Expected value is the long-run value of a decision
Every DFS lineup has a range of possible outcomes. Most of those outcomes do not happen. One does. Expected value asks what that decision is worth across the full distribution of possible outcomes rather than judging it only by the result that happened once.
That matters because DFS is noisy. A strong lineup can fail because a touchdown goes to the wrong player, a hitter lines out four times, or a golfer loses strokes putting. A weak lineup can win because several low-probability events break perfectly.
Tournament EV is shaped by the payout curve
In a top-heavy tournament, moving from a decent finish to a first-place outcome can be worth far more than increasing the probability of a min-cash. That means tournament strategy can rationally accept more variance when the added ceiling, leverage, or correlation improves top-end outcomes.
Cash games generally ask a different question: how do we maximize the probability of clearing a relatively flat payout threshold without taking unnecessary risk?
Do not confuse the result with the decision
A losing lineup can still have been a good pre-lock decision.
A winning lineup can still have relied on fragile assumptions.
Review the information available at lock, not information learned after the games ended.
Track whether your process consistently identifies strong projection, role, leverage, and correlation — not just short-term ROI.
How JARVIS uses EV thinking
JARVIS does not invent simulation metrics
When expected ROI, win probability, optimal rate, or duplicate estimates are not actually available from modeled data, JARVIS should leave them unavailable rather than manufacture precision. It can still use clearly labeled proxies and strategy heuristics, but those are kept distinct from measured simulation outputs.