Applying the box-score paradigms from the NBA analytics community to the world of esports. A small adventure into using regularized adjusted ratings to get a gold standard and using statistical tools to approximate the resulting ratings. The canvas is the team modes in a competitive first person shooter called QuakeLive.
With COVID-19 continuing the shutdown of sports leagues, the itch to do sports analytics leaves us with eSports. Following my previous post analyzing Counter-Strike, here I decide to answer who were the greatest 1v1 FPS players of all-time?
In an era of shelter-in-place, we’ve seen the temporary shutdown of many sports leagues. The biggest sporting news of the day seems to revolve around discussions of greatness. These events led me to e-Sports, specifically Counter-Strike, and interest in answering who were the greatest Counter-Strike teams of all-time?
Inspired by FiveThirtyEight’s update of their CARMELO NBA player projections, a recent conversation on Twitter brought up the value of Elo Ratings. I decided to re-implement 538’s NBA Ratings and tried out a few improvements and variations. The code is all available on this GitHub repo. TLDR: Elo is simple, robust, and works pretty well.
BasketballGM is a remarkable, free, browser-based game. I decided to automatically generate roster files from real NBA data using some basic data science methods. The results are all on this GitHub repo and the roster files are available here
A mentor of mine once told me that the young build their careers by spearing an older fish. Sometimes they set their sights on the wrong fish.
Some notes from visiting a small town restaurant alongside the Pacific Coast Highway.