This paper presents a Weighted Association Rule Mining algorithm for analyzing the Indian cricket team in one day international cricket matches against Sri Lanka and South Africa. This analysis is used by the team for framing game winning plans. The factors such as result of toss, taking part in home condition or away condition, first bat or second bat, and the game result are taken into consideration.

Analyzing the performance of the Indian Cricket Team using Weighted Association Rule Mining

Sport grown from their levels of country to international, essential part of any sport is measure performance analysis of players based on past statistics. Selection of team like cricket, based on player performance as strength and weakness is useful for team selector to make decision. Arrival of world cup, every team finds their best team combination to present on ground to get maximum result in their favour. Large dataset contain much useful information to identify that information uses association rule mining technique. This paper contains association rule mining technique based on individual specific Indian players and make set of players for cricket team. Player's statistics as past records, unknown relation of factors impact player performance, analyzing is helpful to identify best team as per terms. The factors as grounds, venue, strike rate, average, match played by particular player, runs scored, ball faced, high score, not out, match played, numbers of fours an sixes, number of hundreds and fifty's, position of batsman, dismissal, for bowlers factors as wicket taken, maiden's, over bowled, runs given(per match), economy rate, inning played. The result of this study is useful for Indian team selector and manager of team, for strategic planning and decision making and increase winning for team India for the icc world cup 2017.

Selection for balanced cricket team fourth coming ICC Championship 2017

In this paper, an optimized model has been proposed exclusively for the game of cricket whereby a team of fifteen members can be chosen in an unbiased strategy. The proposed method involves a hybrid technique that uses the concept of genetic algorithm (GA) and recurrent neural networks (RNN) for selecting efficient players. Suitable preprocessing is applied to the individual players historical statistics and an initial feature matrix is generated for a player. This feature matrix is fed to the proposed mathematical function used in GA. The GA utilizes a novel fitness function for the minimization of loss-factor. This results in a refined feature matrix. This refined feature matrix is further subjected to RNN to assess a final score for the individual player. Finally, the proposed model comes up with a concurrent rank table that can be referred by the team selectors for an easy and efficient player selection for the upcoming match. It may be essentially considered that the proposed model provides results for the upcoming match or tournament only. Three different authentic datasets have been referred for the purpose of experimental evaluation of the proposed model.
 The results are compared with the recent match performances of each players. Surprisingly, almost equivalent results are obtained which supports the robustness of the scheme. In cases, the proposed scheme outperforms the manual team selection in terms of performance, that indicates that a slight better team could have been created in certain cases. The overall rate of accuracy in terms of predicted list of players for each match vis-a-vis the manually selected players comes out to be satisfactorily 98.5%.

Generic model for automated player selection for cricket teams using recurrent neural networks

Cricket is among the most popular game of the planet. It has all the potential to grab the attention of sports-persons, spectators, financial institutions, and thus ultimately the researchers. Data mining approaches, being the backbone, several prediction schemes and analytics have been presented related to cricket. In this paper, we intend to present a vivid review of several important data mining schemes specifically meant for analytics related to cricket matches that are reported in the literature. The review is made in three distinct approaches based on the format of cricket matches played around the world. These are the one-day matches, T-20 matches, and test matches. Finally, comparative analysis have also been made and are presented separately for all the three formats among the available schemes. Suitable discussion is presented at the end of the paper that may be utilized in the direction of future works.

A Review of Data Analytic Schemes for Prediction of Vivid Aspects in International Cricket Matches

In late days1 because of wide watcher deliver and vast business potential in sports has turned into a dynamic research subject in sports information In each game analysis of the performance of particular player is fundamental to discover out the shortcomings and qualities of the players, which supports the selectors to do their work perfectly The data mining issue with regards to sports data analysis is to separate helpful data covered up in the editorial of cricket match database In this paper we have used association rule mining technique to gain different factors for example venue, bowling position, wickets per inning and so forth Measurable or statistical software will be utilized for data analysis after text extraction Result after data analysis will help the team selector in decision making to select the appropriate combination of team in given condition

Detection of bowler's strong and weak area in cricket through commentary

Performance analysis in every sport is essential to find out the weaknesses and strengths of the players. In a team game like cricket, analysis of career-data is indispensable to get the insight of the players' performance, which helps the selectors to do their job flawlessly and also helps the players' themselves to identify their weaknesses and their strengths. And, when the time comes for world cup cricket, every team looks for their best team-combination to be available on the ground to achieve the desired result in their favour. Association rule mining techniques reveal the unknown information from a huge set of data, and this technique can be used in extracting the information from the performance-data of the players. In this paper, we have applied association rule mining technique on individual Indian players' career-record to obtain the underlying unknown relations of several factors impacting the players' performances. This analysis could help in selecting the best-suited team-combination in a given match condition. The relation among various intrinsic factors such as venue of the match, batting first or second, batting position (of batsmen), strike-rate and runs (of batsmen), economy-rate and wickets taken (of bowlers) is analyzed. The result of this study could be useful for Indian team-captain and team-manager in decision making and strategy planning and could also boost the chances of success for team India for the world cup 2015.

Sanmoy Bhattacherjee

Papers

Association Rule Mining Approach in Strategy Planning for Team India in ICC World Cup 2015