Ensuring Reasonable Randomness: QA Perspectives on Pseudo‑Random Distribution in Games

Players often share anecdotal beliefs about luck and probability in games, such as "opening a chest now yields better rewards" or "paying for draws increases success odds," reflecting a desire for favorable outcomes and the mystique of randomness.

QA engineers should strive to understand the full probability model, using empirical data to verify that the implemented randomness matches design specifications and provides reliable evidence for quality assurance.

While many games implement guaranteed‑drop or pity systems to mitigate poor luck, unadjusted random draws can still produce unsatisfying experiences; for example, a 1% chance of a rare item over 300 draws results in a 4.9% chance of never obtaining it, affecting a noticeable portion of the player base.

In competitive titles like Dota 2, true random chance can lead to extreme outcomes (e.g., a 25% critical hit chance may require 20 attacks before a crit occurs), which can unfairly influence match results. To address this, Dota 2 employs a pseudo‑random distribution (PRD) that adjusts the probability after each attempt, ensuring critical hits appear around the expected frequency and preventing long streaks without a crit.

PRD works by increasing the effective chance of an event after each failure, so a 25% crit is more likely to occur around the fourth attack and rarely exceeds ten attacks without triggering, aligning the experience with player expectations.

However, such interventions add complexity for QA: testers must verify not only the raw probabilities but also the correctness and intended effect of the PRD algorithm, ensuring it functions as designed without unintended side effects.

Examples from World of Warcraft’s orange‑item system illustrate how PRD‑like mechanics can unintentionally favor players who have already received a reward, leading to bugs where probability accumulation behaves incorrectly, ultimately requiring bug fixes after player feedback.

Therefore, QA must design comprehensive test cases and simulations to evaluate both the statistical distribution and the impact of any probability‑adjusting mechanisms, confirming they achieve their intended purpose without introducing new issues.

Does the intervention work as intended?

Does it achieve the expected effect?

Does it produce any unintended consequences?

Future articles will continue to share case studies and best practices for probability testing in game development.