We introduce a new technique for analyzing combination models.The technique allows us to make qualitative conclusions about which IR systems should be combined.We achieve this by using a linear regression to accurately (T ' = 0.98) predict the performance of the combined system based on quantitative measurements of individual component systems taken from TREC5.When applied to a linear model (weighted sum of relevance scores), the technique supports several previously suggested hypotheses: one should maximize both the individual systems' performances and the overlap of relevant documents between systems, while minimizing the overlap of nonrelevant documents.It also suggests new conclusions: both systems should distribute scores similarly, but not rank relevant documents similarly.It furthermore suggests that the linear model is only able to exploit a fraction of the benefit possible from combination.The technique is general in nature and capable of pointing out the strengths and weaknesses of any given combination approach.l The Skimming Effect happens when "retrieval approaches that represent their collection items differently may retrieve different relevant items, so that a combination method that takes the topranked items from each of the retrieval approaches will push non-relevant items down in the ranking."l The Chorus Effect occurs "when several retrieval approaches suggest that an item is relevant to a query...this tends to be stronger evidence for relevance than a single approach doing so."l The Dark Horse Effect in which "a retrieval approach may produce unusually accurate (or inaccurate) estimates of relevance for at least some items, relative to the other retrieval approaches."