Video Game Genre Analysis (2015-2025)

Analyzing player retention and popularity across different game genres based on Steam data

Websites Scraped

This project utilized data scraped from multiple sources to create a comprehensive dataset of Steam game metrics from 2015-2025:

SteamDB Charts (steamdb.info/charts/)

  • Current player counts for top 100 games
  • 24-hour peak player counts
  • All-time peak player counts
  • Game names and unique identifiers
  • Historical player count data with timestamps

SteamCharts (steamcharts.com/top)

  • Monthly average player counts
  • Hours played statistics
  • 30-day player count trends
  • Peak player counts by month
  • Player count gain/loss percentages

Internet Archive (web.archive.org)

  • Historical Steam store pages from 2015-2023
  • Archived player statistics from previous years
  • Historical game pricing and monetization models
  • Past game categorizations and genre classifications
  • Developer and publisher information across time

Steam Store (store.steampowered.com)

  • Current game genres and tags
  • Release dates and developer information
  • Payment models (Free-to-Play, Premium, Subscription)
  • User review scores and review counts
  • DLC and expansion information

Data was collected using Python-based web scrapers with appropriate rate limiting to respect website terms of service. Historical data from 2015-2023 was combined with current data to establish trends, while 2025 projections were created using statistical modeling of existing trends.

Libraries Used

  • D3.js (d3)
  • React
  • Next.js
  • Tailwind CSS
  • shadcn/ui
  • Lucide React
  • clsx
  • tailwind-merge

Research Questions

This visualization project explores the following high-level questions:

  • What factors contribute to long-term player retention in video games across different genres?
  • How have player preferences for game genres evolved over the past decade (2015-2025)?
  • What is the relationship between game monetization models and player engagement?

Specific questions addressed by our visualizations:

  • Which game genres maintain the highest player retention rates?
  • How does player engagement (measured in hours played) correlate with retention?
  • Which genres have grown or declined in popularity over time?
  • How do different studio types (AAA, mid-tier, indie) perform across genres?
  • What payment models (free-to-play, premium, subscription) are most effective for different genres?

Target Audience

This visualization dashboard is designed for:

  • Game Developers and Publishers: To inform strategic decisions about genre focus, monetization models, and player retention strategies
  • Game Industry Analysts: To understand market trends and predict future shifts in player preferences
  • Game Design Students: To learn about the factors that contribute to successful, long-lasting games
  • Investors: To identify promising genres and business models for investment in the gaming industry

Data Source

This analysis uses a comprehensive dataset of Steam games from 2015-2025, including metrics such as:

  • Current and peak player counts
  • Hours played
  • Game genres and release years
  • Payment models (Free-to-Play, Premium, Subscription)
  • Studio types (AAA, Mid-tier, Indie)

Note: The 2025 data includes projections based on current trends and announced games.

Genre Popularity Distribution
Analysis of the most popular game genres based on player count and hours played

Research Question Addressed

This visualization addresses the question: "Which game genres are most popular among players, and how does this popularity vary across different metrics?" It helps game developers and publishers identify which genres attract the largest player bases and maintain the highest engagement levels.

Data Dimensions

  • Game genre (x-axis): Categorical variable representing different game types
  • Popularity metrics (y-axis): Quantitative variables including current player count, total hours played, or retention rate
  • Game count (tooltip): Number of games in each genre
  • Peak players (tooltip): Maximum concurrent players for games in each genre
  • Time period (filter): Year filter to analyze trends across different time periods

Visual Mapping

  • Position (y-axis): Maps to the selected popularity metric, creating a clear visual ranking
  • Length (bar height): Represents the magnitude of the selected metric
  • Color: Distinguishes between different genres with a consistent color scheme
  • Labels: Show exact values for precise comparison

Visualization Idiom Justification

A bar chart was chosen for this visualization because:

  • It effectively shows the relative popularity of different genres, making it easy to compare and rank them
  • Bar charts excel at comparing a single quantitative value across multiple categories
  • The vertical orientation emphasizes the magnitude differences between genres
  • It allows for clear labeling of genres and values
  • The interactive filtering by year and metric type enables multidimensional analysis within a simple visual framework