Do pictures help to memorize? The influence of item presentation and executive functions on everyday memory in older adults

Ageing is associated with a declining memory performance. This phenomenon has been extensively investigated in different laboratory settings, while the transferability from laboratory findings to everyday life situations is rather unclear. In fact, everyday life situations have been found to enhance as well as impair older adults’ memory performance. The present study deals with the question which kind of factors influence memory performance of older adults during everyday life situations. Therefore, participants (70.16 ± 5.8 years) were exposed to a supermarket scenario. Their task was to collect previously presented objects in a specified order while objects were either presented as words or pictures in correct or randomized order. Additionally, participants performed the Stroop test, Trail making test and Bochumer Matrizen test, in order to determine a possible predictability of the performance of these tasks and everyday life performance. Results showed that older adults had more problems to memorize items in the more challenging (randomized item presentation) task but presentation via pictures could offset this effect

Early Prediction of Physical Performance in Elite Soccer Matches—A Machine Learning Approach to Support Substitutions

Verschenen in het kader van het onderzoeksgebied: 'Monitoring and Predicting on Physical Performance in Daily Life and Sports

The heat is on:Investigating the effect of psychological pressure on competitive performance in elite surfing

Competitive sport often creates a high-stake and thus a high-pressure environment for its athletes. In the past, research has pointed to the negative effect that competitive pressure might have on skills and movement executions that have been perfected through prior practice. The Attentional Control Theory: Sport (ACTS) suggests that specifically high situational pressure and prior performance failures may negatively affect an athlete’s subsequent performance. This study aimed to investigate the influence of situational pressure and previous performance errors on performance (i.e., wave score) in elite surfing while considering various contextual factors. A total of 6497 actions, performed by 80 elite surfers (female n = 28; male n = 52), were annotated based on video recordings of the 2019 World Championship Tour (WCT). A multi-level model was used to analyse the effect of pressure, previous errors and other contextual factors on the wave scores of individual surfers (i.e., events were nested within athletes). Partially confirming previous research, prior errors caused a significant decrease in surfing performance on the following ride. However, neither a significant effect of situational pressure on performance nor inter-individual differences in how prior-errors and situational pressure affected performance were found.</p

Attacking Key Performance Indicators in Soccer: Current Practice and Perceptions from the Elite to Youth Academy Level

Key Performance Indicators (KPIs) are used to evaluate the offensive success of a soccer team (e.g. penalty box entries) or player (e.g. pass completion rate). However, knowledge transfer from research to applied practice is understudied. The current study queried practitioners (n = 145, mean ± SD age: 36 ± 9 years) from 42 countries across different roles and levels of competition (National Team Federation to Youth Academy levels) on various forms of data collection, including an explicit assessment of twelve attacking KPIs. 64.3% of practitioners use data tools and applications weekly (predominately) to gather KPIs during matches. 83% of practitioners use event data compared to only 52% of practitioners using positional data, with a preference for shooting related KPIs. Differences in the use and value of metrics derived from positional tracking data (including Ball Possession Metrics) were evident between job role and level of competition. These findings demonstrate that practitioners implement KPIs and gather tactical information in a variety of ways with a preference for simpler metrics related to shots. The low perceived value of newer KPIs afforded by positional data could be explained by low buy-in, a lack of education across practitioners, or insufficient translation of findings by experts towards practice

Predicting match outcome in professional Dutch football using tactical performance metrics computed from position tracking data

Quality as well as quantity of tracking data have rapidly increased over the recent years, and multiple leagues have programs for league-wide collection of tracking data. Tracking data enables in-depth performance analysis, especially with regard to tactics. This already resulted in the development of several Key Performance Indicators (KPI’s) related to scoring opportunities, outplaying defenders, numerical balance and territorial advantage. Although some of these KPI’s have gained popularity in the analytics community, little research has been conducted to support the link with performance. Therefore, we aim to study the relationship between match outcome and tactical KPI’s derived from tracking data. Our dataset contains tracking data of all players and the ball, and match outcome, for 118 Dutch premier league matches. Using tracking data, we identified 72.989 passes. For every pass-reception window we computed KPI’s related to numerical superiority, outplayed defenders, territorial gains and scoring opportunities using position data. This individual data was then aggregated over a full match. We then split the dataset in a train and test set, and predicted match outcome using different combinations of features in a logistic regression model. KPI’s related to a combination of off-the-ball features seemed to be the best predictor of match outcome (accuracy of 64.0% and a log loss of 0.67), followed by KPI’s related to the creation of scoring opportunities (accuracy of 58% and a log loss of 0.69). This indicates that although most (commercially) available KPI’s are based on ball-events, the most important information seems to be in off-the-ball activity. We have demonstrated that tactical KPI’s computed from tracking data are relatively good predictors of match outcome. As off-the-ball activity seems to be the main predictor of match outcome, tracking data seems to provide much more insight than notational analysis

Shortcomings of applying data science to improve professional football performance:Takeaways from a pilot intervention study

Positional tracking data allows football practitioners to derive features that describe patterns of player behavior and quantify performance. Existing research using tracking data has mostly focused on what occurred on the pitch, such as the determinants of effective passing. There have yet to be studies attempting to use findings from data science to improve performance. Therefore, 24 professional players (mean age = 21.6 years, SD = 5.7) were divided into a control team and an intervention team which competed against each other in a pre-test match. Metrics were gathered via notational analysis (number of passes, penalty box entries, shots on goal), and positional tracking data including pass length, pass velocity, defensive disruption (D-Def), and the number of outplayed opponents (NOO). D-Def and NOO were used to extract video clips from the pre-test that were shown to the intervention team as a teaching tool for 2 weeks prior to the post-test match. The results in the post-test showed no significant improvements from the pre-test between the Intervention Team and the Control Team for D-Def (F = 1.100, p = 0.308, η2 = 0.058) or NOO (F = 0.347, p = 0.563, η2 = 0.019). However, the Intervention Team made greater numerical increases for number of passes, penalty box entries, and shots on goal in the post-test match. Despite a positive tendency from the intervention, results indicate the transfer of knowledge from data science to performance was lacking. Future studies should aim to include coaches' input and use the metrics to design training exercises that encourage the desired behavior

The “hockey” assist makes the difference:validation of a defensive disruptiveness model to evaluate passing sequences in elite soccer

With the growing availability of position data in sports, spatiotemporal analysis in soccer is a topic of rising interest. The aim of this study is to validate a performance indicator, namely D-Def, measuring passing effectiveness. D-Def calculates the change of the teams’ centroid, centroids of formation lines (e.g., defensive line), teams’ surface area, and teams’ spread in the following three seconds after a pass and therefore results in a measure of disruption of the opponents’ defense following a pass. While this measure was introduced earlier, in this study we aim to prove the usefulness to evaluate attacking sequences. In this study, 258 games of Dutch Eredivisie season 2018/19 were included, resulting in 13,094 attacks. D-Def, pass length, pass velocity, and pass angle of the last four passes of each attack were calculated and compared between successful and unsuccessful attacks. D-Def showed higher values for passes of successful compared to unsuccessful attacks (0.001 28) needs to be present. In addition, the penultimate pass (“hockey assist”) of an attack seems crucial in characterizing successful attacks

Technical–tactical skill assessments in small-sided soccer games:A scoping review

Skill assessments are essential to elite soccer coaches and clubs, to provide an evidence-based approach to player evaluation. Valid methods thereby support talent identification and development procedures (e.g. scouting and training strategies). However, it remains a complex challenge. Small-sided games have emerged as a promising tool, due to high ecological validity. Until now, no review has focused on their discriminative power. Therefore, we aimed to investigate whether technical–tactical skill assessments of small-sided games can discriminate between individual players and between teams of different skill levels (i.e. higher vs. lower playing levels and older vs. younger players) in soccer. A scoping review of PubMed, Web of Science, and MEDLINE databases was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. A total of 23 studies were included, all but one of which showed at least good methodological quality (i.e. > 50% score in quality assessment). For technical skills, small-sided games indicate discriminative power for passing skills, but only when comparing players of different playing levels, as supported by two of the included studies. Tactical categories, such as movement variability and decision-making, were more pronounced in higher level and more experienced players. However, the most conclusive finding among individual skill assessments was that the technical–tactical overall performance (a total score comprised of different subcategories) of individual players showed a positive relation to skill level in three studies. Team performance assessments in small-sided games showed that older and higher level teams mainly distinguish themselves from less-skilled peers by using the available space more efficiently. With the influence of different assessment instruments and several small-sided games modifications in mind, it may be concluded that technical–tactical skills in small-sided games can discriminate between players and teams of different skill levels. An interesting future avenue is to examine a more consistent approach to skill assessments in small-sided games, which can warrant their use for scouting and talent identification purposes

The “hockey” assist makes the difference—validation of a defensive disruptiveness model to evaluate passing sequences in elite soccer

With the growing availability of position data in sports, spatiotemporal analysis in soccer is a topic of rising interest. The aim of this study is to validate a performance indicator, namely D-Def, measuring passing effectiveness. D-Def calculates the change of the teams’ centroid, centroids of formation lines (e.g., defensive line), teams’ surface area, and teams’ spread in the following three seconds after a pass and therefore results in a measure of disruption of the opponents’ defense following a pass. While this measure was introduced earlier, in this study we aim to prove the usefulness to evaluate attacking sequences. In this study, 258 games of Dutch Eredivisie season 2018/19 were included, resulting in 13,094 attacks. D-Def, pass length, pass velocity, and pass angle of the last four passes of each attack were calculated and compared between successful and unsuccessful attacks. D-Def showed higher values for passes of successful compared to unsuccessful attacks (0.001 28) needs to be present. In addition, the penultimate pass (“hockey assist”) of an attack seems crucial in characterizing successful attacks