  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.
