The objective of this study was to compose an objective and detailed notational analysis system for 3 vs. 2GK smallsided soccer games, in which three roles are examined: attacker with ball, attacker without ball and defender. The actions and the outcome of the actions were registered for each player and in each role. Players earn points for each action and outcome according to an a priori determined scheme. Performance scores for each role are calculated as the average number of points a participant earns per trial. This notation system was tested on 19 highly talented female soccer players and validity and reliability of the system were determined. In addition, practical applications were discussed and the most important items of the notation system were determined and using only these items, a simplified notation system was proposed. The notation system has high ecological validity and can discriminate the high and low categorized players, but further development is necessary to increase the reliability of the system.
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.
Pitch size varies in official soccer matches and differently sized pitches are adopted for tactical purposes in small-sided training games. Since interactive team behaviour emerges under constraints, the authors evaluate the effect of pitch size (task) manipulations on interactive team behaviour in small-sided soccer games. Four 4-a-side (plus goalkeepers) small-sided games were played: a reference game (30×20 m), length manipulation (24×20 m), width manipulation (30×16 m), and a combination (24×16 m). Using position data (100Hz), three measures quantifying the teams' interaction were calculated: longitudinal inter-team distance, lateral inter-team distance, and surface area difference. Means and standard deviations, correlations and coupling values were calculated. Running correlations were calculated over a 3-s window to evaluate interaction patterns. As expected, a shorter pitch results in smaller longitudinal inter-team distance, lateral inter-team distance decreased for narrow pitches, and smaller total playing area resulted in decreased surface area. Unanticipated, a crossover effect was present; length and width manipulations also triggered changes in lateral and longitudinal direction respectively. Inter-team distances and surface area difference differed significantly across conditions. Interaction patterns differed across conditions for all measures. So, highly tactically relevant, soccer teams seem to adapt their interactive behaviour according to pitch size in small-sided games. © 2013 Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg.
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Aanleiding Nieuwsuitgeverijen bevinden zich in zwaar weer. Economische malaise en toegenomen concurrentie in het pluriforme medialandschap dwingen uitgeverijen om enerzijds kosten te besparen en tegelijkertijd te investeren in innovatie. De verdere automatisering van de nieuwsredactie vormt hierbij een uitdaging. Buiten de branche ontstaan technieken die uitgeverijen hierbij zouden kunnen gebruiken. Deze zijn nog niet 'vertaald' naar gebruiksvriendelijke systemen voor redactieprocessen. De deelnemers aan het project formuleren voor dit braakliggend terrein een praktijkgericht onderzoek. Doelstelling Dit onderzoek wil antwoord geven op de vraag: Hoe kunnen bewezen en nieuw te ontwikkelen technieken uit het domein van 'natural language processing' een bijdrage leveren aan de automatisering van een nieuwsredactie en het journalistieke product? 'Natural language processing' - het automatisch genereren van taal - is het onderwerp van het onderzoek. In het werkveld staat deze ontwikkeling bekend als 'automated journalism' of 'robotjournalistiek'. Het onderzoek richt zich enerzijds op ontwikkeling van algoritmes ('robots') en anderzijds op de impact van deze technologische ontwikkelingen op het nieuwsveld. De impact wordt onderzocht uit zowel het perspectief van de journalist als de nieuwsconsument. De projectdeelnemers ontwikkelen binnen dit onderzoek twee prototypes die samen het automated-journalismsysteem vormen. Dit systeem gaat tijdens en na het project gebruikt worden door onderzoekers, journalisten, docenten en studenten. Beoogde resultaten Het concrete resultaat van het project is een prototype van een geautomatiseerd redactiesysteem. Verder levert het project inzicht op in de verankering van dit soort systemen binnen een nieuwsredactie. Het onderzoek biedt een nieuw perspectief op de manier waarop de nieuwsconsument de ontwikkeling van 'automated journalism' in Nederland waardeert. Het projectteam deelt de onderzoekresultaten door middel van presentaties voor de uitgeverijbranche, presentaties op wetenschappelijke conferenties, publicaties in (vak)tijdschriften, reflectiebijeenkomsten met collega-opleidingen en een samenvattende white paper.
The scientific challenge is about unraveling the secret of Brazilian and Dutch soccer by capturing successful elements of game play of both countries,, combining expertise from data science, computer science and sport science. Suggested features from literature, as well as several novel ones, will be considered and filtered on how they capture success in soccer. A manageable set of features will then be obtained from various available Dutch datasets (focusing on successful play). Subsequently, the same features will be used to compare playing styles between both countries. Features of game play will be approached from two different angles. The first angle (spearheaded by the Brazilian computer science partner) concerns features that capture the dynamics of game play and characterize aspects of formation on the pitch. The second angle (lead by the Dutch data science partner) will focus on how an attack is built up, and how key events (shots on goal, transitions from defenders to midfielders, etc.) can help to characterize this. For the comparison between countries data will be collected in four different age categories in Brazil and the Netherlands during official games, in order to compare (the development of) game play between both countries. Data will be collected by means of the Local Position Measurement System, for reasons of accuracy and consistency. The applied science part of this proposal is focusing on bridging the gap between fundamental science and soccer practice, i.e. coaches, trainers, clubs and federations. The outcomes of the fundamental part will be implemented in a coach-cockpit, a software application which trainers and coaches can use to (1) decide upon their strategy before a game, (2) analyze player- and team behaviour during a game enabling to adjust the strategy accordingly, and (3) choose and/or design training forms to improve player- and team behaviour.
The scientific challenge is about unraveling the secret of Brazilian and Dutch soccer by capturing successful elements of game play of both countries,, combining expertise from data science, computer science and sport science. Suggested features from literature, as well as several novel ones, will be considered and filtered on how they capture success in soccer. A manageable set of features will then be obtained from various available Dutch datasets (focusing on successful play). Subsequently, the same features will be used to compare playing styles between both countries. Features of game play will be approached from two different angles. The first angle (spearheaded by the Brazilian computer science partner) concerns features that capture the dynamics of game play and characterize aspects of formation on the pitch. The second angle (lead by the Dutch data science partner) will focus on how an attack is built up, and how key events (shots on goal, transitions from defenders to midfielders, etc.) can help to characterize this. For the comparison between countries data will be collected in four different age categories in Brazil and the Netherlands during official games, in order to compare (the development of) game play between both countries. Data will be collected by means of the Local Position Measurement System, for reasons of accuracy and consistency. The applied science part of this proposal is focusing on bridging the gap between fundamental science and soccer practice, i.e. coaches, trainers, clubs and federations. The outcomes of the fundamental part will be implemented in a coach-cockpit, a software application which trainers and coaches can use to (1) decide upon their strategy before a game, (2) analyze player- and team behaviour during a game enabling to adjust the strategy accordingly, and (3) choose and/or design training forms to improve player- and team behaviour.
