Almost a year ago, having my laptop and a sleeping bag in my backpack, I attended an AI-Hackathon in Germany. Right after the kick-off meeting at 9:00 AM I teamed up with two UX Designers and one Business Developer. We immediately started brainstorming to identify a potential project using open data and AI. Our first idea was to find a new particle in the CERN data or new physics. However, we dropped that idea real quick and decided to build a service for visually impaired people. The idea was basically to create an audiobook from any video content. Usually, Hackathons often aren’t long enough to create something entirely from scratch. Nevertheless, as I was working with Deep Learning models for quite some time, it wouldn’t take too long to recycle a couple of thousands line of code and wrap it around some video feed. Given my professional experience, my task was to develop the back-end of a minimalistic prototype within 24 hours while my teammates were focused on the user interface, presentation, and a bulletproof business case.
The elo rating system is a by Aprad Elo created system for calculating relative skill levels in games such as chess or video games. Although this system couldn’t establish its implementation in many other forms of sport, there are several websites publishing these elo rankings (e.g. Word Football Elo Ratings).
The elo rating number is based on pairwise comparisons. Players‘ ratings are not measured absolutely, but rather depend on their own rating, the rating of their opponents and the results of the game.