Expected value turns a messy set of maybes into a single comparable number. Instead of asking whether you will win, you ask what a case is worth on average if you could run it many times: each possible outcome is weighted by how likely you believe it to be, and the weighted results are summed. A one-in-three shot at a large verdict and a two-in-three chance of a defense win are not a coin flip; expected value forces you to say, in numbers, how good or bad the bet actually is.

The inputs are estimates, not facts. The probabilities are judgment calls, the recovery figure depends on a damages theory, and the cost side keeps growing as the case moves toward trial. That fragility is the point: expected value is a lens for comparing choices under uncertainty, not a forecast of what will happen. Small changes in the win probability or the damages number can flip which decision looks better, which is exactly why the estimate should be stress-tested rather than trusted.

Juricratic treats expected value as an output of a modeled matter rather than a guess typed into a spreadsheet. The case becomes a game with dials for the contested facts, and seeded simulations report a distribution of outcomes instead of a single headline figure. That keeps the analysis honest about what is assumed and lets you watch the number move as the assumptions move.

Lawyers use expected value to decide whether to file, whether to settle, and where a settlement number should land. Compared against the cost of continued litigation and the best settlement on the table, it tells you whether pressing forward is a positive-value bet or a negative one. Because the inputs are contestable, the disciplined move is to run the number across a plausible range and see whether the recommendation is stable or knife-edge.