Understanding how humans master complex skills has the potential for wide-reaching societal benefit. Research has shown that one important aspect of effective skill learning is the temporal distribution of practice episodes (i.e., distributed practice). Using a large observational sample of players (n= 162,417) drawn from a competitive and popular online game (League of Legends), we analysed the relationship between practice distribution and performance through time. We compared groups of players who exhibited different play schedules using data slicing and machine learning techniques, to show that players who cluster gameplay into shorter time frames ultimately achieve lower performance levels than those who space their games across longer time windows. Additionally, we found that the timing of intensive play periods does not affect final performance—it is the overall amount of spacing that matters. These results extend some of the key findings in the literature on practice and learning to an ecologically valid environment with hugen. We discuss our work in relation to recent studies that have examined practice effects using Big Data and suggest solutions for salient confounds.