This study explores the intricate dynamics between player performance, reward frequency, and unpredictability in gaming experiences within the context of game refinement theory. It introduces three key concepts: m (inherent game risk), k (player skill level), and N (reward frequency concerning reinforcement learning), and investigates how their interplay influences player engagement and enjoyment. The research aims to contribute theoretical advancements in game design, user experience, and behavioral psychology by employing the motion-in-mind model. The findings reveal that highly skilled players (lower k) derive greater enjoyment due to their ability to navigate uncertainties efficiently, reaching an equilibrium where the measure of game refinement (GR) equals the measure of unpredictability (AD). The study examines how players transition from k=3 (average skill) to k=2 (high skill), leading to a heightened gaming perception akin to perfect players. The research uncovers relationships between player skill (k), game risk (m), and reward frequency (N). Furthermore, we extend previous research by observing the ratio of GR and AD values (denoted as ϕ) to explore the balance between entertainment and surprise in different game types, providing valuable perspectives for practitioners and academicians.