This paper is about a model that can select best playing 11 in the Indian cricket team. The performance of each player depends on several factors like the pitch type, the opposition team, the ground, and several others. The proposed model contains data from the One Day International of the past several years of team India. The dataset used for this model created using data from trusted sites like espn.com. This method is distinct in the sense that it gives you a 360-degree view of the player's skill set, be it, batting, bowling, and fielding. The vital part of this model is to find the best all-rounder player. Random forest algorithm used for predicting performance. The player performance classified into several classes, and a random forest classifier used to predict the player's performance. This model gives 76% accuracy for batsmen, around 67% accuracy for bowlers, and 95% for an all-rounder. A model is developed with some extra features like weather, matches played that have not considered in any existing model. Using this model, the best team can be selected to play in given conditions.

Machine learning-based Selection of Optimal sports Team based on the Players Performance

Cricket is an outdoor sport that is extremely popular in Asian countries. Generally, the game is played in three different formats. Each format throws its own set of challenges and teams that are chosen in accordance to the format demands. The outcome of a cricket match is highly unpredictable if the gap between the skill levels of opponent teams is very narrow. It should also be noted that, Cricket is a team game, it is not one player, who determines the course of outcome of the match. Hence selecting the right team for a match by assessing the available player performance and playing conditions has a significant bearing on the outcome of the match. This work is further focused towards developing a robust prediction model that can classify players and accordingly help in predicting the Cricket team especially in the ODI format.

Data mining based Classification of Players in Game of Cricket

This chapter shall provide an overview of the sport of beach soccer as well as sepak takraw. It then provides a brief review on existing literature that employs machine learning on team sports. The clustering techniques, as well as machine learning models that are used in the book, are also deliberated. The participants (subjects) and the performance measures utilised in the study are also reflected in this chapter.

An Overview of Beach Soccer, Sepak Takraw and the Application of Machine Learning in Team Sports

Machine learning in sports analytics is a hot field in computer science. Using machine learning algorithms, we can predict the outcome of a game or performance of teams or individual players and building new strategies for upcoming competitions. Cricket is one of the foremost popular games in the world. Choosing the right player is one of the most challenging work for all kinds of sport and no exception in cricket. In the field of machine learning, several algorithms are used for prediction and classifications. Machine learning algorithms like linear regression, support vector machine, random forest, and naive Bayes with linear and polynomial kernel showed good results to predict the runs scored by a batsman and runs given by a bowler. In this work, we explored the techniques that have been applied to solve the challenges in cricket.

A Survey on Predicting Player’s Performance and Team Recommendation in Game of Cricket Using Machine Learning

Currently, there is a system which can calculate the current run rate and from it calculates the final score of the team. It doesn’t consider the fact about the no of wickets and also where the game is being played. The problem with the current system is that it is unable to predict the score of the 2nd team and also unable to predict the win percentage This system which is developed will have 2 model in it the 1st model predict the score a team will get after playing 50 over from the current situation. The second method predicts the win percentage of both teams even before the match has started this done by player selection. We found that error in regression toward the mean classifier could be a smaller quantity than Naive mathematician in predicting match outcome has been sixty-eight ab initio from 2-15 overs to ninety-one until the top of 42th over.

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Cricket Score Prediction Using Machine Learning

Nowadays Data mining is an emerging field in sports analysis. To choose a most effective team or to predict suitable formation for winning a game or to analyze weakness of the opponent, data mining plays a vital role. However, no research has been done yet for the Bangladesh cricket team. So, we analyzed One Day International cricket data of Bangladesh, based on seventeen features and find out the most important features that are enough for better prediction, not only important features but also can take much decision in our analysis. Our analysis divided into three sections; before starting the game, after one innings played and continuous fall of wickets which leads to the probable prediction of the chances of winning and losing even while the game is in progress. In our analysis, we used the latest version of the decision tree algorithm that is C5.0 on our own collected data set and successfully get the accuracy of 63.63% for before starting the game, 72.72% and 81.81% when Bangladesh played in the first and second innings, finally 80% and 70% for fall of wicket analysis. We also used other classification algorithms and shown the accuracy level of our data set.

Md. Muhaimenur Rahman

Papers

An Analysis of Bangladesh One Day International Cricket Data: A Machine Learning Approach