Physically-interpretable classification of biological network dynamics for complex collective motions

Fujii, Keisuke; Takeishi, Naoya; Hojo, Motokazu; Inaba, Yuki; Kawahara, Yoshinobu

doi:10.1038/s41598-020-58064-w

Cited by 14 publications

(16 citation statements)

References 59 publications

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“…In our case, the validation data was not used and hyperparameters are predetermined as default in Python library “xgboost” (version 1.4.1). “Cross validation procedure” we used here is a test using the test dataset in an analogous way of the usual cross-validation to analyze all data even using a small dataset [ 27 – 29 ]. In the cross-validation, the original sample is randomly partitioned into five equal sized subsamples.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study

et al. 2022

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With the development of measurement technology, data on the movements of actual games in various sports can be obtained and used for planning and evaluating the tactics and strategy. Defense in team sports is generally difficult to be evaluated because of the lack of statistical data. Conventional evaluation methods based on predictions of scores are considered unreliable because they predict rare events throughout the game. Besides, it is difficult to evaluate various plays leading up to a score. In this study, we propose a method to evaluate team defense from a comprehensive perspective related to team performance by predicting ball recovery and being attacked, which occur more frequently than goals, using player actions and positional data of all players and the ball. Using data from 45 soccer matches, we examined the relationship between the proposed index and team performance in actual matches and throughout a season. Results show that the proposed classifiers predicted the true events (mean F1 score > 0.483) better than the existing classifiers which were based on rare events or goals (mean F1 score < 0.201). Also, the proposed index had a moderate correlation with the long-term outcomes of the season (r = 0.397). These results suggest that the proposed index might be a more reliable indicator rather than winning or losing with the inclusion of accidental factors.

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Section: Methodsmentioning

confidence: 99%

Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study

et al. 2022

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“…2) Data Transformation: We hypothesize that the relationship between different time-series data carries more information than individual data streams. In [14], this dependence among observations is explored in a data-driven spectral analysis using the Koopman operator with the objective of understanding complex biological network dynamics. For this purpose, using a ballgame as an example, the authors transformed the data using the Gaussian kernel…”

Section: Methodology For Incident Detectionmentioning

confidence: 99%

Data-Driven Incident Detection in Power Distribution Systems

Aguiar

Gupta

Trevizan

et al. 2021

2021 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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In a power distribution network with energy storage systems (ESS) and advanced controls, traditional monitoring and protection schemes are not well suited for detecting anomalies such as malfunction of controllable devices. In this work, we propose a data-driven technique for the detection of incidents relevant to the operation of ESS in distribution grids. This approach leverages the causal relationship observed among sensor data streams, and does not require prior knowledge of the system model or parameters. Our methodology includes a data augmentation step which allows for the detection of incidents even when sensing is scarce. The effectiveness of our technique is illustrated through case studies which consider active power dispatch and reactive power control of ESS.

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“…In this study, an effective attack is defined as being attacked from the defender's perspectives. When calculating VDEP and VAEP values, we used a cross-validation procedure, which repeats the learning of classifiers using the data of four weeks (36 games) and a prediction using the data of one week (9 games) five times (i.e., data of all five weeks were finally predicted and evaluated) to analyze all games [24][25][26].…”

Section: Datasetmentioning

confidence: 99%

Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study

Toda,

Teranishi,

Kushiro

et al. 2021

Preprint

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With the development of measurement technology, data on the movements of actual games in various sports are available and are expected to be used for planning and evaluating the tactics and strategy. In particular, defense in team sports is generally difficult to be evaluated because of the lack of statistical data. Conventional evaluation methods based on predictions of scores are considered unreliable and predict rare events throughout the entire game, and it is difficult to evaluate various plays leading up to a score. On the other hand, evaluation methods based on certain plays that lead to scoring and dominant regions are sometimes unsuitable to evaluate the performance (e.g., goals scored) of players and teams. In this study, we propose a method to evaluate team defense from a comprehensive perspective related to team performance based on the prediction of ball recovery and being attacked, which occur more frequently than goals, using player actions and positional data of all players and the ball. Using data from 45 soccer matches, we examined the relationship between the proposed index and team performance in actual matches and throughout a season. Results show that the proposed classifiers more accurately predicted the true events than the existing classifiers which were based on rare events (i.e., goals). Also, the proposed index had a moderate correlation with the long-term outcomes of the season. These results suggest that the proposed index might be a more reliable indicator rather than winning or losing with the inclusion of accidental factors.

show abstract

Physically-interpretable classification of biological network dynamics for complex collective motions

Cited by 14 publications

References 59 publications

Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study

Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study

Data-Driven Incident Detection in Power Distribution Systems

Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study

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