Where do our edges in fantasy football come from? Some believe a sustainable edge comes from player evaluation. Put another way, this is the theory that you can win by being better at picking the right players more often than your competitors.
I was happy to see Rotoworld’s Josh Norris recognize the importance of sample size while recently predicting that the analytical wizards with the Cleveland Browns will prefer Deshaun Watson to Mitchell Trubisky come draft day.
Let’s play a game.
We have roughly a month until the 2017 NFL draft, when we will learn where our favorite (or not so favorite) prospects will land this coming season. While draft position and landing spot are huge factors for forecasting the success of any running back prospect, I’ve found that we can
Last year I created a model to predict quarterback success at the NFL level for prospects. The model was useful in that it helped identify traits that transfer to the NFL level, but it was certainly overfit and didn’t bring in enough data. I’ve updated the model for
This glossary is an index of frequently used RotoViz terminology.
This is the time of year when the “I want to be a fantasy writer” itch starts, and perhaps you’re wondering how to scratch it. The RotoViz writers took some time out of their busy schedules to give you their thoughts.
In this series of posts I’ll discuss which of the various box score and advanced stats forecasters should pay attention to when projecting teams and players into the upcoming season.
Zero RB appears to be in trouble, and its creator saw it coming. Last August in an article about the 2016 Apex Expert’s League, Shawn Siegele wrote the following about the impending decline of Zero RB as the top fantasy drafting strategy:
The greatest trick the devil ever pulled was convincing fantasy footballers that efficiency is useful. Unless you are talking about quarterbacks, I can’t really think of a good efficiency metric. When you test them to see if they predict anything, you always find that they come crashing back to