How Accurate Are Roblox CCU Forecasts? A Backtest of 2,002 Experiences

A Roblox concurrent-player forecast made today is typically off by about a quarter over the next four weeks. In ROLearn’s backtest of 2,002 experiences, the best model’s median error was 23.0% and the forecast ROLearn actually serves was 25.1%, against 26.8% for simply repeating the same weekday from last week. Error is about 15% to 17% for the next three days and rises with distance; past four weeks it reaches 43%. This report sets out where forecasts work, where they fail, and why the more accurate model is not the one in production.

Executive Summary

1. 1Forecasts are useful for about four weeks.Median error runs from about 15% to 17% for the next three days to 27% to 29% for days 15 to 28, and reaches 43% beyond day 28.
2. 2Lower error alone does not earn production.The model's 80% ranges held 76% of outcomes and it leaned low, so the promotion gate kept the baseline.
3. 3Size and history matter as much as the model.The top 20 games forecast best and the baseline wins there; games under 28 days old are the hardest.
4. 4Production is close to the rehearsal.Served forecasts scored a 26.8% median error across 63,807 forecasts for 17 to 23 October 2026.

Forecast	Median error, days 1 to 28	80% range coverage	Bias
LightGBM model	23.0%	76.4%	-4.7%
Served baseline	25.1%	83.1%	-0.8%
Same weekday last week	26.8%	n/a	+3.3%
Mean of last 3 days	30.7%	n/a	+7.1%
Last day carried forward	32.9%	n/a	+1.8%

Accuracy by forecast distance#

The further ahead, the larger the error, for every method. The model’s lead over the baseline is largest in the first three days and narrows but holds out to four weeks.

• LightGBM model
• Served baseline
• Same weekday last week

Source: ROLearn forecast backtest, evaluation run 48

Show the data

Category	LightGBM model	Served baseline	Same weekday last week
Days 1 to 3	15%	17%	19%
Days 4 to 7	18%	20%	19%
Days 8 to 14	22%	25%	26%
Days 15 to 28	27%	29%	32%

One result runs against the grain: for days 4 to 7, repeating the same weekday from last week (19.2%) slightly beats the served baseline (19.8%). Within a week, the weekly rhythm of Roblox play carries most of the signal.

Why the more accurate model is not the one served#

On error alone, the LightGBM model wins: 23.0% against 25.1%, and it beats “same weekday last week” on 62% of forecasts. But a forecast is also a promise about its range and its direction, and there the model falls short.

• Its ranges are too narrow. The stated 80% range held the real value 76.4% of the time, against 83.1% for the baseline. A planner relying on it would be surprised more often than they were told.
• It leans low. Its median forecast was about 5% under the actual, against under 1% for the baseline.

ROLearn only serves a model after it beats the baseline on start dates it never trained on, keeps its 80% range coverage between 72% and 88%, and keeps its bias within about 5%. The most recent candidate, scored on 24 October 2026, had a lower error than the baseline (24.7% against 26.4%) but under-predicted by about 10%, so it was rejected and the baseline stayed in production. Lower error that comes with a systematic lean is not a better forecast for someone sizing a launch.

Big games are easier to forecast, and the model helps least there#

Experiences by size rank	Forecasts	LightGBM model	Served baseline	Same weekday last week
Top 20	110	18.9%	17.1%	18.6%
21 to 300	1,109	18.7%	18.7%	19.4%
301 to 1,000	3,267	22.4%	23.8%	25.9%
Below 1,000	5,026	24.4%	27.2%	29.1%

The largest experiences are the most predictable, and for the top 20 the simple baseline is the best forecast of the three. Their audiences are large and their weekly rhythm is stable, so a model has little to add. The model’s advantage appears lower down: for experiences ranked below 1,000 it cuts the median error from 27.2% to 24.4%.

New games are where forecasts are weakest#

Experiences with under 28 days of history had a median error of 40.2% with the model and 51.8% with the baseline, against 18% to 24% for established games. A new game’s audience has not settled into a weekly pattern, and the first weeks are exactly when a breakout or a collapse happens. A forecast for a game in its first month should be read as a wide range, not a number.

Beyond four weeks, forecasts are a direction, not a plan#

A separate test scored days 29 to 63 from start dates in mid-August 2026. The median error rose to 43% for both the model and the baseline, and both over-predicted, by about 27% for the baseline. Those start dates sat near the end of the summer holidays, which is consistent with forecasts carrying summer levels into a quieter autumn. Any decision that turns on a 60-day player count, such as when one game will overtake another, inherits that uncertainty.

How the forecasts did in production#

The backtest is a rehearsal. ROLearn also scores the forecasts it actually served once their target days pass. For target days 17 to 23 October 2026, across 63,807 forecasts and horizons of up to 28 days, the median error was 26.8%, the 80% range held the real value 76.6% of the time, and the median bias was close to zero.

Forecast distance	Served forecasts	Median error	80% range coverage
Days 1 to 3	15,601	18.8%	76.8%
Days 4 to 7	17,541	20.0%	77.2%
Days 8 to 14	25,016	25.4%	75.7%
Days 15 to 28	38,408	32.5%	76.7%

Production error is a little higher than in the backtest, and the ranges covered about 77% rather than the 83% the baseline achieved in rehearsal. A backtest over six start dates cannot anticipate every shift in real play, which is why the live scores are published alongside it.

How to use a Roblox CCU forecast#

• Use the range, not the line. Even at its best the forecast is off by about 15% in the next few days. Plan against the 80% range and expect the real value to fall outside it about one time in four.
• Trust it most for established, larger games and least for games in their first month.
• Treat anything past four weeks as a direction. Re-forecast weekly rather than committing to a 60-day number.
• Compare against the simple benchmark. If a forecast you are shown does not beat repeating last week’s same weekday, it is not adding information.

Frequently asked questions#

How accurate are Roblox CCU forecasts?#

In ROLearn’s October 2026 backtest of 2,002 Roblox experiences, the best model had a median error of 23.0% over the next 28 days and the forecast ROLearn serves had 25.1%. Error is about 15% to 17% for the next three days and about 27% to 29% for days 15 to 28.

Are forecasts for big Roblox games more accurate?#

Yes. For the 20 largest experiences the median error was 17.1% for the served baseline, against 27.2% for experiences ranked below 1,000. Games with under 28 days of history are the hardest, at 40% to 52%.

How far ahead can Roblox player counts be forecast?#

Usefully, about four weeks. Beyond day 28 the median error rose to 43% and forecasts over-predicted by about 27%, so a 60-day number is a rough direction, not a plan.

What is sMAPE?#

Symmetric mean absolute percentage error: the gap between forecast and actual as a share of their average size. It is bounded at 200%, so experiences near zero players cannot swamp the result the way they do with ordinary percentage error.