Effective Value Calculations Using Ownership Projections

I think that projecting ownership is just as important as projecting the potential statistical outcome for a given player on a given day, and becomes more so as the player pool increases. I would agree, most if not all value calculations (ours included) are a Point Projection over Cost analysis.

To find the true value of a player, it’s a logical step to include ownership projections, but how to arrive at these is a question of debate. First and foremost, as I’m sure you’ve found, finding concrete ownership data for all players in a given tournament is next to impossible without parsing the data yourself. Further, parsing that data requires a decent to above average excel skill set along with a decent to above average computer. DraftKings certainly doesn’t do any favors to those looking to investigate if any trends exist for Week to Week ownership, which makes it a pain to determine what factors might drive said ownership.

We’ve found that to determine ownership for a specific GPP, we have to download the entire results the next day. From there you have to clean up the data so that you can sort and eventually determine what % of lineups own each player. Slate wide numbers are ?impossible? to determine, as in order to get the results CSV you have to have first entered the tournament. Even if you could get all results, the coding alone is enough to keep all but the elite away from accessing this info.

To answer your question, you’re on the right track.

To further it, I would surmise that the same trends and ideas which influence sports betting decisions also drive the players which end up in an average lineup on a given day.

Has this player seen recent success?
Is their team an overwhelming favorite?
Does “Vegas Say” that the game will be high scoring?
What do the pundits and talking heads say?
Which players are being trumpeted by ESPN?
Has this player moved from a reserve to a starting role? Has this move been recent (on game day) or within the last week, ie, are contest entrants aware of the switch?

Obviously this just scratches the surface, but I do hypothesize that were you to go back through ownership levels of the past year, regardless of sport/season, you could easily line them up with the same trends that influence public betting money.

– Larry

*Thanks for the forum name tip*

I definitely agree with your overall point, that you need to try to predict ownership and thus base your ownership and allocations based on what the field is projected to have. I have yet to actually model the projected ownership percentages but I do it when I enter my lineups. It’s mostly based on instinct at this point which makes it impossible to quantify my overall edge in each contest.

The trickiest situations are when you project a person to have a high point total, a high value, and a high projected ownership. Essentially you think he’s going to be really good but everyone is going to have him (e.g. Westbrook when Durant was out last year). In those instances, I find it tough to fade completely even in GPP’s because I trust the projections so much so I often look to differentiate my lineups in other ways.

I think any serious DFS player has considered ownership in relation to valuation. I have started several models to project ownership….several failed attempts. Which makes the “rouge” FD employee even more infuriating. I offer the following considerations:

The Efficienct Market Hypothesis(EMH) states valuations in a market are always correct as all known information is baked into the price of the asset. Of course this theory can hold true as the term market indicates that pricing is dynamic and of course determined by supply and demand. DFS pricing offers a near inverse situation. Pricing is static and salary algorithms are simply linear performance models that do not consider all possible pricing factors. In this environment demand is totally forgotten and supply is infinite as theoretically ownership of a given asset could be 100%. Which is a long way of saying the pricing algos are highly simplistic and sharp money can leverage variations. The 1st step in leveraging the prices is to understand implied value in relation to a given model’s EV. The 2nd way to leverage static pricing is to understand what other factors should have been considered in the assets valuation. For example a hitter got moved recently to the 2 hole or an NBA player has been playing with a bad wrist. Both situations will be missed in near term pricing and mostly undetectable in a model’s EV and valuation calculations. The 3rd is to monitor on-going news and make adjustments accordingly as late as possible till lock.

To answer the initial post, it is impossible to be successful at DFS without a sound understanding of game theory. 2 years ago making your own numbers and having an idea of implied valuations was all that was required to be +EV. Today with sites like DFSDoctors, quality numbers are available readily to the public, as such game theory, lineup construction and being able to leverage static pricing are the new keys to being truly +EV.

Lastly, YES simplistic point/cost valuation models are not as holistic as they possibly could be. They still, however, hold great value in beginning to find the +EV players in comparison to implied values.

• This reply was modified 8 years, 8 months ago by mlbmodel.

“1) With the prevalence of sites like yours, do you feel like your quantifiable edge has decreased or have you been able to leverage your other skills (game theory, lineup construction, etc.) to maintain or even increase your edge? From a theorycrafting perspective, the reason why DFS always remained nothing more than a low hanging fruit / learning exercise for me (in addition to being an old man who unless I automate real-time breaking news consumption I have no desire to put in the requisite work for point #3 that you raised) is because with static pricing and the availability of ever increasing accurate sites like yours, one would think the edge diminishes. Throw in the almost to double to sports wagering vig and I wonder how more serious DFS players such as yourself feel about your future quantifiable edge.”

I would question how quantifiable an Advantage Players’ edge ever was considering the pool sizes and vig. That is not to say it was not there it is just not as easily quantifiable as say monitoring CLV in sports betting markets. To cut to the chase, yes, advantage has diminished with the influx of quality information.

“2) Just to make sure I fully understand your EMH explanation, am I correct in concluding that the sharper the pricing gets on a site, the greater the edge for the DFS pro vs average user and, conversely, the softer the pricing is (cough DK), the more variance you will run across which actually decreases your edge. Are these statements correct?”

The theory would argue that if all pricing is perfect then there is no edge on the market. In other words simply solving for implied value would produce the optimal lineup. In which case the market is perfect and no one has an advantage. In this environment game theory as described above would yield the only edge and yes advantage players would benefit most. Softer pricing and for that matter softer lineup building rules leave a novice player a much better chance of lucking into success. Also with sites finding the weak salaries these variations are easily spotted.