Context-Dependent Pitch Prediction with Neural Networks

This semester I took the machine learning class at UMass, and for my final project I developed models for predicting characteristics of pitches based on the context that they were thrown in.  The context consists of relevant and available information known before the pitch is thrown, such as the identities of the batter and pitcher, the batter height and stance, the pitcher handedness, the current count, and many others.  In particular, I looked into modeling the distribution over pitch types and locations.  This problem is challenging primarily because for a particular context (a specific setting of the different features that make up the context), there is usually very little data available for pitches thrown in that context.  In general, for each context feature we include, we have to split up our data into $k$, groups where $k$ is the number of values that the context feature can take on (for batter stance it is $k=2$, for count it is $k=12$, etc).  Thus, developing models by partitioning it into groups based on context then individually constructing models for the pitch type and location would not work well.  To overcome this problem, I developed a neural network that can automatically learn the complex dependencies between the context and the distribution over pitch types and locations.  To model the pitch type, I used a standard neural network for probabilistic classification.  And to model the pitch location, I used something called a mixture density network, which I explain in more detail in the write-up.  I demonstrated that these models outperformed models that don't account for context in a principled fashion, and showed that they generalize effectively to pitches thrown in new contexts.
For more information on this project and these models, please refer to the following resources:

• Slides
• Poster
• Paper
• GitHub

Feel free to get in touch with me over email if you have any questions or want to extend the work.