Divergent production in gifted adolescents using timed vs. untimed stimuli with creative prompting

Although creativity and expertise are related, they are nonetheless very different things. Expertise does not usually require creativity, but creativity generally does require a certain level of expertise. There are similarities in the relationships of both expertise and creativity to domains, however. Research has shown that just as expertise in one domain does not predict expertise in other, unrelated domains, creativity in one domain does not predict creativity in other, unrelated domains. People may be expert, and people may be creative, in many domains, or they may be expert, or creative, in few domains or none at all, and one cannot simply transfer expertise, or creativity, from one domain to another, unrelated domain. The domain specificity of creativity matters crucially for creativity training, creativity assessment, creativity research, and creativity theory. The domain specificity of creativity also means that interdisciplinary thinking, interdisciplinary collaboration, and interdisciplinary creativity are even more important than one would assume if creativity were domain general.     

Serious leisure: the sport of target shooting and leisure satisfaction

This study is designed to identify the underlying satisfaction dimensions gained through engagement in target shooting as a serious leisure pastime. The instrument design was based on the Leisure Satisfaction Scale. A total of 5502 useable surveys were collected through the Internet and a website related to target shooting and gun ownership. The results revealed eight dimensions: self-actualization, social interaction, location aesthetic, respite, physical benefit, connection and hedonic pleasure, which supplied the primary satisfiers of engaging in target shooting as a serious leisure activity. The demographic profile and dimensions of satisfaction may serve to dispel commonly held stereotypes of shooting hobbyists and those involved in gun ownership. The findings have important implications for leisure researchers and practitioners, to industry, both leisure and firearm related, and the target shooting community at large. This study adds to the literature of both target shooting and serious leisure.     

Efficacy of massage on muscle soreness,perceived recovery,physiological restoration and physical performance in male bodybuilders

It is believed that sport massage after intensive exercise might improve power and perceptual recovery in athletes. However, few studies have been done in this area. This study aimed to examine the effect of massage on the performance of bodybuilders. Thirty experienced male bodybuilders were randomly assigned to either a massage group (n = 15) or a control group (n = 15). Both groups performed five repetition sets at 75–77% of 1RM of knee extensor and flexor muscle groups. The massage group then received a 30-min massage after the exercise protocol while the control group maintained their normal passive recovery. Criteria under investigation included: plasma creatine kinase (CK) level, agility test, vertical jump test, isometric torque test, and perception of soreness. All variables were measured over 6 time periods: baseline, immediately after the DOMS inducing protocol, right after the massage, and 24, 48, and 72 h after the massage. Both groups showed significant (P < .001) decreases in jumping, agility performance, and isometric torque, but significant (P < .001) increases in CK and muscle soreness levels. The massage group in general demonstrated a better recovery rate. As such, a post-exercise massage session can improve the exercise performance and recovery rate in male bodybuilders after intensive exercise.     

Tier-specific evolution of match performance characteristics in the English Premier League: it’s getting tougher at the top

This study investigated the evolution of physical and technical performances in the English Premier League (EPL), with special reference to league ranking. Match performance observations (n = 14,700) were collected using a multiple-camera computerised tracking system across seven consecutive EPL seasons (2006–07 to 2012–13). Final league rankings were classified into Tiers: (A) 1st–4th ranking (n = 2519), (B) 5th–8th ranking (n = 2965), (C) 9th–14th ranking (n = 4448) and (D) 15th–20th ranking (n = 4768). Teams in Tier B demonstrated moderate increases in high-intensity running distance while in ball possession from the 2006–07 to 2012–13 season (P < 0.001; effect size [ES]: 0.68), with Tiers A, C and D producing less pronounced increases across the same period (P < 0.005; ES: 0.26, 0.41 and 0.33, respectively). Large increases in sprint distance were observed from the 2006–07 to 2012–13 season for Tier B (P < 0.001; ES: 1.21), while only moderate increases were evident for Tiers A, C and D (P < 0.001; ES: 0.75, 0.97 and 0.84, respectively). Tier B demonstrated large increases in the number of passes performed and received in 2012–13 compared to 2006–07 (P < 0.001; ES: 1.32–1.53) with small-to-moderate increases in Tier A (P < 0.001; ES: 0.30–0.38), Tier C (P < 0.001; ES: 0.46–0.54) and Tier D (P < 0.001; ES: 0.69–0.87). The demarcation line between 4th (bottom of Tier A) and 5th ranking (top of Tier B) in the 2006–07 season was 8 points, but this decreased to just a single point in the 2012–13 season. The data demonstrate that physical and technical performances have evolved more in Tier B than any other Tier in the EPL and could indicate a narrowing of the performance gap between the top two Tiers.     

Discriminating talent-identified junior Australian football players using a video decision-making task

This study examined if a video decision-making task could discriminate talent-identified junior Australian football players from their non-talent-identified counterparts. Participants were recruited from the 2013 under 18 (U18) West Australian Football League competition and classified into two groups: talent-identified (State U18 Academy representatives; n = 25; 17.8 ± 0.5 years) and non-talent-identified (non-State U18 Academy selection; n = 25; 17.3 ± 0.6 years). Participants completed a video decision-making task consisting of 26 clips sourced from the Australian Football League game-day footage, recording responses on a sheet provided. A score of “1” was given for correct and “0” for incorrect responses, with the participants total score used as the criterion value. One-way analysis of variance tested the main effect of “status” on the task criterion, whilst a bootstrapped receiver operating characteristic (ROC) curve assessed the discriminant ability of the task. An area under the curve (AUC) of 1 (100%) represented perfect discrimination. Between-group differences were evident (P < 0.05) and the ROC curve was maximised with a score of 15.5/26 (60%) (AUC = 89.0%), correctly classifying 92% and 76% of the talent-identified and non-talent-identified participants, respectively. Future research should investigate the mechanisms leading to the superior decision-making observed in the talent-identified group.     

Explaining match outcome in elite Australian Rules football using team performance indicators

The relationships between team performance indicators and match outcome have been examined in many team sports, however are limited in Australian Rules football. Using data from the 2013 and 2014 Australian Football League (AFL) regular seasons, this study assessed the ability of commonly reported discrete team performance indicators presented in their relative form (standardised against their opposition for a given match) to explain match outcome (Win/Loss). Logistic regression and decision tree (chi-squared automatic interaction detection (CHAID)) analyses both revealed relative differences between opposing teams for “kicks” and “goal conversion” as the most influential in explaining match outcome, with two models achieving 88.3% and 89.8% classification accuracies, respectively. Models incorporating a smaller performance indicator set displayed a slightly reduced ability to explain match outcome (81.0% and 81.5% for logistic regression and CHAID, respectively). However, both were fit to 2014 data with reduced error in comparison to the full models. Despite performance similarities across the two analysis approaches, the CHAID model revealed multiple winning performance indicator profiles, thereby increasing its comparative feasibility for use in the field. Coaches and analysts may find these results useful in informing strategy and game plan development in Australian Rules football, with the development of team-specific models recommended in future.