Unrailed! Gameplay Analysis

A human-computer interaction (HCI) metrics research project analyzing multiplayer video gaming and its applications for real-world team dynamics

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Unrailed! video game demo

Abstract

Using the game Unrailed!, this study examines team success in virtual collaborative environments focusing on task distribution, response time, team communication, team trust, and team cohesion. Team A, with higher communication frequency (8.53 messages per minute) and trust (2.54), achieved a success index of 2.48 compared to Team B’s 0.80, despite weaker cohesion. Communication emerged as the strongest predictor of success followed by trust and task distribution. The findings of this study highlight the importance of real-time communication and trust in teamwork, offering insights applicable to gaming, the workplace, and other team-related contexts.

Framework

Scope: Quantitative & Qualitative

Tools: WebEx, Kinovea, Excel

Timeline: 9 weeks

Context: why unrailed!?

Unrailed! is a cooperative game where players build train tracks in real-time to avoid derailment. Success depends entirely on how players self-organize, communicate, and adapt. Whether it’s mining resources, placing tracks, or extinguishing fires, players must switch tasks and rely on one another, offering a rich environment to study trust, cohesion, and decision-making in dynamic team settings.

Gameplay overview

Screenshot of Unrailed! gameplay with items tracked in analysis identified

To understand the mechanics of collaboration and team dynamics in Unrailed!, it is important to examine the game’s structure and overall goals.

Players work together to build a train track through procedurally generated landscapes, collecting resources like wood and iron to keep the train moving and avoid it derailing. The team must strategize, manage tools, and adapt quickly as the train speeds up and environmental challenges like rivers, forests, and deserts become increasingly difficult to navigate.

Research Objective

Understanding what determines team success is a fundamental question in collaborative environments. In this study, the game Unrailed! serves as the context to explore team dynamics in real-time gameplay scenarios.

By observing and analyzing player interactions, communication patterns, and decision-making processes, the goal is twofold: (1) to understand the key elements that contribute to successful team outcomes and (2) to develop a predictive model for team success based on the insights gleaned from key elements in goal 1.

Methodology

Team Setup:

Two teams (Team A and Team B), each consisting of four players
Team A consisted of our team players (the researchers of this analysis)
Team B was comprised of a pre-existing group from the YouTube channel Stumpt.
Both team compositions varied and included a mix of novice and experienced video gamers to reflect diverse gameplay strategies.

Data Collection:

Video Analysis: Kinovea was used to track in-game actions and create task logs.
Audio Capture: WebEx recorded team communication in real time.
Transcription & Coding: Manual transcription of team audio was followed by coded tagging of key communication events and task switches.

Tools:

Kinovea: Action logging from gameplay footage.
WebEx: Audio data recording.
Excel: Preliminary metric tabulation and visualization.
Python: Data cleaning, analysis, and feature engineering.
ML Models: Logistic regression for identifying predictors of team success.

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Screenshot of Kinovea being used for video analysis

A portion of Python script outlining implementation

Metric Design

We developed five core metrics to evaluate team dynamics and predict team success using a logistic regression model.

Task Distribution

Measures workload equity. Calculated as each player’s share of total tasks (e.g., mining, building, extinguishing) using a proportional formula. High imbalance indicated adaptability challenges.

1

Cohesion

Based on synchronized gameplay and collaborative dialogue. Measured via alignment in task switching and verbal affirmations.

3

Response Time

Average delay between a critical event and a player’s response. Extracted via Python from timestamped Kinovea logs. Faster response (e.g., Team A’s 3s) was linked to higher coordination.

2

Communication Frequency

Messages per minute from WebEx transcripts.

4

Team Trust (Composite Metric)
Combined task distribution, communication frequency, and response time into a unified score:

Team Trust = w1(Task Distribution) + w2(Comm Frequency) − w3(Response Time)

5

Key Results

Team A demonstrated stronger performance across key collaboration metrics, including faster response times (2.8s vs. 6.0s), significantly higher communication frequency (8.53 vs. 1.83 messages/min), and a superior trust score (2.54 vs. 0.86).

While Team B showed better task distribution (0.72 vs. 0.63) and higher cohesion (0.15 vs. 0.01), these advantages were outweighed by their slower reactivity and limited communication. Ultimately, Team A achieved a much higher success index (2.48 vs. 0.80), with feature analysis confirming that communication (60% weight) and trust (20%) were the most critical predictors of team success.

✅ Communication = #1 predictor
✅ High trust + fast response = high performance
✅ Cohesion alone wasn’t enough

Insights & discussion

Team A outperformed Team B in Unrailed! due to faster response times, higher communication frequency, and greater trust, which enabled better adaptability under pressure.

However, they struggled with cohesion and task balance. Team B, while strong in task distribution and teamwork alignment, was held back by slower reactions and limited communication—highlighting that responding to teammates quickly and clarity of interaction are key drivers of team success in dynamic settings.

Limitations & Future work

This study faced several limitations, including transcription errors and missing audio during overlapping speech, which reduced data accuracy and required manual correction. The reliance on verbal-only communication in Unrailed! also limited the capture of nonverbal cues, affecting the assessment of teamwork dynamics.

Bias may have been introduced due to the self-analysis of Team A, whose members had pre-existing rapport and a horizontal team structure. These factors likely influenced trust and cohesion. Future work should integrate nonverbal data, refine transcription methods, and explore silent periods and pre-existing relationships to better understand team dynamics.

Conclusion

In conclusion, the study shows that Team A’s success in Unrailed! was largely due to strong communication and trust, which enabled quick decision-making and task execution. While Team B excelled in cohesion and balanced task distribution, their lower communication and trust limited adaptability.

Overall, the findings emphasize that effective teamwork relies on the interplay of multiple factors, with communication being the most critical driver of success in dynamic, cooperative settings.