PENGARUH KELELAHAN TUBUH TERHADAP AKURASI PASSING SEPAK BOLA

Tujuan penelitian ini adalah untuk mengetahui kelelahan tubuh apakah dapat berpengaruh terhadap akurasi passing sepak bola.Metode penelitian ini menggunakan deskriptif dengan pendekatan kuantitatif.Sample berjumlah 10 mahasiswa prodi ilmu keolahragaan yang mengikuti UKM (Unit Kegiatan Mahasiswa) sepakbola di Universitas Pendidikan Indonesia (UPI).Instrument dalam penelitian ini menggunakan 2 yaitu : RAST (Runing Anaerobic Sprint Tess) untuk mengetahui indeks kelelahan dan Short Passed Test untuk mengetahui akurasi passing .Hasil penelitian berdasarkan Tes RAST yaitu lari sejauh 35 meter dengan 6 kali repetisi dan istirahat selama 10 detik per repetisi yang dilakukan sample.Hasil tes menunjukan sample yang memiliki indeks kelelahan sebanyak 7 orang (70%) sedangkan sample tidak kelelahan sebanyak 3 (30%) .Kondisi fisik seseorang dapat diprediksi dengan menggunakan instrument tes yang tepat dilihat menggunakan Test RAST.Dari hasil tes Short Passed yaitu dengan mempassing 4 bola menuju target yang telah di buat menunjukan sample yang sukses memasukan bola menuju target yaitu hanya sebanyak 1(10%) sedangkan sample sebanyak 9 (90%) terdapat kesulitan atau tidak ada yang sukses melakukan passing terhadap 4 target.Berdasarkan hasil uji kolerasi antara kelelahan terhadap akurasi passing dapat dinyatakan mempunyai nilai signifikansi P (0,02) < 0,05 Maka dapat disimpulkan Terdapat hubungan atau pengaruh yang signifikan antara kelelahan tubuh terhadap akurasi passing sepak bola. The purpose of this study was to find out whether body fatigue can affect the accuracy of soccer passing. The research method used a descriptive quantitative approach. The sample consisted of 10 sports science study program students who took part in soccer UKM (Student Activity Unit) at the Indonesian University of Education (UPI). The instruments in this study used 2, namely: RAST (Running Anaerobic Sprint Test) to determine the fatigue index and the Short Passed Test to determine passing accuracy. The results of the research were based on the RAST test, namely running 35 meters with 6 repetitions and resting for 10 seconds per repetition samples were carried out. The test results showed that 7 people (70%) had a fatigue index, while 3 (30%) were not exhausted. The physical condition of a person can be predicted using the right test instrument seen using the RAST test. From the results of the Short Passed test namely by shortpass 4 balls to ta The targets that have been made show that only 1 (10%) of the samples have successfully put the ball towards the target, while 9 (90%) of the samples have difficulty or no one has succeeded in passing 4 targets. Based on the results of the correlation test between fatigue and accuracy passing can be stated to have a significance value of P (0.02) <0.05. So it can be concluded that there is a significant relationship or influence between body fatigue on the accuracy of football shortpass

ANALYSIS ON CLASSIFICATION OF FOOTBALL TECHNIQUE

Football is a situation sport where football players are characterized by a great variety and complexity of activities during the game, both in contact with the opponent and in the immediate vicinity of the opponent, while cooperating with other teammates. Sports scientists and coaches need to find out more about the set of techniques, its diversity and the amount of technique in top level football games in order to guide the optimal planning and implementation of the training process in the technical training of football players. Due to the diversity of techniques used by football players, there are different approaches to the classification of these techniques. However, it is very important to group all techniques according to their common features in order to adequately assess the structure of a football game, its development tendencies and issues. Research aim: to compare and analyse the essence of the classification of football technique and its development tendencies. Research methods: analysis of scientific articles (electronic scientific databases ScienceDirect, Google Scholar), search keywords – football, football technique, classification of football technique. Main results of the research:  two main approaches dominate in the classification of football technique: the technique approach and the game approach. Initially, football technique was mainly grouped by taking into account the player’s actions with or without the ball, the level of difficulty of the technique element, the role of the players and the player’s actions on the spot or in motion. As football develops, football technique is classified by taking into account the effectiveness of its application in specific tactical situations.

Using Player's Body-Orientation to Model Pass Feasibility in Soccer

Given a monocular video of a soccer match, this paper presents a computational model to estimate the most feasible pass at any given time. The method leverages offensive player's orientation (plus their location) and opponents' spatial configuration to compute the feasibility of pass events within players of the same team. Orientation data is gathered from body pose estimations that are properly projected onto the 2D game field; moreover, a geometrical solution is provided, through the definition of a feasibility measure, to determine which players are better oriented towards each other. Once analyzed more than 6000 pass events, results show that, by including orientation as a feasibility measure, a robust computational model can be built, reaching more than 0.7 Top-3 accuracy. Finally, the combination of the orientation feasibility measure with the recently introduced Expected Possession Value metric is studied; promising results are obtained, thus showing that existing models can be refined by using orientation as a key feature. These models could help both coaches and analysts to have a better understanding of the game and to improve the players' decision-making process.Comment: Accepted at the Computer Vision in Sports Workshop at CVPR 202

A risk-reward assessment of passing decisions:comparison between positional roles using tracking data from professional men's soccer

Introduction: Performance assessment in professional soccer often focusses on notational assessment like assists or pass accuracy. However, rather than statistics, performance is more about making the best possible tactical decision, in the context of aplayer's positional role and the available options at the time. With the current paper, we aim to construct an improved model for the assessment of pass risk and reward across different positional roles, and validate that model by studying differences in decision-making between players with different positional roles. Methods: To achieve our aim, we collected position tracking data from an entire season of Dutch Eredivisie matches, containing 286.151 passes of 336 players. From that data, we derived several features on risk and reward, both for the pass that has been played, as well as for the pass options that were available at the time of passing. Results: Our findings indicate that we could adequately model risk and reward, outperforming previously published models, and that there were large differences in decision-making between players with different positional roles. Discussion: Our model can be used to assess player performance based on what could have happened, rather than solely based on what did happen in amatch

What Do Publicly-available Soccer Match Data Actually Tell Us?

Media analyses of soccer statistics and game play has accelerated in recent years. This is evident in visual displays of ball and player tracking, average player locations and distances they run. These media depictions aim to be attractive, entertaining and informative for viewers. But are such statistics predictive of goal scoring and match outcome? To answer this question we review the sixty-four matches of the 2014 World Cup and examine nine common match statistics, and others, to evaluate their predictive value for goal production and match outcome

Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations

Skateboarding has made its Olympic debut at the delayed Tokyo 2020 Olympic Games. Conventionally, in the competition scene, the scoring of the game is done manually and subjectively by the judges through the observation of the trick executions. Nevertheless, the complexity of the manoeuvres executed has caused difficulties in its scoring that is obviously prone to human error and bias. Therefore, the aim of this study is to classify five skateboarding flat ground tricks which are Ollie, Kickflip, Shove-it, Nollie and Frontside 180. This is achieved by using three optimized machine learning models of k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM) from features extracted via eighteen transfer learning models. Six amateur skaters performed five tricks on a customized ORY skateboard. The raw data from the inertial measurement unit (IMU) embedded on the developed device attached to the skateboarding were extracted. It is worth noting that four types of input images were transformed via Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and synthesized raw image (RAW) from the IMU-based signals obtained. The optimized form of the classifiers was obtained by performing GridSearch optimization technique on the training dataset with 3-folds cross-validation on a data split of 4:1:1 ratio for training, validation and testing, respectively from 150 transformed images. It was shown that the CWT and RAW images used in the MobileNet transfer learning model coupled with the optimized SVM and RF classifiers exhibited a test accuracy of 100%. In order to identify the best possible method for the pipelines, computational time was used to evaluate the various models. It was concluded that the RAW-MobileNet-optimized-RF approach was the most effective one, with a computational time of 24.796875 seconds. The results of the study revealed that the proposed approach could improve the classification of skateboarding tricks

The tactics of successful attacks in professional association football:large-scale spatiotemporal analysis of dynamic subgroups using position tracking data

Association football teams can be considered complex dynamical systems of individuals grouped in subgroups (defenders, midfielders and attackers), coordinating their behaviour to achieve a shared goal. As research often focusses on collective behaviour, or on static subgroups, the current study aims to analyse spatiotemporal behaviour of dynamic subgroups in relation to successful attacks. We collected position tracking data of 118 Dutch Eredivisie matches, containing 12424 attacks. Attacks were classified as successful (N = 1237) or non-successful (N = 11187) based on the potential of creating a scoring opportunity. Using unsupervised machine learning, we automatically identified dynamic formations based on position tracking data, and identified dynamic subgroups for every timeframe in a match. We then compared the subgroup centroids to assess the intra- and inter-team spatiotemporal synchronisation during successful and non-successful attacks, using circular statistics. Our results indicated subgroup-level variables provided more information, and were more sensitive to disruption, in comparison to team-level variables. When comparing successful and non-successful attacks, we found decreases (p < .01) in longitudinal inter- and intra-team synchrony of interactions involving the defenders of the attacking team during successful attacks. This study provides the first large-scale dynamic subgroup analysis and reveals additional insights to team-level analyses