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Expected payoff

As explained in Statistics Every Programmer Needs

An expected payoff is a probability-weighted average of the payoffs associated with possible outcomes.

An expected payoff is a probability-weighted average of possible outcomes. In a decision tree, calculate each chance node by multiplying every payoff by its conditional probability, then summing the results. section 10.2.1

Contract decision

For the healthy scenario, calculate 0.10 × $90M + 0.20 × $120M + 0.70 × $150M = $138M. For the unhealthy scenario, the corresponding value is $117M. Combining health probabilities gives 0.75 × $138M + 0.25 × $117M = $132.75M; subtracting $13M in agent fees produces a Dodgers expected payoff of $119.75M. section 10.2.1

How Statistics Every Programmer Needs covers it

How deep it goes
Explains conditional weighting, nested aggregation, fee subtraction, and programmatic node labeling without developing statistical theory.
Examples
Uses a Dodgers contract with health states, performance incentives, terminal payoffs, and agent fees.
What it assumes
Assumes defined probabilities, payoffs, and costs while demonstrating their weighted combination from individual outcomes upward.
What you can do after
Apply it via a decision tree to quantify uncertain alternatives and display expected values on its nodes.
How it connects
Connects probability, payoff, chance nodes, decision trees, conditional outcomes, and decision analysis.
Cover of Statistics Every Programmer Needs
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Statistics Every Programmer Needs. The section links point to the parts of the book it draws on.