Superflex 101: Building the Foundation – Part 4

Adam Bendzick


There are so many things in life that come in all shapes and sizes. This is clearly evidenced in my Tuesday night pick-up basketball league. In one corner, you have the super athletic former college athlete that still can jump out of the gym and drain threes. In the other corner, you have the post middle-aged phenom, who is sporting his thick goggles and has a tendency to double dribble. When the five foot three inch 63 year old is on fire, his baby hook comes slamming off the upper corner of the glass, instead of missing the backboard all together. It’s truly a diverse crop of talent in my local gym.

I’ve been thinking about that basketball league, and how I can compare it to this sport of dynasty football that we all love. Let’s say we were to host a draft. Maybe not even just one draft, maybe we host six individual mock drafts to formulate a general market value of the players within that league. Couldn’t we then have a general idea of player value in terms of average draft position data? Now obviously we would all go into it thinking that we would draft the stud former college athlete first, and then trickle down to the lowest talent on the totem pole, our 63 year old phenom. But what if the stakes were changed?

In basketball we have a general understanding of rules and scoring procedures. Sometimes in pickup basketball, scoring setup will change, but only minimally. You would still draft the most skilled basketball players regardless. But let’s say we changed the rule to allow the use of dribbling with two hands. Maybe any form of a baby hook shot from just inside the arc would be worth ten points instead of only two. How would this effect our phenom’s value? How would this effect his average draft position in comparison to the rest of the guys within this league? Truth is, it would be difficult to determine, unless we hosted an equal amount of mock drafts to form a relevant market value for this particular scoring setup. At a minimum, our barely roster-able phenom in a standard league, is now a boom or bust type asset.

In part four of this series, I want to give you a different view point instead of just focusing on individual players and how I feel about their value. Rest assured, now that I have gained writer status and have access to a bit more tools for my research, you can expect a deeper look at individual players in the coming weeks. But for now, I want to share with you a tool I have used since DLF has assembled the ADP data, to determine a “market” value in regards to average draft position.

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With respect to the team here at DLF, they have assembled some of the best information that we can hope for as owners of dynasty teams. I personally feel the ADP data that is collected is some of the best information you can use to formulate an expected “market” value in terms of trading. You don’t have to agree with the data and who is drafted where, and that’s honestly the beauty of it. What you need to take from it is the effect it has on the minds of most dynasty owners, and how that data can collectively sway the market. As superflex and 2QB leagues become more common, I’m sure the folks at DLF will have more data in terms of monthly ADP’s specific to these types of leagues. But honestly, I’m glad that data isn’t there yet.

I’m here to help you with a formula I have been using for roughly the last couple years. It’s actually really easy, and has proven to be pretty accurate for projecting a “market” value in the superflex leagues that I take part in. All I do is take the ADP data for the most recent month, divide the number in half, then subtract roughly the amount of teams in the league that I’m playing in, which is typically 12. So for instance, Andrew Luck’s ADP in January is 25.67. I would divide in half, which would be 12.835, then subtract 12 to give me my no doubt top pick in a 2QB league. Cam Newton at 29 ADP for January would end up being 2.5 ADP with that calculation. Aaron Rodgers is still my second highest rated quarterback, and I think there is value to be had there if you take his 37.67 January ADP and use my calculation to give 6.835 ADP. I think the draft order for a 2QB league startup should be Luck top of the list, followed by Rodgers and then Newton as your top three picks.roethlisberger

Looking further down the list, does the formula hold true? I believe it does when you look at the numbers. Ben Roethlisberger would translate to roughly the 30th player drafted, and I believe you can expect to draft him in the middle of the third round in a startup. When we compare Roethlisberger’s adjusted ADP with my formula, to a guy like Devante Parker’s January ADP of 31.00, you have two players that I believe should be valued at near the same level. Obviously every owner is going to favor one guy or the other, but I think the use of that as a “market” value is extremely relevant. This way you have enough mock drafts to feel comfortable that there is enough data to have a solid baseline of data.

As you go deeper into the later rounds, guys will start to show a ton of value in comparison to the actual points produced. But this doesn’t prove the formula as being wrong. It actually supports the fact that ADP data will tend to favor youth, and value can be had if you search for it. Take a guy like Eli Manning for instance. In a 1QB league his value is almost non-existent at 162.67 January ADP. My formula would project out to him having a 69.335 adjusted ADP. In comparison to rookies, that would put him somewhere between a mid to late first round draft pick, which is actually a good comparison to what you could probably buy him for. For the tenth highest scoring quarterback through week 16 of the 2016 NFL season, that type of value could elevate your QB2 to a back end QB1 producer. At roughly the same price as what Brandon Marshall cost to acquire around mid-season, Eli could have provided the same type of value.


As I was completing this article, Ryan McDowell formulated two mock drafts and calculated the data. The information presented is great to show the drastic differences of quarterback value when jumping from a 1QB league to a 2QB league. But just as Ryan presented, I don’t believe there was enough people present to do a substantial amount of mock drafts to formulate a reliable set of data. Furthermore, 2QB leagues are still not the most common type of league, so I wonder if the people that participated in the two mock drafts have a legitimate amount of 2QB league experience. What I do take from Ryan’s article and data, is that the team at DLF is at least attempting to provide information to our readers in every aspect and type of league possible. Along with the other writers that have contributed to this case, I believe I was brought in to write articles to help bolster DLF’s representation to the growing popularity of 2QB leagues.

As always, I appreciate your feedback, and hope you enjoy my latest installment to the series. Please leave comments below, and you can also follow me on Twitter @BenzyAB21.