# In-Season Articles

### Understanding Win Rates: The Picks that Won MFL10s in 2017

WARNING: unbalanced footnote start tag short code found.If this warning is irrelevant, please disable the syntax validation feature in the dashboard under General settings > Footnote start and end short codes > Check for balanced shortcodes.Unbalanced start tag short code found before:““2X,” or double-up,…”The following looks at the win rates((Win rate is a ratio defined by the number of times a player was on a...

### 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…....

### 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…....

### The Picks that Won MFL10s in 2016

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 250 MFL10 leagues. of players drafted in close to 5,000 MFL10 best-ball leagues in 2016…. Footnotes[+]Footnotes[−] Footnotes ↑1 Win rate is...

### Hyper-Fragility: Exploiting Roster Construction and ADP in MFL10s

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

### 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 only player who has been with his team for more than two years. For more…...

### 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…....

### Julian Edelman is a High Floor Receiver – Or is he?

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 can influence your week-to-week performance in very different ways. Consider the case of Rueben Randle…...

### 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…....

### 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…....

### The Picks that Won MFL10s in 2016

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 250 MFL10 leagues. of players drafted in close to 5,000 MFL10 best-ball leagues in 2016…. Footnotes[+]Footnotes[−] Footnotes ↑1 Win rate is...

### Hyper-Fragility: Exploiting Roster Construction and ADP in MFL10s

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

### 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 only player who has been with his team for more than two years. For more…...

### 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…....

### Julian Edelman is a High Floor Receiver – Or is he?

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 can influence your week-to-week performance in very different ways. Consider the case of Rueben Randle…...

### 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…....

### 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…....

### The Picks that Won MFL10s in 2016

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 250 MFL10 leagues. of players drafted in close to 5,000 MFL10 best-ball leagues in 2016…. Footnotes[+]Footnotes[−] Footnotes ↑1 Win rate is...

### Hyper-Fragility: Exploiting Roster Construction and ADP in MFL10s

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

### 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 only player who has been with his team for more than two years. For more…...

### 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…....

### Julian Edelman is a High Floor Receiver – Or is he?

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 can influence your week-to-week performance in very different ways. Consider the case of Rueben Randle…...

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