Methodology

We collect player activity, store rankings, review metrics, and news from public Steam sources and process the results on a schedule. The goal is to surface useful signals without adding noise.

Collection

Jobs run on a rolling cadence and write to a denormalized view for fast reads. We prefer stable queries and clear cache rules over complex pipelines. When data is sparse, we avoid guessing.

Scoring

Trending and hype scores combine ranking positions, velocity, review sentiment and volume, wishlist movement, and news activity. Scale limiters keep small bases from swinging results. Scores are designed to be steady enough for browsing and fresh enough to reflect real changes.

Freshness

You will see update notices on listing pages. These timestamps reflect the latest successful runs from our data jobs.

Limitations

Public APIs change, storefronts evolve, and some titles have incomplete data. We improve coverage over time and favor honest gaps over made up values.