Maximum Ratio Combining vs. Selection Combining
Very quick explanation: SC just throws away $N-1$ of $N$ observations, whereas MRC combines all $N$. 

MRC does to a signal in spatial domain what a matched filter does in frequency domain: it maximizes the inner product of signal vector and weights. The exact same derivation as for the matched filter's optimal performance under AWGN apply to the MRC under the same assumption.

You can also do the same consideration on a basic stochastic level.

Assume the combiner has correct knowledge of each observation's SNR.

Then, SC picks the best one. Derivation is simple: if channel realizations are Rayleigh distributed, then the best of these channels has $\sum_{n=1}^N \frac1N$ increased SNR in expectation, $N$ being the number of concurrent observations. 

For MRC, you literally get the weighted average of the received signals, so your overall SNR literally is $\sum_{n=1}^N \frac {S_k}{N_k}$, which yields a diversity gain of $N$ (the maximum possible).

Regarding your "I've read papers": the 1959 Brennan paper is the only one you need to read for this :) But, honestly, this is knowledge that's 60 years old, so you don't have to read the original paper, and should probably rather read a good modern textbook or lecture slides explaining combining methods, as these tend to didactically process the paper's content to make it more accessible. A really quick google for "diversity gain MRC" instantly yielded a link to these lecture notes, and you could have found them, too! They're easy to read, I find after quickly scanning them.