Esports: The Chess of the 21st Century Pluss, Matthew A; Bennett, Kyle J M; Novak, Andrew R ...
Frontiers in psychology,
01/2019, Volume:
10
Journal Article
Peer reviewed
Open access
For many decades, researchers have explored the true potential of human achievement. The expertise field has come a long way since the early works of de Groot (1965) and Chase and Simon (1973). Since ...then, this inquiry has expanded into the areas of music, science, technology, sport, academia, and art. Despite the vast amount of research to date, the capability of study methodologies to truly capture the nature of expertise remains questionable. Some considerations include (i) the individual bias in the retrospective recall of developmental activities, (ii) the ability to develop ecologically valid tasks, and (iii) difficulties capturing the influence of confounding factors on expertise. This article proposes that expertise research in electronic sports (esports) presents an opportunity to overcome some of these considerations. Esports involves individuals or teams of players that compete in video game competitions via human-computer interaction. Advantages of applying the expert performance approach in esports include (i) developmental activities are objectively tracked and automatically logged online, (ii) the constraints of representative tasks correspond with the real-world environment of esports performance, and (iii) expertise has emerged without the influence of guided systematic training environments. Therefore, this article argues that esports research provides an ideal opportunity to further advance research on the development and assessment of human expertise.
To investigate the construct and discriminant validity of a video-based decision-making assessment for talent identification in youth soccer.
Observational study.
A total of 328 academy youth soccer ...players (tier one, tier two, and tier three) from three developmental stages (late childhood, early adolescence, and mid-adolescence) participated in this study. The control group consisted of 59 youth athletes with no soccer experience in the last five years. Players completed a video-based decision-making assessment on an iPad, with response accuracy and response time recorded for various attacking situations (2 vs. 1, 3 vs. 1, 3 vs. 2, 4 vs. 3, and 5 vs. 3).
The video-based decision-making assessment showed some construct validity. Response times were significantly faster in early and mid-adolescent players when compared to those in the late childhood group. Furthermore, an overall decline in decision-making performance (i.e. decrease in response accuracy and increase in response time) was observed from the 2 vs. 1 to the 4 vs. 3 situations. The video-based decision-making assessment lacked discriminant validity as minimal differences between academies were evident for response accuracy and response time. Only response accuracy was able to discriminate youth academy soccer players from the control group to some extent.
Coaches and sporting professionals should apply caution when interpreting data from practical, video-based decision-making assessments. There is currently limited conclusive evidence supporting the effectiveness of these assessments for talent identification.
Performance analysis is a well-established discipline in sports science, supported by decades of research. Comparatively, performance analysis in electronic sports (esports) is limited. Therefore, ...there is an opportunity to accelerate performance outcomes in esports by applying methods grounded in sports science. This study adopted a coach-centred approach to model performance at the 2018 League of Legends World Championship. Three expert coaches rated the proposed relationship between 43 variables and match outcomes in professional League of Legends competition using a Likert scale (1–10). The Likert scale was anchored with ‘no relationship’ at 1 and ‘very strong relationship’ at 10. The coaches’ median ratings were calculated for each variable. Variables with a median score ≥6 were retained for analyses. A total of 14 variables were collected from the 2018 League of Legends World Championship (n = 119) matches via video annotations and match histories. Generalized Linear Mixed Effects Models with binomial logit link function were implemented with respect to the Blue Side winning or losing the match, and individual teams were specified as random effects. Variables were screened for multicollinearity before using a step-up approach. The best model of performance included Tower Percentage (p = 0.006) and Number of Inhibitors (p = 0.029). This model achieved classification accuracy of 95.8%. While Tower Percentage and Number of Inhibitors contributed to winning or losing, further research is required to determine effective strategies to improve these variables, to understand the relevance of these variables across the complete time-series of the match, and to determine whether performance indicators remain stable across game updates.
The current study aimed to investigate the performance characteristics that discriminate Australian youth soccer players according to their academy status. A total of 165 youth soccer players ...participated in this study and were sub-divided into either an early adolescence (n = 92, age = 13.0 ± 0.6 years) or mid-adolescence (n = 73 age = 14.8 ± 0.6 years) group. Players completed multifactorial assessments of anthropometry, motor competence, physical fitness, decision-making and psychological traits. Statistical significance was set at p ≤ 0.05. Multivariate analysis of variance identified dynamic balancing ability (both age groups), object manipulation (mid-adolescence), lateral jumping ability (both age groups), linear speed over 5 m (both age groups), change of direction skill (mid-adolescence), intermittent aerobic endurance (mid-adolescence) and total response time on a decision-making assessment (early adolescence) to discriminate academy status. Interestingly, a binomial logistical regression showed that a 0.1 s decrease in sprint time (i.e. running faster) increased the odds of a player belonging to a tier one academy by 19% and 47% for early and mid-adolescent players, respectively. Overall, performance in the motor competence and physical fitness assessments were in favour of the tier one academy players. These findings are indicative of a potential selection bias in the Australian talent pool or a training effect whereby tier one academy programmes emphasise the development of physical attributes. However, future research is required to further substantiate this in a larger sample of youth soccer players from other playing regions within Australia.
This study followed a longitudinal design to objectively monitor practice behaviors of professional and semi-professional esports players over a year. Publicly available data were collected from 30 ...male Counter-Strike: Global Offensive players (age: 23.76 ± 2.88y). Players were classified into two groups: professional (n = 18) or semi-professional (n = 12). The total hours of practice (all game-specific practice) and the competitive hours of practice (time spent in competitive modes only) were collected weekly. Generalised Estimating Equations were used to compare the practice behaviors of the two groups. Professional and semi-professional esports players completed an average of 30.9 ± 8.2 h and 24.7 ± 3.6 h per week of total game-specific practice, respectively, and 19.6 ± 6.9 and 15.0 ± 2.7 h of competitive practice, respectively. A significant week∗group interaction was observed for total practice time (Wald χ2 = 9.48, p = 0.002) and total competition practice time (Wald χ2 = 7.54, p = 0.006). Specifically, professional esports players completed 6.6 (SE = 2.2) hr per week more of total practice hours than semi-professional players, of which 4.8 (SE = 1.8) hr were competitive practice. This sample of expert esports performers complete high volumes of practice which can be monitored via publicly accessible repositories.
•Public data repositories can be used to objectively and longitudinally monitor practice behaviors of esports players.•Practice behaviors of professional and semi-professional esports players can involve >30 h/week of practice.•Professional players accumulate more hours of practice per week than semi-professional players.•A high volume of expert esports practice is performed in competitive, game-specific activities.
This study aimed to investigate the test-retest reliability and discriminant validity of the Mobalytics Proving Ground™ assessment for League of Legends. Forty participants (age: 24.15 ± 3.68 y, sex: ...male = 31, female = 9) were a priori classified into two expertise groups: (1) esports players (age: 22.98 ± 3.64 y, sex: male = 18, female = 2), and (2) controls (age: 25.31 ± 3.42 y, sex: male = 13, female = 7). Participants completed three separate trials (60 s each) online. To assess test-retest reliability, variables displaying normal distributions were analysed using intraclass correlation coefficient (ICC) estimates for two-way mixed-effects models with 95% confidence intervals. The average ICC for all the independent variables in the esports group and control group were moderate (ICC esports = 0.53 and ICC control = 0.72). The average 95% confidence intervals for the independent variables in the esports and control group were ICC = 0.30–0.75 and ICC = 0.55–0.86, respectively. A Friedman test revealed an effect size of 0.11 in the esports group and 0.07 in the control group. In terms of discriminant validity, there were significant differences for 17 variables when comparing the best scores of each group. Overall, the Mobalytics Proving Ground™ assessment used in the current study can, to some extent, distinguish esports players from controls.
Purpose: This study adopted an exploratory approach to investigate the use of small-sided games as a talent identification tool to determine youth soccer players' skill proficiency. Methods: A total ...of 73 male youth soccer players (age = 13.3 ± 1.2 years) were subdivided into two groups in accordance with their playing level (high-level: n = 36, low-level: n = 37). Within their levels, players completed 4 vs. 4 small-sided games on a 30 × 20 m playing surface under two conditions (condition 1: 5 × 3 min, condition 2: 3 × 5 min). Attempted and completed skill involvements were analysed using retrospective video analysis. Skill proficiency was determined as the total completed involvements relative to amount attempted. Results: Repeated measures multivariate analysis of variance identified that high-level players displayed a significantly greater number of attempted and completed passes, touches, and total skill involvements compared with low-level players. Only the number of attempted passes and total involvements differed between conditions for high-level players. High-level players' total skill proficiency was significantly greater than their lower level counterparts. Conclusion: This study supports the use of small-sided games as a tool to assess soccer-specific skill proficiency, which coaches and sporting practitioners can apply in a talent identification setting.
Purpose: Monitoring athletic development in youth soccer can help sporting professionals monitor athletic development and evaluate the effectiveness of training interventions. However, long-term ...follow up in talent development programmes in youth soccer is complicated. Therefore, the aim of this study was to forecast the developmental trajectories of explosive leg power in youth soccer players. Methods: Mixed effects regression models were used to forecast explosive leg power (countermovement (CMJ) and standing broad jump (SBJ)) in a longitudinal sample of 2201 youth soccer players from two high level Belgian acedemies and the Belgian National teams. Players were aged between 6 and 20 years (mean age = 13.04 ± 3.18 y) and were subdivided into three age cohorts (6-10, 11-15, and 16-20 y) for CMJ and two age cohorts (6-16, and 17-20 y) for SBJ. Results: This study was able to accurately forecast explosive leg power using different regression equations in each age cohort. Conclusions: Researchers, coaches, and sporting professionals can use these methods to either predict future explosive leg power from current performance measures, monitor the development of explosive leg power, or assess the effectiveness of training interactions aimed at altering predicted developmental trajectories.
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