Implementation of New Ways of Artificial Intelligence in Sports

Nadikattu, Rahul Reddy

doi:10.2139/ssrn.3620017

Cited by 16 publications

(16 citation statements)

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“…The authors summarised existing academic work, in a range of sports, tackling issues such as match outcome modelling, in-game tactical decision making, player performance in fantasy sport games, and managing professional players' sport injuries. Work by Nadikattu ( 2020 ) presents, at an abstract level, discussions on how AI can be implemented in (American) sports from enhancing player performance, to assisting coaches to come up with the right formations and tactics, to developing automated video highlights of sports matches and supporting referees using computer vision applications.…”

Section: Research On Ai and ML In Sportsmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists

Chmait

Westerbeek

2021

Front. Sports Act. Living

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In the last two decades, artificial intelligence (AI) has transformed the way in which we consume and analyse sports. The role of AI in improving decision-making and forecasting in sports, amongst many other advantages, is rapidly expanding and gaining more attention in both the academic sector and the industry. Nonetheless, for many sports audiences, professionals and policy makers, who are not particularly au courant or experts in AI, the connexion between artificial intelligence and sports remains fuzzy. Likewise, for many, the motivations for adopting a machine learning (ML) paradigm in sports analytics are still either faint or unclear. In this perspective paper, we present a high-level, non-technical, overview of the machine learning paradigm that motivates its potential for enhancing sports (performance and business) analytics. We provide a summary of some relevant research literature on the areas in which artificial intelligence and machine learning have been applied to the sports industry and in sport research. Finally, we present some hypothetical scenarios of how AI and ML could shape the future of sports.

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Section: Research On Ai and ML In Sportsmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists

Chmait

Westerbeek

2021

Front. Sports Act. Living

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“…These algorithms have led to developments in the field of sports analysis, particularly in the evaluation of video. Deep learning techniques have impacted how athletes plan for and play the game as well as how sports analysts monitor players, analyze performance on individual and collective levels, determine appropriate wages, and help the coach achieve better results [9]. This leads, in the end, to increased player performance and spectator enjoyment.…”

Section: Introductionmentioning

confidence: 99%

Automatic Team Assignment and Jersey Number Recognition in Football Videos

Alhejaily¹,

Alhejaily²,

Almdahrsh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Football is one of the most-watched sports, but analyzing players' performance is currently difficult and labor intensive. Performance analysis is done manually, which means that someone must watch video recordings and then log each player's performance. This includes the number of passes and shots taken by each player, the location of the action, and whether or not the play had a successful outcome. Due to the time-consuming nature of manual analyses, interest in automatic analysis tools is high despite the many interdependent phases involved, such as pitch segmentation, player and ball detection, assigning players to their teams, identifying individual players, activity recognition, etc. This paper proposes a system for developing an automatic video analysis tool for sports. The proposed system is the first to integrate multiple phases, such as segmenting the field, detecting the players and the ball, assigning players to their teams, and identifying players' jersey numbers. In team assignment, this research employed unsupervised learning based on convolutional autoencoders (CAEs) to learn discriminative latent representations and minimize the latent embedding distance between the players on the same team while simultaneously maximizing the distance between those on opposing teams. This paper also created a highly accurate approach for the real-time detection of the ball. Furthermore, it also addressed the lack of jersey number datasets by creating a new dataset with more than 6,500 images for numbers ranging from 0 to 99. Since achieving a high performance in deep learning requires a large training set, and the collected dataset was not enough, this research utilized transfer learning (TL) to first pretrain the jersey number detection model on another large dataset and then fine-tune it on the target dataset to increase the accuracy. To test the proposed system, this paper presents a comprehensive evaluation of its individual stages as well as of the system as a whole. All codes, datasets, and experiments are available on GitHub (https://github.com/ragadalhejaily/project).

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“…In recent years, there has been a marked evolution in AI technology to have a significant impact across various sectors. The sports industry has emerged as a beneficiary of the advancements in AI due to the facilitation of a transition from conventional to contemporary practices [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review

2023

J Biomed Phys Eng

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Background: Artificial neural network helps humans in a wide range of activities, such as sports. Objective: This paper aims to investigate the effect of artificial intelligence on decision-making related to human gait and sports biomechanics, using computer-based software, and to investigate the impact of artificial intelligence on individuals’ biomechanics during gait and sports performance. Material and Methods: This review was conducted in compliance with the PRISMA guidelines. Abstracts and citations were identified through a search based on Science Direct, Google Scholar, PubMed, Elsevier, Springer Link, Web of Science, and Scopus search engines from 1995 up to 2023 to obtain relevant literature about the impact of artificial intelligence on biomechanics. A total of 1000 articles were found related to biomechanical characteristics of gait and sport and 26 articles were directly pertinent to the subject. Results: The extent of the application of artificial intelligence in sports biomechanics in various fields. In addition, various variables in the fields of kinematics, kinetics, and the field of time can be investigated based on artificial intelligence. Conventional computational techniques are limited by the inability to process data in its raw form. Artificial Intelligence (AI) and Machine Learning (ML) techniques can handle complex and high-dimensional data. Conclusion: The utilization of specialized systems and neural networks in gait analysis has shown great potential in sports performance analysis. Integrating AI into this field would be a significant advancement in sport biomechanics. Coaches and athletes can develop more precise training regimens with specialized performance prediction models.

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Implementation of New Ways of Artificial Intelligence in Sports

Cited by 16 publications

References 0 publications

Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists

Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists

Automatic Team Assignment and Jersey Number Recognition in Football Videos

Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review

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