Streampace docs

Reports library

Streampace ships 32 reports across 7 categories. Every report answers one question, runs one statistical method, and surfaces one decision you can act on.

The math underneath is borrowed from places that take this stuff seriously — survival analysis from medical research (Kaplan-Meier), regression from econometrics, paired t-tests from clinical trials, Gini from economics, z-score from engineering quality control. The plain-language explanations below describe what each one tells you and what to do about it.

Performance

How a stream went.

Daily brief

Question
What happened across my whole roster yesterday?
Plain English
A morning newspaper for your creators — yesterday’s totals, who earned most, who did unusually well or badly, who went quiet, the biggest gifts, and the battles, all on one screen so you don’t click through 24 creator pages.
Method
Roster-wide recap of the most recent complete UTC day: a pulse strip (diamonds, creators live, airtime, gifters, battles, follows — each vs the prior day, with a 7-day average for the additive ones), a diamond leaderboard, movers (each creator’s swing across three baselines — vs the prior day, vs their prior-week per-active-stream-day average, and month-to-date vs last month), a went-dark watch list (active creators silent 3+ days), a full-roster table breaking down every creator’s day (diamonds, streams, airtime, swing vs their baseline, last live), a per-creator spotlight carousel (each active creator’s own top gifters, top gifts, peak viewers, and battles), the top statistically-gated recommendations, a battles recap, and upcoming scheduled battles. Agency-only.
Use it for
The agency principal/manager’s start-of-day screen. Catch a creator going dark early, surface lighthouse wins for partner pitches, and drive who-to-check-in-on today. Pull any past day with the date nav; export the core as a PDF.

Stream recap

Question
How did tonight’s stream go?
Plain English
A box-score for the stream you just finished — totals, top names, and how tonight stacks up against your typical night.
Method
Just numbers — total ◆, top gifters tonight, pace, vs your lifetime, vs your last stream.
Use it for
The post-stream debrief. The first thing a creator opens after going offline.

Top gifters leaderboards

Question
Who are my biggest fans, this stream / this week / this month / lifetime?
Plain English
Your biggest fans by tonight, this week, this month, and all-time — four leaderboards on one page.
Method
Just rankings, four windows.
Use it for
Know who to thank by name, spot a new top gifter rising, see who’s slipping.

Stream shape

Question
How long should my stream be, and how soon should I go again?
Plain English
Two questions, two charts. One: at what duration do longer streams stop paying back? Two: do back-to-back streams help, or do you earn more if you take a day off in between?
Method
Two charts — avg ◆ per stream by duration band (finds the sweet spot before diminishing returns kick in), and ◆ vs gap since prior stream (does back-to-back help or hurt).
Use it for
Schedule decisions. “Streams over 3h drop in ◆/min” or “I earn more if I take a day off between.”

Cohort retention matrix

Question
Which streams built a sticky audience vs flash-in-the-pan?
Plain English
For each stream, how many of your first-time gifters came back next time, the time after, the time after that. Shows which streams built lasting fans vs. one-night spikes.
Method
Triangle grid — for each stream, what % of its first-time gifters came back 1, 2, 3, …N streams later.
Use it for
Identify what kinds of streams build a real fan base vs one-night spikes.

Stream cadence optimizer

Question
Should I stream more often or less often?
Plain English
Are you better off going live every day, every other day, or once a week? Answers from your own history, not a generic benchmark.
Method
Buckets your closed streams by gap-since-prior-stream into five cadence bands (<1d, 1–2d, 2–3d, 3–5d, 5+d) and reports the median ◆ per band. The eligible band with the highest median is the recommendation — the one that pays best for you specifically, not a generic benchmark.
Use it for
Schedule decisions. Some creators leave money on the table by streaming daily (audience fatigue); others by waiting a week between sessions. Answers the question from your own data.

Go-live time optimizer

Question
What hour should I start streaming?
Plain English
Which start time pays you most? Looks at every stream you’ve run and ranks your top-three start-hours by what you actually earn at each.
Method
Buckets your closed streams by start-hour (in your local timezone) and reports the per-hour median ◆ with p25/p75. Surfaces the top-3 eligible start-hours with their % uplift vs your overall median. The prescriptive counterpart to the descriptive day×hour gift heatmap — that one shows when gifts land; this one shows what start-time pays.
Use it for
Pre-stream planning. "Going live at 8pm beats 10pm by 18% on a typical Saturday for me." Validate the recommendation with an A/B-tagged stream on the recommended hour.

Creator outliers

Question
Which of my creators are outperforming or underperforming their expected pace?
Plain English
After accounting for how long each creator streams and when, who actually outperforms expectations? Tells you who’s real talent vs. who just got a lucky schedule.
Method
Multi-variable OLS regression of pace on stream duration + time-of-day + day-of-week, fit globally across the roster. Each creator gets a residual (their actual pace minus what the model predicts for their typical schedule + duration). Positive residual = overperformer; negative = underperformer. 95% confidence interval on the mean per-creator residual — the "sig" badge marks creators whose CI excludes zero, i.e. statistically distinguishable from the roster baseline.
Use it for
Agency principal's killer report. "Whose pace is real talent vs. luck of their schedule?" Coach the underperformers; learn from the overperformers. Read alongside Stream cadence + Go-live time — they tell you WHAT to change for one creator; creator outliers tells you WHO needs the change.

Audience

Who’s gifting and why.

Gifter survival curves

Question
How long do new gifters stick around after their first gift?
Plain English
After a new fan gifts you for the first time, how likely are they to keep coming back? Shows the drop-off curve so you can spot whether you’re losing them fast or keeping them around.
Method
Kaplan-Meier — the same survival math hospitals use for "how long do patients stay healthy." Plots the % of gifters still active after N streams. Handles the "we don’t know yet, they might still be active" case properly instead of pretending those are losses.
Use it for
Spot the drop-off cliff (e.g. "60% of new gifters never come back after their first stream") and watch whether your retention is improving or eroding over time.

Audience health (RFM)

Question
Who’s loyal, who’s slipping, who’s about to churn?
Plain English
Tags every gifter with a vibe — "loyal champion," "at-risk," "about to ghost," "lapsed" — so you know exactly who to thank, who to DM, and who’s already gone.
Method
Recency, Frequency, Monetary — each gifter scored on three axes, bucketed into segments (champions, loyal, at-risk, lapsed).
Use it for
Personal outreach. DM the at-risk top gifters before they ghost.

Top gifter concentration (Gini)

Question
How dependent am I on a few big gifters?
Plain English
Are you one bad month away from disaster if your top fan vanishes? Measures how much your income leans on a few top gifters vs. a broad base.
Method
Gini coefficient. 0 = every gifter contributes equally; 1 = one top gifter gives everything. A score of 0.85 means losing your top 5 gifters is catastrophic; 0.40 means you’ve got a healthy diversified base.
Use it for
Risk gauge. High Gini = chase diversification; low Gini = lean into your loyalists.

Lapsed top gifters

Question
Which big gifters have gone quiet?
Plain English
Your top gifters who haven’t shown up in two weeks. Your re-engagement DM list, ready to go.
Method
Top-30 lifetime gifters who haven’t shown up in 14+ days.
Use it for
Re-engagement list. DM them, shout them out when they return.

Engagement

Audience action — likes, shares, comments, follows.

Engagement health per stream

Question
Which streams pulled audience action vs which were quiet?
Plain English
Was last night actually loud, or just quietly profitable? Shows likes, comments, and shares per minute for each stream, with up/down arrows vs your typical pace.
Method
Per-minute rates of likes / shares / comments / diamonds for each stream, vs the creator’s median across the window. Above-median rows get a ▲ marker; below get a ▼.
Use it for
Vital signs check. Diamonds tell you who paid; engagement rates tell you how alive the room felt. A stream with great engagement but low diamonds is a re-monetisation puzzle, not a quality problem.

Engagement velocity over time

Question
Are engagement rates trending up or down across the last N weeks?
Plain English
Is engagement going up, flat, or fading? Catches the cooling weeks before gifts start dropping — your earliest warning sign.
Method
Weekly buckets of likes/shares/comments/diamonds per minute, with a linear regression slope per metric. ±2%/week is the noise floor — anything inside reads as flat.
Use it for
Early warning. Engagement cools weeks before revenue catches up. A falling-likes / flat-diamonds combo says push for fresh content before the gift-rate trails the engagement-rate.

Follow → gift conversion funnel

Question
Of viewers who joined, how many engaged, followed, and gifted?
Plain English
Of everyone who watched, how many engaged, then followed, then actually gifted? Shows where the bleed happens — and how many gifters were already followers vs. impulse buyers.
Method
Five-stage cohort funnel: joiners → engaged → followers → gifters → repeat gifters. Plus a sub-stat — of the gifters, what fraction followed BEFORE their first gift (loyal-fan signal) vs after (impulse gifters).
Use it for
Audience-building diagnostic. A funnel that bleeds at engaged→followed = "audience is here but not committing." Bleeds at followers→gifters = "they like me but won’t pay yet."

Engagement day × hour heatmap

Question
When does the audience actually engage — follows, joins, shares, subs?
Plain English
A grid of day-of-week × hour-of-day showing when the audience does stuff (not just when they gift). Pairs with the gift heatmap to spot "alive but under-monetized" time slots.
Method
7×24 grid of event counts by day-of-week and hour-of-day (creator’s local timezone). Optional filter to one event type. Likes and chat aren’t here — those are aggregate-only by design; see engagement-health.
Use it for
Mirror of the gift heatmap. Looking at the two together surfaces "audience engages here but doesn’t gift" mismatches — useful for finding under-monetised time slots.

Forecasts

Where things are headed.

End-of-stream forecast

Question
Mid-stream — where will I likely finish tonight?
Plain English
A mid-stream prediction: "at this pace, you’re tracking to about X tonight, give or take Y." Helps you decide whether to push harder or wrap it up.
Method
Exponential smoothing on per-minute pace. Projects the rest of the stream with a confidence band.
Use it for
Live decisions. "Push for a bigger goal" or "wrap up, this stream’s done."

Pace decay

Question
Are my streams trending up, flat, or eroding?
Plain English
Is your per-minute pace climbing, flat, or sliding? Catches slow drift months before it shows up as a bad month you actually feel.
Method
Linear regression on pace across the last N streams. Outputs a slope (◆/min change per stream) and a category: rising, flat, falling.
Use it for
Monthly check-in. Catches slow drift before it shows up as a "bad month." Sustained negative slope = something needs to change; flat = economy stable.

Live anomaly detector

Question
Alert me NOW when something unusual is happening on the stream.
Plain English
Pings you mid-stream when something out-of-pattern is happening — a surprise surge to lean into, or a sudden drop to course-correct.
Method
Real-time z-score of per-minute pace vs the creator’s typical pattern. Fires push alerts on big surges or dips while live.
Use it for
Live decisions. Surge alert at minute 47 = "lean in, this is your moment"; dip alert = "pivot, what you’re doing isn’t landing."

What-if simulator

Question
What pace would I expect if I streamed at a different time or for a different length?
Plain English
A flight simulator for your schedule. Slide the time-of-day, day-of-week, and length sliders; the model predicts what your pace would be — with a clear honesty bar about how wide the uncertainty is.
Method
Per-creator OLS regression of pace on cyclical hour-of-day + cyclical day-of-week + duration. Slide the sliders; the model predicts the pace at those settings with a 95% bootstrap-percentile confidence interval. Compares your counterfactual to your "usual" baseline (your mode hour, mode day, median duration). Warns when sliders extrapolate beyond your observed schedule range — predicting Tuesday-3am from Friday-9pm history is a guess, not an inference.
Use it for
Schedule experimentation before you run the experiment. "If I moved to Saturday 7pm with 90-minute streams, what would my pace look like?" The CI is honest about uncertainty — wider when your history doesn't cover the counterfactual. Pair with A/B compare to validate the prediction after running a real stream at the chosen settings.

Experiments

Did the change actually work.

A/B compare

Question
Did changing X actually move the needle, or was it just luck?
Plain English
Tag two batches of streams ("short intros" vs "long intros") and the report tells you whether the difference is a real pattern or just three lucky streams in a row.
Method
Welch’s t-test. Tag streams "version A" vs "version B" (e.g. long intro vs short, weekday vs weekend), the report tells you whether the difference is real or could be chance.
Use it for
Validate strategy changes before committing. Don’t roll out "shorter intros" based on three good streams in a row.

Effect size (Cohen’s d)

Question
Even if the change is real, is it BIG enough to care?
Plain English
Even when a change is real, was it big? Tells you "yeah, you earn 1% more but you’d never feel it" vs. "this changed your career."
Method
Cohen’s d quantifies how much two groups actually differ. 0.2 = small (barely feel it), 0.5 = medium (noticeable), 0.8+ = large (career-altering).
Use it for
Use alongside A/B compare. A statistically-significant 1% lift isn’t worth changing your strategy for.

Power & sample size

Question
How many streams do I need to actually prove a hypothesis?
Plain English
Before you run an experiment, how many streams do you actually need to prove the idea? Tells you "three streams isn’t enough — you’d need 24."
Method
Power analysis. Plug in expected effect size + confidence level → tells you the streams needed.
Use it for
Plan experiments before running them. "Test whether 9pm beats 8pm — you’d need 24 streams to detect a 10% lift; 6 streams is wasted time."

Battles

Battle profitability.

Battle profitability

Question
Which opponents and partners are worth my time?
Plain English
Ranks every opponent and teammate by how much they actually boost your pace. Schedule rematches with the profitable ones; skip the duds.
Method
For each battle, calculate the lift in ◆/min vs the creator’s solo baseline. Group by counterpart. Paired t-test flags which lifts are real vs noise.
Use it for
Schedule rematches with profitable opponents, avoid duds, identify partners who carry their weight. Compare-mode shows two creators’ lists side-by-side.

Battle lift over time

Question
Is my battle strategy still working — or has the lift faded?
Plain English
Are battles still working? Tracks the lift week-over-week so you catch "battles used to help, but stopped three weeks ago" before it costs you a month.
Method
Same per-battle deltas, bucketed into weekly buckets. Paired t-test per week tells you which weeks the lift is real, which are noise.
Use it for
Catch "battles used to lift my pace but stopped working three weeks ago" patterns. Compare-mode overlays two creators’ weekly lines.

Power-up inventory

Question
Which of my gifters are holding match power-ups right now, and which type?
Plain English
After you win a battle, TikTok awards match power-ups (boosting glove, magic mist, stun hammer, time maker, top-2 / top-3 booster) to up to three of your top contributors — each card good for 5 days. This page shows every card currently outstanding, grouped by type, with the gifters holding them and how long until each expires.
Method
Every match power-up awarded to one of your top contributors is recorded with the recipient, the card tier, and a 5-day expiry; every time a recipient later spends one in a future battle on your stream, that activation is recorded too. Activations are paired back to their originating award by first-in-first-out within recipient and tier. Active inventory at any time = awards not yet spent whose 5-day window is still open. Activation rate = spent / awarded across the chosen window.
Use it for
Pre-battle planning. "Which gifters are armed right now?" — DM them before a scheduled battle so they bring the cards. Spot expiring cards (under a day left) and ping the recipient. Read the activation rate over time: high % = loyal repeat-battlers; low % means cards keep aging out (a quiet churn signal even when the diamond totals look fine).

Upcoming battles

Question
What battles are coming up across my whole roster?
Plain English
One board of every proposed and confirmed battle between your creators — any format, soonest first, shown in your local time. Managers schedule and confirm matchups on each creator’s page; this is the at-a-glance "what’s on the calendar" view across the whole roster.
Method
Reads every scheduled battle with a start time in the future, ordered soonest-first and rendered in the viewer’s local zone. Supports every format (1v1 through 2v2 / free-for-all). Read-only — proposing, confirming, and cancelling all happen on the individual creator pages.
Use it for
Plan the week across the roster, avoid accidentally booking your own creators against each other, and line up the right gifters (and their power-up cards) ahead of each confirmed matchup.

Battle format mix

Question
What share of a creator’s battles is each format?
Plain English
A pie of how a creator splits their battles across formats — mostly 1v1, or do they play a lot of 2v2 and free-for-all? One glance tells you their battle style.
Method
Counts every captured battle by its format (1v1 / 2v2 / 1v2 / 2v1 / 1v3 / 3v1 / free-for-all) over the chosen window and shows each format’s share as a donut + per-format table. Descriptive — no model. The same donut is embedded on each creator’s battles tab.
Use it for
See a creator’s battle style at a glance, then pair it with Battle profitability to check whether the formats they play most are the ones that actually pay.

Gifter battle rivalries

Question
Which of my gifters rally hardest against which opponents?
Plain English
Your top gifters pick favorites. This shows which opponents each gifter goes hardest against — so you learn that @bigfan only engages with the battle when you’re up against @rival, and you can book that matchup.
Method
Counts the diamonds each gifter sent during battles, attributed to the battle’s opponent (a gift counts as “vs X” when it landed during a battle vs X — timing, not a TikTok-tagged side). Ranks the top gifter×opponent pairs. In 2v2 / free-for-all the gift is attributed to each opponent on the other side and flagged, so a shared total isn’t mistaken for exclusive. The per-gifter version lives on each gifter’s page.
Use it for
Schedule the battles your top gifters care about — line up @rival when the gifter who rallies against them is around, and brief creators on who fires up which fans.

Scouting

The competitive field your roster plays in.

League standings

Question
Who is ranked in each TikTok Creator League, and where do my creators sit?
Plain English
The full TikTok Creator League leaderboard — the top 99 creators in every league (A1 at the top down to D5), with your roster highlighted wherever they land. See exactly who your creators are competing against this period, and which leagues your roster spans.
Method
For each league we pull TikTok’s own live Creator League board — the top 99 creators in that class, ranked by diamonds this period. Pick any league (A1…D5); your roster creators are highlighted “ROSTER” across all of them, with a count of how many of yours sit in each. The per-creator page also embeds the board for that creator’s own league. (A creator’s class — A1…D5 — comes from the Creator League card; this report is the full field behind it.)
Use it for
Competitive intelligence + scouting. See the field your creators are fighting in, spot rising talent in lower leagues before they climb, and know at a glance which leagues your roster spans.

Rising talent

Question
Which up-and-coming creators in the lower leagues are climbing fastest?
Plain English
A scout board for the lower leagues. We watch the C and D league leaderboards day by day and surface the creators who are moving up — climbing the board, getting promoted a rung, or just breaking into the top 99 — so you can find talent on the way up before they arrive.
Method
Each lower-league board (top 99 per class) is captured once a day. We line the daily boards up and turn each creator’s standing into one number — their rung on the 20-step ladder combined with their rank within it — then measure how many ladder positions they gained from the start of your chosen window (7, 14, or 30 days) to the latest day. A creator who was not on the board at the start is treated as climbing in from just below the cutoff, so a genuine break-in counts. We keep only positive movers still sitting in a lower league, and rank by positions gained. Scores reset each league period, so we rank by ladder position rather than raw diamonds. Pick the league band (D, C–D, or B–D) and the region — any market we capture league boards for, not just North America. Agency-only.
Use it for
Roster expansion + recruiting. Find rising creators to sign before a competitor does, line up battle opponents who are heating up, and watch which lower leagues are producing momentum.

Creator diamond history

Question
How much does any creator earn per day — even one who’s not on my roster?
Plain English
Look up any TikTok creator and see their daily diamond totals over time — one finalized figure per day. Search by handle or name; if a creator isn’t indexed yet, an owner or manager can add them by @handle and they start showing up from the next day. It’s a scouting lookup — not live tracking, and not a roster add.
Method
A daily index of finalized diamond totals harvested across every region we cover — one settled figure per creator per day, not a live counter. Search any indexed creator, then window the daily series and totals to the last 7, 30, 90 days, all-time, or a custom range. Owners and managers can add a creator by @handle; the handle is resolved to its identity and its region auto-detected on the next harvest cycle, after which the daily figures accumulate. Agency-only.
Use it for
Scouting and competitive intel beyond your roster. Size up a prospect’s earnings before you reach out, track a rival creator’s day-by-day trajectory, and build a watch-list of creators to court — without adding them to (or paying for) your roster.

Want to see this in action against your roster?

Streampace ingests the moment you connect a creator — your first stream is fully captured. Demo against your own creator data on a follow-up call.

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