Predicting Participants' Performance in Programming Contests using Deep Learning Techniques

TL;DR

This paper presents a deep learning framework to predict individual programmers' contest performance and future ratings on the Codeforces platform, aiding in understanding and forecasting competitive programming outcomes.

Contribution

The study introduces a novel deep learning-based approach for predicting contest results and ratings, leveraging participants' historical performance data.

Findings

Accurately predicts contest performance and ratings.

Demonstrates effectiveness of deep learning in competitive programming analytics.

Provides insights into factors influencing programming contest success.

Abstract

In recent days, the number of technology enthusiasts is increasing day by day with the prevalence of technological products and easy access to the internet. Similarly, the amount of people working behind this rapid development is rising tremendously. Computer programmers consist of a large portion of those tech-savvy people. Codeforces, an online programming and contest hosting platform used by many competitive programmers worldwide. It is regarded as one of the most standardized platforms for practicing programming problems and participate in programming contests. In this research, we propose a framework that predicts the performance of any particular contestant in the upcoming competitions as well as predicts the rating after that contest based on their practice and the performance of their previous contests.