FRESH TAKE WITH MGFRESH

Fresh Takes is back this week to examine another basic DFS concept.  

Inspired by a question that has popped up a few times in the Judge’s slack channel, let’s dig into… “What is the difference between a GPP play and a Cash play?”

In a previous article I broke down why I believe individual player risk can sometimes be over emphasized when building lineups for different competitions, including why “cash” plays can make their way into gpp lineups and vice versa.

(For a quick refresher on the different types of DFS contests check out the article from 10/24/17)

BUT – it is important to understand the range of outcomes you can expect from different players when creating a lineup.  While I tend to spend most of my time focusing on expected production, it is critical to recognize that some players produce more consistently in a tighter range and have “higher floors” than riskier players.  

GPP Plays = Boom or bust players tend to be categorized as “gpp” plays since their booms can produce the big scores needed to finish at the top of tournaments.  However, their potential for busts make them risky in cash game lineups.

Cash Plays = consistency monsters who have high floors and are unlikely to single handedly destroying your chances to finish in the top half of entries.

But just how predictable are these booms and busts?  Are certain players really more susceptible to terrible nights?  

I find myself fixating on the outcomes that affect me more.  I will remember player performances when they make my lineups much more than how they perform on average or when I do not include them in my lineup.  My guess is this is common among DFS players and it leads to anchoring expectations to those dream killing or dream loving performances, versus actual production throughout the season.

Sooooo…. I decided to take a look at 12 of the top FD scorers to see if the data backs up the idea that some players are materially riskier than others.

One note on methodology: I wanted to focus on unexpected performance variance so I concentrated on points/salary rather than just points.  This should reduce noise associated with favorability of matchups, role changes, etc which can be predicted beforehand.  By including salary in the denominator, the range of outcomes zeros in on the unexpected output, since the DFS sites will try and price players as efficiently as possible for each slate.

Charts below are sorted by average ratio

  • Average pts = average FD points using 2017/2018 scoring
  • SD pts = standard deviation of FD points using 2017/2018 scoring
  • Avg Ratio = average FD points / 1,000 salary
  • SD Ratio = standard deviation of FD points / 1,000 salary
  • Min Ratio = Worst FD points / salary night of the year
  • Max Ratio = Best FD points / salary night of the year
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A few patterns worth noting

  • In both 2016/2017 and 2017/2018 Anthony Davis had the second highest standard deviation when it comes to pts/salary. This backs up his reputation as a fragile unicorn whose basketball ability is only outmatched by his ability to find an excuse to limp to the locker room in the first quarter.
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  • John Wall, while uninspiring so far this year, might be the most consistent player on the list.  This makes sense for a guy who fills the stat sheet and is not reliant on his jumper falling to score FD points.
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  • Jimmy Butler has really fallen off from last year.  Not only are his average FD points and average ratio down, his variance is down as well to start the year.  In other words, he has been consistently bad, only hitting 5x value in three of his first thirteen games this season.  
  • In 2016/2017 if you were willing to put up with a few stinkers, you were compensated for taking more risk with Boogie Cousins and Jimmy Butler as they had the highest average ratios.  However, the next four guys produced almost as much on average with much more consistency to their results.
  • Over this sample Lebron had the rare combination of a high floor and high ceiling to go along with a solid average.  He has not scored worse than 2.5x salary in the last year and has produced at least 4.5x salary in 64% of his games.  Long live the King.
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Not so Fresh Take

When looking at the top players over the past few years, I can’t say I found some undiscovered nugget in the data.  If anything, the data broadly backs up intuition that injury (or ejection) prone players can be dream killers that may give you slightly more on average if you tolerate the risk.

I do believe revisiting this kind of data can be helpful from time to time to shake off personal biases.  For example, I have not loved Lebron in matchups this year despite his dominance.  In the few times I have played him, his performance has not been great.  Looking at the bigger picture it is clear I should rethink that bias going forward.

Find this helpful?  Let me know @mgfresh on Twitter or in the Slack channel.  If desired, I would be happy to follow up on this type of analysis for an expanded set of players.