Long-horizon prediction of who quits : League of Legends as a case study

We explore long-term prediction of quitting behavior, a challenge across many domains, using League of Legends (LoL) as our test case. The objective is to present a replicable and stable methodology for long-horizon game quitting prediction when many and potentially highly collinear predictors are available, as is often the case in practice. We show that we can robustly identify long-term predictors of quitting behavior by selecting features of importance using Random Survival Forest analysis to constrain the number of Cox models subsequently used for statistical inferences. In the case of LoL, defensive deficit (a combination of class-normalized damage taken and number of deaths), computed from the early season matches (first 50), was found to consistently predict the quitting hazard over the entire season. It did so to the same extent across two different datasets, such that higher damage taken by one standard deviation, early in the season, lowered the quitting hazard by about 18% across the two datasets.