Search Results for: models and metrics

jalin hyatt

Jalin Hyatt: Where Fantasy and Reality Diverge

Rookie / By /

One of the most respected draft experts, Lance Zierlein, set off a Twitter firestorm this week when he placed Jalin Hyatt as his first wide receiver off the board, 12th overall, in his first mock draft.

He states:

“Let me first start off by saying I believe this draft is very similar to the 2016 draft in terms of wideouts. I didn’t see any WR as a true WR1 for a team. Here was how I graded them. Obviously, Mike Thomas ended up being a dude but the rest? Here is how I had it (old scale)”

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Using Regression and Neural Networks To Predict Fantasy Football

Dynasty / By /

Modeling is the most powerful and underutilized tool in how we as a population analyze and understand football. Often I see people use metrics to try and understand/predict football. They use these metrics individually to understand small segments of a grand picture. For instance, one popular metric I see used is Market Share (MS), but

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kirk cousins

Big Money Leagues vs. Small Buy-ins

Dynasty / By /

Welcome back, DFF family! On the docket today, I wanted to discuss Dynasty strategy as it pertains to buy-in size. Dynasty leagues have differing annual dues, so strategy should be tailored to the specific buy-in cost of each league. “High stakes” is a relative term. I consider this to be annual dues of $100 or more. For others, this may be $50, $250, $500…you get the point. However you categorize your small buy-in vs. big money leagues is fine. The purpose of this article is simply to get you thinking about how strategy should change depending on the buy-in requirement each season. Many fantasy owners take the same approach for all the leagues they play in, which is not necessarily wrong, but I choose to go about things differently. Let me explain what I mean. 

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The WR1 Wide Receiver Prospect Model Ratings

Rookie / By /

For those new to the WR1 rating, this is a highly accurate proprietary forecasting formula I developed to provide a data-driven forecast of future dynasty value for incoming rookie wide receivers. The WR1 model scores rookies on nine predictive metrics and then combines those individual metric scores into a single WR1 rating. The goal is to provide a single easy-to-use score for those who don’t want to spend all the time dissecting different metrics. 

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