Hybrid design for sports data visualization using AI and big data analytics

Liu, Aijun; Mahapatra, Rajendra Prasad; Mayuri, A.V.R.

doi:10.1007/s40747-021-00557-w

Cited by 11 publications

(5 citation statements)

References 26 publications

(25 reference statements)

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“…Additionally, due to the availability of new measurement devices (e.g., higher-resolution cameras and wearable sensors) [47][48][49][50][51]57], the volume, variety, and velocity of data have also increased [5]. The era of Big Data presents exciting challenges for sports analysts, with one of the major hurdles being the processing of vast multimedia data, and translating them into practical, applied, and valuable information [5,174]. Ultimately, it is this information that will be communicated to various stakeholders, including athletes, coaches, delegates, and the media.…”

Section: Discussionmentioning

confidence: 99%

Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis

Cossich,

Carlgren,

Holash

et al. 2023

Applied Sciences

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We are currently witnessing an unprecedented era of digital transformation in sports, driven by the revolutions in Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Data Visualization (DV). These technologies hold the promise of redefining sports performance analysis, automating data collection, creating immersive training environments, and enhancing decision-making processes. Traditionally, performance analysis in sports relied on manual data collection, subjective observations, and standard statistical models. These methods, while effective, had limitations in terms of time and subjectivity. However, recent advances in technology have ushered in a new era of objective and real-time performance analysis. AI has revolutionized sports analysis by streamlining data collection, processing vast datasets, and automating information synthesis. VR introduces highly realistic training environments, allowing athletes to train and refine their skills in controlled settings. AR overlays digital information onto the real sports environment, providing real-time feedback and facilitating tactical planning. DV techniques convert complex data into visual representations, improving the understanding of performance metrics. In this paper, we explore the potential of these emerging technologies to transform sports performance analysis, offering valuable resources to coaches and athletes. We aim to enhance athletes’ performance, optimize training strategies, and inform decision-making processes. Additionally, we identify challenges and propose solutions for integrating these technologies into current sports analysis practices. This narrative review provides a comprehensive analysis of the historical context and evolution of performance analysis in sports science, highlighting current methods’ merits and limitations. It delves into the transformative potential of AI, VR, AR, and DV, offering insights into how these tools can be integrated into a theoretical model.

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Section: Discussionmentioning

confidence: 99%

Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis

Cossich,

Carlgren,

Holash

et al. 2023

Applied Sciences

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“…Outlier detection played a crucial role in identifying extreme values within key metrics, such as matches played, batting and bowling averages, catches taken, and performance predictions, ensuring data integrity. Data preprocessing was crucial to ensure data accuracy and involved deduplication and the detection and removal of outliers [3,24,25]. The dataset was split into training and testing sets to assess model performance, and key performance indicators were selected as features for prediction.…”

Section: Methodsmentioning

confidence: 99%

Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023

Sanjaykumar,

Udaichi,

Rajendiran

et al. 2024

HSR

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Background and purpose Cricket, a globally renowned bat and ball sport, is the second most popular sport worldwide. The objective of the study is to utilize machine learning algorithms to predict the performance probabilities of Indian cricket players participating in the ODI Cricket World Cup 2023. Furthermore, we aim to assess and compare the predictive precision of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost. Materials and Methods Data collection centered on Indian One Day International cricket statistics, encompassing matches played, batting and bowling averages, catches taken, and performance predictions. We sourced this data from reputable platforms such as ESPNcricinfo and the International Cricket Council website. Our performance prediction utilized of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost. Comparative analysis was conducted, evaluating these models through essential metrics including Mean Squared Error, Root Mean Squared Error, Mean absolute Error, and R-squared. Results The comparative analysis revealed that the XGBoost model consistently outperformed the others. It exhibited lower errors with the lowest Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error, signifying greater predictive accuracy. XGBoost achieved the highest R-squared value, indicating a robust relationship between predictions and actual performance probabilities. Random Forest produced satisfactory results but fell short of XGBoost's accuracy, while Support Vector Regression displayed less accurate predictions across all metrics. Conclusions This research demonstrates the superior predictive ability of the XGBoost model in the performance probabilities of Indian cricket players in the ODI Cricket World Cup. The practical implications underscore the significance of data-driven insights for team selection and strategy.

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“…Because of advancements in information and communication technologies, AI is increasingly used in sports [16][17][18][19][20]. There is a need to improve the precision of deep learning for use in diagnosing sports injuries.…”

Section: Application Of Ai In Sportsmentioning

confidence: 99%

Cloud IoT-Oriented Neural Network-Based Taekwondo Teaching Scheme

2024

J Multimed Inf Syst

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As Taekwondo is widely accepted, there is a rapid increase in Taekwondo learners. Traditional Taekwondo teaching mode is challenging to meet the increasing educational needs. In Taekwondo teaching, in addition to teaching the basic skills of combat, Taekwondo Poomsae teaching is also required. The quality of Taekwondo Poomsae learning is critical to the overall strength of Taekwondo. For a long time, the judgment of the quality of Poomsae action has relied on the manual evaluation of teachers, which could be more conducive to forming an objective and accurate quality score. Therefore, it is necessary to introduce in-depth learning to find new solutions to these problems. First, a multi-sensor data fusion method is proposed to collect Taekwondo Poomsae action. Then, a Taekwondo Poomsae expertise integrated multiview feature extraction method is proposed. Finally, Convolutional Neural Network (CNN)-Mogrifier Long Short-Term Memory (LSTM) is proposed to train the generated Taekwondo Poomsae action scoring model, identifying whether Taekwondo Poomsae action meets the standard and improve Taekwondo teaching. The effect test of Taekwondo Poomsae action intelligent evaluation shows that the results of the proposed method follow the evaluation given by the teachers, and the discrimination of the scores is moderate, which indicates that the proposed method has good Taekwondo Poomsae action quality evaluation ability. Furthermore, the experimental results show that CNN-Mogrifier LSTM achieves high recognition accuracy of Taekwondo Poomsae, gives comprehensive evaluation and improvement suggestions for action completion quality, breaks away from the restrictions of teachers and venues, and realizes automatic and smart Taekwondo teaching.

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Hybrid design for sports data visualization using AI and big data analytics

Cited by 11 publications

References 26 publications

Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis

Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis

Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023

Cloud IoT-Oriented Neural Network-Based Taekwondo Teaching Scheme

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