Model CLV: aggregate audit

Closing-line-value audit of the platform's picks: the ones we recommended, and every market we priced. CLV = closing price − the price we took, raw implied on both sides. The model is not in that formula, which is what makes it a test rather than a restatement of our own opinion. Positive mean CLV means our picks beat the close, the standard proxy for sharp skill.

-0.76pp
Too few picks with a closing line in this window to grade.
8 recommended picks with a closing line in this window; we grade from 30.

Since 5 October 2026 our model starts from the market's price, so calls are rarer. Earlier calls stay in this record.

-1.48pp
Not beating the close.
Every market we priced, recommended or not: 1,487 markets; the price did not move our way on 66.3% of them. 95% range -1.8 to -1.2pp, 2.48pp short of our +1.00pp target.

This is the model's raw number, before the selection gate, and the one the plan below is about.

Bar for selling picks +0.00pp Target +1.00pp
Window (by game date):
30 days 90 days All time Since 12 Sep fix
View:
Per sport By sport + bet type
Methodology: The close is the last price captured before kickoff, and a row is recomputed whenever a fresher close is captured. Buckets with N<30 are hidden. The Since 12 Sep fix window starts at the closing-line capture fix of 12 September 2026, the cohort the selling test is judged on. CLV compares the best price available when we posted the pick with the best price available at the last capture before kickoff. Both ends are best-of-board, and the close is captured across at least as many books as the board we post from, Pinnacle where it is quoted, so the comparison does not tilt in our favour.

Every market we priced, by sport

Every market we priced, by sport: model CLV per bucket.
Sport N Mean CLV Median CLV % beating close
MLB 274 -0.80pp -0.57pp 36.1%
NCAAF 249 -2.36pp -2.10pp 24.5%
NHL 211 -0.82pp -0.57pp 34.6%
WTA Tennis 183 -0.57pp -0.65pp 38.3%
ATP Tennis 154 -1.05pp -1.13pp 33.1%
UFC 93 -2.83pp -0.82pp 41.9%
Champions League 75 -1.23pp -0.87pp 38.7%
NFL 67 -3.71pp -2.16pp 35.8%
La Liga 37 -2.54pp -2.85pp 27.0%
Serie A 36 -2.32pp -2.05pp 27.8%
EPL 33 -0.87pp -1.90pp 27.3%
Ligue 1 31 -1.37pp +0.00pp 48.4%

Cumulative-mean CLV trajectory

Cumulative mean CLV, every market we priced · N=1,487 · 22 Sep 2026 to 22 Oct 2026 · ends at -1.48pp

What we are doing about it

A number like the one above is worth little without the response to it, so here is ours, and the figure that will show whether it worked.

  1. Narrow to where we are least behind. CLV is not evenly distributed. Today the largest sample is MLB, 274 markets at -0.80pp, and the widest gap is NFL, -3.71pp on 67. Coverage follows the evidence rather than the calendar.
  2. Pull the model toward the market where it disagrees most. In the band where our probability departs furthest from the closing price, the market has been the better estimator. That is a calibration problem with a known direction, not a mystery.
  3. Fix the instrument before trusting it. Some of what this page measured was measurement error, found and corrected in September 2026. Numbers here predate and postdate those fixes; the trajectory above, by game date, is the honest view.

The test: mean CLV on the picks we recommend, non-negative at 95% confidence. Today, on this window: 8 picks. Too few picks to say. Until it clears that bar we are not selling picks, and this page will keep saying so.

See /trust for live calibration buckets across sports and /methodology for the signal pipeline behind each pick.