# Real-time eSports Match Result Prediction

arXiv: https://arxiv.org/abs/1701.03162 · HTML: https://mlsanity.com/paper/1701.03162/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2016.

## Built on by (most cited first, 7 of 7)

- [Graph Neural Networks to Predict Sports Outcomes](https://mlsanity.com/paper/2207.14124/) (2022)
- [Machine learning models for DOTA 2 outcomes prediction](https://mlsanity.com/paper/2106.01782/) (2021)
- [Bandit Modeling of Map Selection in Counter-Strike: Global Offensive](https://mlsanity.com/paper/2106.08888/) (2021)
- [Optimal Team Economic Decisions in Counter-Strike](https://mlsanity.com/paper/2109.12990/) (2021)
- [Sequential Item Recommendation in the MOBA Game Dota 2](https://mlsanity.com/paper/2201.08724/) (2022)
- [Predicting Outcomes in Video Games with Long Short Term Memory Networks](https://mlsanity.com/paper/2402.15923/) (2024)
- [Round Outcome Prediction in VALORANT Using Tactical Features from Video Analysis](https://mlsanity.com/paper/2510.17199/) (2025)

## Abstract (arXiv)

In this paper, we try to predict the winning team of a match in the multiplayer eSports game Dota 2. To address the weaknesses of previous work, we consider more aspects of prior (pre-match) features from individual players' match history, as well as real-time (during-match) features at each minute as the match progresses. We use logistic regression, the proposed Attribute Sequence Model, and their combinations as the prediction models. In a dataset of 78362 matches where 20631 matches contain replay data, our experiments show that adding more aspects of prior features improves accuracy from 58.69% to 71.49%, and introducing real-time features achieves up to 93.73% accuracy when predicting at the 40th minute.