# ESTA: An Esports Trajectory and Action Dataset

arXiv: https://arxiv.org/abs/2209.09861 · HTML: https://mlsanity.com/paper/2209.09861/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://mlsanity.com/paper/1807.01281/) (2018)
- [Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks](https://mlsanity.com/paper/1810.00825/) (2018)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [A Bayesian Approach to In-Game Win Probability in Soccer](https://mlsanity.com/paper/1906.05029/) (2019)
- [STARDATA: A StarCraft AI Research Dataset](https://mlsanity.com/paper/1708.02139/) (2017)
- [Valuing Player Actions in Counter-Strike: Global Offensive](https://mlsanity.com/paper/2011.01324/) (2020)
- [Graph Neural Networks to Predict Sports Outcomes](https://mlsanity.com/paper/2207.14124/) (2022)
- [Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset](https://mlsanity.com/paper/2011.00958/) (2020)
- [ggViz: Accelerating Large-Scale Esports Game Analysis](https://mlsanity.com/paper/2107.06495/) (2021)

## Built on by (most cited first, 6 of 6)

- [Learning to Move Like Professional Counter-Strike Players](https://mlsanity.com/paper/2408.13934/) (2024)
- [X-Ego: Acquiring Team-Level Tactical Situational Awareness via Cross-Egocentric Contrastive Video Representation Learning](https://mlsanity.com/paper/2510.19150/) (2025)
- [A Data-Driven Discretized CS:GO Simulation Environment to Facilitate Strategic Multi-Agent Planning Research](https://mlsanity.com/paper/2509.06355/) (2025)
- [EgoEsportsQA: An Egocentric Video Benchmark for Perception and Reasoning in Esports](https://mlsanity.com/paper/2604.12320/) (2026)
- [Round Outcome Prediction in VALORANT Using Tactical Features from Video Analysis](https://mlsanity.com/paper/2510.17199/) (2025)
- [Multi-Task Learning for Heterogeneous Prediction from Video Game State with Transfer Learning](https://mlsanity.com/paper/2607.21290/) (2026)

## Abstract (arXiv)

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.