A multi-mechanism partial counterbalancing approach for multi-session esports performance research in Counter-Strike: Global Offensive

Esports performance research increasingly relies on repeated-measures designs, in which each player serves as their own control. Such designs are efficient, but they are vulnerable to order effects: participants improve with practice, tire within a session, and carry the influence of one condition into the next. Competitive gaming makes this problem unusually acute, because skill acquisition in digital games is rapid and session fatigue develops quickly. The conventional remedy, complete counterbalancing, is unattainable once a study tests more than a handful of conditions. This article describes and evaluates a partial counterbalancing strategy that layers four mechanisms: category-level Latin square sequencing, randomisation of values within categories, forward–reverse distribution of sequences across participants, and temporal spacing with session truncation. The strategy was implemented in an 18-week laboratory study of 170 South African Counter-Strike: Global Offensive players, testing six categories of hardware and in-game settings at three values each. Three independent checks were used to evaluate whether order effects had been controlled. Conditions were evenly distributed across testing positions in every setting category. Performance at the first testing position did not differ from performance at later positions on any of five metrics, with all effect sizes below the threshold for a small effect. Adding order covariates to the study’s primary analyses did not reverse the direction of any effect, and left the great majority essentially unchanged; a small number of effects involving kill and death counts lost significance once order was controlled, and are identified as requiring additional care. The contribution is a transparent, auditable worked example rather than a new technique. For researchers designing multi-session studies, it offers a template for layering and then verifying order controls. For coaches and practitioners, it addresses a prior question that applied esports research depends on: whether performance differences attributed to equipment, settings or training reflect the intervention rather than the order in which conditions happened to be tested.