Dit artikel behandelt het concept Maincontracting als referentiekader voor contracten in de vastgoedsector.
DOCUMENT
Het bekende Amazon recept van "anderen die dit boek bestelden, kochten ook..." wordt ook steeds populairder voor persoonlijke verzamelingen van bookmarks: "anderen die dit artikel of deze website bookmarkten, vonden ook..." Zet je bookmarks online, op je naam of onder pseudoniem. Importeren vanuit Explorer of Firefox is geen probleem. En met iin klik kun je grasduinen door de verzamelingen van gelijkgeonteresseerden. De kans dat je kwalitatief goede bronnen vind is daardoor groot, en op basis van een annotatie, trefwoord of waarderingsscore kun je snel scannen of er iets voor je bij zit. Vergelijk dat eens met de zoekresultaten van bijvoorbeeld Google waarbij je zelf telkens het kaf weer van het koren moet scheiden. Social bookmarks: hoe werkt het precies, en wat kan dit betekenen voor het Hoger Onderwijs?
DOCUMENT
Substitution is an essential tool for a coach to influence the match. Factors like the injury of a player, required tactical changes, or underperformance of a player initiates substitutions. This study aims to predict the physical performance of individual players in an early phase of the match to provide additional information to the coach for his decision on substitutions. Tracking data of individual players, except for goalkeepers, from 302 elite soccer matches of the Dutch ‘Eredivisie’ 2018–2019 season were used to enable the prediction of the individual physical performance. The players’ physical performance is expressed in the variables distance covered, distance in speed category, and energy expenditure in power category. The individualized normalized variables were used to build machine learning models that predict whether players will achieve 100%, 95%, or 90% of their average physical performance in a match. The tree-based algorithms Random Forest and Decision Tree were applied to build the models. A simple Naïve Bayes algorithm was used as the baseline model to support the superiority of the tree-based algorithms. The machine learning technique Random Forest combined with the variable energy expenditure in the power category was the most precise. The combination of Random Forest and energy expenditure in the power category resulted in precision in predicting performance and underperformance after 15 min in a match, and the values were 0.91, 0.88, and 0.92 for the thresholds 100%, 95%, and 90%, respectively. To conclude, it is possible to predict the physical performance of individual players in an early phase of the match. These findings offer opportunities to support coaches in making more informed decisions on player substitutions in elite soccer.
DOCUMENT
