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International Journal of Conceptions on Information Technology and Computing Vol. 4, Issue. 2, March’ 2016; ISSN: 2345 - 9808 Data mining technique for Best 11 Dr. Manjula Sanjay, Shreyas Srinivasan and Rahul Kulkarni Dept. of Master of Computer Applications Dayananda Sagar Academy of Technology and Management Bangalore, India {manjula.dsce, shreyastatachar and rahulkulkarni}@gmail.com The entertainment industry is no exception; for instance, football statistics flood the Internet every now and then. The English Premier League, in particular, produces a great deal of data because it is so popular. Fixed odds betting markets and researchers make use of these data to analyze and predict football match starting XI. Different statistical techniques have been used to develop models for football match starting XI prediction. Although some of these predictions have reasonable levels of accuracy, limitations remain, and including the fact that some features affecting matches are not considered due to their complexity. Abstract—According to recent studies, sports produce significant statistical information about each player, team, games, and seasons. In order to improve the performance of sports, physical education provides scientific principles and techniques. A football team consists of coach, assistant coach, manager, team head, sponsors and team players. This paper deals with prediction of football team using data mining technique and also we can rectify players profile and his performance so that the coach can form a team for a winning combination. We have extracted the data for our analysis from premier league and fantasy premier league. Keywords-Sport matches; data mining techniques; prediction; prediction accuracy; players II. LITERATURE SURVEY In [1], the author has discussed a new generation of techniques and instruments that are being developed to aid humans with intelligent analysis of the high volume of data and that result in critical knowledge. In [2], the author has elaborated on how sports provide huge data about each player, team, game, and season and are thus perfect for testing data mining techniques and instruments. In [3], the authors describe about the data mining techniques that are employed to assist the experts or to be used independently in decision making. The authors [4] have given insights into how sports teams can gain advantage over their rivals by converting data to applied knowledge through appropriate data extraction and interpretation. In [5], the authors have given how the referees are and also gives the data about minute-to-minute data about data about the data how the player plays and what influence the players have on the match. The authors [6] using the regression model using the past 10 years data of the football matches and the significance of the end of the season and how that indirectly affects the players. The authors speak about the home team advantage in association football as played in English Premier League [7]. In [8], the authors have discussed about the level of injuries in the English football association. The home team have advantage of home ground and the effect it has on the players and the team performance has been discussed [9]. In 2010 World cup, there was a display of sheer brilliance by Paul the Octopus. The sea dweller predicted the winning team correctly 8 times when he was tested. There are other predicting techniques, which can predict the outcome only after half time; however, the accuracy is not good. So, for the love of the game and the eagerness to learn new techniques of I. INTRODUCTION The main objective of Data mining systems is to assist coaches and sports managers in not only result prediction, but also player performance assessment, player injury prediction, sports talent identification, and game strategy evaluation. The most popular sport to be played and viewed in most European and South American countries is the football game. In Indian continent, this game has gained the popularity since 21st century, due to televisions and global broadcasting of the games. India is steadily becoming a global figure in the Football (American: Soccer) world, with more and more official football events happening and also major international stars participating in the new Indian Super League. It is the game played on the field with 11 player in each team and team of two playing with each. The goalkeepers are the only players allowed to touch the ball with their hands or arms while it is in play and only in their penalty area. Outfield players mostly use their feet to strike or pass the ball, but may also use their head or torso to do so instead. The team that scores the most goals by the end of the match wins. If the score is level at the end of the game, either a draw is declared or the game goes into extra time and/or a penalty shootout depending on the format of the competition. The Laws of the Game were originally codified in England by The Football Association in 1863. Association football is governed internationally by the International Federation of Association Football (FIFA; French: Fédération Internationale de Football Association), which organizes World Cups for both men and women every four years. It is very clear that a market of growing enthusiasts required to be tapped in with a “Fantasy” football concept. 10 International Journal of Conceptions on Information Technology and Computing Vol. 4, Issue. 2, March’ 2016; ISSN: 2345 - 9808 prediction, we have made an attempt to devise our own method to predict the outcome of a football match [10]. III. DATA SET USED The data set used for analysis is from the official website of premier league and football fantasy. The data set comprises of the attributes of the players at the position which they play, also describes how best the players can play at their position (Goal keeper, defense, mid-field and forwards). IV. METHODOLOGY Prediction system usually works by learning from the past for which the data is gathered[11]. The data available follows a particular pattern. The job of a prediction system is to observe the data and determine the pattern so as to predict the future results. The prediction system is not as easy as it seems. Complexity is its intrinsic characteristic. We have applied regression technique to predict football teams also we can rectify players profile and their performance so that the coach can form a team for a winning combination. From Table 2, it is clear that if the height of the defender is more then the chances of him having the high tackling ability is more, but in some cases other attributes also determine the ability but height being the main attribute. From the above table after the analysis “ NEMANJIA VIDIC & CHRIS SMALLING “ are the best to start in the playing XI ahead of others. Table 3 : Full Back/ Wide defenders Regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. V. EXPERIMENTS AND RESULTS We have used Tanagra for analyzing the football data which was collected from premier league and football fantasy. Table 1: Goal Keeper From table 3, it is clear that the tackling ability of the player can be high if he is able to mark the players assigned for him properly, then his tackling skills will automatically be high. From the above analysis “ MARCOS ROJO & LUKE SHAW” are the favorites to start in the playing XI. Table 4: Central Defenders Table 1 illustrates us which player have the highest goal keeping reflex skills, this can be told by the command he has of the penalty area when he in one on one situation, In other ways if the 1-1 abilities is more then the reflexes will be more, and from the above analysis we can say that the player “ DAVID DE GEA” is the best among all and can be started in playing 11. Table 2: Central defenders 11 International Journal of Conceptions on Information Technology and Computing Vol. 4, Issue. 2, March’ 2016; ISSN: 2345 - 9808 From table 4, the positioning and the vision of the player at that position is perfect, then the chances of passing the ball perfectly is more. Here, passing of the ball refers to passing the ball upfront in the opponent half from the above analysis we can say that “ MICHEAL CARRICK , PAUL SCHOLES & BASTIAN SCHWINSTIEGER “ are the players players in that position . VI. CONCLUSION We have discussed the prediction of football team using regression technique and also we can rectify players profile and his performance so that the coach can form a team for a winning combination. Data mining systems aim to assist coaches and sports managers in not only result prediction, but also player performance assessment, player injury prediction, sports talent identification, and game strategy evaluation. By analyzing the tables 1- table 6 , the best goalkeeper , central defender , wide defenders , central midfielders , attacking midfielders and strikers . Table 5 : Attacking Midfielders The best XI predicted according the analysis David De Gea (GK), Marcos Rojo (RWB), Nemanjia Vidic (CB), Chris Smalling (CB), Luke Shaw (LWB) Micheal Carrick (CM), Paul Scholes (CM), Bastian Schwinstieger (RM), Memphis Depay (LM), JuanMata (CAM), WayneRooney (CF). REFERENCES [1] Binesh, M., “Position of Data Mining in Knowledge Management”, Automobile Industry (In Persian), Vo. 36, 2009. [2] Cao, C., “Sports data mining technology used in basketball outcome prediction”, Master dissertation, Dublin Institute of technology, Ireland, 2012. [3] Schumaker, R., Solieman, O., Chen, H.” Sports data mining. Springer”, 2010. [4] O’Reilly, N., Knight, P., “Knowledge management best practices in national sport organizations”, International Journal of sport management and marketing, 2, 3, 2007, pp.264-280. [5] Buraimo, B., Forrest, D., & Simmons, R.,The 12th man, Refereeing bias in English and German football. Journal of Royal Statistics Society. Series A: Statistician in Society,173(2), 2012, 431-449. [6] Goddard, J. &AsimakopoulosI., Forecasting football results and the efficiency of fixed-odds betting. Journal of Forecasting 23(1), 2004, 51– 66. [7] Clarke, S. R. & Norman, J. M. Home ground advantage of individual clubs in English football. Statistician, 44, 1995, 509- 521. [8] Drawer S. & Fuller C. W. Evaluating the level of injury in English professional football using a risk based assessment process. Br J Sports Med 36. , 2002, 446–451. [9] Carmichael, F., & Thomas, D. (2005). Home-field effect and team performance: Evidence from English premiership football. Journal of Sports Economics, 6, 264–281. [10] Kushal Gevaria, Harshal Sanghavi , Saurabh Vaidya, Prof. Khushali Deulkar. Football Match Winner Prediction: (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 10, October 2015) [11] PK Srimani, MS Koti Medical diagnosis using ensemble classifiersa novel machine-learning approach, Journal of Advanced Computing, pg 9-27, 2013 From Table 4, the dribbling and ball control of the player in these positions depend upon the pace , if the player has good pace , he can dribble the ball well , and inturn his ball control will be at the best . from the above analysis “ JUAN MATA & MEMPHIS DEPAY “ are the favourites to start in the playing XI. Table 6: Attackers/Forwards From the table 6 , we can analyze how good the strikers of the team are from their finishing ability , the finishing ability depends on the composure and the balance. If the composure isn’t good enough the chances of him scoring the goal is less, “ WAYNE ROONEY “ is the best striker to be in the playing XI. 12