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Bayesian Space-Time Models for Expected Possession added Value

2021 Big Data Cup Submission

Brendan Kumagai, Mikael Nahabedian, Thibaud Chatel, Tyrel Stokes

Final Paper

Contains a pdf with our submission for the 2021 Big Data Cup.

Code

Our code for this project is broken down into the following sub-folders:

  1. Functions: Contains plotting functions used to create INLA spatial maps and rink plots as well as sampling functions used in our simulations.

  2. Data Preparation: All data preparation required to extract entry-to-exit sequences and reformat data to fit the Markov decision process as described in our paper.

  3. Action-Transition Models: The spatiotemporal poisson and logistic models that were used to estimate the transition probabilities of our Markov decision process.

  4. Movement and Time Models: Additional models required to make our simulation run smoothly such as predicting whether or not there is traffic on a shot, predicting the subsequent xy-location following an event, predicting whether an entry will be controlled or dumped, etc.

  5. Value Models: The expected goals models (with and without pre-shot passes).

  6. Simulations: Contains the generation of sample parameters to be used in our simulations and the simulation chain itself used to get expected possession value samples from our model.

Data

Contains the outputs of our "Code/1. Data Preparation/Data Preparation.Rmd" file. Most notably, "otters_data_for_modelling.csv", the data set used for all of our model fitting. Additionally, we include fitted values of expected goals based off of our models in this folder.

Figures

The figures folder contains pngs of various spatial maps from our core and glue transition models.

Models

Due to file size constraints, we could not include any of our models in this repository. However, the code for our models is available in "Code/2. Action-Transition Models", "Code/3. Movement and Time Models" and "Code/4. Value Models".

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