# Group evolution patterns in running races

## TL;DR

This paper introduces algorithms to track and analyze the evolution of runner groups during races, providing insights into race dynamics through geometric modeling and pattern detection.

## Contribution

It presents novel algorithms for detecting and analyzing group evolution patterns in running races, including long-term patterns, with real-time processing capabilities.

## Key findings

- Algorithms effectively identify group patterns in real and synthetic data.
- The methods provide valuable insights into race development.
- Algorithms operate in real time even with dense data.

## Abstract

We address the problem of tracking and detecting interactions between the different groups of runners that form during a race. In athletic races control points are set to monitor the progress of athletes over the course. Intuitively, a {\it group} is a sufficiently large set of athletes that cross a control point together. After adapting an existing definition of group to our setting we go on to study two types of group evolution patterns. The primary focus of this work are {\it evolution patterns}, i.e. the transformation and interaction of groups of athletes between two consecutive control points. We provide an accurate geometric model of the following evolution patterns: survives, appears, disappears, expands, shrinks, merges, splits, coheres and disbands, and present algorithms to efficiently compute these patterns. Next, based on the algorithms introduced for identifying evolution patterns, algorithms to detect {\it long-term patterns} are introduced. These patterns track global properties over several control points: surviving, traceable forward, traceable backward and related forward and backward. Experimental evaluation of the algorithms provided is presented using real and synthetic data. Using the data currently available, our experiments show how our algorithms can provide valuable insight into how running races develop. Moreover, we also show how, even if dense (synthetic) data is considered, our algorithms are also able to process it in real time.