# Deep Neural Networks for Optimal Team Composition

arXiv: https://arxiv.org/abs/1805.03285 · HTML: https://mlsanity.com/paper/1805.03285/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

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

- [QuickSkill: Novice Skill Estimation in Online Multiplayer Games](https://mlsanity.com/paper/2208.07704/) (2022)
- [Individualized Context-Aware Tensor Factorization for Online Games Predictions](https://mlsanity.com/paper/2102.11352/) (2021)
- [Graph Embedding Augmented Skill Rating System](https://mlsanity.com/paper/2304.08257/) (2023)
- [Modeling Teams Performance Using Deep Representational Learning on Graphs](https://mlsanity.com/paper/2206.14741/) (2022)
- [Explaining Expert Search and Team Formation Systems with ExES](https://mlsanity.com/paper/2405.12881/) (2024)
- [A QUBO Framework for Team Formation](https://mlsanity.com/paper/2503.23209/) (2025)

## Abstract (arXiv)

Cooperation is a fundamental social mechanism, whose effects on human performance have been investigated in several environments. Online games are modern-days natural settings in which cooperation strongly affects human behavior. Every day, millions of players connect and play together in team-based games: the patterns of cooperation can either foster or hinder individual skill learning and performance. This work has three goals: (i) identifying teammates' influence on players' performance in the short and long term, (ii) designing a computational framework to recommend teammates to improve players' performance, and (iii) setting to demonstrate that such improvements can be predicted via deep learning. We leverage a large dataset from Dota 2, a popular Multiplayer Online Battle Arena game. We generate a directed co-play network, whose links' weights depict the effect of teammates on players' performance. Specifically, we propose a measure of network influence that captures skill transfer from player to player over time. We then use such framing to design a recommendation system to suggest new teammates based on a modified deep neural autoencoder and we demonstrate its state-of-the-art recommendation performance. We finally provide insights into skill transfer effects: our experimental results demonstrate that such dynamics can be predicted using deep neural networks.