A machine learning approach to analyze ODI cricket predictors

Jayalath, Kalanka P.

doi:10.3233/jsa-17175

Cited by 20 publications

(15 citation statements)

References 15 publications

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“…The article studied these algorithms due to the binary nature of the classification problem. The winner prediction classification problem is y=f(x), where y indicates the dependent variable and x is a single or set of independent variables [6][7][8][9][10]18,23,26].…”

Section: Learning Algorithms/ Model Selectionmentioning

confidence: 99%

“…As a result, their average or expected performance is determined using the last ten matches performance. External features may not directly impact the player's results but can indirectly affect the match due to circumstances favoring one side over the other [8]. The classification of the feature set is shown in Figure 2.…”

Section: Model Formulation and Feature Constructionmentioning

confidence: 99%

“…A match outcome depends greatly on each player's skill and their success on a given day. However, the game's outcome is also determined by external parameters like toss, home field advantage, pitch, etc., and numerous environmental parameters [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Learning Algorithms/ Model Selectionmentioning

confidence: 99%

Section: Model Formulation and Feature Constructionmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…Kapadia et al [25] used machine learning techniques to solve the same problem but for the cricket world in the Indian Premier League (IPL). Jayalath [24] considered the popular logistic regression model to study the significance of one-day international (ODI) cricket predictors. Kerr [28] presented three experiments in his thesis.…”

Section: Machine Learning Based Sport Data Analysismentioning

confidence: 99%

A Dockerized big data architecture for sports analytics

Özgüven

Gönener

Eken

2022

ComSIS

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The big data revolution has had an impact on sports analytics as well. Many large corporations have begun to see the financial benefits of integrating sports analytics with big data. When we rely on central processing systems to aggregate and analyze large amounts of sport data from many sources, we compromise the accuracy and timeliness of the data. As a response to these issues, distributed systems come to the rescue, and the MapReduce paradigm holds promise for large scale data analytics. We describe a big data architecture based on Docker containers with Apache Spark in this paper. We evaluate the architecture on four data-intensive case studies in sport analytics including structured analysis, streaming, machine learning approaches, and graph-based analysis.

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“…A large number of studies have attempted to focus on Test Cricket players in various ways around distinct theoretical frameworks [5,[11][12][13][14][15][16][17]. Recent research studies on cricket have highlighted the need of examining and comprehending prematch indicators such as toss, ground effects, home ground, and rating of both participating teams, among others [18][19][20][21][22]. Kimber and Hansford studied cricket batting strategy at various levels [23].…”

Section: Introductionmentioning

confidence: 99%

Rating Batters in Test Cricket

Akhtar

Awais

Hussain

et al. 2022

Mathematical Problems in Engineering

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In test cricket, we rated batters’ performance. We have proposed rating criteria as introduced by Scarf, Akhtar, and Rasool in 2014 with additional explanatory variables on the updated data set. The newly added covariates that we included in our research are the home factor and the ground influence. The same rating system is applied in the previous study. Using multinomial logistic regression, sessions from all days of a test match are modeled to determine match outcome probabilities at the end of each session. These models are based on all of the factors that can influence the outcome of a match. It is discovered that the predictors of home factor and pitch quality have a significant impact on the outcome of the test match. We used multinomial logistic regression to model data and estimate the parameters in the models. We forecasted match outcomes using these models at the end of each session and measured batters’ performances by using these probabilities. This process is repeated in a test match at the end of a session, and batters’ contributions to their team score are accumulated. Both teams’ batters are then ranked based on their rating points. The batsmen are rated based on their performance in the match by adding new factors (pitch effect and home advantage) in the models. The proposed ranking is compared with the ICC’s traditional ranking of batters in the test cricket series.

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A machine learning approach to analyze ODI cricket predictors

Cited by 20 publications

References 15 publications

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

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

A Dockerized big data architecture for sports analytics

Rating Batters in Test Cricket

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