A speedrun data store, analysis engine, and racing platform for sharing and analyzing split-by-split run history.
Splits.io is a web platform built for the speedrunning community to store, analyze, and share detailed data from their gaming runs. It solves the problem of isolated performance data by providing tools for split-by-split analysis, historical trend comparison, and live racing, helping runners identify precise areas for improvement over time.
Speedrunners and gaming communities who use timers like LiveSplit and want to deeply analyze their performance data, share attempt histories, and participate in live races.
Developers choose Splits.io for its specialized focus on speedrunning data, its support for a wide range of timer integrations via an open exchange format, and its comprehensive suite of analysis and community features not typically found together in other platforms.
a speedrunning data store and analysis engine
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Provides split-by-split and historical comparisons, enabling runners to pinpoint improvement areas through detailed data visualization and statistics, as highlighted in the README's focus on long-term insights.
Supports over 15 speedrunning timers like LiveSplit via an open exchange format, allowing for self-integration and extensive compatibility, making it versatile for the community.
Includes real-time competitive racing features, enhancing practice and community engagement by integrating with analysis tools for a complete speedrunning ecosystem.
Facilitates sharing of entire attempt histories, successful or not, fostering collaborative feedback and learning, which aligns with the project's philosophy of data-driven improvement.
The platform has ceased operations for financial reasons, as stated in the README, meaning no active support, updates, or hosted service, limiting its practical use.
Requires Docker and Docker Compose, with additional steps for Windows involving WSL2, making initial deployment and development environment setup time-consuming and challenging.
Relies on AWS services like RDS, S3, and Lambda, along with a monolithic Rails architecture, which can be resource-intensive to self-host and maintain for smaller teams.
Focused exclusively on speedrunning data, so adapting it for other performance tracking domains would require significant code modification, reducing its versatility.