Application of Association Rule Mining: A case study on team India
21 Feb 2013-pp 1-6
TL;DR: The outcome of the analysis reveals that Team India has performed well in the last ten years as compared to entire period since the team started playing its first match.
Abstract: This paper applies Association Rule Mining algorithm to sports management, especially mining relationship from data on performance of Indian cricket team in one day international (ODI) matches This analysis will help in determining factors associated with the match outcome so as to enable the team to formulate match winning strategies Data has been obtained from secondary sources to obtain deeper insights on playing conditions and the match outcome The association among factors such as outcome of toss, playing in a home ground or playing abroad, batting first or batting second, and the match outcome, ie, win or loss is examined The outcome of the analysis reveals that Team India has performed well in the last ten years (since 2001 to 2010) as compared to entire period since the team started playing its first match (since 1974 to 2010)
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01 Jan 2014
TL;DR: A prediction system that takes in historical match data as well as the instantaneous state of a match, and predicts future match events culminating in a victory or loss is built, demonstrating the performance of the algorithms in predicting the number of runs scored, one of the most important determinants of match outcome.
Abstract: Cricket is a popular sport played by 16 countries, is the second most watched sport in the world after soccer, and enjoys a multi-million dollar industry. There is tremendous interest in simulating cricket and more importantly in predicting the outcome of games, particularly in their one-day international format. The complex rules governing the game, along with the numerous natural parameters affecting the outcome of a cricket match present significant challenges for accurate prediction. Multiple diverse parameters, including but not limited to cricketing skills and performances, match venues and even weather conditions can significantly affect the outcome of a game. The sheer number of parameters, along with their interdependence and variance create a non-trivial challenge to create an accurate quantitative model of a game Unlike other sports such as basketball and baseball which are well researched from a sports analytics perspective, for cricket, these tasks have yet to be investigated in depth. In this paper, we build a prediction system that takes in historical match data as well as the instantaneous state of a match, and predicts future match events culminating in a victory or loss. We model the game using a subset of match parameters, using a combination of linear regression and nearestneighbor clustering algorithms. We describe our model and algorithms and finally present quantitative results, demonstrating the performance of our algorithms in predicting the number of runs scored, one of the most important determinants of match outcome.
41 citations
Cites background from "Application of Association Rule Min..."
...Raj and Padma [15] analyze the Indian cricket team’s One-Day International (ODI) match data and mine association rules from a set of features, namely toss, home or away game, batting first or second and game outcome....
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01 Oct 2015
TL;DR: In this article, a model has been proposed that has two methods, first predicts the score of first innings not only on the basis of current run rate but also considers number of wickets fallen, venue of the match and batting team.
Abstract: Currently, in One Day International (ODI) cricket matches first innings score is predicted on the basis of Current Run Rate which can be calculated as the amount of runs scored per the number of overs bowled. It does not include factors like number of wickets fallen and venue of the match. Furthermore, in second innings there is no method to predict the outcome of the match. In this paper a model has been proposed that has two methods, first predicts the score of first innings not only on the basis of current run rate but also considers number of wickets fallen, venue of the match and batting team. The second method predicts the outcome of the match in the second innings considering the same attributes as of the former method along with the target given to the batting team. These two methods have been implemented using Linear Regression Classifier and Naive Bayes Classifier for first innings and second innings respectively. In both methods, 5 over intervals have been made from 50 overs of the match and at each interval above mentioned attributes have been recorded of all non-curtailed matches played between 2002 and 2014 of every team independently. It has been found in the results that error in Linear Regression classifier is less than Current Run Rate method in estimating the final score and also accuracy of Naive Bayes in predicting match outcome has been 68% initially from 0–5 overs to 91% till the end of 45th over.
38 citations
Cites background from "Application of Association Rule Min..."
...There are certain rules defined to get the batsman out by the bowlers or the fielders....
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TL;DR: Attempts are made to investigate the feasibility of using collective knowledge obtained from microposts posted on Twitter to predict the winner of a Cricket match to classify winning team prediction in a Cricket game before the start of game.
Abstract: Social media has become a platform of first choice where one can express his/her feelings with freedom. The sports and matches being played are also discussed on social media such as Twitter. In this article, efforts are made to investigate the feasibility of using collective knowledge obtained from microposts posted on Twitter to predict the winner of a Cricket match. For predictions, we use three different methods that depend on the total number of tweets before the game for each team, fans sentiments toward each team and fans score predictions on Twitter. By combining these three methods, we classify winning team prediction in a Cricket game before the start of game. Our results are promising enough to be used for winning team forecast. Furthermore, the effectiveness of supervised learning algorithms is evaluated where Support Vector Machine (SVM) has shown advantage over other classifiers.
30 citations
Cites background from "Application of Association Rule Min..."
...Raj and Padma [11] analyzed the data of Indian Cricket team ODI matches and mine association rules from the following set of features: toss, home/away game, batting first or second and game result....
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01 Nov 2018
TL;DR: A model using machine learning algorithms that can predict winning team based on past data available based on individual competency of each player, coordination and team work of whole team evolving and technique followed by each team in each match is proposed.
Abstract: Cricket, the most exciting and fascinating game that the people of all age group are very crazy to see and play. It is considered to be the most interesting and uncertain game. For many it becomes a billion dollar market as they speculate financially, hope of being able to earn profit. Every year the gambling market is going to be on hike as there is much great concern about spot fixing. In this paper, we have studied the problem of predicting the uncertainty of who will win the upcoming IPL match based on the individual competency of each player, coordination and team work of whole team evolving and technique followed by each team in each match. In this paper we propose a model using machine learning algorithms that can predict winning team based on past data available. We applied three machine learning algorithms namely Support Vector Machine, CTree and Naive Bayes and achieved an accuracy of 95.96%, 97.98% and 98.99% respectively.
19 citations
Cites methods from "Application of Association Rule Min..."
...[4] studied data of One Day International Cricket matches of the Indian Cricket team and mined different association rules by using market basket tools with attributes....
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01 May 2015
TL;DR: This paper has applied association rule mining technique on individual Indian players' career-record to obtain the underlying unknown relations of several factors impacting the players' performances, which could help in selecting the best-suited team-combination in a given match condition.
Abstract: 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.
9 citations
Cites methods from "Application of Association Rule Min..."
...Association rule mining technique was used by Raj and Padma [6] for analysing India’s performance in ODIs....
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...[6] Raj, K. Antony Arokia Durai, and Panchapakesan Padma....
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