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Thanks for the feedback. Ideally, I would like to do it the way I think you are describing it. Get a bunch of factors and see how they all interact with each ot

by socrates1998 10y ago

Thanks for the feedback. Ideally, I would like to do it the way I think you are describing it. Get a bunch of factors and see how they all interact with each other. And try to get the probabilities of all the different scenarios. For example, if the coach runs the ball to the right in the red zone, what is the probability that he runs it again to the right on the next play. The problem with trying to input a bunch of factors and trying to get the conditional probabilities of all those situations is that it can get exceedingly complex and I don't think it will be particularly useful given my small data sets. I normally only have about 100 offensive plays to analyze and use data from. My theory is that if I compared the R/L direction of the plays that the opposing coach has historically done in the last two or three games with a truly randomization of R/L data, that the coach will severely regress to the mean because he doesn't like the idea of going 5 times to the right in a row. However, I need some type of percentage comparison or data analysis to show this instead of a subjective picture.

usgroup 10y ago
Have a look at http://rattle.togaware.com/ http://rattle.togaware.com/ . It's a UI you can use with R and it's fully capable of doing what I described. You'll need to massage the format of your files a bit. Once you've got some outputs going, post back and I'll try and help you avoid the analysis pitfalls.
almostkorean 10y ago
What usgroup said is correct. To give a little more background, this is a classification problem (predicting L or R) and it is a supervised learning problem (since you are training it on historical data). There are many supervised learning models that can be used for classification. The simplest ones that first come to mind are logistic regression and decision trees. If you want to get into more complex models, look into boosting models and random forest. I'm not sure what your programming background is, but creating classification models is very easy in Python using the scikitlearn library. Creating the features that are used to train your model would be more difficult, but you can always start with something simple and iterate. It's also important to know how to measure the performance of your model. In this situation, plain accuracy will probably be OK assuming predictions are roughly 50/50 chance. But AUC is typically the standard used to measure model performance.

usgroup 10y ago

Have a look at http://rattle.togaware.com/ http://rattle.togaware.com/ . It's a UI you can use with R and it's fully capable of doing what I described. You'll need to massage the format of your files a bit. Once you've got some outputs going, post back and I'll try and help you avoid the analysis pitfalls.

almostkorean 10y ago

What usgroup said is correct. To give a little more background, this is a classification problem (predicting L or R) and it is a supervised learning problem (since you are training it on historical data). There are many supervised learning models that can be used for classification. The simplest ones that first come to mind are logistic regression and decision trees. If you want to get into more complex models, look into boosting models and random forest. I'm not sure what your programming background is, but creating classification models is very easy in Python using the scikitlearn library. Creating the features that are used to train your model would be more difficult, but you can always start with something simple and iterate. It's also important to know how to measure the performance of your model. In this situation, plain accuracy will probably be OK assuming predictions are roughly 50/50 chance. But AUC is typically the standard used to measure model performance.