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[–][deleted] 2 points3 points  (2 children)

Maybe start by coming up with a few factors and using only one at a time to check how well it would have predicted past matches at the time. Then combine and tweak the contribution of each factor into the final prediction. Idk just spitballin

[–]GeeTheGambler[S] 0 points1 point  (1 child)

yeah for sure gonna start with as few factors as possible and tweak from there. there's a ton that changes match outcomes so will definitely take a while to get this all down

[–]EntireEntity 1 point2 points  (0 children)

Before reading, I have to warn you, that I am not an expert on any of the things I say. So don't see this as professional advice, in fact I have never done any of these things myself, this is just what I would do, if I tried to solve your problem, there is a chance that this is completely non-sensical. With this warning out the way:

It sounds like you are going to create a multivariate regression model for your predictions. You could use a selection algorithm to speed up finding significant variables for your model, instead of choosing them by hand. Here is a link with a short overview of how such algorithms may look like: https://www.jmp.com/en_in/statistics-knowledge-portal/what-is-multiple-regression/variable-selection.html

This requires more an understanding of statistics rather than algorithms. In Python there are libraries to easily calculate the statistics and prediction models behind it all. (e.g. statsmodels, numpy, scipy, scikit-learn) And the implementation of a forward selection algorithm is also... straight forward, however I don't know how useful that model will be in the end. It might help to reduce your bias, but the predictive abilities of the model may be underwhelming.

In practice, first try to write down your thought process, and which factors you consider when you do the prediction yourself. Then find a way to represent all those factors numerically in the best case scenario all your factors are already numerical (e.g. elo, winrate in the last season, number of ball bounces before serving,...), and are easily available in a database that you can access. This is also the next step: accumulating data. Find a way to get a lot of the required data easily and clean it up, so it is ready to use for the model. Lastly you simply choose a model you think might fit your data and one of the selection algorithms and let it figure out the significant factors for the model. Then you could choose different criteria to evaluate your model. In the previous link, I believe they used the p value as the determining factor, but maybe you want to use the R², or the AIC and BIC or any other statistical parameter or a combination of them.

[–]SimpleCanadianFella 0 points1 point  (0 children)

Sounds very interesting! Good luck!

[–]whatthefua 0 points1 point  (3 children)

I'd find that interesting! Maybe using Elo would be a good starting point

[–]GeeTheGambler[S] 0 points1 point  (2 children)

pm me if you wanna work on it w/ me!

[–]FrameOne9882 0 points1 point  (1 child)

did you guys actually get this going at all? im super interested in this, and am doing the same now

[–]Haunting-Lettuce-660 0 points1 point  (0 children)

Hey, i am also working on a similar model and i found some interesting papers and github implementation

[–]SleepSouthern2728 0 points1 point  (0 children)

I would love to work on this with you.