I'm working on a project which is predicting the win rate of one team or one person. (could be any kind of sports like baseball, basketball or e-sport games) The data I have is more like a classification problem. Data looks like this, A v.s B, the winner is A. So, I trained a classification model with those data. The model should input two units(a team or a person) and output a probability from sigmoid, so I should set a threshold to determine the output is 1 or 0. But I'm curious about if the output of the sigmoid can be considered as the win rate? I think the answer might be yes? But I got next question. My goal is to predict the win rate of one unit(team or person), which means I don't care about opponents. So When using this model to predict, for example, I want to predict Laker's win rate, and I make the match-up with all the others teams(29 teams) as the opponents and make the 29 pieces data like Laker vs Hawks, Laker vs Mavericks,...and so on, then I got the 29 output from sigmoid. Here are two choices I have. 1. setting the threshold for the output of sigmoid and determine the result is 1 or 0. Then calculate the win rate. For example, 3 output, 1 0 0 ---> win rate=0.33 2. just get the mean of all the 29 games' output from sigmoid(no threshold setting). For example, 3 output 0.6, 0.3, 0.4 ---> win rate = 0.433
I think the first one is more reasonable, but the second one is outperforming the first one in my experiment. So, does the second one make any sense? For the second one, is it a regression problem? or still a classification problem? I I'm not fluent in English. Please bear with me... I would be grateful for your response.