Antonio Brown – Buy the Boom, Skip the Bust

The first article of this series introduced the concept of using High Floor and Boom/Bust “prototypes” to better understand the week-to-week risk a player brings to your roster. The rest of the series will step through the wide receiver tiers, WR1 down to WR4. The following focuses on the WR1’s.

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Understanding Win Rates: The Picks that Won MFL10s in 2017

The following looks at the win rates[1]Win rate is a ratio defined by the number of times a player was on a first place roster divided by the total number of times that player was drafted. This analysis encompasses players drafted in at least 500 MFL10, MFL25, MFL50 or MFL100 leagues. of players drafted in close to 5,000 MFL10 best-ball leagues in 2017.((“2X,” or double-up,…...

MFL10 Optimization Part 2: Quarterback, Tight End and Defense

In part one of this project, we followed the evolution of a fictional 2016 MFL10 draft from an inefficient mess into a league full of optimized rosters. That first step provided us with some general guidelines for roster construction. Now we dig a bit deeper, in search of more detailed and more actionable conclusions. We’ve already learned, for example, that we want to roster either two or three…...

MFL10 Optimization: Evolution of a Perfect Draft

What would an MFL10 draft look like if all twelve players knew exactly what they were doing? You may never have asked yourself that question, but I have spent more time thinking about roster construction than I care to admit, so I decided to find out how an “optimized” draft might play out…....

Understanding Win Rates: The Picks that Won MFL10s in 2017

The following looks at the win rates[1]Win rate is a ratio defined by the number of times a player was on a first place roster divided by the total number of times that player was drafted. This analysis encompasses players drafted in at least 500 MFL10, MFL25, MFL50 or MFL100 leagues. of players drafted in close to 5,000 MFL10 best-ball leagues in 2017.((“2X,” or double-up,…...

MFL10 Optimization Part 2: Quarterback, Tight End and Defense

In part one of this project, we followed the evolution of a fictional 2016 MFL10 draft from an inefficient mess into a league full of optimized rosters. That first step provided us with some general guidelines for roster construction. Now we dig a bit deeper, in search of more detailed and more actionable conclusions. We’ve already learned, for example, that we want to roster either two or three…...

MFL10 Optimization: Evolution of a Perfect Draft

What would an MFL10 draft look like if all twelve players knew exactly what they were doing? You may never have asked yourself that question, but I have spent more time thinking about roster construction than I care to admit, so I decided to find out how an “optimized” draft might play out…....

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