How the list is built
Public Discovery Score V2
A transparent public-data model for discovering active cybersecurity podcasts. It is not an audited measure of global listeners, downloads, or absolute show quality.
Five weighted signals
The synchronized inputs add to 100%. Missing platform data is not converted to zero.
25%
Listen Notes reach
Synchronized Listen Notes reach. This is a relative popularity estimate, not an audited listener count.
15%
Platform engagement
Apple Podcasts US and Spotify rating counts, normalized separately before the available platform scores are averaged.
30%
Listener satisfaction
Bayesian-adjusted listener satisfaction across available platforms. It is a public satisfaction signal, not a staff review score.
25%
Publishing activity
Freshness and cadence adherence, so a monthly show is not judged as though it should publish every day.
5%
Durability
A capped, logarithmic durability signal. Its small weight prevents age from dominating the result.
Eligibility gate
Eligibility decides which shows can enter the model; it does not award rank.
- A public RSS feed.
- Original cybersecurity podcast content.
- A qualifying original episode within the previous 90 days.
- Sufficient verified public evidence for scoring.
Four views, four jobs
Fresh episode data helps listeners decide what to play; it does not rewrite the published score.
- Top Podcasts
- The current synchronized Public Discovery Score V2 ranking.
- Tal's Choice
- Tal's Choice is a curator order that is separate from the Public Discovery Score rank.
- Latest
- Latest follows live RSS chronology across the current catalog. In other words, it is live RSS chronology rather than a new ranking.
- Homepage hero
- The homepage hero shows the newest available episode among the current Top 5. It is the newest available episode among the current Top 5.
Limitations and uncertainty
Public reach and rating data are proxies, not first-party listener analytics. Platform coverage can be incomplete, and different formats have different publishing economics. Weight-sensitivity tests produce rank ranges and Top-15 probabilities, so close positions should not be read as absolute quality boundaries.
How updates work
Live RSS freshness never silently changes the published ranking; ranking inputs refresh together under a new synchronized model run. A model refresh re-verifies the scoring inputs together and preserves its version, snapshot date, sources, and uncertainty outputs.
Inputs excluded from the numeric score
These signals may provide context, but they are not comparable enough to enter the V2 calculation directly.
- Feedspot rank
- Social-media followers
- Unverified download or listener claims
- YouTube reach
- Editorial-list appearances