Identifying individuality and variability in team tactics by means of statistical shape analysis and multilayer perceptrons

Jäger, Jörg M.; Schöllhorn, Wolfgang I.

doi:10.1016/j.humov.2010.09.005

Cited by 11 publications

(8 citation statements)

References 13 publications

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“…Accordingly, organization of the optimal position requires players to use different displacement speeds in both competition ( Folgado et al, 2014 , 2015 ) and training scenarios ( Sampaio et al, 2014 ). These dynamic positioning behaviours lead to different time dependent configurations that reflect team specific tactical concepts ( Jager and Schollhorn, 2012 ). Moreover, a better understanding of these sub-units’ behaviour may be achieved when the variability in their structures is considered, which can be measured through the coefficient of variation (CV) and the regularity level of the EPS.…”

Section: Introductionmentioning

confidence: 99%

Changes in Effective Playing Space When Considering Sub-Groups of 3 to 10 Players in Professional Soccer Matches

Gonçalves

Folgado

Coutinho

et al. 2018

Journal of Human Kinetics

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Success in soccer is much dependent on how players and teams create and restrict space and time. In match situations, players constitute small sub-groups to improve their collective synchronization and achievement of specific goals. This study aimed to identify changes in the effective playing space (EPS, defined as the smallest polygonal area delimited by the peripheral outfield players) when considering sub-groups of 3 to 10 players. Twenty outfield professional players participated in this study. The EPS, its regularity pattern (measured by the approximate entropy), coefficient of variation and players’ mean speed were calculated for sub-groups of 3, 4, 5, 6, 7, 8, 9 and 10 players, considering the smallest inter-player distance as the criterion. The EPS presented a most likely increase with a higher number of players, especially considering the transition from 3 to 4 players (~440% of variation, very large). As the EPS increased with the number of players, the correspondent regularity presented a trend of a most likely increase (from EPS3 vs. EPS4: ~25%, very large; to EPS9 vs. EPS10: ~11%, moderate). The mean speed results suggest that players may achieve different states of collective coordination, mainly between ~6 to 8 km.h-1. Overall, three different match scenarios should require additional attention when aiming to design more match transferable tasks: i) transition from EPS3 to EPS4; ii) transition from EPS4 up to EPS8; and iii) transition from EPS8 to EPS9. These results help to understand match self-organized behaviours and, consequently, allow to optimize task characteristics in practice sessions.

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Section: Introductionmentioning

confidence: 99%

Changes in Effective Playing Space When Considering Sub-Groups of 3 to 10 Players in Professional Soccer Matches

Gonçalves

Folgado

Coutinho

et al. 2018

Journal of Human Kinetics

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“…The output that has the smallest distance wins and if the k th output is equal to the smallest distance d k , then the k th output y k gets a value of 1 or 0, as shown in Eq. (10).…”

Section: Competitive Learning Networkmentioning

confidence: 99%

“…In another study, multilayer perceptrons were used to determine different team tactics in volleyball. The team tactic patterns were used to train multilayer perceptrons [10]. Another study dealt with a neural network of motion perception and speed discrimination [11].There were also studies that used different types of cameras, as catadioptric [12] and multicamera systems [13,14].…”

Section: Introductionmentioning

confidence: 99%

Motion clustering on video sequences using a competitive learning network

Görgünoğlu¹,

Altay²

2014

Turk J Elec Eng & Comp Sci

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Abstract:It is necessary to track human movements in crowded places and environments such as stations, subways, metros, and schoolyards, where security is of great importance. As a result, undesired injuries, accidents, and unusual movements can be determined and various precautionary measures can be taken against them. In this study, real-time or existing video sequences are used within the system. These video sequences are obtained from objects such as humans or vehicles, moving actively in various environments. At first, some preprocesses are made respectively, such as converting gray scale, finding the edges of the objects existing in the images, and thresholding the images. Next, motion vectors are generated by utilizing a full search algorithm. Afterwards, k-means, nearest neighbor, image subdivision, and a competitive learning network are used as clustering methods to determine dense active regions on the video sequence using these motion vectors, and then their performances are compared. It is seen that the competitive learning network significantly determines the classification of dense active regions, including motion. Moreover, the algorithms are analyzed in terms of their time performances.

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“…Pfeiffer et al [12] propose that simple linear models are inadequate in understanding and explaining human behaviour or movement and more complex, non-linear, methods of analysing movement characteristics are needed. In recent sports related studies, different neural network models have been used in: the identification of swimming talent [13], the identification of tactical patterns in handball [14], predicting the flight of javelins [15] and analyzing interlimb coordination during a golf chip shot [16]. Machine learning methods are also being applied in running and gait analysis problems.…”

Section: Artificial Intelligence Modelsmentioning

confidence: 99%

Automatic detection of collisions in elite level rugby union using a wearable sensing device

et al. 2012

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Elite rugby union teams currently employ the latest technology to monitor and evaluate the physical demands of training and games on their players. Tackling has been shown to be the most common cause of injury in rugby union, yet current player monitoring technology does not effectively evaluate player tackling measurements. Currently, to evaluate measurements specific to player tackles, a time-consuming manual analysis of player sensor data and video footage is required. The purpose of this work is to investigate tackle modeling techniques which can be utilised to automatically detect player tackles and collisions using sensing technology already being used by elite international and club level rugby union teams. This paper discusses issues relevant to automatic tackle analysis, describes a technique to detect tackles using sensing data and validates the technique by comparing automatically detected collisions to manually labeled collisions using data from elite club and international level players. The results of the validation show that the system is able to consistently identify collisions with very few false positives and false negatives, achieving a recall and precision rating of 0.933 and 0.958, respectively. The aim is that the automatically detected tackles can provide coaching, medical and strength and conditioning staff with objective tackle-specific measurements, in real time, which can be used in injury prevention and rehabilitation strategies.

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Identifying individuality and variability in team tactics by means of statistical shape analysis and multilayer perceptrons

Cited by 11 publications

References 13 publications

Changes in Effective Playing Space When Considering Sub-Groups of 3 to 10 Players in Professional Soccer Matches

Changes in Effective Playing Space When Considering Sub-Groups of 3 to 10 Players in Professional Soccer Matches

Motion clustering on video sequences using a competitive learning network

Automatic detection of collisions in elite level rugby union using a wearable sensing device

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