# Competitive Balance in Team Sports Games

arXiv: https://arxiv.org/abs/2006.13763 · HTML: https://mlsanity.com/paper/2006.13763/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

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

- [RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games](https://mlsanity.com/paper/2307.01676/) (2023)
- [Evaluating Team Skill Aggregation in Online Competitive Games](https://mlsanity.com/paper/2106.11397/) (2021)
- [The Evaluation of Rating Systems in Team-based Battle Royale Games](https://mlsanity.com/paper/2105.14069/) (2021)
- [Player Modeling using Behavioral Signals in Competitive Online Games](https://mlsanity.com/paper/2112.04379/) (2021)
- [Behavioral Player Rating in Competitive Online Shooter Games](https://mlsanity.com/paper/2207.00528/) (2022)
- [An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis](https://mlsanity.com/paper/2402.00031/) (2024)
- [Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis](https://mlsanity.com/paper/2408.17180/) (2024)

## Abstract (arXiv)

Competition is a primary driver of player satisfaction and engagement in multiplayer online games. Traditional matchmaking systems aim at creating matches involving teams of similar aggregated individual skill levels, such as Elo score or TrueSkill. However, team dynamics cannot be solely captured using such linear predictors. Recently, it has been shown that nonlinear predictors that target to learn probability of winning as a function of player and team features significantly outperforms these linear skill-based methods. In this paper, we show that using final score difference provides yet a better prediction metric for competitive balance. We also show that a linear model trained on a carefully selected set of team and individual features achieves almost the performance of the more powerful neural network model while offering two orders of magnitude inference speed improvement. This shows significant promise for implementation in online matchmaking systems.