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PLAYCURVE
Steam concurrent players, done right
SNAPSHOT
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Players in-game now — across the top 100
#1 right now — —
Biggest climber — vs last week
Top genre — by players
Free-to-play — of players in-game

Who commands Steam player share by genre · click to filter

Trending rank vs last week

▲ Climbing
▼ Falling
⌕
# Game Players now Peak today Week
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    UNDER THE HOOD

    A real ETL pipeline feeds this page

    Three keyless Steam/SteamSpy sources are extracted, joined, cleaned and loaded by a scheduled pipeline.py. Every run also appends to a history.jsonl — so a genuine concurrent-player time series accumulates from first deploy.

    01

    Extract

    Steam GetMostPlayedGames + GetNumberOfCurrentPlayers + SteamSpy appdetails.

    3 sources
    →
    02

    Transform

    Join on appid; engineer fill %, rank movement, engagement, review score.

    17 fields
    →
    03

    Clean

    Drop games with no live count; rank by current players.

    — raw
    →
    04

    Load

    games.json for the dashboard + tidy CSV for Tableau.

    — games
    pipeline.py — transform()

    Tableau-ready in one click

    The joined, cleaned dataset exports as a tidy CSV — exactly what you'd drop into Tableau or pandas.

    TABLEAU PUBLIC

    Embed slot — publish the CSV and drop the iframe here.

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