Classification of exercise fatigue levels by multi-class SVM from ECG and HRV.

Among the various physiological signals, electrocardiogram (ECG) is a valid criterion for the classification of various exercise fatigue. In this study, we combine features extracted by deep neural networks with linear features from ECG and heart rate variability (HRV) for exercise fatigue classification. First, the ECG signals are converted into 2-D images by using the short-term Fourier transform (STFT), and image features are extracted by the visual geometry group (VGG) . The extracted image and linear features of ECG and HRV are sent to the different types of classifiers to distinguish distinct exercise fatigue level. To validate performance, the proposed methods are tested on (i) an open-source EPHNOGRAM dataset and (ii) a self-collected dataset (n = 51). The results reveal that the classification based on the concatenated features has the highest accuracy, and the calculation time of the system is also significantly reduced. This demonstrates that the proposed novel hybrid approach can be used to assist in improving the accuracy and timeliness of exercise fatigue classification in a real-time exercise environment. The experimental results show that the proposed method outperforms other recent state-of-the-art methods in terms of accuracy 96.90%, sensitivity 96.90%, F1-score of 0.9687 in EPHNOGRAM and accuracy 92.17%, sensitivity 92.63%, F1-score of 0.9213 in self-collected dataset.

Analysis of defensive playing styles in the professional Chinese Football Super League.

AIM: Describing and measuring different team styles of play during matches is a key step towards a more predictive and prescriptive performance analysis. The current study aimed to identify and measure different defensive playing styles associated with technical-tactical and physical performance indicators in professional football via Principal Component Analysis (PCA). METHODS: The sample comprised all 240 matches played in the Chinese Football Super League (CSL) during the 2018 season. RESULTS: Seventeen key performance indicators (KPIs), 15 defense-related and 2 physical-related were identified from a total of 62 defensive performance indicators, which significantly differed between when teams lost and did not lose (p<0.05, ES=0.03-0.22). Then the PCA model based on 17 KPIs (Kaiser-Meyer-Olkin value being 0.81), outputted 8 factors representing 7 different styles of play (factor 6 and 8 represented one style) and explaining 83.01% of the total variance. Of all styles, defense close to own goal, including clearance, ball gain in the zone 1, deep completion and unsuccessful cross of the opponent, was the most dominant style (31.92% of the total variance). The champion of the league showed better scores in all styles of play, while the last ranked team got the highest score in defense close to the own goal style. CONCLUSION: The study indicated that a team's defensive style could be defined by specific KPIs, and teams are suggested to attune their styles consciously rather than maintaining a consistent strategy so as to achieve better performance. Moreover, such categorization of defensive styles could be used during scouting and match preparation.

Quantifying the Effectiveness of Defensive Playing Styles in the Chinese Football Super League.

Establishing and illustrating a predictive and prescriptive model of playing styles that football teams adopt during matches is a key step toward describing and measuring the effectiveness of styles of play. The current study aimed to identify and measure the effectiveness of different defensive playing styles for professional football teams considering the opponent's expected goal. Event data of all 1,120 matches played in the Chinese Football Super League (CSL) from the 2016 to 2020 seasons were collected, with fifteen defense-related performance variables being extracted. The PCA model (KMO = 0.76) output eight factors that represented 7 different styles of play (factor 6 and 8 represent one style of play) and explained 85.17% of the total variance. An expected goal (xG) model was built using data related to 27,852 shots. Finally, the xG of the opponent was calculated in the multivariate regression model, outputting five factors that (p < 0.05) explained 41.6% of the total variance in the xG of the opponent and receiving a dangerous situation (factor 7) was the most apparent style (31.3%). Finally, the predicted model with defensive styles correlated with actual xG of the opponent at r = 0.62 using the 2020 season as testing data which showed that the predicted xG was correlated moderately with the actual. The result indicated that if the team strengthened the defense closed to the own goal, high intensity confrontation, and defense of goalkeeper, meanwhile making less errors and receiving less dangerous situations, the xG of the opponent would be greatly reduced.

Impact of Match Type and Match Halves on Referees' Physical Performance and Decision-Making Distance in Chinese Football Super League.

The purpose of this study was to explore how Chinese Football Super League (CSL) referees' physical performance and decision-making distance varied according to match type and match halves. Data from 107 matches played by top-4 ranked and bottom-4 ranked teams during 2018-2019 CSL seasons were collected. Level of matches was classified into three groups: (a) upper-ranked (top-4) teams against top-4 teams, (b) top-4 teams against lower-ranked teams (bottom-4), and (c) bottom-4 teams against bottom-4 teams. Two-way ANOVA and Scheirer-Ray-Hare test were used to examine the statistical differences of referees' physical and spatial related distance variables among different match levels and halves. The Euclidean distance to the ball at the following three variables were statistically different among three match types: clearance (p = 0.03, E R 2 = 0.03), running with the ball (p = 0.01, E R 2 = 0.04), and shot off target (p = 0.04, E R 2 = 0.03). In addition, referees' distance to the ball at three events were statistically different between both match halves: pass (p < 0.001, r = 0.69), reception (p < 0.001, r = 0.76), and running with the ball (p < 0.001, r = 0.77). The total running distance was statistically different between both match halves (p = 0.001, d = 0.05). The findings indicated that although CSL referees showed little difference in physical performance when officiating matches of three competitive levels and two halves, distinct rhythms of competitions determined that they needed to adjust running strategies to maintain proper distance to the ball. This study implied that the CSL referees' match performance was affected by the teams' style of play and match status.