Using python I conducted a few different analyses:
Proportion of character wins vs other characters: Proportion of character wins when paired with other characters:
With these I gave each possible team a score, equal to the sum over characters (sum over enemy characters(proportion of wins for said character) + sum over other teammates(proportion of wins when paired with said character)), and the highest scoring team was:
This was much more pleasant than using Excel! I think I might try and learn R or some other dedicated statistical language for the next one.
PvP team (without having the time to estimate anything about my enemies’ teams so highly likely to get countered) has actually come out the same. There’s a good chance something is up with my analysis or my method is too biased towards synergistic teams.
Tidehollow Tyrant, Rock-n-Roll Ranger, Nullifying Nightmare, Greenery Giant, Blaze Boy
Using python I conducted a few different analyses:
Proportion of character wins vs other characters:
Proportion of character wins when paired with other characters:
With these I gave each possible team a score, equal to the sum over characters (sum over enemy characters(proportion of wins for said character) + sum over other teammates(proportion of wins when paired with said character)), and the highest scoring team was:
Rock-n-Roll Ranger, Blaze Boy, Nullifying Nightmare, Greenery Giant, Tidehollow Tyrant
This was much more pleasant than using Excel! I think I might try and learn R or some other dedicated statistical language for the next one.
PvP team (without having the time to estimate anything about my enemies’ teams so highly likely to get countered) has actually come out the same. There’s a good chance something is up with my analysis or my method is too biased towards synergistic teams.
Tidehollow Tyrant, Rock-n-Roll Ranger, Nullifying Nightmare, Greenery Giant, Blaze Boy