  Sports, due to their global reach and impact-rich prediction tasks, are an
exciting domain to deploy machine learning models. However, data from
conventional sports is often unsuitable for research use due to its size,
veracity, and accessibility. To address these issues, we turn to esports, a
growing domain that encompasses video games played in a capacity similar to
conventional sports. Since esports data is acquired through server logs rather
than peripheral sensors, esports provides a unique opportunity to obtain a
massive collection of clean and detailed spatiotemporal data, similar to those
collected in conventional sports. To parse esports data, we develop awpy, an
open-source esports game log parsing library that can extract player
trajectories and actions from game logs. Using awpy, we parse 8.6m actions,
7.9m game frames, and 417k trajectories from 1,558 game logs from professional
Counter-Strike tournaments to create the Esports Trajectory and Actions (ESTA)
dataset. ESTA is one of the largest and most granular publicly available sports
data sets to date. We use ESTA to develop benchmarks for win prediction using
player-specific information. The ESTA data is available at
https://github.com/pnxenopoulos/esta and awpy is made public through PyPI.
