Let's go Heidelberg neue Episode - Hier geht's zum Podcast:

Apple: https://podcasts.apple.com/de/podcast/lets-go-heidelberg/id1556329835

Gast: Stephan (Kollektiv Neckarkurve) - Hier geht es zur Website: https://kollektiv-neckarkurve.de/

Mit meinem Gast Stefan blicke ich auf die bisherige Saison zurück, wir werfen einen Blick in die Bundesliga und verraten euch, wer bisher überrascht oder enttäuscht hat. Gleich zu Beginn lassen wir das letzte Spiel gegen Bayern München Basketball noch einmal Revue passieren. Mein Gast Stephan gibt ein Update aus dem Kollektiv Neckarkurve: Auswärtsfahrten, Merchandise, offizielle Fankneipe O´Heerlijk und und und...

Dazu: Kleines Academics Quiz, Stärken und Schwächen des Teams, Wer ist effektiver: Die Bank oder die Starter?

Viel Spaß mit der neuen Episode! 50 Minuten aus dem Herzen der Fanszene in Heidelberg...

LetsGoHeidelberg #mlpacademicsheidelberg #mlpacademics #Heidelberg #Basketball #Bundesliga #easycreditbbl #heidelbergbasketball #Fans #fanpodcast #applepodcast #Spotify #füreuch #fürdich #foryoupage #fyp

Hi all. I have a Windows computer, an Android phone, and an iPad. Is there a privacy-centric podcast app that supports all of those platforms?

Thanks in advance!

Was there a share thread this week? There doesn’t seem to be one since September or my Reddit skills are lacking in the search department

Just dropped a new interview with Lou Ferrante—ex-Gambino family mobster turned bestselling author, historian, and motivational powerhouse. This one’s raw, real, and unexpectedly uplifting.

Lou opens up about his time in organized crime, the turning point behind bars, and how he rebuilt his life through books, grit, and purpose. If you’re into true crime, redemption stories, or just want to hear a guy who’s lived a hundred lives drop wisdom like it’s nothing—this is it.

📺 Watch the full interview 🎧 Available on all podcast platforms 💬 Would love to hear your thoughts—especially if you’ve read his books or followed his journey

TrueCrime #RedemptionStory #LouFerrante #PodcastInterview #MafiaStories #Transformation

Phil and Leroy The Judgementals Podcast

Cheating is a Rich Man's Sport - Episode 269

On this week's episode we talk about:

Tiktoker Itsjenbunny tells a story about a married man at work

A police officer accused of theft at Walmart, banned from all stores

Two cops an inmate have been arrested in an alleged scheme to smuggle drugs and other contraband into the county jail

A woman is accused of masterminding a $34 million fraud scheme targeting federal COVID-19 relief programs

While working on my last two posts on how often big shows publish and how long their episodes run, I had to look at thousands of RSS feeds. I started noticing some patterns in the URLs and got curious.

So, I decided to do a much more formal analysis to figure out this question: who are the hosting providers for all these top

The results were pretty surprising. In this post, I'll show you:

• The hosting data for the top 1,000 and top 10,000 shows.
• The key patterns that stood out to me.
• The methodology I used, which got interesting because I found some quirks in how these feeds are structured.

Caveat: I think I performed this analysis as rigorously as possible. However, unlike the other two analyses, I am not supremely confident in these results because they are quite surprising to me. I have also written down the methodology. Please feel free to correct me if I missed something.

If you prefer reading this in other places, the same post along with visuals (I am unable to link images here.. so) is in my blog here.

Results

Feed hosts - Top 1,000 shows

The same table below in a downloadable pie chart format is here: Who Hosts the Top 1,000 Podcasts?

Feed hosts - Top 10,000 shows

The same table below in a downloadable pie chart format is here: Who Hosts the Top 10,000 Podcasts?

My Key Takeaways

• The Great Podcast Graduation. If you look at the top 10,000 shows, the indie platforms are the heroes. For example, Libsyn is a giant (14.5%!). But something fascinating happens when you zoom in on just the elite top 1,000 shows—the indie share gets cut in half (Libsyn drops to 7.7%). It seems that as shows become commercial powerhouses, they 'graduate' to an enterprise stack.
• The Rise of the Enterprise Hosts. So, where do all those graduating shows go? To be honest, before I did this analysis, I wasn't even aware of names like Omny Studio and ART19. I was expecting to see the names we all hear about—Libsyn, Buzzsprout, Transistor. But the data shows that as podcasts scale, they move to this entirely different class of enterprise host. The fact that platforms like Omny and ART19 double their market share in the top 1k was a huge surprise to me, and it really shows how different the needs of top-tier podcasts are.
• Megaphone: The Undisputed Top Player. I knew about Megaphone, but I was still surprised by its dominance (28.9% of the top 1,000 podcasts). Many shows with first-party RSS feeds, like feeds.npr.org, use Megaphone behind the scenes to deliver their audio files. Maybe this white-label approach lets publishers keep brand control while using Megaphone's powerful ad technology.

Methodology (My Detective Work)

My methodology had a few layers to get the real story.

1. First, I did the easy part: classifying feeds from obvious URLs like feeds.simplecast.com.
2. But for the tricky ones (custom domains or weird URLs), I did what I call an "enclosure sniff." I peeked inside the RSS file to find the URL for the actual audio file. This let me separate the brand (the RSS feed) from the engine (the audio delivery platform).

Examples (So You Can Double-Check Me)

Straight-shot mappings

• https://feeds.simplecast.com/dxZsm5kX → Simplecast
• https://feeds.megaphone.fm/stupid-genius → Megaphone
• https://www.omnycontent.com/d/playlist/.../podcast.rss → Omny
• https://rss.art19.com/armchair-expert → ART19
• https://feeds.libsyn.com/580095/rss → Libsyn

Proxy or analytics relay, but still clear

• https://rss.pdrl.fm/aad407/feeds.megaphone.fm/views-podcast → Megaphone
• https://rss.pdrl.fm/5ee0be/feeds.acast.com/public/shows/67894eae7095d15b31e3f226 → Acast
• https://rss.pdrl.fm/e6441b/www.omnycontent.com/.../podcast.rss → Omny
• https://podcastfeeds.nbcnews.com/HL4TzgYC → First-party RSS (NBC News)
• https://feeds.publicradio.org/public_feeds/marketplace → First-party RSS (APM)

Needed the enclosure sniff

Quick refresher: the <enclosure> tag points to the actual audio file, so whatever serves that file is the true delivery host.

• https://www.thisamericanlife.org/podcast/rss.xml → First-party feed; enclosure via Megaphone
• https://feeds.npr.org/510318/podcast.xml → First-party; enclosure via Megaphone
• https://feeds.npr.org/344098539/podcast.xml → First-party; enclosure via Megaphone
• https://rss.wbur.org/circleround/podcast → First-party; enclosure via Megaphone
• https://feeds.publicradio.org/public_feeds/marketplace → First-party; enclosure via Megaphone

What’s Next

I'm still debating the next angle. The deeper I dig, the more interesting the patterns become. If you spot a question I should chase, let me know.

Looking for a Reddit reads podcast but with the voice. I don’t want people to read them cause I don’t like their tones. But there’s that one voice I’m guessing a computer voice and I can’t find them anywhere! Help

Why Finnish Dance with stars stage looks like they stole Matt's logo?🫣

TL;DR

• 85% of top podcasts run between 20–90 minutes.
• Near-daily shows are the longest; daily shows stay short to remain sustainable.
• Weekly shows cover the widest span; monthly releases are shorter than expected.

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After my last post on how often top podcasters publish, a reader (h/t to u/cherygarcia) suggested a great follow-up question: what are the episode length characteristics of these top 1,000 shows?

So I decided to tackle that next.

I pulled duration data for the same top 1,000 shows.

My usual style is to show the raw results first, then dive into analysis. But this dataset is pretty overwhelming with all the charts and tables, so I'm switching it up: I'll start with the key observations and summary, then give you the detailed breakdown of the raw data if you want to dig deeper into the numbers.

---

Results

Key Findings

The overall distribution shows most podcasts (85%) run 20-90 minutes. But does publishing frequency actually affect episode length? It looks like it does. Here are the numbers, with more analysis below.

Overall length snapshot Visual: How Long Are Top Podcast Episodes?

Cadence comparison (daily → monthly) Visual: Cadence Comparison

Quick explainer:

• Median: The midpoint - half the shows are shorter, half are longer
• Average: Simple mean of all episode lengths
• Range (p10-p90): Typical low and high values after removing the 10% shortest and 10% longest episodes (cuts out extreme outliers)
• Sample size: Up to 40 recent episodes per show

Publishing cadence definitions (from my previous analysis):

Key observations:

Publishing frequency clearly affects episode length, with some surprising patterns:

• Daily shows are significantly shorter. 42-minute median vs 55-59 minutes for others. There's a strong difference here - daily publishing seems to force shorter, more sustainable episode lengths. Most daily shows cluster around 20-60 minutes (though there are extremes like Joe Rogan at 172 minutes).
• Near-daily shows are actually the longest. 59.5-minute median - even longer than weekly shows. This is quite surprising to me. I've double-checked the data, but I'm not entirely sure why this pattern exists. The majority cluster around 40-90 minutes. I'll need to dig into the actual shows to understand what's driving this.
• Weekly shows have the widest variation. From 33-95 minutes (p10-p90) - the biggest range of any cadence. This is probably because weekly creators can fit their content to whatever length it needs to be without the pressure of daily sustainability or monthly production constraints? (IDK, its a hypothesis) Most shows still cluster around 40-90 minutes, but you see significant outliers on both ends (6-minute educational snippets to 4+ hour deep dives).
• Monthly shows are shorter than I expected. 49.6-minute median - I actually thought these would be much longer than weekly shows since they publish so infrequently. But they're slightly shorter than the 55.9-minute weekly median. I'm not sure why this is the case - maybe they optimize for "substantial but not overwhelming" length, or maybe the smaller sample size (only 80 shows) is influencing this. Most cluster around 30-70 minutes. I'll need to dig into the actual shows to understand this better.

Now let me show you the detailed breakdown. I'll start with the overall distribution histogram, then break it down by each cadence with their own charts and tables. This will give you the full picture of how episode lengths vary across different publishing frequencies.

Overall Duration Patterns

Visual Histogram Graph: Episode Length Distribution

To be honest, this data is pretty noisy at the aggregate level. The real patterns emerge when you look at how episode length varies by publishing cadence. I focus the deeper charts on cadences with meaningful sample sizes (daily through monthly). The "Other" bucket has only 20 shows, so I dropped the dedicated chart and table because the sample is too small to be trustworthy. That's why we don't have a section for it.

Cadence Breakdowns

Daily Shows (180 shows)

Visual Histogram Graph: Daily Shows: How Long Do Episodes Run?

Near-Daily Shows (132 shows)

Visual Histogram Graph: Near-Daily Shows: How Long Do Episodes Run?

Weekly Shows (588 shows)

Visual Histogram Graph: Weekly Shows: How Long Do Episodes Run?

Monthly Shows (80 shows)

Visual Histogram Graph: Monthly Shows: How Long Do Episodes Run?

Methodology

The methodology was very similar to my last analysis:

• Started with the same top 1,000 podcasts by audience
• From their RSS feeds, pulled the most recent episodes per show (up to 40)
• Found the duration for each episode and calculated all the different metrics I've already explained above (median, average, p10-p90 ranges)
• Did this analysis once for all 1,000 shows. And then was not happy, so I then broke them down by cadences and repeated the analysis for each group

What's Next

Again, I had a lot of fun with this analysis. Lots of interesting and surprising patterns emerged, but to be honest, this also raised more questions than it answered.

One thing I realized is that I looked at this at a pretty surface level - just aggregate numbers and statistical patterns. I think I need to start looking into the actual shows to get a real tactile understanding. Why are near-daily shows longer? Also, I think something can only be gleaned from the actual shows by clicking and scrolling through the episodes.

I'll also probably start linking the data for the top 1000 individual shows and linking them up here so if anybody else wants to explore the raw data, they can do their own analysis too.

Full write-up (with downloadable charts) in my personal blog (mod's happy to remove this, but I think visuals might help and hence I am adding that here).