TimSort is essentially merge sort plus optimizations. The major optimization is that it identifies already-sorted subsequences within the input, and sorts these "runs" plus some surrounding elements using a fast binary insertion sort. This means that for input that isn't completely random, it doesn't need to do as many comparisons as a pure mergesort. TimSort has also been tuned based on empirical testing on real-world data/hardware.
Academic work on subjects like sorting tends to focus more on things like big-O runtimes and less on implementation details that lead to reducing the various constants that big-O simplifies out, or that arise from real-world data patterns rather than totally randomized or idealized data.
