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.

+1.44pp
Beating the close. Above our target, on a sample too small to call that settled.
Mean closing-line value across 241 recommended picks with a closing line; the price moved our way on 51.9% of them. 95% range +0.6 to +2.3pp.

Both prices are the best on the board at their moment, when we posted and at the last capture before kickoff: this measures whether the market came toward us after we posted, not any one book's close.

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

-0.56pp
Not beating the close.
Every market we priced, recommended or not: 9,576 markets; the price did not move our way on 60.8% of them. 95% range -0.7 to -0.5pp, 1.56pp 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 3,976 -0.48pp -0.49pp 39.6%
WTA Tennis 1,089 -0.53pp -0.38pp 38.6%
ATP Tennis 1,049 -0.72pp -0.52pp 40.3%
NHL 589 +0.03pp +0.00pp 41.6%
UFC 581 -1.03pp +0.00pp 46.6%
NCAAF 513 -1.88pp -1.11pp 21.1%
La Liga 287 -1.66pp -1.76pp 34.1%
NBA 268 -0.49pp +0.00pp 44.0%
EPL 245 +0.02pp -1.32pp 40.0%
Serie A 240 -0.50pp -1.74pp 32.9%
Bundesliga 186 +1.51pp +0.14pp 50.5%
Ligue 1 138 -0.91pp -0.61pp 46.4%
Champions League 129 +1.33pp -1.20pp 40.3%
NFL 110 -2.36pp -1.45pp 37.3%
NCAAB 82 +1.30pp +0.71pp 61.0%
IPL Cricket 41 -0.60pp +0.00pp 41.5%

Cumulative-mean CLV trajectory

Cumulative mean CLV, every market we priced · N=9,576 · 17 Mar 2026 to 22 Oct 2026 · ends at -0.56pp

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, 3,976 markets at -0.48pp, and the widest gap is NFL, -2.36pp on 110. 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: +1.44pp, range +0.6 to +2.3pp, on 241 picks. Clears the bar. Declaring the record open is a decision we make in the open, with the reason stated here, not a switch this page flips.

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