Every belief in the wisdom canon has been tested against this channel's data. This page shows the current verdicts, recent changes, and the full history of how each belief has fared over time. Data through 2026-04-30.
Conventional beliefs about YouTube — does each apply to this channel?
“Older channels become less dependent on algorithmic recommendations.”
This belief depends on data not currently tracked: cross-channel cohort data. It would activate when more channels contribute their data.
algorithmic_dependency_index (1 − sticky_traffic_ratio) vs channel age. Cross-channel cohort-level negative correlation at r ≤−0.4.
“Distribution happens in two phases — test (low impressions, narrow audience) then scale.”
On this channel: — the belief inconclusive here.
Per video, fit a piecewise function to the impression-by-day curve. Identify the 'elbow' where slope changes most. Agrees if ≥50% of mature videos show a detectable elbow within days 3–14.
“Videos with average view percentage above 50% receive sustained impression delivery.”
On this channel: — the belief inconclusive here.
Per video, compare impression delivery rate in days 8–14 against days 1–7. Bucket by retention tier (<30%, 30–50%, 50%+). Agrees if 50%+ bucket has impression-rate ratio ≥0.7 AND lower-retention buckets ≤0.4.
“Browse / Homepage impressions are weighted toward existing subscribers.”
This belief depends on data not currently tracked: owner-mode-only data (subscribed-state, search queries, etc.). It would activate when the channel owner connects private analytics.
Browse views / Browse impressions broken out by subscribed state. Agrees if subscribed-state share of Browse views ≥50%.
“Browse pickups are more durable than Suggested-driven impressions.”
This belief depends on data not currently tracked: cross-channel cohort data. It would activate when more channels contribute their data.
For videos with Browse-source rank-1 day, compare Browse impression decay rate to Suggested decay rate over the next 14 days. Cross-channel cohort baseline ideal. Agrees if Browse decay half-life > 1.5× Suggested.
“Cards contribute roughly 1% of traffic.”
This belief depends on data not currently tracked: Awaiting Round 4 cards-source data ingestion.. It would activate when Future round.
card_views / total_views. Cards are usually grouped under Direct/Unknown or a specific source.
“Channel-page-driven views come from highly engaged viewers.”
On this channel: 28.0 — the belief inconclusive here.
Compare AVD, retention pct, sub conversion, comment rate of Channel Page traffic vs Suggested traffic. Agrees if all four are higher for Channel Page.
“As library grows, Channel Page share of views falls.”
On this channel: 16.0 — the belief inconclusive here.
Per channel, Pearson correlation between days_since_first_video and trailing-7d Channel Page view share. Agrees if r ≤−0.5.
“Comments-per-view tracks audience resonance better than likes-per-view.”
On this channel: — the belief inconclusive here.
For each engagement metric, Pearson correlation with sub conversion rate at the video level. Agrees if comments/v has the highest correlation.
“Comments are weighted more than likes by the algorithm.”
On this channel: — the belief inconclusive here.
For videos with comparable view counts, regress impression-rate-after-day-7 on (comments per view) and (likes per view) separately. Agrees if |β_comments| > 2 × |β_likes|.
“High click-through-rate drives video success more than high retention.”
On this channel: 0.43× — the belief disagrees here.
Per video, scale CTR and retention to channel-relative units (each value's distance from the channel's median, divided by the channel's typical spread). Fit a linear regression of total views against both. If the magnitude of the CTR coefficient is at least 1.5× the retention coefficient, the data agrees that CTR dominates on this channel.
“Neither click-rate nor retention alone matters; the product (or geometric mean) does.”
This belief depends on data not currently tracked: Awaiting Round 4 retention-curve-derived precision.. It would activate when Future round.
Define combined = sqrt(ctr_z × retention_z) per video. Regress total views on combined vs on CTR and retention separately. Agrees if combined- only regression has higher R² than either-alone.
“Channels publishing daily need pre-recorded inventory; those without it burn out.”
This belief depends on data not currently tracked: cross-channel cohort data. It would activate when more channels contribute their data.
Cross-channel only. Cadence break frequency over time for channels classified as `daily`. Cohort-level: agrees if daily-cadence channels with ≥30-video pre-publish queue have lower break rates than ad-hoc.
“YouTube's algorithm tests a video primarily in its first 1–3 days, after which performance is largely set.”
On this channel: — the belief inconclusive here.
For each video with ≥14 days tracked, what fraction of total lifetime views came from days 1–3? Channel median across qualifying videos.
“Once 30 days post-publish, a video's daily view rate stabilizes and varies little.”
On this channel: — the belief inconclusive here.
For videos ≥60 days old, coefficient of variation of daily views in days 30–60. Channel median. Agrees if median CoV ≤0.5.
“Editing title or thumbnail post-publish kills momentum.”
On this channel: 0.00 — the belief inconclusive here.
Same as #12, but agrees if post-change views stay ≤50% of pre-change baseline for 14+ days. Tests for sustained damage rather than reset+recovery.
“Videos with high end-screen click-through have higher overall retention.”
This belief depends on data not currently tracked: Awaiting Round 4 cards/end-screen data ingestion.. It would activate when Future round.
Per video, end-screen view share of total views vs lifetime weighted retention. Agrees if r ≥0.4. Requires Round 4 cards/end-screen data.
“End-screen impressions and clicks contribute 5–15% of traffic.”
This belief depends on data not currently tracked: Awaiting Round 4 retention/cards data ingestion.. It would activate when Future round.
end_screen_views / total_views channel-wide. Reads from per-source breakdown. Note: requires retention curve / cards data from Round 4 to test fully.
“Viewers from external sites have the highest engagement rates.”
On this channel: 1.00 — the belief inconclusive here.
AVD, retention, sub conversion of External vs all other sources. Agrees if External AVD ≥ p75 of cross-source AVD distribution. Sample-size flag triggers when External views < 100 channel-wide.
“Days to first 100 subscribers is the slowest sub-acquisition leg.”
On this channel: — the belief inconclusive here.
Days from 1 sub to 100 subs vs days from 100 to 200, 200 to 300, etc. Channel-relative growth rates per sub-bucket. Agrees if days-per-sub in 0–100 is ≥2× days-per-sub in 100–500.
“Performance trajectory of the first 10 videos predicts month-12 channel state.”
This belief depends on data not currently tracked: cross-channel cohort data. It would activate when more channels contribute their data.
Cross-channel. Needs ≥12 months of data per channel and a consistent 'predict' definition.
“A video's lifetime trajectory is largely determined by its first 24–48 hour performance.”
On this channel: n = 0 — the belief inconclusive here.
Pearson correlation of day1_views vs total_views across all videos with ≥30 days tracked. Same correlation for day1_ctr vs lifetime_weighted_ctr. Higher r = belief agrees on this channel.
“First video's day-30 performance correlates with month-3 channel trajectory.”
This belief depends on data not currently tracked: cross-channel cohort data. It would activate when more channels contribute their data.
Cross-channel only. Within-channel, capture first_video_day30_views and compare to channel month-3 cumulative views. Across cohort, Pearson correlation.
“Publishing video N consumes impressions that would otherwise have gone to video N−1, slowing N−1's trajectory.”
On this channel: 0.50 — the belief agrees here.
For each video pair (N−1, N) where N−1 is still in days 1–7 of its lifecycle when N publishes, compare N−1's view trajectory in the 3 days after N's publish to N−1's trajectory in the 3 days before. Channel median of (views_3d_after / views_3d_before) across eligible pairs.
“The 0–15-second retention drop is the strongest single predictor of overall performance.”
This belief depends on data not currently tracked: Awaiting Round 4 retention curve ingestion.. It would activate when Future round.
From video retention curve, retention_15s_pct. Pearson correlation with total views and weighted_ctr_post_day_3. Agrees if r ≥0.7. Requires retention curve data (Round 4).
“Hour-1 view rate predicts the video's lifetime performance.”
This belief depends on data not currently tracked: YouTube Reporting API does not expose hourly granularity.. It would activate when Future round.
Requires hourly data not exposed by the YouTube Reporting API.
“Longer videos accumulate more total watch minutes per viewer.”
On this channel: — the belief inconclusive here.
Pearson correlation of duration_seconds and (total_watch_minutes / total_views). Agrees if r ≥0.5.
“Videos 8–10 minutes long perform best for ad revenue and watch time.”
On this channel: 20 min — the belief disagrees here.
Bucket videos by duration (60/180/360/600/1200/1800/3600+ sec). Median weighted CTR, AVD, retention pct, sub conversion per bucket. Agrees if 600s bucket has the highest median weighted retention pct.
“A new publish steals impression budget from the rest of the catalog.”
On this channel: 0.49 — the belief agrees here.
When video N publishes, change in non-N videos' impression share over the next 3 days. Agrees if median impression-share-of-others drops ≥15% in the post-publish window.
“Channels with narrow topical focus outgrow broad-topic channels.”
This belief depends on data not currently tracked: cross-channel cohort data. It would activate when more channels contribute their data.
Cross-channel — needs topic similarity scoring (owner-mode titles) plus growth rate. Score channels by topical entropy of titles. Correlate with 30-day growth.
“Titles with numbers (5 Ways… / 3 Things…) perform better.”
This belief depends on data not currently tracked: owner-mode-only data (subscribed-state, search queries, etc.). It would activate when the channel owner connects private analytics.
Median weighted CTR and median total views for title_has_number = 1 vs 0. Agrees if both metrics ≥1.2× the non-numbered group.
“Late-afternoon publishing optimizes day-0 reach.”
On this channel: — the belief inconclusive here.
Median day-1 views by hour-of-publish. Identify mode. Agrees if mode falls in 14:00–18:00 local time. Many channels lock to one hour, making this inconclusive.
“Channels that post on a consistent cadence outperform those that don't.”
On this channel: 22.0 — the belief inconclusive here.
Cadence regularity score = the typical day-to-day spread of inter-publish gaps, divided by the mean gap (coefficient of variation). Lower = more consistent. Correlate against trailing-30-day view growth.
“Publishing too often cannibalizes per-video performance.”
On this channel: 0.50 — the belief agrees here.
Same calculation as #1.
“Long gaps between uploads cause the algorithm to deprioritize the channel.”
On this channel: — the belief inconclusive here.
For gaps of ≥5 days between publishes, compare the post-gap video's day-1/day-3 performance against the channel's typical day-1/day-3.
“Titles ending in ? outperform on click-through rate.”
This belief depends on data not currently tracked: owner-mode-only data (subscribed-state, search queries, etc.). It would activate when the channel owner connects private analytics.
Median weighted CTR for videos where title_is_question = 1 vs 0. Owner-mode (titles are coarsened in public).
“Once a channel has 30+ videos, key ratios (CTR, AVD, sub conversion) settle.”
On this channel: — the belief inconclusive here.
Coefficient of variation of weekly weighted CTR and AVD across the channel's history. Compare CoV in weeks 1–10 vs weeks 11+. Agrees if both CoVs drop by ≥30% after the 30-video mark.
“High retention drives video success more than high click-through-rate.”
On this channel: 2.31× — the belief agrees here.
Same regression as #7; agrees if |β_retention| > 1.5 × |β_ctr|. Note: can be `inconclusive` simultaneously with #7 if neither dominates.
“Videos with growing Search-source share over time become long-term assets.”
On this channel: 15.0 — the belief inconclusive here.
Per video, slope of weekly Search-source view share over the video's lifetime. Channel-level: share of mature videos with positive slope.
“Shorter videos accumulate more views than longer ones.”
On this channel: — the belief inconclusive here.
Pearson correlation of duration_seconds and total_views. Agrees if r ≤−0.3.
“Most subscribers come from a few breakout videos, not from many videos contributing equally.”
On this channel: 0.86 — the belief agrees here.
Gini coefficient on subs_gained distribution across videos. Agrees if Gini ≥0.7.
“Channels with more subs per view have more engaged audiences.”
This belief depends on data not currently tracked: cross-channel cohort data. It would activate when more channels contribute their data.
Cross-channel: Pearson correlation between (current_subscribers / total_views) and (sub conversion rate, comment rate, AVD). Agrees if all three ≥0.3.
“Subscribed-state viewers have higher retention than non-subscribers.”
This belief depends on data not currently tracked: owner-mode-only data (subscribed-state, search queries, etc.). It would activate when the channel owner connects private analytics.
AVD per (video, subscribed_state). Agrees if subscribed AVD median is ≥1.3× non-subscribed AVD.
“Day-0 traffic is dominated by subscribed-state viewers.”
This belief depends on data not currently tracked: owner-mode-only data (subscribed-state, search queries, etc.). It would activate when the channel owner connects private analytics.
Per-video, what % of day-0 views came from subscribed-state viewers? Channel median. Reads from summary_video_subscribed (owner-mode only).
“Suggested impressions reach mostly non-subscribed viewers.”
This belief depends on data not currently tracked: owner-mode-only data (subscribed-state, search queries, etc.). It would activate when the channel owner connects private analytics.
Suggested views per subscribed state. Agrees if non-subscribed share of Suggested views ≥70%.
“Channels accelerate after 1,000 subscribers.”
This belief depends on data not currently tracked: cross-channel cohort data. It would activate when more channels contribute their data.
Cross-channel only. Compare 30-day view growth rate before vs after 1,000-sub milestone. Agrees if median post-1k growth is ≥1.5× pre-1k.
“Renaming a video resets its testing cycle.”
On this channel: 0.00 — the belief inconclusive here.
For videos with a title change, compare daily views/impressions/CTR for the 7 days before vs 7 days after the change. Agrees if median ratio drops below 0.5 then recovers above 1.0 within 14 days.
“Most videos peak in their first three days.”
On this channel: — the belief inconclusive here.
Distribution of peak_views_day across mature videos. Agrees if ≥60% of videos peaked on days 0–2.
“YouTube ranks by session length, not per-video metrics.”
This belief depends on data not currently tracked: cross-channel cohort data. It would activate when more channels contribute their data.
Cross-channel; requires session inference. Approximation: total channel watch minutes per active viewer per day. Marked low_confidence due to proxy nature.
“Saturday and Sunday uploads get less engagement than weekday uploads.”
On this channel: — the belief inconclusive here.
Median weighted CTR, AVD, sub conversion for videos published on Sat/Sun vs Mon–Fri. Agrees if all three are lower on weekend uploads. Inconclusive with <10 weekend uploads.
| Belief | Status | Confidence | Flips | Last changed | Streak (days) |
|---|---|---|---|---|---|
| Older channels become less dependent on algorithmic recommendations. | not_currently_testable | low | 0 | — | 22 |
| Distribution happens in two phases — test (low impressions, narrow audience) then scale. | inconclusive | low | 0 | — | 22 |
| Videos with average view percentage above 50% receive sustained impression delivery. | inconclusive | low | 0 | — | 22 |
| Browse / Homepage impressions are weighted toward existing subscribers. | not_currently_testable | low | 0 | — | 22 |
| Browse pickups are more durable than Suggested-driven impressions. | not_currently_testable | low | 0 | — | 22 |
| Cards contribute roughly 1% of traffic. | not_currently_testable | low | 0 | — | 22 |
| Channel-page-driven views come from highly engaged viewers. | inconclusive | low | 0 | — | 22 |
| As library grows, Channel Page share of views falls. | inconclusive | low | 0 | — | 22 |
| Comments-per-view tracks audience resonance better than likes-per-view. | inconclusive | high | 0 | — | 22 |
| Comments are weighted more than likes by the algorithm. | inconclusive | low | 0 | — | 22 |
| High click-through-rate drives video success more than high retention. | disagrees | high | 4 | 2026-04-26 | 5 |
| Neither click-rate nor retention alone matters; the product (or geometric mean) does. | not_currently_testable | low | 0 | — | 22 |
| Channels publishing daily need pre-recorded inventory; those without it burn out. | not_currently_testable | low | 0 | — | 22 |
| YouTube's algorithm tests a video primarily in its first 1–3 days, after which performance is largely set. | inconclusive | low | 0 | — | 22 |
| Once 30 days post-publish, a video's daily view rate stabilizes and varies little. | inconclusive | low | 0 | — | 22 |
| Editing title or thumbnail post-publish kills momentum. | inconclusive | low | 0 | — | 22 |
| Videos with high end-screen click-through have higher overall retention. | not_currently_testable | low | 0 | — | 22 |
| End-screen impressions and clicks contribute 5–15% of traffic. | not_currently_testable | low | 0 | — | 22 |
| Viewers from external sites have the highest engagement rates. | inconclusive | low | 0 | — | 22 |
| Days to first 100 subscribers is the slowest sub-acquisition leg. | inconclusive | low | 0 | — | 22 |
| Performance trajectory of the first 10 videos predicts month-12 channel state. | not_currently_testable | low | 0 | — | 22 |
| A video's lifetime trajectory is largely determined by its first 24–48 hour performance. | inconclusive | low | 0 | — | 22 |
| First video's day-30 performance correlates with month-3 channel trajectory. | not_currently_testable | low | 0 | — | 22 |
| Publishing video N consumes impressions that would otherwise have gone to video N−1, slowing N−1's trajectory. | agrees | high | 5 | 2026-04-24 | 7 |
| The 0–15-second retention drop is the strongest single predictor of overall performance. | not_currently_testable | low | 0 | — | 22 |
| Hour-1 view rate predicts the video's lifetime performance. | not_currently_testable | low | 0 | — | 22 |
| Longer videos accumulate more total watch minutes per viewer. | inconclusive | high | 0 | — | 22 |
| Videos 8–10 minutes long perform best for ad revenue and watch time. | disagrees | high | 1 | 2026-04-14 | 17 |
| A new publish steals impression budget from the rest of the catalog. | agrees | high | 1 | 2026-04-14 | 17 |
| Channels with narrow topical focus outgrow broad-topic channels. | not_currently_testable | low | 0 | — | 22 |
| Titles with numbers (5 Ways… / 3 Things…) perform better. | not_currently_testable | low | 0 | — | 22 |
| Late-afternoon publishing optimizes day-0 reach. | inconclusive | low | 0 | — | 22 |
| Channels that post on a consistent cadence outperform those that don't. | inconclusive | low | 0 | — | 22 |
| Publishing too often cannibalizes per-video performance. | agrees | high | 5 | 2026-04-24 | 7 |
| Long gaps between uploads cause the algorithm to deprioritize the channel. | inconclusive | low | 0 | — | 22 |
| Titles ending in ? outperform on click-through rate. | not_currently_testable | low | 0 | — | 22 |
| Once a channel has 30+ videos, key ratios (CTR, AVD, sub conversion) settle. | inconclusive | low | 0 | — | 22 |
| High retention drives video success more than high click-through-rate. | agrees | high | 4 | 2026-04-26 | 5 |
| Videos with growing Search-source share over time become long-term assets. | inconclusive | low | 0 | — | 22 |
| Shorter videos accumulate more views than longer ones. | inconclusive | high | 0 | — | 22 |
| Most subscribers come from a few breakout videos, not from many videos contributing equally. | agrees | high | 1 | 2026-04-15 | 16 |
| Channels with more subs per view have more engaged audiences. | not_currently_testable | low | 0 | — | 22 |
| Subscribed-state viewers have higher retention than non-subscribers. | not_currently_testable | low | 0 | — | 22 |
| Day-0 traffic is dominated by subscribed-state viewers. | not_currently_testable | low | 0 | — | 22 |
| Suggested impressions reach mostly non-subscribed viewers. | not_currently_testable | low | 0 | — | 22 |
| Channels accelerate after 1,000 subscribers. | not_currently_testable | low | 0 | — | 22 |
| Renaming a video resets its testing cycle. | inconclusive | low | 0 | — | 22 |
| Most videos peak in their first three days. | inconclusive | low | 0 | — | 22 |
| YouTube ranks by session length, not per-video metrics. | not_currently_testable | low | 0 | — | 22 |
| Saturday and Sunday uploads get less engagement than weekday uploads. | inconclusive | high | 4 | 2026-04-22 | 9 |