GooglyPlusPlus: Win Probability using Deep Learning and player embeddings

In my last post'GooglyPlusPlus now with Win Probability Analysis for all T20 matches' I had discussed the performance of my ML models, created with and without player embeddings, in computing the Win Probability of T20 matches. While the Random Forest gave excellent accuracy, it was bulky and also took an unusually long time to predict the Win Probability of a single T20 match. The above 2 ML models were built using R's Tidymodels. I had initially tried to use Tensorflow, Keras in Python but then abandoned it, since I did not know how to port the Deep Learning model to R and use in my app GooglyPlusPlus. But later, since I was stuck with a bulky Random Forest model, I decided to again explore options for saving the Keras Deep Learning model and loading it in R. I found out that saving the model as .h5, Hence, I rebuilt a Deep Learning model using Keras, Python with player embeddings and I got excellent performance.