DAILY PACE CARD — THE BLOG

Tuesday, November 3, 2026

Yesterday’s settled scorecard, and today’s free race.

📄 Today’s Consensus Sheet — every race on one printable page (PDF): Download today’s consensus sheet

Yesterday, settled — Monday, November 2, 2026

39
PICKS SETTLED
12
WINNERS
12-4-6
WIN-PLACE-SHOW
$78
$2 WIN BETS
$44.18
RETURNED
-43.4%
ROI
As filed (before scratches): 37 settled · 11 winners · $74 win bets → $36.58 · ROI -50.6%

Single-pick accounting — official picks: 34 settled · 12 winners · ROI -35.0%. Winner was the official pick in 12 of 34 races. 2 official pick(s) were scratched and the leg settled on the first backup in the chain that ran (second scenario's pick, then the filed alternates).

In the money: 22 of 39 pick legs (56%) finished 1st, 2nd or 3rd.

The 12 winners paid an average of $3.68 on a $2 win ticket.

Across the board: $2 win-place-show on every pick — $202 → $139.82 · ROI -30.8%. (1 pool stake(s) refunded — the chart printed no price for that pool.)

Full settled card, every pick graded: results.thoroughwiz.com/2026-11-02/selections/

Watch the races

▶ Parx Race 1 · Nov 2 — the race that produced the sheet's biggest winner ($7.60) · full-race replay from runner-up Brandenburg Gate's connections, UTCB Stud (YouTube)

Today’s free race

Mountaineer — Race 7
Post 9:30 PM ET · 6 Furlongs · Dirt · Allowance 21000b
If the pace stays honest (55%): #4 Cybertime (5/2)
Anthony Farrior · Luis R Rivera
Cybertime carries a sanctioned profile edge — the clear-margin blended late-graph lead (100, 21 points clear) with the field's best raw late figure — at 135 lb, the heaviest weight on our scale today
If the pace is controlled (45%): #6 Uncashed (3/1)
Larry Rivelli · Walter A Rodriguez
Uncashed is the field's clear early-graph leader (97) and second on the blended late graph (78) — if the pace is controlled, the lone-speed picture is his and his finish holds the closers off
🎧 Hear the pick — Alex walks through the selection

The picks, the pace sheets, and this podcast are all produced by AI. The system behind them was built and tested using many of today's leading AI systems in a strict deterministic environment set up as a handicapping lab, and it has been independently audited by other Large Language Models. Rankings follow the morning line.