SteamSpy: Understanding Steam Game Analytics with Python

This article is part of in the series
Published: Friday 28th February 2025

steamspy explained in detail

SteamSpy is a very useful tool for interpreting and understanding your game sales, player statistics, and market trends on Steam. This tool was originally developed by Sergey Galyonkin. It even provides estimated sales data and user engagement metrics based on historical data already available for general public. It is commonly used by game developers, publishers, and business analysts to understand the market trends and make informed decisions.

How does SteamSpy Work

SteamSpy combines all these features and brings out meaningful data:

  • Public user profiles: SteamSpy collects data from users who have their profiles set to public.
  • Statistical sampling: By extrapolating from sampled users, SteamSpy estimates the total number of owners and playtime of a game.
  • Machine Learning (ML) and data processing: Advanced algorithms refine data accuracy by filtering anomalies and normalizing figures.
  • API access and data scraping: SteamSpy gathers information using web crawlers and Steam's API to fetch publicly available game statistics.
  • Historical data tracking: The system maintains a log of past data to conduct an analysis, long-term trends, and growth patterns.

Important SteamSpy Metrics that can be Tracked

Here are some important SteamSpy metrics that can be useful for you, based on your persona:

  • Game owners: The estimated number of people who own a game.
  • Playtime (median and average): Data on how long players engage with a game.
  • Players in the last two weeks: Measures active engagement.
  • Price changes and discounts: Tracks historical pricing trends.
  • Release date and genre data: Useful for comparing different game categories.
  • Concurrent player count: Evaluates real-time engagement metrics for trending titles.
  • Regional distribution: Provides breakdowns of player demographics based on location.

Real World Applications of SteamSpy

Different stakeholders use SteamSpy differently. Let us explain how different professionals use this tool to their advantage so that you can choose for yourself.

Game Developers and Publishers

Famous game publishing teams and developers use this tool to do:

  • Market research: Identifying successful game genres and pricing strategies.
  • Competitor analysis: Understanding what makes competitors successful.
  • Pre-Launch planning: Estimating potential sales based on similar titles.
  • User retention analysis: Evaluating engagement levels over time to improve game mechanics.
  • Feature impact study: Monitoring how updates or expansions affect user statistics.

Investors, Business Analysts, and CXOs

People with a business reason to go through Steam analytics data use SteamSpy to:

  • Market trends: Evaluating the viability of investing in certain game studios.
  • Player engagement: Assessing which games have long-term retention.
  • Revenue estimation: Predicting potential revenue streams.
  • ROI analysis: Comparing the performance of different titles over time.

Content Creators, Journalists, and Game Reviewers

Here is how people working on game content use this tool:

  • Industry reports: Writing data-backed articles on gaming trends.
  • Fact-checking developer claims: Verifying game success through independent data.
  • Consumer guides: Helping gamers make informed purchasing decisions.
  • Data visualization: Creating charts and graphs to showcase trends in gaming.

SteamSpy and Python Integration

SteamSpy has an API that allows developers to access game and players data programmatically. Python, being a versatile and widely used programming language, has libraries and tools that can interact with the SteamSpy API.

One such tool is SteamSpyPI, a Python library that provides an interface to the SteamSpy API. With this library, you can easily download and analyze data from SteamSpy using Python code. For example, you can retrieve details for thousands of games, get information about specific games, or find games in a particular genre or with specific tags.

Here's a simple example of how you can use SteamSpyPI to get data about the top 100 games with the most players in the last two weeks:

import steamspypi

data_request = {
'request': 'top100in2weeks'
}

data = steamspypi.download(data_request)
print(data)

This script shows you how to make a request to the SteamSpy API and print the resulting data. The library handles the API calls and data parsing, making it easy to work with SteamSpy data in Python.

Wrapping Up

Though SteamSpy has its own limitations (a separate topic for another day), it is still an essential tool for understanding the gaming market on Steam, providing crucial insights into player behaviour, sales trends, and market opportunities. While it has limitations due to Steam’s privacy policy updates, it is still widely used by developers, publishers, and analysts to make data-driven decisions.

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