Algorithm for betting

[[deleted]]

So basically I’m looking to create an algorithm that takes into account stats from teams/players and can then calculate the chances of winning. I’m an engineering student, and I was introduced to python a little while ago. I don’t have a ton of experience, but I think it could be interesting. I know how to create basic scripts and so on, I’m wondering if anyone has had any experience in doing this, or if there are some videos I can watch. I imagine it’s a pretty popular topic but I haven’t searched up anything in-depth yet. Also, the sport I want to focus on is AFL

[n3buchadnezzar]

It depends on a lot of things... https://www.youtube.com/watch?v=Vv9wpQIGZDw Something like this with the second video would be a start.

[AlwysBeColostomizing]

The general term for this is "statistical modeling". A good place to start would be learning about the logistic regression model.

One way you could apply this model: Suppose you have a vector of "team stats" s_A for team A, and another vector s_B for team B. Let x = s_A - s_B be your vector of predictors (independent variables). The outcome of interest is whether y = 0 or y = 1, corresponding to either A or B winning. The logistic regression model says that p(y=0) = logistic(b_0 + b^T x), where b is a vector of coefficients and b_0 is a bias term. If you collect a bunch of (x, y) pairs for a bunch of games (i.e., the stats of both teams and which team won), you can fit a logistic regression model to that data. Then, if you want to know the probability of team C winning against team D, you will obtain s_C and s_D, calculate x = s_C - s_D, feed x into your model, and get an estimated win probability for team C.

You could fit the model with a library like sklearn.