Have you ever felt like your chess rating doesn't represent your actual playing strength? Sometimes we want to be able to estimate playing strength based on individual games rather than rating (which changes more slowly). During the past few months, I've been taking a number of online courses and learning python for data analysis. In one of the courses, the final project allowed me to choose my own dataset. So surprise surprise! I chose something chess related. (Not really surprised, are you?) When we play games online, getting a computer evaluation is just a few clicks away. And a commonly used statistic is the average centipawn loss, or simply the average deviation from the computer's best move. Many of us tend to think that centipawn loss (CPL) is a good estimate of playing strength. And, of course, it gives some indication, but it's far from a perfect predictor. Fellow chess/statistics blogger Patrick Coulombe has investigated the correlation between rating and CPL
I am one of many. I am an amateur chess player trying to improve, but I have limited time because of, well, life and stuff. If you can identify with this description, then this site is for you. On the site I post book reviews, game analyses and tips for chess improvement and training. I am also proud to be a founding member of the #chesspunks community.