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.

-0.47pp
Behind the close by a small, real margin.
Every market we priced, recommended or not: 8,810 markets; the price did not move our way on 60.2% of them. 95% range -0.6 to -0.3pp, 1.47pp short of our +1.00pp target. At -110 that is the gap between -110 and -108: about 2 cents of price.

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 3,925 -0.48pp -0.49pp 39.7%
WTA Tennis 1,013 -0.48pp -0.31pp 38.9%
ATP Tennis 970 -0.69pp -0.46pp 41.0%
UFC 537 -0.87pp +0.00pp 47.5%
NHL 459 +0.28pp +0.00pp 44.0%
NCAAF 414 -1.78pp -0.92pp 18.6%
NBA 251 -0.25pp +0.00pp 46.2%
La Liga 250 -1.53pp -1.56pp 35.2%
EPL 212 +0.16pp -0.96pp 42.0%
Serie A 204 -0.18pp -1.69pp 33.8%
Bundesliga 159 +2.01pp +0.22pp 53.5%
Ligue 1 107 -0.77pp -0.72pp 45.8%
NCAAB 82 +1.30pp +0.71pp 61.0%
NFL 79 -1.11pp -0.92pp 39.2%
Champions League 54 +4.89pp† -2.16pp 42.6%
IPL Cricket 41 -0.60pp +0.00pp 41.5%

† mean and median differ by 3pp or more: a few large moves drive this cell.

Cumulative-mean CLV trajectory

Cumulative mean CLV, every market we priced · N=8,810 · 17 Mar 2026 to 7 Oct 2026 · ends at -0.47pp

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, 3,925 markets at -0.48pp, and the widest gap is NCAAF, -1.78pp on 414. 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: +1.52pp, range +0.7 to +2.3pp, on 233 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.