Casefile True Crime
Casefile Presents · Society & Culture
YAPP has Smart‑Skip data for 12 episodes of Casefile True Crime. Recent episodes average 4.5 minutes of ads per 88.3‑minute episode.
Listen to the good stuff in Casefile True Crime
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Recent episodes
| Episode | Published | Length | Ad time | Breaks |
|---|---|---|---|---|
| Case 353: Jaidyn Leskie | 1:49:53 | 0:22 | 1 | |
| Case 352: Gregory Smart | 1:29:02 | 10:14 | 4 | |
| Case 351: Neelma, Kunal & Sidhi Singh | 1:36:47 | 0:22 | 1 | |
| Case 350: Alice Sebold & Tony Broadwater | 1:28:50 | 8:19 | 5 | |
| Case 349: Michael Wallace & David Castor | 1:26:54 | 7:36 | 4 | |
| Case 348: Toyah Cordingley | 1:37:11 | 9:09 | 5 | |
| Casefile Extra: Maria Cruz | 1:06:18 | 7:44 | 4 | |
| Casefile Revisited: Pamela Lawrence | 1:23:43 | 0:22 | 1 | |
| Casefile Extra: Greg Fleniken | 46:57 | 0:22 | 1 | |
| Case 347: Zahra Baker | 1:44:50 | 0:22 | 1 | |
| Case 346: Megumi Yokota | 1:30:26 | 8:17 | 4 | |
| Case 345: The Barking Murders | 1:38:34 | 0:22 | 1 |
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 Casefile True Crime with YAPP
Casefile True Crime typically runs 2.7 ad breaks per episode, usually a pre-roll and one or two mid-rolls. Across the episodes we have processed, that comes to about 4.5 minutes per episode, or 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 12 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 0% of detected segments were refined by audio fingerprinting; the rest rest on transcript timing and are less precise.
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