A uniform approach for advancing athlete assessment: A tutorial on the Lens Model.

In athlete assessment, coaches or scouts typically judge athletes by observing and combining information about their attributes. However, how accurate is the expert's eye in combining this information, and can its accuracy be improved? To address these questions, this paper introduces the Lens Model, a framework for studying human judgment that has been widely successful in other performance domains. Since the framework offers both theoretical and practical benefits and is new to sports scientists and practitioners, our paper is presented in the form of a tutorial. First, we discuss the need for the Lens Model in sports; second, we demonstrate its proven value outside of sports. Third, we provide a conceptual explanation of the Lens Model, detailing, among other aspects, how experts' judgmental policies can be modeled and how judgmental accuracy can be determined and evaluated. This is followed by an empirical example: a study on the judgments of soccer scouts, along with suggestions to improve their accuracy. To inspire further Lens Model research across sports, we conclude with prospective research directions.

Predicting special forces dropout via explainable machine learning.

Selecting the right individuals for a sports team, organization, or military unit has a large influence on the achievements of the organization. However, the approaches commonly used for selection are either not reporting predictive performance or not explainable (i.e., black box models). In the present study, we introduce a novel approach to selection research, using various machine learning models. We examined 274 special forces recruits, of whom 196 dropped out, who performed a set of physical and psychological tests. On this data, we compared four machine learning models on their predictive performance, explainability, and stability. We found that a stable rule-based (SIRUS) model was most suitable for classifying dropouts from the special forces selection program. With an averaged area under the curve score of 0.70, this model had good predictive performance, while remaining explainable and stable. Furthermore, we found that both physical and psychological variables were related to dropout. More specifically, a higher score on the 2800 m time, need for connectedness, and skin folds was most strongly associated with dropping out. We discuss how researchers and practitioners can benefit from these insights in sport and performance contexts.

Resilience in sports: a multidisciplinary, dynamic, and personalized perspective.

Athletes are exposed to various psychological and physiological stressors, such as losing matches and high training loads. Understanding and improving the resilience of athletes is therefore crucial to prevent performance decrements and psychological or physical problems. In this review, resilience is conceptualized as a dynamic process of bouncing back to normal functioning following stressors. This process has been of wide interest in psychology, but also in the physiology and sports science literature (e.g. load and recovery). To improve our understanding of the process of resilience, we argue for a collaborative synthesis of knowledge from the domains of psychology, physiology, sports science, and data science. Accordingly, we propose a multidisciplinary, dynamic, and personalized research agenda on resilience. We explain how new technologies and data science applications are important future trends (1) to detect warning signals for resilience losses in (combinations of) psychological and physiological changes, and (2) to provide athletes and their coaches with personalized feedback about athletes' resilience.

Dynamical Systems Principles Underlying Resistance, Resilience, and Growth.

Resilience has traditionally been conceptualized as resisting, bouncing back from, and growing from a stressor. However, recent literature has pointed out that these are different processes with bouncing back coming closest to the literal meaning of the term resilience. To detect whether an individual demonstrates one of these three stressor-responses, different analysis strategies have been suggested. However, deeper theoretical explanations for how patterns of resistance, resilience, and growth come about, have been lacking. To address this gap, this paper proposes a coherent framework based on a dynamical systems approach. We first discuss how adapting to stressors emerges from complex interactions between multiple levels of organization within the system. These interactions unfold on different time scales: What appears as resistance on slower or macro scales may actually consist of bouncing back at micro scales that change much faster. Next, we discuss how the different trajectories that distinguish resistance, resilience, and growth can be understood through attractor dynamics. We address the fixed-point attractors, which are commonly used in the resilience literature to detect early warning signals of bifurcations following resilience losses. Moreover, we describe the implications of limit cycles and strange attractors which capture multiple pathways to adapt to stressors that can lead to growth patterns. We conclude that resisting, bouncing back from, or growing from a stressor represent distinct phenomena that can be distinguished both empirically and theoretically from a dynamical systems perspective. These distinctions may drive future development of theoretical models, empirical measurements, and theory-driven interventions.

Monitoring the recovery-stress states of athletes: Psychometric properties of the acute recovery and stress scale and short recovery stress scale among Dutch and Flemish athletes.

The Acute Recovery and Stress Scale (ARSS) and the Short Recovery and Stress Scale (SRSS) are recently-introduced instruments to monitor recovery and stress processes in athletes. In this study, our aims were to replicate and extend previous psychometric assessments of the instruments, by incorporating recovery and stress dimensions into one model. Therefore, we conducted five confirmatory factor analyses (CFA) and determined structural validity, internal consistency, and construct validity. Dutch and Flemish athletes (N = 385, 213 females, 170 males, 2 others, 21.03 ± 5.44 years) completed the translated ARSS and SRSS, the Recovery Stress Questionnaire for Athletes (RESTQ-Sport-76), the Rating of Perceived Exertion (RPE) and the Total Quality of Recovery (TQR). There was a good model fit for the replicated CFA, sub-optimal model fit for the models that incorporated recovery and stress into one model, and satisfactory internal consistency (α=.75 - .87). The correlations within and between the ARSS and SRSS, as well as between the ARSS/SRSS and the RESTQ-Sport-76 (r = .31 - -.77 for the ARSS, r = .28 - -.63 for the SRSS), the RPE (r = .19 - -.23), and the TQR (r = .63 - -.63) also supported construct validity. The combined findings support the use of the ARSS and SRSS to assess stress and recovery in sports-related research and practice.

How the Complexity of Psychological Processes Reframes the Issue of Reproducibility in Psychological Science.

In the past decade, various recommendations have been published to enhance the methodological rigor and publication standards in psychological science. However, adhering to these recommendations may have limited impact on the reproducibility of causal effects as long as psychological phenomena continue to be viewed as decomposable into separate and additive statistical structures of causal relationships. In this article, we show that (a) psychological phenomena are patterns emerging from nondecomposable and nonisolable complex processes that obey idiosyncratic nonlinear dynamics, (b) these processual features jeopardize the chances of standard reproducibility of statistical results, and (c) these features call on researchers to reconsider what can and should be reproduced, that is, the psychological processes per se, and the signatures of their complexity and dynamics. Accordingly, we argue for a greater consideration of process causality of psychological phenomena reflected by key properties of complex dynamical systems (CDSs). This implies developing and testing formal models of psychological dynamics, which can be implemented by computer simulation. The scope of the CDS paradigm and its convergences with other paradigms are discussed regarding the reproducibility issue. Ironically, the CDS approach could account for both reproducibility and nonreproducibility of the statistical effects usually sought in mainstream psychological science.

The Dynamics Underlying the Rise of Star Performers.

Across different domains, there are 'star performers' who are able to generate disproportionate levels of performance output. To date, little is known about the model principles underlying the rise of star performers. Here, we propose that star performers' abilities develop according to a multi-dimensional, multiplicative and dynamical process. Based on existing literature, we defined a dynamic network model, including different parameters functioning as enhancers or inhibitors of star performance. The enhancers were multiplicity of productivity, monopolistic productivity, job autonomy, and job complexity, whereas productivity ceiling was an inhibitor. These enhancers and inhibitors were expected to influence the tail-heaviness of the performance distribution. We therefore simulated several samples of performers, thereby including the assumed enhancers and inhibitors in the dynamic networks and compared their tail-heaviness. Results showed that the dynamic network model resulted in heavier and lighter tail distributions, when including the enhancer- and inhibitor-parameters, respectively. Together, these results provide novel insights into the dynamical principles that give rise to star performers in the population.

Nonergodicity in Load and Recovery: Group Results Do Not Generalize to Individuals.

PURPOSE: The study of load and recovery gained significant interest in the last decades, given its important value in decreasing the likelihood of injuries and improving performance. So far, findings are typically reported on the group level, whereas practitioners are most often interested in applications at the individual level. Hence, the aim of the present research is to examine to what extent group-level statistics can be generalized to individual athletes, which is referred to as the "ergodicity issue." Nonergodicity may have serious consequences for the way we should analyze, and work with, load and recovery measures in the sports field. METHODS: The authors collected load, that is, rating of perceived exertion × training duration, and total quality of recovery data among youth male players of a professional football club. This data were collected daily across 2 seasons and analyzed on both the group and the individual level. RESULTS: Group- and individual-level analysis resulted in different statistical outcomes, particularly with regard to load. Specifically, SDs within individuals were up to 7.63 times larger than SDs between individuals. In addition, at either level, the authors observed different correlations between load and recovery. CONCLUSIONS: The results suggest that the process of load and recovery in athletes is nonergodic, which has important implications for the sports field. Recommendations for training programs of individual athletes may be suboptimal, or even erroneous, when guided by group-level outcomes. The utilization of individual-level analysis is key to ensure the optimal balance of individual load and recovery.

How soccer scouts identify talented players.

Scouts of soccer clubs are often the first to identify talented players. However, there is a lack of research on how these scouts assess and predict overall soccer performance. Therefore, we conducted a large-scaled study to examine the process of talent identification among 125 soccer scouts. Through an online self-report questionnaire, scouts were asked about (1) the players' age at which they can predict players' soccer performance, (2) the attributes they consider relevant, and (3) the extent to which they predict performance in a structured manner. The most important results are as follows. First, scouts who observed 12-year-old and younger players perceived they could predict at older ages (13.6 years old, on average) whether a player has the potential to become a professional soccer player. This suggests that scouts are aware of the idea that early indicators of later performance are often lacking, yet do advise on selection of players at younger ages. Second, when identifying talented players, scouts considered more easily observable attributes, such as technical attributes. However, scouts described these often in a broad sense rather than in terms of specific predictors of future performance. Finally, scouts reported that they assess attributes of players in a structured manner. Yet, they ultimately based their prediction (i.e. final score) on an intuitive integration of different performance attributes, which is a suboptimal strategy according to existing literature. Taken together, these outcomes provide specific clues to improve the reliability and validity of the scouting process. HighlightsBased on a large sample of soccer scouts, we examine three issues that are important in the process of identifying talented soccer players: The age at which good performance predictions can be made, which attributes are relevant predictors, and how performance predictions are formed.Scouts who observe players in young age cohorts believe that the age at which they can predict performance is older than the players they typically scout, suggesting that they are aware that early indicators of performance are often lacking.Technical performance attributes are considered as most important performance predictors by scouts, but these are often described in a broad - rather than specific - sense.Scouts indicate that they predict performance in a structured manner, but form their overall performance prediction on an intuitive integration of different performance attributes, which can be a suboptimal approach.

The Relation Between Complexity and Resilient Motor Performance and the Effects of Differential Learning.

Complex systems typically demonstrate a mixture of regularity and flexibility in their behavior, which would make them adaptive. At the same time, adapting to perturbations is a core characteristic of resilience. The first aim of the current research was therefore to test the possible relation between complexity and resilient motor performance (i.e., performance while being perturbed). The second aim was to test whether complexity and resilient performance improve through differential learning. To address our aims, we designed two parallel experiments involving a motor task, in which participants moved a stick with their non-dominant hand along a slider. Participants could score points by moving a cursor as fast and accurately as possible between two boxes, positioned on the right- and left side of the screen in front of them. In a first session, we determined the complexity by analyzing the temporal structure of variation in the box-to-box movement intervals with a Detrended Fluctuation Analysis. Then, we introduced perturbations to the task: We altered the tracking speed of the cursor relative to the stick-movements briefly (i.e., 4 s) at intervals of 1 min (Experiment 1), or we induced a prolonged change of the tracking speed each minute (Experiment 2). Subsequently, participants had three sessions of either classical learning or differential learning. Participants in the classical learning condition were trained to perform the ideal movement pattern, whereas those in the differential learning condition had to perform additional and irrelevant movements. Finally, we conducted a posttest that was the same as the first session. In both experiments, results showed moderate positive correlations between complexity and points scored (i.e., box touches) in the perturbation-period of the first session. Across the two experiments, only differential learning led to a higher complexity index (i.e., more prominent patterns of pink noise) from baseline to post-test. Unexpectedly, the classical learning group improved more in their resilient performance than the differential learning group. Together, this research provides empirical support for the relation between complexity and resilience, and between complexity and differential learning in human motor performance, which should be examined further.

Injury Prediction in Competitive Runners With Machine Learning.

PURPOSE: Staying injury free is a major factor for success in sports. Although injuries are difficult to forecast, novel technologies and data-science applications could provide important insights. Our purpose was to use machine learning for the prediction of injuries in runners, based on detailed training logs. METHODS: Prediction of injuries was evaluated on a new data set of 74 high-level middle- and long-distance runners, over a period of 7 years. Two analytic approaches were applied. First, the training load from the previous 7 days was expressed as a time series, with each day's training being described by 10 features. These features were a combination of objective data from a global positioning system watch (eg, duration, distance), together with subjective data about the exertion and success of the training. Second, a training week was summarized by 22 aggregate features, and a time window of 3 weeks before the injury was considered. RESULTS: A predictive system based on bagged XGBoost machine-learning models resulted in receiver operating characteristic curves with average areas under the curves of 0.724 and 0.678 for the day and week approaches, respectively. The results of the day approach especially reflect a reasonably high probability that our system makes correct injury predictions. CONCLUSIONS: Our machine-learning-based approach predicts a sizable portion of the injuries, in particular when the model is based on training-load data in the days preceding an injury. Overall, these results demonstrate the possible merits of using machine learning to predict injuries and tailor training programs for athletes.

Facing Repeated Stressors in a Motor Task: Does it Enhance or Diminish Resilience?

The aim of the present research is to test whether resilience in a motor task enhances or diminishes when encountering stressors. We conducted a lateral movement task during which we induced stressors and tracked the movement accuracy of each participant over time. Stressors corresponded to organismic constraints (i.e., visual occlusion), task constraints (i.e., movement sensitivity), or both types of constraints in an alternating pattern. In order to determine resilience, we introduced a measure combining the strength of a stressor and the relaxation time. Across the three conditions, we found that resilience was enhanced rather than diminished over time. This supports the notion that stressors in the form of constraint alterations can be beneficial to human motor performance.

The validity of small-sided games in predicting 11-vs-11 soccer game performance.

Predicting performance in soccer games has been a major focus within talent identification and development. Past research has mainly used performance levels, such as elite vs. non-elite players, as the performance to predict (i.e. the criterion). Moreover, these studies have mainly focused on isolated performance attributes as predictors of soccer performance levels. However, there has been an increasing interest in finer grained criterion measures of soccer performance, as well as representative assessments at the level of performance predictors. In this study, we first determined the degree to which 7-vs-7 small-sided games can be considered as representative of 11-vs-11 games. Second, we assessed the validity of individual players' small-sided game performance in predicting their 11-vs-11 game performance on a continuous scale. Moreover, we explored the predictive validity for 11-vs-11 game performance of several physiological and motor tests in isolation. Sixty-three elite youth players of a professional soccer academy participated in 11 to 17 small-sided games and six 11-vs-11 soccer games. In-game performance indicators were assessed through notational analysis and combined into an overall offensive and defensive performance measure, based on their relationship with game success. Physiological and motor abilities were assessed using a sprint, endurance, and agility test. Results showed that the small-sided games were faster paced, but representative of 11-vs-11 games, with the exception of aerial duels. Furthermore, individual small-sided game performance yielded moderate predictive validities with 11-vs-11 game performance. In contrast, the physiological and motor tests yielded small to trivial relations with game performance. Altogether, this study provides novel insights into the application of representative soccer assessments and the use of continuous criterion measures of soccer performance.

Predicting Resilience Losses in Dyadic Team Performance.

In the current study, we applied the dynamical systems approach to obtain novel insights into resilience losses. Dyads (n = 42) performed a lateral rhythmical pointing (Fitts) task. To induce resilience losses and transitions in performance, dyads were exposed to ascending and descending scoring scenarios. To assess changes in the complexity of the dyadic pointing performance, reflecting their resilience, we performed cross-recurrence quantification analyses. Then, we tested for temporal patterns indicating resilience losses. We applied lag 1 autocorrelations to assess critical slowing down and mean squared successive differences (MSSD) to assess critical fluctuations. Although we did not find evidence that scoring scenarios produce performance transitions across individuals, we did observe transitions in each condition. Contrary to the lag 1 autocorrelations, our results suggest that transitions in human performance are signaled by increases in the MSSD. Specifically, both positive and negative performance transitions were accompanied with increased fluctuations in performance. Furthermore, negative performance transitions were accompanied with increased fluctuations of complexity, signaling resilience losses. On the other hand, complexity remained stable for positive performance transitions. Together, these results suggest that combining information of critical fluctuations in performance and complexity can predict both positive and negative transitions in dyadic team performance.

Antifragility in Climbing: Determining Optimal Stress Loads for Athletic Performance Training.

In the past decades, much research has examined the negative effects of stressors on the performance of athletes. However, according to evolutionary biology, organisms may exhibit growth under stress, a phenomenon called antifragility. For both coaches and their athletes, a key question is how to design training conditions to help athletes develop the kinds of physical, physiological, and behavioral adaptations underlying antifragility. An answer to this important question requires a better understanding of how individual athletes respond to stress or loads in the context of relevant sports tasks. In order to contribute to such understanding, the present study leverages a theoretical and methodological approach to generate individualized load-response profiles in the context of a climbing task. Climbers (n = 37) were asked to complete different bouldering (climbing) routes with increasing loading (i.e. difficulty). We quantified the behavioral responses of each individual athlete by mathematically combining two measures obtained for each route: (a) maximal performance (i.e. the percentage of the route that was completed) and (b) number of attempts required to achieve maximal performance. We mapped this composite response variable as a function of route difficulty. This procedure resulted in load-response curves that captured each athlete's adaptability to stress, termed phenotypic plasticity (PP), specifically operationalized as the area under the generated curves. The results indicate individual load-response profiles (and by extension PP) for athletes who perform at similar maximum levels. We discuss how these profiles might be used by coaches to systematically select stress loads that may be ideally featured in performance training.

The Link between Microdevelopment and Long-Term Learning Trajectories in Science Learning.

Long-term learning trajectories evolve through microdevelopmental sequences (i.e., short-term processes of change during learning tasks) and depend on variability during and across learning tasks. The aim of this study is to examine the coupling between short-term teacher-student dynamics and students' long-term learning trajectories, thereby providing empirical support for the link between the short- and long-term time scale in cognitive development. For 31 students (ages 3-5 years) from regular and special education, five teacher-student interactions during science tasks were filmed and coded in real time with regard to the student's level of understanding and the teacher's support throughout the task. A hierarchical cluster analysis resulted in four different learning trajectories over the course of 1.5 years, labeled as a high-scoring, mid-scoring, fluctuating, and low-scoring group of students. When connecting these trajectories to microdevelopmental data, the interactions of the high-scoring students were characterized by more moment-to-moment variations in the teacher's support and student's level of understanding, while the low-scoring group had the least variability compared to the other groups. Students with emotional and behavioral disabilities were represented across all learning trajectories, despite frequent accounts in the literature on their significant academic delays.

Methodological Issues in Soccer Talent Identification Research.

Talent identification research in soccer comprises the prediction of elite soccer performance. While many studies in this field have aimed to empirically relate performance characteristics to subsequent soccer success, a critical evaluation of the methodology of these studies has mostly been absent in the literature. In this position paper, we discuss advantages and limitations of the design, validity, and utility of current soccer talent identification research. Specifically, we draw on principles from selection psychology that can contribute to best practices in the context of making selection decisions across domains. Based on an extensive search of the soccer literature, we identify four methodological issues from this framework that are relevant for talent identification research, i.e. (1) the operationalization of criterion variables (the performance to be predicted) as performance levels; (2) the focus on isolated performance indicators as predictors of soccer performance; (3) the effects of range restriction on the predictive validity of predictors used in talent identification; and (4) the effect of the base rate on the utility of talent identification procedures. Based on these four issues, we highlight opportunities and challenges for future soccer talent identification studies that may contribute to developing evidence-based selection procedures. We suggest for future research to consider the use of individual soccer criterion measures, to adopt representative, high-fidelity predictors of soccer performance, and to take restriction of range and the base rate into account.

Rowing together: Interpersonal coordination dynamics with and without mechanical coupling.

Although most research on interpersonal coordination focuses on perceptual forms of interaction, many interpersonal actions also involve interactions of mechanical nature. We examined the effect of mechanical coupling in a rowing task from a coupled oscillator perspective: 16 pairs of rowers rowed on ergometers that were physically connected through slides (mechanical coupling condition) or on separate ergometers (no mechanical coupling condition). They rowed in two patterns (in- and antiphase) and at two movement frequencies (20 and 30 strokes per minute). Seven out of sixteen pairs showed one or more coordinative breakdowns, which only occurred in the antiphase condition. The occurrence of these breakdowns was not affected by mechanical coupling, nor by movement frequency. For the other nine pairs, variability of steady state coordination was substantially lower in the mechanical coupling condition. Together, these results show that the increase in coupling strength through mechanical coupling stabilizes coordination, even more so for antiphase coordination.

Perceiving affordances in sports through a momentum lens.

In this experimental study, we tested whether athletes' judgments of affordances and of environmental features vary with psychological momentum (PM). We recruited golf, hockey, and tennis players, who were assigned to a positive or negative momentum condition. We designed a golf course on which participants made practice putts, after which they were asked to place the ball at their maximum "puttable" distance and to judge the hole size. Next, participants played a golf match against an opponent, in which the first to take a lead of 5 points would win the match. Participants were told that they could win a point by making the putt or by being closest to the hole. They wore visual occlusion goggles to prevent them from seeing the actual result, and the experimenter manipulated the scoring pattern to induce positive or negative PM. Participants in the positive momentum condition came back from a four-point lag to a four-point lead, whereas those in the negative momentum condition underwent the opposite scenario. We then asked the participants again to indicate their maximum puttable distance from the hole and to judge the hole size. After the manipulation, participants judged the maximum puttable distance to be longer in the positive momentum condition and shorter in the negative momentum condition. For the hole-size judgments, there were no significant effects. These results provide first indications for the idea that athletes' affordances change when they experience positive PM compared to negative PM. This sheds a new light on the dynamics of perception-action processes and PM in sports.