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 -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.
