Jan 3, 2023
Boosting Win Probability accuracy with player embeddings
Boosting Win Probability accuracy with player embeddings
In my previous post Computing Win Probability of T20 matches I had discussed various approaches on computing Win Probability of T20 matches. I had created ML models with glmnet and random forest using TidyModels. This was what I had achieved glmnet : accuracy – 0.67 and sensitivity/specificity – 0.68/0.65 random forest : accuracy – 0.737 and roc_auc- 0.834 DL model with Keras in Python :…
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