Climbing Gold

Duct Tape Then Beer · Sports

YAPP has Smart‑Skip data for 10 episodes of Climbing Gold. Recent episodes average 5.1 minutes of ads per 58.4‑minute episode.

5.3ad minutes per hourof runtime
8.8%of runtimeis advertising
3.9ad breaksper episode, on average
10episodesmeasured by YAPP

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Recent episodes

Ad time detected in the 10 most recent episodes of Climbing Gold that YAPP has processed.
Episode Published Length Ad time Breaks
The Last Dirtbag 55:04 3:26 5
Do Grades Matter? 1:10:16 5:43 4
Jan Conn: Catching Pinecones 50:16 4:11 4
Palestinian Climbers 54:59 6:57 4
Jordan Cannon: A Place In History 54:06 3:17 3
Cheat Code: Tanner Wanish and Michael Vaill 1:17:50 5:50 4
Pete Whittaker: Beyond Wide 47:25 5:42 4
Wide Ponies and Offwidths 56:14 5:37 4
Will Moss: Happy Little Accident 57:23 4:40 3
Jon Krakauer: The Mountain's Shadow 1:00:04 6:02 4

Ad load varies by listener. Most podcast advertising is dynamically inserted, so figures reflect ad load as observed by YAPP's processing pipeline at the time each episode was fetched, not what any individual listener hears. Methodology

Listening to Climbing Gold with YAPP

Climbing Gold runs 3.9 ad breaks in a typical episode, so there is a fair amount between you and what you came for. Across the episodes we have processed, that comes to about 5.1 minutes per episode, or 5.3 minutes for every hour of listening.

YAPP maps each episode before you play it, so you get the show and not the rest of it. Because the ads are stitched in per listener, YAPP matches what you are actually hearing rather than replaying a fixed set of timestamps.

How these numbers are measured

YAPP transcribes each episode, classifies advertising segments, and refines their boundaries using audio fingerprinting. Figures cover the 10 episodes of this show that YAPP has processed. Because most podcast ads are dynamically inserted and vary by listener, these figures reflect what our pipeline observed rather than what every listener hears. Full methodology

Boundaries for 20% of detected segments were refined by audio fingerprinting; the rest rest on transcript timing and are less precise.

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