Suppose I have 5 players on each team in a game where each player selects a character (League of legends, Valorant, etc.).
I am considering the overall win rates of the characters, and the unique win rate attached to the player is not considered (i.e some players focus on using X character, so their win rate may be higher with this specific character, I am ignoring this, I am using the win rate of a character across all games played in a given season).
Each character has been played (in a game that has matches consisting of 2 teams formed by 5 players each, selected at random) anywhere from at least 4,000 to even 60,000 times in a given patch (lets just use season for simplicity).
Upon entry, team A may have overall individual character win rates (calculated across all matches played from and up to a certain point) of [40%, 50%, 55%, 50%, 60%] and team B may have individual win rates of [50%, 45%, 60%, 40%, 65%] for each character (not player) respectively.
Is it naïve to just average the win rates and consider that the team win rate, or is there a more robust method of doing this?
Despite all the characters having 4000+ games, some may have only around 4000, some may have 8000, some may have even more depending on popularity, so I imagine there may need to be sort of weighting, but I am not sure if 4000+ games is enough to disregard the need for a weighting system (as opposed to a basketball player playing less than 20 games a season and then 70 games the next, clearly weighting there may be more important).
My goal is to determine a predicted win rate for each team given the win rates of each character selected.
I can come up with everything, I am just trying to figure out that last key data point (predicted team win rate), it seems almost too easy to just average the 5 win rates, or maybe I am just overthinking it =)