# On Quitting: Performance and Practice in Online Game Play

## TL;DR

This study analyzes online game players to understand how practice affects performance, revealing that players tend to quit after significant improvements and that persistence correlates with success, with implications for real-time performance optimization.

## Contribution

The paper introduces a detailed analysis of player performance dynamics, including session segmentation and a hidden Markov model to predict quitting behavior and performance.

## Key findings

- Performance improves with practice across skill levels.
- Players tend to quit after large score improvements.
- Persistence correlates with higher success and lower quitting rates.

## Abstract

We study the relationship between performance and practice by analyzing the activity of many players of a casual online game. We find significant heterogeneity in the improvement of player performance, given by score, and address this by dividing players into similar skill levels and segmenting each player's activity into sessions, i.e., sequence of game rounds without an extended break. After disaggregating data, we find that performance improves with practice across all skill levels. More interestingly, players are more likely to end their session after an especially large improvement, leading to a peak score in their very last game of a session. In addition, success is strongly correlated with a lower quitting rate when the score drops, and only weakly correlated with skill, in line with psychological findings about the value of persistence and "grit": successful players are those who persist in their practice despite lower scores. Finally, we train an epsilon-machine, a type of hidden Markov model, and find a plausible mechanism of game play that can predict player performance and quitting the game. Our work raises the possibility of real-time assessment and behavior prediction that can be used to optimize human performance.