18 Apr 2017

MFL10 Optimization Part 2: Solving the Onesie Positions


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

10 Apr 2017

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

27 Feb 2017

MFL10 Roster Construction: What Worked in 2016?


In January, I took at look at the players with the best and worst win rates across 2016 MFL10 best-ball leagues. Here, the focus shifts to roster construction. Roster construction is defined by the number of players a team drafts at each position.

06 Jan 2017

How the Best(ball) was Won: Player Win Rates in 2016 MFL10s


The following looks at the win rates1 of players drafted 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 250 MFL10

11 Aug 2016

Hyper-Fragility: Your Answer to Zero-RB in MFL10s


Is there a way to beat MFL10s without relying on player evaluation and nailing late-round sleepers? I think there is.

Photo via flickr/denverjeffrey
20 Jul 2015

Booms, Busts and Emmanuel Sanders


This series started by introducing a new way of looking at weekly risk and then focused on WR1s.  Now the spotlight is on the WR2s. Within the WR2 tier, there are only three players who have played more than one season with their current team, and Julian Edelman is the

02 Jul 2015

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

23 Jun 2015

Using Historical Data to Identify Boom/Bust Receivers


While gearing up for the 2015 fantasy football season, you will inevitably see the terms High Floor and Boom/Bust in reference to particular players.  Which type of player you are drafting can be useful information as you build your roster.  Two players with the same total points over a season