I am developing a predictive model in sports vertical. As the game will progress, my model will predict the winning probability of each team playing. The problem I am facing is to what metric would be appropriate to quantify accuracy for the model. What I have in mind to quantify accuracy is as follows :Consider n matches for which my model predicts the winning probability of Team A at time instance t1, t2 ... tn. For each match, I will quantify the loss as the hinge loss at that time. i.e. if probability greater than threshold and team_A actually won, then accurate. As the game progresses, model gets new info and I can expect the model performance to improve, so I thought about giving weights to loss function acc. to Time of prediction also. i.e. if model predicts wrongly at later point of the game then it should be penalized heavily. But I think it can be better. Any leads on the apt. loss function for this is highly appreciated.Hoping to learn something great from the answers.  
Right choice of accuracy metric or loss function
machine learning;deep learning;loss function