# Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The Division

## TL;DR

This study demonstrates that gameplay data can accurately predict player motivation levels in Tom Clancy's The Division, using preference learning methods with high accuracy.

## Contribution

It introduces a novel approach to infer player motivation from gameplay data using support vector machine-based preference learning.

## Key findings

- Gameplay features predict motivation with 92-94% accuracy.
- Motivation can be effectively modeled from gameplay data.
- Preference learning is suitable for modeling player motivation.

## Abstract

Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division. On the other end, we ask them to report their levels of competence, autonomy, relatedness and presence using the Ubisoft Perceived Experience Questionnaire. After processing the survey responses in an ordinal fashion we employ preference learning methods based on support vector machines to infer the mapping between gameplay and the reported four motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the best obtained models reach accuracies of near certainty, from 92% up to 94% on unseen players.