Enhancing Sports Strategy with Video Analytics and Data Mining: Automated Video-Based Analytics Framework for Tennis Doubles

• URL: http://arxiv.org/abs/2507.02906v1
• Date: Tue, 24 Jun 2025 06:48:55 GMT
• Title: Enhancing Sports Strategy with Video Analytics and Data Mining: Automated Video-Based Analytics Framework for Tennis Doubles
• Abstract summary: The framework integrates advanced machine learning techniques including GroundingDINO for precise player localisation through natural language grounding and YOLO-Pose for robust pose estimation.<n>We evaluate our approach on doubles tennis match data and demonstrate that CNN-based models with transfer learning substantially outperform pose-based methods for predicting shot types, player positioning, and formations.
• Score: 3.1130310881807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a comprehensive video-based analytics framework for tennis doubles that addresses the lack of automated analysis tools for this strategically complex sport. Our approach introduces a standardised annotation methodology encompassing player positioning, shot types, court formations, and match outcomes, coupled with a specialised annotation tool designed to meet the unique requirements of tennis video labelling. The framework integrates advanced machine learning techniques including GroundingDINO for precise player localisation through natural language grounding and YOLO-Pose for robust pose estimation. This combination significantly reduces manual annotation effort whilst improving data consistency and quality. We evaluate our approach on doubles tennis match data and demonstrate that CNN-based models with transfer learning substantially outperform pose-based methods for predicting shot types, player positioning, and formations. The CNN models effectively capture complex visual and contextual features essential for doubles tennis analysis. Our integrated system bridges advanced analytical capabilities with the strategic complexities of tennis doubles, providing a foundation for automated tactical analysis, performance evaluation, and strategic modelling in professional tennis.

Related papers

• Topological Data Analysis and Graph-Theoretic Approaches for Tennis Match Prediction [67.45502291821956]
  We present two approaches for predicting tennis match outcomes using topological data analysis and graph theory.<n>The first method applies lower-star filtration to player competitive networks, extracting topological features through persistent homology.<n>The second method uses a modified Katz similarity index with temporal edge weighting, achieving 62.48% accuracy on held-out test data.
  arXiv  Detail & Related papers  (2026-07-26T07:18:43Z)
• TennisExpert: Towards Expert-Level Analytical Sports Video Understanding [16.625250626542208]
  Tennis is one of the most widely followed sports, generating extensive broadcast footage with strong potential for professional analysis, automated coaching, and real-time commentary.<n>However, automatic tennis understanding remains underexplored due to lack of large-scale benchmarks with fine-grained annotations and expert-level commentary.<n>To address these challenges, we introduce TennisVL, a large-scale tennis benchmark comprising over 200 professional matches (471.9 hours) and 40,000+ rally-level clips.<n>We propose TennisExpert, a multimodal tennis understanding framework that integrates a video semantic with a memory-augmented model built on Qwen3-VL-8B
  arXiv  Detail & Related papers  (2026-03-11T15:12:10Z)
• Understanding Long Videos via LLM-Powered Entity Relation Graphs [51.13422967711056]
  GraphVideoAgent is a framework that maps and monitors the evolving relationships between visual entities throughout the video sequence.<n>Our approach demonstrates remarkable effectiveness when tested against industry benchmarks.
  arXiv  Detail & Related papers  (2025-01-27T10:57:24Z)
• Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies [46.1232919707345]
  We present the tennis match simulation environment textitMatch Point AI, in which different agents can compete against real-world data-driven bot strategies. First experiments show that generated shot-by-shot data of simulated tennis matches show realistic characteristics when compared to real-world data. At the same time, reasonable shot placement strategies emerge, which share similarities to the ones found in real-world tennis matches.
  arXiv  Detail & Related papers  (2024-08-12T07:22:46Z)
• ViSTec: Video Modeling for Sports Technique Recognition and Tactical Analysis [19.945083591851517]
  ViSTec is a Video-based Sports Technique recognition model inspired by human cognition. Our approach integrates a graph to explicitly model strategic knowledge in stroke sequences and enhance technique recognition with contextual inductive bias. Case studies with experts from the Chinese national table tennis team validate our model's capacity to automate analysis.
  arXiv  Detail & Related papers  (2024-02-25T02:04:56Z)
• Classification of Tennis Actions Using Deep Learning [0.0]
  We investigate the potential and the challenges of using deep learning to classify tennis actions. Three models of different size were trained and evaluated on the academic tennis dataset THETIS. The best models achieve a generalization accuracy of 74 %, demonstrating a good performance for tennis action classification.
  arXiv  Detail & Related papers  (2024-02-04T15:48:20Z)
• Towards Active Learning for Action Spotting in Association Football Videos [59.84375958757395]
  Analyzing football videos is challenging and requires identifying subtle and diverse-temporal patterns. Current algorithms face significant challenges when learning from limited annotated data. We propose an active learning framework that selects the most informative video samples to be annotated next.
  arXiv  Detail & Related papers  (2023-04-09T11:50:41Z)
• Sports Video Analysis on Large-Scale Data [10.24207108909385]
  This paper investigates the modeling of automated machine description on sports video. We propose a novel large-scale NBA dataset for Sports Video Analysis (NSVA) with a focus on captioning.
  arXiv  Detail & Related papers  (2022-08-09T16:59:24Z)
• P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos [64.57435509822416]
  This work consists of 2,721 video clips collected from the broadcasting videos of professional table tennis matches in World Table Tennis Championships and Olympiads. We formulate two sets of action detection problems -- emphaction localization and emphaction recognition. The results confirm that TheName is still a challenging task and can be used as a special benchmark for dense action detection from videos.
  arXiv  Detail & Related papers  (2022-07-26T08:34:17Z)
• Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation [0.0]
  We introduce a novel method for collecting table tennis video data and perform stroke detection and classification. A diverse dataset containing video data of 11 basic strokes obtained from 14 professional table tennis players has been collected. A temporal convolutional neural network model developed using 2D pose estimation performs multiclass classification of these 11 table tennis strokes.
  arXiv  Detail & Related papers  (2021-04-20T11:32:43Z)
• Unified Graph Structured Models for Video Understanding [93.72081456202672]
  We propose a message passing graph neural network that explicitly models relational-temporal relations. We show how our method is able to more effectively model relationships between relevant entities in the scene.
  arXiv  Detail & Related papers  (2021-03-29T14:37:35Z)
• EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos [26.516909452362455]
  EventAnchor is a data analysis framework to facilitate interactive annotation of racket sports video. Our approach uses machine learning models in computer vision to help users acquire essential events from videos.
  arXiv  Detail & Related papers  (2021-01-13T09:32:05Z)
• Hybrid Dynamic-static Context-aware Attention Network for Action Assessment in Long Videos [96.45804577283563]
  We present a novel hybrid dynAmic-static Context-aware attenTION NETwork (ACTION-NET) for action assessment in long videos. We learn the video dynamic information but also focus on the static postures of the detected athletes in specific frames. We combine the features of the two streams to regress the final video score, supervised by ground-truth scores given by experts.
  arXiv  Detail & Related papers  (2020-08-13T15:51:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.