Download 3: Data mining technique for Best 11

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.
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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
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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
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[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.
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professional football using a risk based assessment process. Br J Sports
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[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.
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