This is really interesting because i ran into a pokemon bot the other day were its training led to calibration of 50% winrste at all levels of play on Pokémon showdown. It was a complete accident.
Making one that realistically plays like a human is an unsolved problem.
Now that i think about it, i remember the people in the alphago documentary talking about the bot giving its moves percentage scores in both how high winning % the move had and how high % chance that a human would have made the same move that it just played. I wonder why they never showed what a full game of the most human-like moves from alphago would look like. Maybe it actually worked, by feeding it all the pro games in existence, and training it to play the high human % instead of the higest win probability moves like they did in the end.
What evidence lead you to think that, and how surprised would you be to be wrong?
Making a computer have a 50% score against a 1300-rated human is way easier than making it play like a 1300-rated human.
For the former, you can take a top-of-the-line program and have it flip a coin in every game whether to make a random move every move or not.