Predicting Participants' Performance in Programming Contests using Deep Learning Techniques
This work addresses performance prediction for competitive programmers on platforms like Codeforces, but it is incremental as it applies existing deep learning techniques to a specific domain.
The researchers tackled the problem of predicting contestant performance in programming competitions by developing a deep learning framework that forecasts both future contest outcomes and post-contest ratings based on historical practice and contest data.
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.