This exploratory study addressed a knowledge gap in the practice behaviours of ranked League of Legends players. We sourced data on a random sample of players (n = 913) from four competitive tiers and eight servers. We derived practice behaviour metrics from their last 100 matches. Challenger players had more matches per day, less variability in total hours, went fewer days without a match, and had the most matches in three- and seven-day blocks than other tiers. Servers with larger player pools tended to have more daily practice than comparatively smaller servers. We devised several hypotheses: (1) the volume of solo/duo ranked practice is associated with expertise, (2) more effective stress-coping strategies explain the lower variability in daily practice hours between tiers, (3) there is an interrelationship between player pool size, competitiveness, and practice behaviors, and (4) there are distinct patterns of practice associated with sustained participation or prolonged disengagement.

The impact of an avatar on real-world behaviors of users is known as the Proteus Effect. Different user avatar bond (UAB) aspects, including identifying, immersing, and compensating via the avatar, influence an individual’s Proteus Effect propensity. This study aimed to use machine learning (ML) classifiers to automate the prediction of those likely to experience Proteus Effect, based on their reports of identifying, immersing, and compensating with their avatar. Participants were 565 gamers (Mage = 29.3 years; SD = 10.6), assessed twice, six months apart, using the User-Avatar-Bond Scale and the Proteus Effect Scale. Tuned and untuned ML classifiers showed ML models could accurately identify individuals with higher Proteus Effect propensity, informed by a gamer’s reported UAB, age, and length of gaming involvement, both concurrently and longitudinally (i.e., six months later). Random forests performed better than other MLs, with avatar identification as the strongest predictor. This suggests higher Proteus Effect propensity for those with a stronger user-avatar bond, informing gamified health applications to introduce adaptive behavioral changes via the avatar. Prevention and practice implications are discussed.

Concerns have arisen regarding the possible addictive nature of videogames, resulting in the provisional recognition of internet gaming disorder (IGD) as a behavioural addiction. However, this classification remains controversial, with arguments abounding regarding its structure and nature. Therefore, the present study examined the network structure/characteristics of nine IGD symptoms and three distress behaviours (i.e. depression, anxiety, and stress). A sample of 462 adults (Mage = 30.8. [SDage = 9.23]; 320 males [69.3%]) were surveyed regarding their experience of IGD symptoms and distress behaviours using the Internet Gaming Disorder Short Form (IGDS9-SF) and the Depression, Anxiety and Stress Scale (DASS21) respectively. Subsequently, a network analysis was undertaken using R. IGD symptoms were found to be stable both cross-sectionally and over time. They were associated with, yet distinct, from, depression, anxiety, and stress. The most central symptoms within the network in terms of expected influence were tolerance, persistence, and stress. With a pathway between depression and mood modification providing the ‘bridge’ between IGD and Distress. The findings support the conceptualisation of IGD as a distinct construct (i.e. behavioural addiction). Further implications for the identification and treatment of IGD are discussed.