Naive Bayes approach to predict the winner of an ODI cricket game

Wickramasinghe, Indika

doi:10.3233/jsa-200436

Cited by 17 publications

(8 citation statements)

References 24 publications

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“…11,12 In literature related to cricket, it can be observed that different feature selection methods have been tried and tested with a variety of approaches. Prominently, Wickramasinghe 8 emphasizes the impact of using different feature selection methods in cricket by comparing the results of univariate, recursive elimination, and principal component analysis. Based on the results, Wickramasinghe claims that the prediction accuracy reaches the highest when using the univariate feature selection method.…”

Section: Feature Analysismentioning

confidence: 99%

“…The results of the study indicate that they have achieved accuracy levels ranging from 65.79% (first over) to 84.15% (19 normalth over) across the second innings. By focusing on ODI cricket, Wickramasinghe 8 presents an in-game predictor with a Naive Bayes based approach where the study predicts the winner after the end of the first innings of the match. In his research study, he has achieved a maximum accuracy of 85.71% while investigating how different combinations of training/testing sample sizes and feature selection methods affect the results.…”

Section: Introductionmentioning

confidence: 99%

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In-game winner prediction and winning strategy generation in cricket: A machine learning approach

Pussella

Silva

Egodawatta

2022

International Journal of Sports Science & Coaching

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This study provides an in-game prediction of the winner for Twenty20 (T20) cricket by focusing on the matches played in the Indian Premier League. For the analysis, data were collected from 812 completed matches played between 2008 and 2020. Initially, several candidate features were identified, and then the LASSO method was applied as a feature selection technique to identify the most important set of features. Based on the identified important features, predictions are provided for each stage of a match where a T20 match can consist of a maximum of 240 stages. For each stage, three classification models were formed using Naive Bayes, Logistic Regression and Support Vector Machines. The prediction accuracy was used for evaluating the findings of the study, and the prediction accuracy of each model indicates the ratio between the number of correctly predicted instances and the total number of predicted instances. Naive Bayes demonstrated prediction accuracies ranging from 53.08% to 91.76% between the first and the 240th stage of matches, whereas the accuracy of Logistic Regression varied from 56.92% to 97.65%. In comparison, Support Vector Machines also displayed comparable outcomes with a prediction accuracy of 55.00% at the first stage, and 90.59% at the 240th stage. Furthermore, a strategy generator that assists the competing teams in the second innings to devise winning strategies, is presented in this study alongside an interactive web-based application for making in-game predictions, and for assisting the end users (players and coaching/managing staff) in decision making, based on the generated winning strategies.

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Section: Feature Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-game winner prediction and winning strategy generation in cricket: A machine learning approach

Pussella

Silva

Egodawatta

2022

International Journal of Sports Science & Coaching

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“…Cricket is a top-rated game. Cricket is reasonably significant within the statistical science community, but this game's unpredictable and unstable nature makes it more challenging to use in standard probability models [1,4,5]. Uncertainty is the feature of cricket, and it increases as the game becomes shorter.…”

Section: Introductionmentioning

confidence: 99%

“…With millions watching cricket, building a model for forecasting the outcome of cricket matches is a real-world task. Sports Analytics is used to determine a cricket match outcome, whether the game is in progress or well before the game begins [3,4].…”

Section: Introductionmentioning

confidence: 99%

Winner Prediction in One Day International Cricket Matches Using Machine Learning Framework: An Ensemble Approach

Ishi¹,

Patil²,

Patil³

et al. 2022

INDJCSE

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Forecasting output of any sports match is in massive demand for the sports industry. Avoiding defeat is the eventual aim of any sports game. Cricket is one of the well-known sports in the world. Cricket is a sport of uncertainty; the state of match gets changed on each ball of the game. Due to that, winner prediction in cricket is becoming a challenging task. In this paper, the focus is given to the prediction of victory in one day international game of cricket using machine learning. In this work, 128 features are considered for the implementation. With these feature sets, three models are proposed based on battingbowling strength of the team, run-scoring pattern for the team, and overall team strength. The concept of ensemble algorithm using voting and stacking classifier is used for prediction with machine learning algorithms. The feature selection methods are used for this work to remove irrelevant or redundant features. The investigation of the prediction model is performed using accuracy, precision, F1-score, recall value. The Logistic Regression and Support Vector Machine give better results than other models with an accuracy of 96.30% for predicting the winner of the ODI match.

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“…Techniques based on computer vision for Physical Activity Recognition (PAR) have mainly used [36]: red-green-blue (RGB) images, optical flow, 2D depth maps, and 3D skeletons. They use diverse algorithms, such as Naive Bayes (NB) [37], Decision Trees (DT) [38], Support Vector Machines (SVM) [39], Nearest Neighbor (NN) [40], Hidden Markov Models (HMM) [41], and Convolutional Neural Networks (CNN) [42].…”

Section: Introductionmentioning

confidence: 99%

Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation

Echeverria

Santos

2021

Sensors

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Technological advances enable the design of systems that interact more closely with humans in a multitude of previously unsuspected fields. Martial arts are not outside the application of these techniques. From the point of view of the modeling of human movement in relation to the learning of complex motor skills, martial arts are of interest because they are articulated around a system of movements that are predefined, or at least, bounded, and governed by the laws of Physics. Their execution must be learned after continuous practice over time. Literature suggests that artificial intelligence algorithms, such as those used for computer vision, can model the movements performed. Thus, they can be compared with a good execution as well as analyze their temporal evolution during learning. We are exploring the application of this approach to model psychomotor performance in Karate combats (called kumites), which are characterized by the explosiveness of their movements. In addition, modeling psychomotor performance in a kumite requires the modeling of the joint interaction of two participants, while most current research efforts in human movement computing focus on the modeling of movements performed individually. Thus, in this work, we explore how to apply a pose estimation algorithm to extract the features of some predefined movements of Ippon Kihon kumite (a one-step conventional assault) and compare classification metrics with four data mining algorithms, obtaining high values with them.

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Naive Bayes approach to predict the winner of an ODI cricket game

Cited by 17 publications

References 24 publications

In-game winner prediction and winning strategy generation in cricket: A machine learning approach

In-game winner prediction and winning strategy generation in cricket: A machine learning approach

Winner Prediction in One Day International Cricket Matches Using Machine Learning Framework: An Ensemble Approach

Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation

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