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 (si

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.