2022, Zuber, Claudia, Sieghartsleitner, Roland, Zibung, Marc, Conzelmann, Achim

2020-09-09, Schmid, Michael J., Conzelmann, Achim, Zuber, Claudia

Talent selection in rowing is often solely based on anthropometric and performance variables, even though psychological characteristics are considered to be important contributors to successful talent development. Because multidimensional talent models and holistic theories represent the state-of-the-art in talent research, we aimed to find patterns connecting psychological and performance variables to future success in rowing. Therefore, 22 coaches rated the achievement-motivated behavior represented by the variables proactivity, ambition and commitment of 65 competitive to high-level athletes (Mage = 17.2 ± 1.55 years) for the past year (t1). Additionally, the athletes performed several 2,000 m ergometer tests during that same period. At t2 (30 months later), each rower’s performance was evaluated based on the success at different competitions. To examine the results, we used the person-oriented Linking of Clusters after removal of a Residue (LICUR) method to identify the relationships between the achievement-motivated behavior and ergometer results at t1 and the success at t2. The rowers could be assigned to five clusters. Although the highly motivated rowers were not the fastest on the ergometer at t1, they were more likely to be in highest performance level at t2 compared to the other clusters (OR = 3.5, p < .05). By contrast, all the ambitionless rowers and unmotivated rowers were either racing at national level or had dropped out. In conclusion, certain patterns of achievement-motivated behavior and current performance are associated with future success (30 months later). The consideration of achievement-motivated behavior in the selection of rowers seems promising in this context.

2019-10-22, Zuber, Claudia, Conzelmann, Achim

2018-02-21, Sieghartsleitner, Roland, Zuber, Claudia, Zibung, Marc, Conzelmann, Achim

2021-02-10, Stegmann, Pascal, Zuber, Claudia, Zibung, Marc, Lenze, Lars, Conzelmann, Achim, Sieghartsleitner, Roland

There is continuing discussion in talent research on the best approach to developing sporting expertise through learning activities during early sport participation. Among other concepts, the specialized sampling model describes a pathway between early specialization and early sampling and yields promising results in Swiss football. As successful constellations of early sport participation might be affected by sport-specific constraints (e.g., age of peak performance, selection pressure, and physiological/psychological requirements), other popular game sports may show similar promising pathways. This study investigates whether ice hockey, another popular game sport in Switzerland, shows similar successful constellations of early sport participation. A sample of 98 former Swiss junior national team players born between 1984 and 1994 reported on early sport participation through a retrospective questionnaire. Using the person-oriented Linking of Clusters after removal of a Residue (LICUR) method, volumes of in-club practice, free play, and activities besides ice hockey until 12 years of age were analyzed, along with player’s age at initial club participation. The results indicate that ice hockey enthusiasts with the most free play and above-average in-club practice had a greater chance of reaching professional level compared to other groups. This implies that high domain specificity with varied sampling experiences is the most promising approach to developing sporting expertise in ice hockey. As similar results were previously found in Swiss football, comparable sport-specific constraints might indeed require similar constellations of learning activities during early sport participation. Therefore, in popular game sports in Switzerland, the specialized sampling model seems to be most promising.

2020-02-24, Zuber, Claudia, Schmid, Michael J., Conzelmann, Achim

2019-06-27, Zuber, Claudia, Conzelmann, Achim

2021-01-20, Schmid, Michael J., Charbonne, Bryan, Conzelmann, Achim, Zuber, Claudia

2019-11-12, Sieghartsleitner, Roland, Zuber, Claudia, Zibung, Marc, Charbonnet, Bryan, Conzelmann, Achim

Recommended multidimensional models for talent selection are difficult to implement for practitioners in the field. Furthermore, their application has not been established from a scientific point of view, with a lack of clarity concerning how to integrate manifold test results with respect to loading, interaction, and compensation phenomena. Consequently, the question of powerful single predictors for future player status are still of interest within talent research in order to determine promising content for less extensive selection procedures. The aim of the current study is an immediate comparison of the prognostic validity of two frequently used areas within talent selection in youth football: general motor performance (e.g., speed and endurance) and specific motor performance (i.e., technical skills). Participants completed four general and four specific motor performance tests at early adolescence (U13/U14, 133 players) and middle adolescence (U16/U17, 85 players). The area under the curve (AUC) from the receiver operating characteristic was used to compare the prognostic validity of both motor performance areas (predicting U20 player status: professional vs. non-professional). Although no comparison at the four different age levels led to a significant difference (.07 ≤ p ≤ .65), there was a continuous superiority of specific over general motor performance in descriptive AUC values. These descriptive differences reached relevant extent within early adolescence (ΔAUCU13 = .09; ΔAUCU14 =.14) and were partially accounted for by the influence of biological maturation. In line with theoretical considerations and earlier research, these results provide further evidence of the superiority of specific over general motor performance in predicting future player status. Until the applicability of multidimensional models is further established, specific motor performance rather than general performance should be included in less extensive talent selection models, especially in early adolescence.

2019-02-11, Sieghartsleitner, Roland, Zuber, Claudia, Zibung, Marc, Conzelmann, Achim

Due to the tremendous popularity of youth football, practitioners in this domain face the ongoing question of the most effective solutions in early talent selection. Although the scientific community has suggested multidimensional models for some time, coach assessments and motor performance tests remain common. Earlier research has determined the strengths and weaknesses within these different approaches. The current investigation directly compared the effectiveness of each approach in talent selection (coach assessment vs. motor performance tests vs. multidimensional data). A sample of 117 youth football players, their parents, and coaches participated in multidimensional measurements in the U14 age category (coach assessments, motor performance tests, psychological characteristics, familial support, training history, and biological maturation). The area under the curve (AUC [95% CI]) from receiver operating characteristic indicated the prognostic validity of each approach in predicting U19 player status five years after the assessments (professional vs. non-professional). Motor performance tests (0.71 [0.58; 0.84]) showed a lower AUC than the multidimensional data (0.85 [0.76; 0.94], p = 0.02), whilst coach assessments did not differ from the two others (.82 [.74; .90]). Further, combined talent selection approaches, especially the use of coach assessments and multidimensional data together, were significantly better at predicting U19 player status (0.93 [0.87; 0.98], p = 0.02 vs. multidimensional data only). Although certain limitations may impede further insights (summation of data, skipped use of non-linear statistics), scientific claims for using multidimensionality within talent selection were confirmed to be fruitful. In particular, the combination of the subjective coaches' eye with scientific data may buffer the mutual weaknesses of these different approaches. Future research should focus on optimizing the output of promising multidimensional models. Knowledge of detailed values relating to specific dimensions within these models and the implementation of enhanced non-linear statistics may enable further improvements in the field of talent selection.