How to reduce prediction combinations without blind cuts

Before you reduce the size of your plan, define what the smaller set must improve or preserve. Possible goals include limiting total card count, preserving minimum scenario coverage, improving estimated expected profit, controlling worst-case loss, or avoiding excessive concentration in one outcome pattern. Choose the primary goal and constraints before ranking cards.

Mark outcomes or scenarios that must remain represented. These may be high-confidence anchors, specific upset paths, or a minimum number of cards for each important scenario. Protected constraints prevent a ranking rule from quietly deleting an entire branch simply because its cards score slightly lower on one metric.

• Set a maximum total card count or allocation.
• Set minimum coverage for important outcomes or scenarios.
• Define concentration limits before removing cards.

A card with a very large possible payout may still add little estimated value if its modeled probability is extremely low or if it repeats coverage already supplied by other cards. Compare the incremental contribution of each card: estimated return, probability-weighted profit, unique coverage, and the diversification it adds to the remaining set.

Two cards can serve nearly the same scenarios while one offers weaker estimated economics under the chosen assumptions. A card is a stronger removal candidate when it adds little unique coverage and is worse across the metrics that matter. This comparison should use consistent allocations and probability inputs; otherwise the ranking mixes different assumptions.

Recalculate total allocation, covered scenarios, estimated expected profit, loss frequency, and the distribution of possible returns after each meaningful cut. Marginal effects are not always constant: removing one card can make a later card much more important because it becomes the only remaining path for a scenario.

Compare the full and reduced sets against historical cases that were not used to design the rule. Track realized profit or loss, retained winning cards, average return, and worst drawdowns. A rule that looks excellent on the examples used to create it may fail on new cases, so use separate development and validation groups and prefer stable improvements over one exceptional result.

Historical cases show how the rule handled known outcomes; use Monte Carlo to simulate possible outcomes beyond that sample. Test changes in probabilities, correlation, and allocation. If a small assumption change reverses the ranking or removes all profitable paths, the reduced plan is fragile and may need wider coverage.