Dodge and survive: modeling the predatory nature of dodgeball

TL;DR

This paper models dodgeball as a complex system using ODE and agent-based simulations, revealing diverse dynamics and strategies that influence game outcomes, with implications for understanding competitive behaviors.

Contribution

It introduces a novel combined ODE and stochastic agent-based model for dodgeball, analyzing strategic dynamics and long game scenarios.

Findings

Different game dynamics depend on team strategies.

Long games can occur due to noise-driven escape from stable states.

Strategies can be devised to increase winning probabilities.

Abstract

The analysis of games and sports as complex systems can give insights into the dynamics of human competition, and has been proven useful in soccer, basketball, and other professional sports. In this paper we present a model for dodgeball, a popular sport in US schools, and analyze it using an ordinary differential equation (ODE) compartmental model and stochastic agent-based game simulations. The ODE model reveals a rich landscape with different game dynamics occurring depending on the strategies used by the teams, which can in some cases be mapped to scenarios in competitive species models. Stochastic agent-based game simulations confirm and complement the predictions of the deterministic ODE models. In some scenarios, game victory can be interpreted as a noise-driven escape from the basin of attraction of a stable fixed point, resulting in extremely long games when the number of players is large. Using the ODE and agent-based models, we construct a strategy to increase the probability of winning.