DFS Dashboard provides actual probability information so users can make pragmatic decisions for DFS lineups and sports betting.
I have taught AI how to read and interpret data on DFS Dashboard and created thousands of lineups. The data on DFS Dashboard provide actual floor probability, Probability good and Probability great, so simulations can further project 90% ceiling and 95% ceiling data.
This dataset is a slate-specific, simulation-driven lineup portfolio for a three-game DraftKings Classic contest. It starts with DFS Dashboard player file as the single source of truth for salaries, positions, teams, projections, and outcome-quality signals (PROB GOOD / PROB GREAT), then uses Monte Carlo sampling to generate realistic distributions of player fantasy outcomes rather than relying on one-point estimates. From those player distributions, I built 1,000 unique lineups that intentionally prioritize the strongest stack environments identified in your data—most notably QB-led stacks (QB+1 and QB+2) with optional opponent “bring-backs”—while staying within a $49,000–$50,000 salary band and satisfying DK roster rules (QB, RB, RB, WR, WR, WR, TE, FLEX, DST). In short, this file is not a list of “optimal” lineups from a single projection set—it’s a diversified set of lineups designed to win when the slate breaks in different ways.
Each lineup is then evaluated head-to-head against the other 999 lineups across 30,000 simulated slates, producing finish-rate metrics that translate directly to tournament outcomes. For every lineup, the file reports the probability of landing in the Top 1%, Top 5%, Top 10%, and Top 20% of this 1,000-lineup universe—giving you an interpretable “how often does this lineup contend?” score at multiple payout-like cutoffs. These finish probabilities are paired with lineup-level score summaries (SIM_MEAN, SIM_P80, SIM_P90) so you can distinguish lineups that are merely consistent from those that have true spike-week potential. The goal is to make lineup selection and trimming objective: instead of choosing based on gut feel or a single projection rank, you can prioritize lineups that repeatedly show up near the top of the distribution, filter by your preferred threshold (e.g., Top 20% rate), and deploy a portfolio that is both correlated and outcome-resilient.
I have included lineups that have a greater than 20% chance to finish in the top 20%.
See Showdown lineups for Cowboys-Redskins SD Slate below
See Showdown Lineups for Lions-Vikings
