Convergence of discrete-time Markov chains with two timescales is a powerful tool to study stochastic evolutionary games in subdivided populations. Focusing on linear games within demes, convergence to a diffusion process for the strategy frequencies as the population size increases yields a strong-migration limit. The same limit is obtained for a linear game in a well-mixed population with effective payoffs that depend on reproductive values and identity measures between individuals. The first-order effect of selection on the fixation probability of a strategy introduced as a single mutant can be calculated by using the diffusion approximation, or alternatively by summing the successive expected changes in the mutant strategy frequency that involve expected coalescence times of ancestral lines under neutrality. These can be approached by resorting to the existence of two timescales in the genealogical process. Keeping the population size fixed but letting the intensity of migration go to zero, convergence to a continuous-time Markov chain after instantaneous initial transitions yields a low-migration limit that depends on fixation probabilities within demes in the absence of migration. In the limit of small uniform dispersal, the fixation probability of a mutant strategy in the whole population exceeds its initial frequency if it is risk-dominant over the other strategy with respect to the overall average payoffs in pairwise interactions. Finally, in the low-mutation limit in the presence of recurrent mutation, the average abundance of a strategy in the long run is determined by the fixation probabilities of both strategies when introduced as single mutants in a deme chosen at random.