
+2.15c
n=18
Overall WR
41%
Failure-mode breakdown (15 negative-CLV trades)
bad_model13
bad_blend2
bad_timing0
bad_clv_label0
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

SOCCER

CLV shown descriptively; runtime state is out of scope
POSITIVE OBS.
descriptive +CLV · not authority to scale
Mean CLV
+1.54c
n=152
30d
+1.37c
n=66
Overall WR
41%
Failure-mode breakdown (87 negative-CLV trades)
bad_model81
bad_blend1
bad_timing0
bad_clv_label5
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

WTA

CLV shown descriptively; runtime state is out of scope
POSITIVE OBS.
descriptive +CLV · not authority to scale
Mean CLV
+1.26c
n=1276
30d
-1.29c
n=269
Overall WR
42%
Failure-mode breakdown (700 negative-CLV trades)
bad_model629
bad_blend46
bad_timing15
bad_clv_label10
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

NHL

CLV shown descriptively; runtime state is out of scope
NEAR ZERO
descriptive band · not a keep/kill rule
Mean CLV
-0.31c
n=205
30d
-0.88c
n=86
Overall WR
61%
Failure-mode breakdown (95 negative-CLV trades)
bad_model66
bad_blend10
bad_timing5
bad_clv_label14
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

MLB

CLV shown descriptively; runtime state is out of scope
NEGATIVE OBS.
review measurement and execution; no causal label
Mean CLV
-1.07c
n=632
30d
+9.79c
n=7
Overall WR
53%
Failure-mode breakdown (309 negative-CLV trades)
bad_model257
bad_blend24
bad_timing3
bad_clv_label25
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

CS2

CLV shown descriptively; runtime state is out of scope
LOW OBS.
review measurement and execution; no causal label
Mean CLV
-3.44c
n=362
30d
—
n=0
Overall WR
48%
Failure-mode breakdown (193 negative-CLV trades)
bad_model115
bad_blend59
bad_timing9
bad_clv_label10
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

ATP

CLV shown descriptively; runtime state is out of scope
LOW OBS.
review measurement and execution; no causal label
Mean CLV
-3.46c
n=363
30d
-8.05c
n=32
Overall WR
38%
Failure-mode breakdown (221 negative-CLV trades)
bad_model174
bad_blend35
bad_timing4
bad_clv_label8
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

CFB

CLV shown descriptively; runtime state is out of scope
LOW OBS.
review measurement and execution; no causal label
Mean CLV
-5.37c
n=72
30d
-5.37c
n=72
Overall WR
33%
Failure-mode breakdown (42 negative-CLV trades)
bad_model41
bad_blend1
bad_timing0
bad_clv_label0
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

NBA

CLV shown descriptively; runtime state is out of scope
LOW OBS.
review measurement and execution; no causal label
Mean CLV
-9.90c
n=41
30d
—
n=0
Overall WR
40%
Failure-mode breakdown (24 negative-CLV trades)
bad_model21
bad_blend3
bad_timing0
bad_clv_label0
What to review before any operational decision

Do not scale, pause, or reactivate from terminal in-play CLV alone. Review fixed-horizon markouts, exchange fills and slippage, calibration, sample uncertainty clustered by game, and the sport's independent risk gates.

Why we publish this. The current aggregate can be unfavorable, and showing it creates an audit trail. It is one descriptive execution measurement, not a verdict on a model or sport.

Investigation tags. Negative-CLV rows receive deterministic heuristic tags such as bad_model, bad_blend, bad_timing, or bad_clv_label. These names are hypotheses for follow-up, not proven causes; the counts cannot identify causality by themselves.

Operational decisions. Scaling, pausing, and reactivation require separate evidence: fixed-horizon markouts, execution quality, calibrated out-of-sample forecasts, clustered uncertainty, and risk controls. This page does not drive those decisions.

Data & methodology. CLV is computed weekly via backfill_clv.py from Polymarket price history. Failure-mode tags are deterministic from clv_failure_tagger.py. Both are reproducible from the public /clv.json + /clv/failures.json endpoints.