Talent Identification and Development in Sport

Höner, Oliver; Larkin, Paul; Leber, Thorsten; Feichtinger, Philip

doi:10.1007/978-3-031-03921-8_23

Cited by 6 publications

(5 citation statements)

References 130 publications

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“…In the sports context, potential can be analogous to talent. Talent is a latent ability that someone has from birth (Höner et al, 2023). Talent is the innate potential of family offspring (especially from parents) and forms the basis of real ability (Ribeiro et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Android-Based Athlete Talent Identification: Preliminary Study

Mahardika,

Siswantoyo,

Yachsie

2023

IJMRA

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This study aims to find out how much the coach's knowledge is related to talent identification in the community environment. This research is descriptive research with a survey method. The data collection technique used a closed questionnaire which totaled 12 valid statements with a reliability value of 0.897. The sample in this study was 40 sports trainers in Yogyakarta, who were taken using a purposive sampling technique, with the criteria of coaches being elite coaches and having coached athletes. The results of the research on how high the trainer's knowledge is to identify android-based talent are: "very low" at 5.00%, "low" at 22.50%, "enough" at 32.50%, "high" at 32.50%, and "very high" of 7.50%.

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Section: Discussionmentioning

confidence: 99%

Android-Based Athlete Talent Identification: Preliminary Study

Mahardika,

Siswantoyo,

Yachsie

2023

IJMRA

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“…In addition to these subjective assessments, talent identification and development (TID) research aims to provide scientifically sound objective diagnostic tools for assessing potential talent predictors. For example, decision-making skills are considered as relevant talent predictors in elite youth soccer ( 2 ) enabling players to select the best action in specific game situations ( 3 ). Empirical findings demonstrate that highly skilled youth players possess superior decision-making skills compared to lower-skilled players ( 4 , 5 ).…”

Section: Introductionmentioning

confidence: 99%

“…The present study focuses on the development of appropriate stimuli for such cognitive skills tests. In this context, the Expert Performance Approach ( 11 ) suggests that domain-specific stimuli provide the most reliable and valid diagnostic results ( 3 , 12 ). In line with that, a meta-analysis by Kalén et al ( 7 ) demonstrated a better suitability of sport-specific stimuli compared to non-specific stimuli for discriminating between higher and lower skilled athletes.…”

Section: Introductionmentioning

confidence: 99%

“…Do you have a good all-around view? Evaluation of a decisionmaking skills diagnostic tool using 360°videos and head-mounted displays in elite youth soccer Oliver Höner 1 * , Damir Dugandzic 2 , Thomas Hauser 2 , Michael Stügelmaier 3 , Nico Willig 3 and Florian Schultz 1 1 Institute of Sports Science, University of Tübingen, Tübingen, Germany, 2 DFB Academy, Frankfurt, Germany, 3 VfB Stuttgart 1893 AG, Stuttgart, Germany Elite youth players' decision-making skills are considered important predictors of adult performance in soccer. The presentation of 360°videos in head-mounted displays offers new potential for the diagnostic of these skills in talent development programs.…”

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confidence: 99%

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Do you have a good all-around view? Evaluation of a decision-making skills diagnostic tool using 360° videos and head-mounted displays in elite youth soccer

Höner,

Dugandzic,

Hauser

et al. 2023

Front. Sports Act. Living

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Elite youth players’ decision-making skills are considered important predictors of adult performance in soccer. The presentation of 360° videos in head-mounted displays offers new potential for the diagnostic of these skills in talent development programs. This study evaluated a new diagnostic tool using soccer-specific 360° videos for assessing decision-making skills in youth academy (YA) players. The evaluation consisted of players' subjective feedback as well as the analysis of diagnostic and prognostic validity. It was hypothesized that high-level YA players achieve better diagnostic results than regional-level players, and U19 outperform U17 players. Moreover, YA players' diagnostic results should be positively associated with future adult performance level. During the 2018/19 season, N = 48 youth players participated in the diagnostic procedures (split-half reliability r = .78). Participants were shown 54 videos which terminated when the central midfielder received a teammate's pass. Participants were then asked how to best continue playing. The subjective evaluation explored YA players' experiences with the diagnostic tool via quantitative ratings (e.g., “How exciting was the task?”, “How involved did you feel in the game situation?”) and additional interviews. Diagnostic validity was examined in a balanced cross-sectional 2 × 2-design (performance level x age group) and prognostic validity in a 3-year prospective design. Sensitivity and case-by-case analyses completed the evaluation. The YA players provided positive quantitative ratings regarding their experienced immersion into the environment. Players’ qualitative feedback indicated general acceptance of the diagnostic tool as well as it offered recommendations for improvements. Confirming the diagnostic validity, ANOVA revealed significant main effects for performance level (p < .001, η2 = .29) and age group (p < .01, η2 = .14). Contributing to the prognostic validity, the diagnostic results discriminated between YA players achieving a higher and a lower adult performance level (“League 1–4” vs. “League 5 or below”) in adulthood (p < .05; d = 0.80). A ROC curve and the AUC showed that the correct assignment to the adult performance levels is possible with a 71% probability. YA players with a high decision-making accuracy had a six times higher chance of playing in “League 1–4”. The results demonstrated empirical evidence for the new diagnostic tool in terms of YA players' acceptance and validity coefficients exceeding effect sizes of former studies. The technology provides opportunities to test soccer-specific situations demanding an all-around view that were not testable in former experimental settings. Further technological advancements will enable the realization of improvements recommended by the players. Nonetheless, case-by-case analyses suggest caution in using such a diagnostic as a selection tool in talent development programs.

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“…If we assume that such data are generated by samples from a set of underlying latent factors, the usual goal is to identify those factors and describe them using information about the data‐generating process (normally by means of a factor loading matrix). For instance, multidimensional, complex performance data on young, promising players in team sports such as soccer are assessed in order to get promising insights into their performance profile [2, 3]. Considering the complexity as well as the abundance of data that are collected here [4], one needs to identify relationships between the assessed performance outcomes and reduce them to the relevant dimensions reflected by the assessed data.…”

Section: Introductionmentioning

confidence: 99%

A deep learning factor analysis model based on importance‐weighted variational inference and normalizing flow priors: Evaluation within a set of multidimensional performance assessments in youth elite soccer players

Kilian

Leyhr

Urban

et al. 2023

Statistical Analysis

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Exploratory factor analysis is a widely used framework in the social and behavioral sciences. Since measurement errors are always present in human behavior data, latent factors, generating the observed data, are important to identify. While most factor analysis methods rely on linear relationships in the data‐generating process, deep learning models can provide more flexible modeling approaches. However, two problems need to be addressed. First, for interpretation, scaling assumptions are required, which can be (at least) cumbersome for deep generative models. Second, deep generative models are typically not identifiable, which is required in order to identify the underlying latent constructs. We developed a model that uses a variational autoencoder as an estimator for a complex factor analysis model based on importance‐weighted variational inference. In order to receive interpretable results and an identified model, we use a linear factor model with identification constraints in the measurement model. To maintain the flexibility of the model, we use normalizing flow latent priors. Within the evaluation of performance measures in a talent development program in soccer, we found more clarity in the separation of the identified underlying latent dimensions with our models compared to traditional PCA analyses.

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Talent Identification and Development in Sport

Cited by 6 publications

References 130 publications

Android-Based Athlete Talent Identification: Preliminary Study

Android-Based Athlete Talent Identification: Preliminary Study

Do you have a good all-around view? Evaluation of a decision-making skills diagnostic tool using 360° videos and head-mounted displays in elite youth soccer

A deep learning factor analysis model based on importance‐weighted variational inference and normalizing flow priors: Evaluation within a set of multidimensional performance assessments in youth elite soccer players

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