Applications of Machine Learning in cricket: A systematic review

Wickramasinghe, Indika

doi:10.1016/j.mlwa.2022.100435

Cited by 12 publications

(2 citation statements)

References 32 publications

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“…However, our research demonstrates the potential of composite approaches, with an updated model incorporating positive results from multiple methods achieving superior accuracy. (19)(20)(21) This finding underscores the efficacy of transcending singular models in favour of a multi-pronged approach to this multifaceted problem.…”

Section: Resultsmentioning

confidence: 76%

Utilizing Machine Learning for Comprehensive Analysis and Predictive Modelling of IPL-T20 Cricket Matches

Gour,

Khan

2024

IJST

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Objective : Current study intends to develop a predictive model for Indian Premier League (IPL) cricket match results using machine learning techniques. In order to provide a precise framework that allows for the prediction of IPL match outcomes, it aims to examine player statistics, match dynamics, and historical data. Method : SVM, Random Forest, Logistic Regression, Decision Tree, and KNN models were used in this study to predict player performance on any given day. Form, fitness, and previous results were among the historical player data that were used as characteristics. Each model preceded through training and testing phases, with accuracy, precision, and recall metrics evaluated to determine the most effective algorithm for forecasting player performance. Findings : Final studies indicated that relative team strength of competitor teams, recent form of players, and opponent pairings are distinguishing features for predicting the performance of both players and teams on any given day. The multi-machine learning approach-based model that was constructed demonstrated an accuracy of 0.71, further indicating improved performance for the given challenge. Modelling team strength is similar to modelling individual player batting and bowling performances, which is the cornerstone of our approach. Novelty : This paper was designed based on a novel approach leveraging combinatorial machine learning methods. This has been found to demonstrate unprecedented performance improvement in predicting a player’s performance on a given day. Additionally, the presented approach may prove valuable in opening new avenues to advance machine learning applications in sports analytics by addressing the limitations of existing methods. Keywords: Machine Learning, Sports analytics, SVM, Random Forest, KNN

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Section: Resultsmentioning

confidence: 76%

Utilizing Machine Learning for Comprehensive Analysis and Predictive Modelling of IPL-T20 Cricket Matches

Gour,

Khan

2024

IJST

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“…The deep and informative features extracted from the frame sequence are fed into the Bi-GRU, which learns the temporal motions in both forward and backward directions at every time step. In addition, Wickramasinghe (24) conducted a comprehensive review of the diverse applications of machine learning within the realm of cricket. Reddy and Santhosh (25) propose a two-stream spatial CNN method for recognizing human actions in long video datasets.…”

Section: Related Workmentioning

confidence: 99%

Umpire’s Signal Recognition in Cricket Using an Attention based DC-GRU Network

Dey,

Biswas,

Abualigah

2024

IJE

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Computer vision has extensive applications in various sports domains, and cricket, a complex game with different event types, is no exception. Recognizing umpire signals during cricket matches is essential for fair and accurate decision-making in gameplay. This paper presents the Cricket Umpire Action Video dataset (CUAVd), a novel dataset designed for detecting umpire postures in cricket matches. As the umpire possesses the power to make crucial judgments concerning incidents that occur on the field, this dataset aims to contribute to the advancement of automated systems for umpire recognition in cricket. The proposed Attention-based Deep Convolutional GRU Network accurately detects and classifies various umpire signal actions in video sequences. The method achieved remarkable results on our prepared CUAVd dataset and publicly available datasets, namely HMDB51, Youtube Actions, and UCF101. The DC-GRU Attention model demonstrated its effectiveness in capturing temporal dependencies and accurately recognizing umpire signal actions. Compared to other advanced models like traditional CNN architectures, CNN-LSTM with Attention, and the 3DCNN+GRU model, the proposed model consistently outperformed them in recognizing umpire signal actions. It achieved a high validation accuracy of 94.38% in classifying umpire signal videos correctly. The paper also evaluated the models using performance metrics like F1-Measure and Confusion Matrix, confirming their effectiveness in recognizing umpire signal actions. The suggested model has practical applications in real-life situations such as sports analysis, referee training, and automated referee assistance systems where precise identification of umpire signals in videos is vital.

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