Users will now have the probability that each player on every main slate for DraftKings and Fanduel ends up in the optimal lineup listed along with the industry-leading customizable rate tools.
We are also introducing our proprietary ownership algorithm, which projects the ownership of each player in large-field tournaments on both Fanduel and DraftKings. Both simulations and ownership models will be updated up until 30-minutes prior to lock.
For savvy tournament players like yourself, you can now compare a player’s actual likely ownership to each player’s odds of landing on the optimal lineup, allowing you to infer actionable information to gain leverage on the field in large field DFS contests.
These are actual simulations. We are not using spreadsheet formulas to extrapolate a probability with assumptions about the distribution of results that are not tested and may or may not be appropriate.
These are decision-making tools. You are the decision-maker. How you utilize these depends on your overall objectives, appetite for risk, and the contest you are playing in. In the largest field MME events, you very likely need something close to the optimal lineup to win the tournaments. In these contests trying to maximize leverage or the difference between optimal probability and ownership may be a profitable strategy. In a small field high-stakes contest you likely will not need the optimal lineup but still will benefit immensely from finding similar probability plays with wide gaps in consensus. In top-heavy qualifiers, you are rewarded more for taking on risk than extremely flat payout structures. All of these are important context for you to make decisions using our tools.
An in-depth article on the specific methodologies is planned for publishing in the new year, but for now, let’s give some examples of how to make informed decisions using the information right in front of you in the optimizer.
The above is sorted in terms of optimal probability, the players most likely to end up in the winning lineups on Draftkings at the end of the night. However, not all plays are equal here, as we have some good chalk – Kyrie Irving, Anthony Davis, and Paul George are projected to be owned at a rate +/- 2 percentage points of their optimal probability – and some bad chalk – Eric Paschall is such an obvious and useful value play on Draftkings that he is being owned 16 percentage points higher than his probability.
If we sort by leverage we can identify the most underutilized plays on the upcoming slate relative to their optimal probability. The following graphic shows Fanduel on the opening night two gamer.
Here we can see that because of the overwhelming popularity of three expensive players in the same game – Irving, Kevin Durant, and Stephen Curry – the expensive players in the Lakers/Clippers game are showing up as under-owned.
It is worth noting that especially on short slates that normally unplayable players may show up high on this list. With a normal slate, anyone projecting for single-digit points will have an optimal probability so low that being sub 1% owned doesn’t actually benefit you. But when there are only seven PG eligible players projected to play all of a sudden a free Alex Caruso or Jordan Poole can potentially help, especially if the two chalk expensive PGs are outscored by expensive players at other positions.
If you have a feeling that a player will be higher or lower rostered than we project, you can adjust that manually and see how the leverage score changes. In the coming weeks, we will roll out constraints to set a minimum sum of optimal probability or leverage score for each lineup, similar to how you can currently set maximum projected ownership or a minimum total salary.