Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

289
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
289
Functional Classification of Joints01:09

Functional Classification of Joints

3.6K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
3.6K
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

57
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
57
Structural Classification of Joints01:20

Structural Classification of Joints

3.0K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.0K
Classification of Systems-I01:26

Classification of Systems-I

154
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
154
Classification of Systems-II01:31

Classification of Systems-II

119
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
119

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Long-Term Effect of Intensive Education in Patients With First-Ever Ischemic Stroke.

Journal of the American Heart Association·2026
Same author

Rasch model analysis of herbal medicine consumption and doping risk awareness among athletes.

Scientific reports·2025
Same author

From Congenital Torticollis to Leigh Syndrome: A Case Report of Diagnostic Evolution in an Infant.

Children (Basel, Switzerland)·2025
Same author

Deep Learning-Based Body Shape Clustering Analysis Using 3D Body Scanner: Application of Transformer Algorithm.

Iranian journal of public health·2025
Same author

Development of Reference Point of Doping Attitude and Dispositions for Anti-Doping Education Notification of Athletes: Application of Reference Group Model.

Iranian journal of public health·2024
Same author

Taekwondo win-loss determining factors using data mining-based decision tree analysis: focusing on game analysis for evidence-based coaching.

BMC sports science, medicine & rehabilitation·2024

Related Experiment Video

Updated: May 9, 2025

Comparison of Kinetic Characteristics of Footwork during Stroke in Table Tennis: Cross-Step and Chasse Step
07:19

Comparison of Kinetic Characteristics of Footwork during Stroke in Table Tennis: Cross-Step and Chasse Step

Published on: June 16, 2021

2.6K

Deep learning-based tennis match type clustering.

Hyo-Jun Yun1, Nara Jang2, Minsoo Jeon3

  • 1Center for Sports and Performance Analysis, Korea National Sport University, Seoul, Republic of Korea.

BMC Sports Science, Medicine & Rehabilitation
|April 28, 2025
PubMed
Summary

This study identified four distinct tennis match types: Net Rusher Defensive, All Courter Defensive, Stroke Placement Offensive, and Serve Placement Offensive. These classifications aid in developing targeted game strategies for improved player performance.

Keywords:
Deep learningMatch typeTennisTransformer

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.1K

Related Experiment Videos

Last Updated: May 9, 2025

Comparison of Kinetic Characteristics of Footwork during Stroke in Table Tennis: Cross-Step and Chasse Step
07:19

Comparison of Kinetic Characteristics of Footwork during Stroke in Table Tennis: Cross-Step and Chasse Step

Published on: June 16, 2021

2.6K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.1K

Area of Science:

  • Sports Science
  • Performance Analysis
  • Tennis Analytics

Background:

  • Understanding diverse tennis match dynamics is crucial for strategic development.
  • Previous research has lacked a systematic classification of playing styles.

Purpose of the Study:

  • To define and cluster distinct tennis match types based on playing characteristics.
  • To provide a data-driven framework for categorizing playing styles in professional tennis.

Main Methods:

  • Analysis of 32 matches from the 2023 International Tennis Open Tournament finals.
  • Inclusion of 27 variables across seven domains, informed by expert knowledge.
  • Application of three clustering models, with silhouette coefficient used for optimal cluster identification.

Main Results:

  • Model 3 demonstrated the highest performance with a silhouette coefficient of 0.406.
  • Four distinct tennis match types were identified: NEt Rusher Defensive, ALl Courter Defensive, STroke Placement Offensive, and SErve Placement Offensive.
  • Significant differences were observed in game record variables across the identified clusters.

Conclusions:

  • The study provides foundational data for classifying tennis match types.
  • Findings can inform the establishment of tailored game strategies for each identified type.
  • This classification has the potential to enhance player performance through strategic optimization.