# An Elo-based rating system for TopCoder SRM

## TL;DR

This paper introduces an Elo-based rating system tailored for TopCoder SRMs, utilizing a logarithmic performance metric and empirical calibration to improve skill prediction and track player development.

## Contribution

It presents a novel Elo-style rating framework that incorporates a logarithmic rank metric and empirical parameter tuning for programming contests.

## Key findings

- Enhanced rank prediction accuracy
- More consistent rating progression with skill development
- Effective calibration of model parameters

## Abstract

This paper presents an Elo-based rating system for programming contests, specifically Topcoder's Single Round Matches (SRMs). We introduce a logarithmic rank-based performance metric that allows single-round, multi-player contest results to be incorporated into an Elo-style continuous rating framework. Model parameters and adjustment factors are calibrated empirically by minimizing absolute prediction error over historical data, accounting for experience level, initial ratings, and competition characteristics. The resulting system demonstrates improved rank predictions and rating progressions consistent with natural skill development over player careers.