I was going to stay out of this, but I couldn't help myself.
To really address the question of what country is best and whether it matters for good players versus poor players, we would need to do more descriptive statistics. You would need to do regression, and in this case, either multinomial logistic regression (win/draw/defeat) or do logistic regression for win and draw separately controlling for players' skills/ghostrating for when they participated in the game and the interactions of players' skills/GR and country-assignment. Then you analyze the odds ratios of how likely a country would win controlling for players' skills, how much players' skills matter, and whether players' skills interacted with country-assignment significantly.
So the dataset would need to contain country assignment, each player's skill at the time the game was played, and the outcomes for each game. Then you would run three main models, one with just the 7 countries, one with the 7 countries and the players' skills, and one with the 7 countries, the players' skills, and the interaction between country and skills.
Note: these are simple models not accounting for things like phase lengths, CDs, tournies, and so forth. Those factors may or may not matter, but the models become significantly more difficult if they do matter and the parameter estimates would then be biased.
If you don't have those covariates in the models, then all you are doing is looking at average statistics and you can't take players' skills into account.
If I have a ton of time one summer, I'll work with Alderian and Kestas to create the dataset and play around with it. But really, just from personal observation, I would hazard a guess that the outcomes for countries would be different for good players versus poor players as players' skills would equalize starting conditions ie countries.