The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity

Nguyen, Nguyen Hoang; Nguyen, Diep Thi Ngoc; Ma, Bingkun; Hu, Jinglu

doi:10.1080/24751839.2021.1977066

Cited by 28 publications

(9 citation statements)

References 36 publications

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“…That type of player-fan engagement could not have been feasible inside the bubble during the previous season or this year when spectators were compelled to wear masks, sit irregularly away from the court, and seldom. MaCfarlane also clears, unfortunately, with the reintroduction of player-fan interactions also occurs the rare reappearance of malicious people [11][12]. Such as the Philadelphia fan who threw a bag of popcorn at Russell Westbrook when he left the floor for a locker during a Washington Wizards match; therefore, wrong spacing and bad environment hit the stadium wizard [11].…”

Section: Issues Linked With Host Spacesmentioning

confidence: 99%

“…Moralde mentioned that putting the specialists together is another bigger developmental challenge for NBA [11][12]. Subsequently, the National Basketball Association & USA Basketball likewise recommended putting together a team of top specialists to discuss their practical research plus solutions for any of these problems.…”

Section: Putting Specialists Togethermentioning

confidence: 99%

“…The team's study of a body of earlier research on juvenile sports participation and the associated health, economic, and psychological consequences is discussed in this assessment. Due to the creation of new teams, the integration of African Americans significantly boosted its number and size [12]. Some clubs experienced financial hardships due to moving from leagues with significantly smaller markets.…”

Section: Putting Specialists Togethermentioning

confidence: 99%

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Research on the Development Mode Status Analysis and Optimization Strategy of the NBA

Bai¹

2022

HBEM

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In 1949, the National Basketball League became the National Basketball Association (NBL). The league’s corporate offices, located at 645 Fifth Avenue in New York City’s Olympic Tower, serve as the base of operations for its numerous corporate executives. The All-Star Game, an amateur basketball competition held in February by the National Basketball Association, features 24 of the best players in the league. It is the main event of the three-day NBA All-Star Week, which runs from Friday to Sunday. However, what makes these athletes practice so much? In essence, Basketball is both livelihood & occupation of choice for such athletes. These players enjoy playing the game but are well compensated for their labor. They must also practice daily to keep great basketball talents at a high level if players want to maintain their NBA professions successfully. The research on the NBA’s development mode status analysis and optimization approach is explained in detail in this paper.

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Section: Issues Linked With Host Spacesmentioning

confidence: 99%

Section: Putting Specialists Togethermentioning

confidence: 99%

Section: Putting Specialists Togethermentioning

confidence: 99%

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Research on the Development Mode Status Analysis and Optimization Strategy of the NBA

Bai¹

2022

HBEM

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“…In the traditional EEG emotion recognition method, first, screen out the hand-made features that are more relevant to the emotion recognition task [15], and then, input these emotional features into the machine learning model for classification. However, because deep learning does not require manual feature making and has better learning effect [16], researchers of EEG emotion recognition mostly use deep learning methods for research in recent years [17,18]. Based on the characteristics of EEG signals, it can be extracted from the time domain, frequency domain, timefrequency domain, and nonlinear dynamical system [19].…”

Section: Introductionmentioning

confidence: 99%

Multiview Feature Fusion Attention Convolutional Recurrent Neural Networks for EEG‐Based Emotion Recognition

et al. 2023

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Emotion recognition is essential for computers to understand human emotions. Traditional EEG emotion recognition methods have significant limitations. To improve the accuracy of EEG emotion recognition, we propose a multiview feature fusion attention convolutional recurrent neural network (multi-aCRNN) model. Multi-aCRNN combines CNN, GRU, and attention mechanisms to fuse features from multiple perspectives deeply. Specifically, multiscale CNN can unite elements in the frequency and spatial domains through the convolution of different scales. The role of the attention mechanism is to weigh the frequency domain and spatial domain information of different periods to find more valuable temporal perspectives. Finally, the implicit feature representation is learned from the time domain through the bidirectional GRU to achieve the profound fusion of features from multiple perspectives in the time domain, frequency domain, and spatial domain. At the same time, for the noise problem, we use label smoothing to reduce the influence of label noise to achieve a better emotion recognition classification effect. Finally, the model is validated on the EEG data of 32 subjects on a public dataset (DEAP) by fivefold cross-validation. Multi-aCRNN achieves an average classification accuracy of 96.43% and 96.30% in arousal and valence classification tasks, respectively. In conclusion, multi-aCRNN can better integrate EEG features from different angles and provide better classification results for emotion recognition.

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“…However, this approach may overlook the influence of team quality, as players from powerful teams tend to have higher performance measures. Additionally, [2] employs machine learning and deep learning techniques to predict player performance. They use the CRISP-DM methodology and identify the top relevant features in their regression model.…”

Section: Introductionmentioning

confidence: 99%

Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts

Liu

2019

Lecture Notes in Computer Science

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The field of machine learning has seen tremendous progress in recent years, with deep learning models delivering exceptional performance across a range of tasks. However, these models often come at the cost of interpretability, as they operate as opaque "black boxes" that obscure the rationale behind their decisions. This lack of transparency can limit understanding of the models' underlying principles and impede their deployment in sensitive domains, such as healthcare or finance. To address this challenge, our research team has proposed an innovative framework designed to reconcile the trade-off between model performance and interpretability. Our approach is centered around modular operations on high-dimensional data, which enable end-to-end processing while preserving interpretability. By fusing diverse interpretability techniques and modularized data processing, our framework sheds light on the decision-making processes of complex models without compromising their performance. We have extensively tested our framework and validated its superior efficacy in achieving a harmonious balance between computational efficiency and interpretability. Our approach addresses a critical need in contemporary machine learning applications by providing unprecedented insights into the inner workings of complex models, fostering trust, transparency, and accountability in their deployment across diverse domains.

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The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity

Abstract: Basketball is known for the vast amount of data collected for each player, team, game, and season. As a result, basketball is an ideal domain to work on different data analysis techniques to gain useful insights. In this study, we continued our previous study

Cited by 28 publications

References 36 publications

Research on the Development Mode Status Analysis and Optimization Strategy of the NBA

Research on the Development Mode Status Analysis and Optimization Strategy of the NBA

Multiview Feature Fusion Attention Convolutional Recurrent Neural Networks for EEG‐Based Emotion Recognition

Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts

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