MLB Injuries
A library for scraping MLB IL/DTD stints and modeling injury risk with Temporal Point Processes. The code in this repo produces the results and plots in this blog.
Installation
conda create -n "injury" python=3.8
pip install -e .
Data Scraping
MLB IL data is available through the stats api and can be scraped using functions in injury.scrape.statsapi. Day to day information is available at Pro Sports through injury.scrape.prosports.
Scraping and aligning of these two datasets is contained in notebooks/scrape_align_injuries.ipynb.
Injury Data Preprocessing
Injury mapping and event processing can be found in injury/preprocess. Injury data is cleaned from both sources, categorized, and combined with player game data to determine injury timing in notebooks/inury_processing.ipynb
Modeling
Injuries are modeled using a Hawkes Process, which inherently model a set of events through an intesity function:
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The input to the model is the events and respective times of each event. For example:
{
't': [642.0, 649.0, 932.0, 934.0, 953.0, 999.0, 1159.0, 1172.0],
'injury_location': ['foot','wrist','hamstring','hamstring','hand','head/neck','wrist','other leg']
}Models can be trained using the command
python -m injury.model.train
There are available options for different types of models. For example, to train a model that splits IL and DTD events, on only pitchers, using a 70/30 train/test split use
python -m injury.model.train -p "pitcher" -t 0.3 --dtd