How GGAID calculates player count trends for Roblox games using statistical analysis
GGAID provides trend analysis for every tracked Roblox game, helping players and developers understand whether a game's player base is growing, declining, or remaining stable. This page explains exactly how we calculate these trends.
We collect concurrent user (CCU) data for each game at regular intervals throughout the day. This gives us multiple data points per day, capturing the natural fluctuations in player activity across different time zones and peak hours.
Rather than comparing raw CCU values directly, we first calculate the daily average CCU for each day. This eliminates intra-day fluctuations caused by time zones and typical peak/off-peak patterns.
This gives us 7 daily averages (one per day) instead of hundreds of raw data points, making trend detection more reliable.
Special events, game updates, or viral moments can cause temporary spikes that don't reflect the true trend. We use the Interquartile Range (IQR) method to handle these outliers.
Instead of removing outliers entirely, we use Winsorization: values below the lower bound are replaced with the lower bound, and values above the upper bound are replaced with the upper bound. This preserves the data point count while reducing the impact of extreme values.
We apply linear regression to the processed daily averages to find the best-fit line. The slope of this line tells us the overall direction and magnitude of the trend.
The slope (m) indicates the average daily change in player count. A positive slope means the game is gaining players over time; a negative slope means it's losing players.
We calculate the percentage change over the 7-day period based on the regression line, then classify the trend using a 10% threshold:
| Change | Classification |
|---|---|
| ≥ +10% | Rising |
| -10% to +10% | Stable |
| ≤ -10% | Declining |
The 10% threshold prevents minor fluctuations from being misclassified as significant trends. A game needs to show meaningful, sustained change to be marked as Rising or Declining.
Eliminates noise from time-of-day variations. A game isn't "declining" just because it's 3 AM in the US.
A single viral day or server outage won't skew the entire trend. Outliers are bounded, not deleted.
Looks at the overall direction across all data points, not just comparing day 1 to day 7.
Natural variation happens. Only meaningful, sustained changes are flagged as trends.
Browse any game on GGAID to see real-time trend analysis based on this methodology.
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