Algorithms for cache replacement are computable approximations of universal sequence predictors, though we don't often think of them that way. As such, they are balancing the number of patterns they can "see" in the workload with the computational cost of representing pattern state. Recency-only algorithms are blind to high-probability patterns in the same way frequency-only algorithms are, so algorithms that can see all high-probability patterns must have elements of both.

For sufficiently complex workload patterns, the details of competent algorithms will have minimal impact on cache hit rates. A good algorithm will capture all of the high-probability cases and the low-probability cases are essentially treated as stochastic. Small differences in synthetic tests of cache hit rates don't map that well to real-world performance.

An under-rated aspect of the algorithms, somewhat ignored in the literature, are their computational cost. In real systems, the cache replacement algorithm is being exercised upwards of 10s of millions of times per second. The design idiom for high-performance cache replacement systems is to filter for algorithms that can see all the standard high-probability cases, of which there are dozens, and then select for optimizability in a classic performance-engineering sense e.g. CPU cache behavior. Marginal differences in cache hit rate become dwarfed by large differences in practical computational cost.