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Prediction of the 2014 FIFA World CupJared NessECE 539

Introduction

For soccer enthusiasts, the World Cup is the Mecca for international play. Being

able to predict the outcome of such an event would be a gold mine for betting purposes,

as well as, great for publicity regarding the games. What makes the tournament even

more valuable is the fact it only happens once every four years involving international

teams from thirty-two countries. A sense of nationalism occurs during these games with

everyone rooting for their home country. To understand how to predict the tournament, it

is important to understand how it is set up.

This tournament is set up a bit differently then other soccer tournaments. Play

begins years in advance with different countries competing for the top spots in each

division. There are six different divisions of countries marked mainly by continent

guidelines. UEFA contains mostly Europe. CONMEBOL comprises of South America.

CAF holds the African teams. AFC covers Asia. CONCACAF consists of both North and

Central America. OFC is made up of many of the islands off of Australia. Only 32 teams

can be chosen overall allowing only a handful from each division.

Once in the tournament, competition consists of five stages amounting to sixty-

four matches with the thirty-two teams competing. The first stage is a group match, A

through H, with each group consisting of four teams. A team obtains points by winning

or tying, three points and one point consecutively. There are no points given for a loss.

The top two teams in each group move onto stage two. Here sixteen teams compete in a

single elimination matches until two teams are left for the final game, stage five. A third

and fourth place game is also played for the losers of the final four (Huang, 2010).

2

Work Performed

To forecast an all out winner of the cup is not an easy chore. Solving such an

equation requires a multi layer perceptron that involves both feed forward and back

propagation. Initially with a neuronal network like this it is important to set features that

correspond most accurately to a team’s performance. In this study, eight features were

chosen based on each teams performance per game. Those were x1= Goals For, x2=

Shots, x3= Shots on Goal, x4= Fouls Committed, x5= Corner Kicks, x6= Direct Free Kicks

to Goal, x7= Indirect Free Kicks to Goal, x8= Time of Possession. Each one of these

features, with the exception of fouls, helps a team to victory (Fouls are detrimental to a

teams performance).

In order to make the most accurate prediction possible, testing and training data

were received from the first stage of the tournament. From each game of stage 1, the

eight features were taken from either side. This data was then normalized against the

opposing team. To achieve this normalization two separate equations were used.

y1a = x1a/(x1a+x1b) and y2a = x2a/(x1a+x1b)

An example of the process is shown in Table 1.

3

FeaturesSpain Ukrainex y x y

Goals For 4 1 0 0Shots 19 0.7917 5 0.2083Shots on Goal 10 0.8333 2 0.1667Fouls Committed 11 0.44 14 0.56Corner Kicks 7 0.875 1 0.125Direct Free Kicks to Goal 2 1 0 0Indirect Free Kicks to Goal 0.5 0.5 0.5 0.5Time of Possession 54 0.54 46 0.46

Table 1 Feature Normalization.

To set up the testing data for the top two teams from each group, the average of

the normalized values from the three games played for each team was taken. The

averages were placed into a matrix N x 16 consisting of the 8 features from opposing

teams to play in stage 2. After each stage, the resulting feature vector and classes of the

winning team were averaged out and used for the next completion of the MLP.

With testing data received, now training data needs to be implemented. To

achieve maximum correctness, the teams who were undefeated and the teams who were

winless in the first stage had their features taken. In the process of training the MLP, the

values of the undefeated teams were compared to the features of the winless teams

mixing up which one takes the first eight set and which one takes the second eight set.

The two output classes are set to one and zero depending which team lead off in the input

vectors. For Stage 2, 72 training data samples were used. After each iteration of the MLP

or each stage the resulting features and classes were then added on to the end of the

training data resulting then in the end 86 training data samples. This helping to keep the

neural network as current as possible with the stats.

4

The multi-layered perceptron with back propagation was set up in two fashions.

One had three layers with one hidden and the other MLP had four layers total. The input

features counted to 16 with the first 8 attributed to one team and the second 8 to the other

team. The two hidden layers comprised of 22 neurons (both layer types) and 4 neurons

(just the four layer) allowing for proper placement of the input data. The output layer

consisted of two classes. If class 1 received a 1 then the team comprising of the first eight

features won and visa versa. The alpha value was set to 0.1, momentum to 0.5, and an

epoch of K = 35.

Figure 1 Single Hidden Layer MLP

The MLP was tested out on a previous World Cup since the upcoming 2014

World Cup hasn’t happened yet. Data from 2006 World Cup in Germany was used as to

compare the correctness of the program. Two trials were conducted with different sizes of

the hidden layer, one layer and two layers.

Figure 2 A MLP with two hidden layers.

5

Results

1 Hidden Layer

STAGE 2Feature1a Feature2a Feaure3a Feature4a Featrue5a

GERvsSWE 2.388888889 1.686825397 1.861111111 0.11379892 0.616666667ARGvsMEX 2.222222222 0.559356725 0.983333333 -0.14849537 -0.024743231ITAvsAUS 3.333333333 0.593518519 1.521008403 -0.476490776 1.176851852SUIvsUKR 3.333333333 -0.060185185 0.208333333 -0.257440476 0.136507937ENGvsECU 3.333333333 1.751207729 1.70995671 -0.464895636 0.965873016PORvsNED 3.888888889 1.050670961 1.96969697 0.128993518 1.957671958BRAvsGHA 4.333333333 1.080808081 1.967320261 -1.117647059 1.129148629ESPvsFRA 4.166666667 2.931929182 2.89089995 -0.957575758 3.374542125

Feature6a Feature7a Feature8a Feature1b Feature2b Feature3b0.25 0.25 0.488333333 1.166666667 0.536835749 1.0709706960.25 0.25 0.305 0.402777778 0.578253968 0.944444444

1.166666667 0.25 0.305 -0.208333333 0.865811966 1.0880952382.083333333 0.25 0.176666667 1.166666667 0.191944444 0.861111111

0.25 0.25 0.616666667 1.166666667 0.148944193 0.2222222221.166666667 0.25 0.616666667 1.472222222 -0.015057208 0.379411765

0 0.25 0.488333333 0.555555556 0.411764706 0.1456582630.555555556 0 0.763333333 1.166666667 1.152916667 1.007936508

Feature4b Feature5b Feature6b Feature7b Feature8b Class1 Class20.457446986 1.574074074 1.166666667 0.25 0.451666667 1 00.071520344 0.518518519 2.083333333 0.25 0.378333333 1 01.06663825 0.581730769 -0.666666667 0.25 0.341666667 1 0

0.247755102 0.020833333 -0.972222222 0.25 0.121666667 1 00.479166667 -0.122710623 2.083333333 0.25 0.305 1 00.792020918 -0.511764706 1.777777778 0.25 0.506666667 1 01.128070175 -0.647079772 -0.666666667 0.25 0.268333333 1 00.285947712 1.991666667 -2.5 0.25 0.415 1 0

Crate is 100%. Compared to 2006 World Cup 75% accurate.

STAGE 3

6

Feature1a Feature2a Feaure3a Feature4a Featrue5aGERvsARG 2.388888889 1.686825397 1.861111111 0.11379892 0.616666667ITAvsSUI 3.333333333 0.593518519 1.521008403 -0.476490776 1.176851852ENGvsPOR 3.333333333 1.751207729 1.70995671 -0.464895636 0.965873016BRAvsESP 4.333333333 1.080808081 1.967320261 -1.117647059 1.129148629

Feature6a Feature7a Feature8a Feature1b Feature2b Feature3b0.25 0.25 0.488333333 2.222222222 0.559356725 0.983333333

1.166666667 0.25 0.305 3.333333333 -0.060185185 0.2083333330.25 0.25 0.616666667 3.888888889 1.050670961 1.96969697

0 0.25 0.488333333 4.166666667 2.931929182 2.89089995

Feature4b Feature5b Feature6b Feature7b Feature8b Class1 Class2-0.14849537 -0.024743231 0.25 0.25 0.305 1 0

-0.257440476 0.136507937 2.083333333 0.25 0.176666667 1 00.128993518 1.957671958 1.166666667 0.25 0.616666667 0 1-0.957575758 3.374542125 0.555555556 0 0.763333333 0 1



GERvsITA 2.388888889 1.686825397 1.861111111 0.11379892 0.616666667PORvsESP 3.888888889 1.050670961 1.96969697 0.128993518 1.957671958


1.166666667 0.25 0.616666667 4.166666667 2.931929182 2.89089995

Feature4b Feature5b Feature6b Feature7b Feature8b Class1 Class2-0.476490776 1.176851852 1.166666667 0.25 0.305 0 1-0.957575758 3.374542125 0.555555556 0 0.763333333 0 1

Crate is 100%. Compared to 2006 World Cup, 75% accurate.


7

ITAvsESP 3.333333333 0.593518519 1.521008403 -0.476490776 1.176851852GERvsPOR 2.388888889 1.686825397 1.861111111 0.11379892 0.616666667


0.25 0.25 0.488333333 3.888888889 1.050670961 1.96969697

Feature4b Feature5b Feature6b Feature7b Feature8b Class1 Class2-0.957575758 3.374542125 0.555555556 0 0.763333333 0 10.128993518 1.957671958 1.166666667 0.25 0.616666667 0 1


WINNER = PLACE2 = PLACE3 = PLACE4 =

ESP ITA POR GER

2 Hidden Layers

STAGE 2Feature1a Feature2a Feaure3a Feature4a Featrue5a Feature6a

GERvsSWE 2.388888889 1.686825397 1.861111111 0.11379892 0.616666667 0.25ARGvsMEX 2.222222222 0.559356725 0.983333333 -0.14849537 -0.024743231 0.25ITAvsAUS 3.333333333 0.593518519 1.521008403 -0.476490776 1.176851852 1.166666667SUIvsUKR 3.333333333 -0.060185185 0.208333333 -0.257440476 0.136507937 2.083333333ENGvsECU 3.333333333 1.751207729 1.70995671 -0.464895636 0.965873016 0.25PORvsNED 3.888888889 1.050670961 1.96969697 0.128993518 1.957671958 1.166666667BRAvsGHA 4.333333333 1.080808081 1.967320261 -1.117647059 1.129148629 0ESPvsFRA 4.166666667 2.931929182 2.89089995 -0.957575758 3.374542125 0.555555556

Feature7a Feature8a Feature1b Feature2b Feature3b Feature4b Feature5b0.25 0.488333333 1.166666667 0.536835749 1.070970696 0.457446986 1.5740740740.25 0.305 0.402777778 0.578253968 0.944444444 0.071520344 0.5185185190.25 0.305 -0.208333333 0.865811966 1.088095238 1.06663825 0.5817307690.25 0.176666667 1.166666667 0.191944444 0.861111111 0.247755102 0.0208333330.25 0.616666667 1.166666667 0.148944193 0.222222222 0.479166667 -0.1227106230.25 0.616666667 1.472222222 -0.015057208 0.379411765 0.792020918 -0.5117647060.25 0.488333333 0.555555556 0.411764706 0.145658263 1.128070175 -0.647079772

0 0.763333333 1.166666667 1.152916667 1.007936508 0.285947712 1.991666667

8

Feature6b Feature7b Feature8b Class1 Class21.166666667 0.25 0.451666667 1 02.083333333 0.25 0.378333333 1 0-0.666666667 0.25 0.341666667 1 0-0.972222222 0.25 0.121666667 1 02.083333333 0.25 0.305 1 01.777777778 0.25 0.506666667 1 0-0.666666667 0.25 0.268333333 1 0

-2.5 0.25 0.415 1 0

For stage 2 the crate was 100%. Compared to actual results from 2006 this data is 75% correct, with France beating Spain and Ukraine beating Switzerland.


GERvsARG 2.388888889 1.686825397 1.861111111 0.11379892 0.616666667ITAvsSUI 3.333333333 0.593518519 1.521008403 -0.476490776 1.176851852ENGvsPOR 3.333333333 1.751207729 1.70995671 -0.464895636 0.965873016BRAvsESP 4.333333333 1.080808081 1.967320261 -1.117647059 1.129148629


1.166666667 0.25 0.305 3.333333333 -0.060185185 0.2083333330.25 0.25 0.616666667 3.888888889 1.050670961 1.96969697

0 0.25 0.488333333 4.166666667 2.931929182 2.89089995

Feature4b Feature5b Feature6b Feature7b Feature8b Class1 Class2-0.14849537 -0.024743231 0.25 0.25 0.305 1 0

-0.257440476 0.136507937 2.083333333 0.25 0.176666667 1 00.128993518 1.957671958 1.166666667 0.25 0.616666667 1 0-0.957575758 3.374542125 0.555555556 0 0.763333333 1 0

For Stage 3 the crate was 100%. Compared to 2006 data this Stage is only 50% correct with England losing to Portugal and France beating Brazil.


GERvsITA 2.388888889 1.686825397 1.861111111 0.11379892 0.616666667ENGvsBRA 3.333333333 1.751207729 1.70995671 -0.464895636 0.965873016

9

Feature6a Feature7a Feature8a Feature1b Feature2b Feature3b0.25 0.25 0.488333333 3.333333333 0.593518519 1.5210084030.25 0.25 0.616666667 4.333333333 1.080808081 1.967320261

Feature4b Feature5b Feature6b Feature7b Feature8b Class1 Class2-0.476490776 1.176851852 1.166666667 0.25 0.305 1 0-1.117647059 1.129148629 0 0.25 0.488333333 1 0

For Stage 4 the crate was 100%. Here none of the results are correct, 0%, but two of the teams involved are still correct.


GERvsENG 2.388888889 1.686825397 1.861111111 0.11379892 0.616666667ITAvsBRA 3.333333333 0.593518519 1.521008403 -0.476490776 1.176851852


1.166666667 0.25 0.305 4.333333333 1.080808081 1.967320261

Feature4b Feature5b Feature6b Feature7b Feature8b Class1 Class2-0.464895636 0.965873016 0.25 0.25 0.616666667 1 0-1.117647059 1.129148629 0 0.25 0.488333333 1 0

For Stage 5 the crate was 100%. Here none of the results are correct compared to 2006. Italy not Germany won with France in second place, Germany in third place, and Portugal in fourth place.

WINNER = PLACE2 = PLACE3 = PLACE4 =

GER ENG ITA BRA

Training Data from Stage 2

Training Data from STAGE 2Number of Samples Feature1a Feature2a Feaure3a Feature4a Featrue5a

1 0.666666667 0.84 0.833333333 0.423076923 0.7

10

2 1 0.761904762 0.727272727 0.552631579 0.7142857143 1 0.681818182 0.818181818 0.45 0.2857142864 0.666666667 0.84 0.833333333 0.423076923 0.75 1 0.761904762 0.727272727 0.552631579 0.7142857146 1 0.681818182 0.818181818 0.45 0.2857142867 0.666666667 0.84 0.833333333 0.423076923 0.78 1 0.761904762 0.727272727 0.552631579 0.7142857149 1 0.681818182 0.818181818 0.45 0.285714286

10 1 0.592592593 0.727272727 0.408163265 0.71428571411 1 0.782608696 0.909090909 0.513513514 0.92857142912 0.666666667 0.44 0.454545455 0.617021277 0.44444444413 1 0.592592593 0.727272727 0.408163265 0.71428571414 1 0.782608696 0.909090909 0.513513514 0.92857142915 0.666666667 0.44 0.454545455 0.617021277 0.44444444416 1 0.592592593 0.727272727 0.408163265 0.71428571417 1 0.782608696 0.909090909 0.513513514 0.92857142918 0.666666667 0.44 0.454545455 0.617021277 0.44444444419 1 0.590909091 0.666666667 0.5 0.41666666720 1 0.533333333 0.6 0.264705882 0.63636363621 0.8 0.7 0.823529412 0.4 0.78571428622 1 0.590909091 0.666666667 0.5 0.41666666723 1 0.533333333 0.6 0.264705882 0.63636363624 0.8 0.7 0.823529412 0.4 0.78571428625 1 0.590909091 0.666666667 0.5 0.41666666726 1 0.533333333 0.6 0.264705882 0.63636363627 0.8 0.7 0.823529412 0.4 0.78571428628 1 0.791666667 0.833333333 0.44 0.87529 0.75 0.857142857 0.769230769 0.272727273 0.92307692330 1 0.730769231 0.764705882 0.5 0.71428571431 1 0.791666667 0.833333333 0.44 0.87532 0.75 0.857142857 0.769230769 0.272727273 0.92307692333 1 0.730769231 0.764705882 0.5 0.71428571434 1 0.791666667 0.833333333 0.44 0.87535 0.75 0.857142857 0.769230769 0.272727273 0.92307692336 1 0.730769231 0.764705882 0.5 0.71428571437 0.333333333 0.16 0.166666667 0.576923077 0.338 0 0.461538462 0.363636364 0.55 0.57142857139 0.333333333 0.545454545 0.416666667 0.375 0.240 0.333333333 0.16 0.166666667 0.576923077 0.341 0 0.461538462 0.363636364 0.55 0.57142857142 0.333333333 0.545454545 0.416666667 0.375 0.243 0.333333333 0.16 0.166666667 0.576923077 0.344 0 0.461538462 0.363636364 0.55 0.571428571

11

45 0.333333333 0.545454545 0.416666667 0.375 0.246 0.333333333 0.16 0.166666667 0.576923077 0.347 0 0.461538462 0.363636364 0.55 0.57142857148 0.333333333 0.545454545 0.416666667 0.375 0.249 0 0.47826087 0.4 0.394736842 0.650 0 0.266666667 0.1 0.611111111 0.57142857151 0.4 0.230769231 0.230769231 0.628571429 0.152 0 0.47826087 0.4 0.394736842 0.653 0 0.266666667 0.1 0.611111111 0.57142857154 0.4 0.230769231 0.230769231 0.628571429 0.155 0 0.47826087 0.4 0.394736842 0.656 0 0.266666667 0.1 0.611111111 0.57142857157 0.4 0.230769231 0.230769231 0.628571429 0.158 0 0.47826087 0.4 0.394736842 0.659 0 0.266666667 0.1 0.611111111 0.57142857160 0.4 0.230769231 0.230769231 0.628571429 0.161 0.333333333 0.36 0.333333333 0.515151515 0.57142857162 0 0.4 0.4375 0.5625 0.33333333363 0 0.32 0.181818182 0.647058824 0.164 0.333333333 0.36 0.333333333 0.515151515 0.57142857165 0 0.4 0.4375 0.5625 0.33333333366 0 0.32 0.181818182 0.647058824 0.167 0.333333333 0.36 0.333333333 0.515151515 0.57142857168 0 0.4 0.4375 0.5625 0.33333333369 0 0.32 0.181818182 0.647058824 0.170 0.333333333 0.36 0.333333333 0.515151515 0.57142857171 0 0.4 0.4375 0.5625 0.33333333372 0 0.32 0.181818182 0.647058824 0.1

Feature6a Feature7a Feature8a Feature1b Feature2b Feature3b1 0.5 0.63 0.333333333 0.16 0.166666667

0.5 0.5 0.57 0 0.461538462 0.3636363640 0.5 0.43 0.333333333 0.545454545 0.4166666671 0.5 0.63 0 0.47826087 0.4

0.5 0.5 0.57 0 0.266666667 0.10 0.5 0.43 0.4 0.230769231 0.2307692311 0.5 0.63 0.333333333 0.36 0.333333333

0.5 0.5 0.57 0 0.4 0.43750 0.5 0.43 0 0.32 0.1818181821 0.5 0.57 0.333333333 0.16 0.1666666671 0.5 0.63 0 0.461538462 0.3636363640 0.5 0.5 0.333333333 0.545454545 0.416666667

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1 0.5 0.57 0 0.47826087 0.41 0.5 0.63 0 0.266666667 0.10 0.5 0.5 0.4 0.230769231 0.2307692311 0.5 0.57 0.333333333 0.36 0.3333333331 0.5 0.63 0 0.4 0.43750 0.5 0.5 0 0.32 0.1818181820 0.5 0.5 0.333333333 0.16 0.1666666671 0.5 0.53 0 0.461538462 0.363636364

0.5 0.5 0.6 0.333333333 0.545454545 0.4166666670 0.5 0.5 0 0.47826087 0.41 0.5 0.53 0 0.266666667 0.1

0.5 0.5 0.6 0.4 0.230769231 0.2307692310 0.5 0.5 0.333333333 0.36 0.3333333331 0.5 0.53 0 0.4 0.4375

0.5 0.5 0.6 0 0.32 0.1818181821 0.5 0.54 0.333333333 0.16 0.166666667

0.666666667 0.5 0.65 0 0.461538462 0.3636363640 0.5 0.59 0.333333333 0.545454545 0.4166666671 0.5 0.54 0 0.47826087 0.4

0.666666667 0.5 0.65 0 0.266666667 0.10 0.5 0.59 0.4 0.230769231 0.2307692311 0.5 0.54 0.333333333 0.36 0.333333333

0.666666667 0.5 0.65 0 0.4 0.43750 0.5 0.59 0 0.32 0.1818181820 0.5 0.37 0.666666667 0.84 0.8333333330 0.5 0.48 1 0.761904762 0.727272727

0.6 0.5 0.49 1 0.681818182 0.8181818180 0.5 0.37 1 0.592592593 0.7272727270 0.5 0.48 1 0.782608696 0.909090909

0.6 0.5 0.49 0.666666667 0.44 0.4545454550 0.5 0.37 1 0.590909091 0.6666666670 0.5 0.48 1 0.533333333 0.6

0.6 0.5 0.49 0.8 0.7 0.8235294120 0.5 0.37 1 0.791666667 0.8333333330 0.5 0.48 0.75 0.857142857 0.769230769

0.6 0.5 0.49 1 0.730769231 0.7647058820.666666667 0.5 0.39 0.666666667 0.84 0.833333333

0.5 0.5 0.43 1 0.761904762 0.7272727271 0.5 0.33 1 0.681818182 0.818181818

0.666666667 0.5 0.39 1 0.592592593 0.7272727270.5 0.5 0.43 1 0.782608696 0.9090909091 0.5 0.33 0.666666667 0.44 0.454545455

0.666666667 0.5 0.39 1 0.590909091 0.666666667

13

0.5 0.5 0.43 1 0.533333333 0.61 0.5 0.33 0.8 0.7 0.823529412

0.666666667 0.5 0.39 1 0.791666667 0.8333333330.5 0.5 0.43 0.75 0.857142857 0.7692307691 0.5 0.33 1 0.730769231 0.764705882

0.333333333 0.5 0.36 0.666666667 0.84 0.8333333330.5 0.5 0.5 1 0.761904762 0.7272727271 0.5 0.43 1 0.681818182 0.818181818

0.333333333 0.5 0.36 1 0.592592593 0.7272727270.5 0.5 0.5 1 0.782608696 0.9090909091 0.5 0.43 0.666666667 0.44 0.454545455

0.333333333 0.5 0.36 1 0.590909091 0.6666666670.5 0.5 0.5 1 0.533333333 0.61 0.5 0.43 0.8 0.7 0.823529412

0.333333333 0.5 0.36 1 0.791666667 0.8333333330.5 0.5 0.5 0.75 0.857142857 0.7692307691 0.5 0.43 1 0.730769231 0.764705882

Feature4b Feature5b Feature6b Feature7b Feature8b0.576923077 0.3 0 0.5 0.37

0.55 0.571428571 0 0.5 0.480.375 0.2 0.6 0.5 0.49

0.394736842 0.6 0.666666667 0.5 0.390.611111111 0.571428571 0.5 0.5 0.430.628571429 0.1 1 0.5 0.330.515151515 0.571428571 0.333333333 0.5 0.36

0.5625 0.333333333 0.5 0.5 0.50.647058824 0.1 1 0.5 0.430.576923077 0.3 0 0.5 0.37

0.55 0.571428571 0 0.5 0.480.375 0.2 0.6 0.5 0.49

0.394736842 0.6 0.666666667 0.5 0.390.611111111 0.571428571 0.5 0.5 0.430.628571429 0.1 1 0.5 0.330.515151515 0.571428571 0.333333333 0.5 0.36

0.5625 0.333333333 0.5 0.5 0.50.647058824 0.1 1 0.5 0.430.576923077 0.3 0 0.5 0.37

0.55 0.571428571 0 0.5 0.480.375 0.2 0.6 0.5 0.49

0.394736842 0.6 0.666666667 0.5 0.390.611111111 0.571428571 0.5 0.5 0.43

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0.628571429 0.1 1 0.5 0.330.515151515 0.571428571 0.333333333 0.5 0.36

0.5625 0.333333333 0.5 0.5 0.50.647058824 0.1 1 0.5 0.430.576923077 0.3 0 0.5 0.37

0.55 0.571428571 0 0.5 0.480.375 0.2 0.6 0.5 0.49

0.394736842 0.6 0.666666667 0.5 0.390.611111111 0.571428571 0.5 0.5 0.430.628571429 0.1 1 0.5 0.330.515151515 0.571428571 0.333333333 0.5 0.36

0.5625 0.333333333 0.5 0.5 0.50.647058824 0.1 1 0.5 0.430.423076923 0.7 1 0.5 0.630.552631579 0.714285714 0.5 0.5 0.57

0.45 0.285714286 0 0.5 0.430.408163265 0.714285714 1 0.5 0.570.513513514 0.928571429 1 0.5 0.630.617021277 0.444444444 0 0.5 0.5

0.5 0.416666667 0 0.5 0.50.264705882 0.636363636 1 0.5 0.53

0.4 0.785714286 0.5 0.5 0.60.44 0.875 1 0.5 0.54

0.272727273 0.923076923 0.666666667 0.5 0.650.5 0.714285714 0 0.5 0.59

0.423076923 0.7 1 0.5 0.630.552631579 0.714285714 0.5 0.5 0.57

0.45 0.285714286 0 0.5 0.430.408163265 0.714285714 1 0.5 0.570.513513514 0.928571429 1 0.5 0.630.617021277 0.444444444 0 0.5 0.5

0.5 0.416666667 0 0.5 0.50.264705882 0.636363636 1 0.5 0.53

0.4 0.785714286 0.5 0.5 0.60.44 0.875 1 0.5 0.54

0.272727273 0.923076923 0.666666667 0.5 0.650.5 0.714285714 0 0.5 0.59

0.423076923 0.7 1 0.5 0.630.552631579 0.714285714 0.5 0.5 0.57

0.45 0.285714286 0 0.5 0.430.408163265 0.714285714 1 0.5 0.570.513513514 0.928571429 1 0.5 0.630.617021277 0.444444444 0 0.5 0.5

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0.5 0.416666667 0 0.5 0.50.264705882 0.636363636 1 0.5 0.53

0.4 0.785714286 0.5 0.5 0.60.44 0.875 1 0.5 0.54

0.272727273 0.923076923 0.666666667 0.5 0.650.5 0.714285714 0 0.5 0.59

Class1 Class2 Class1trained Class2 Trained1 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 01 0 1 0

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0 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 10 1 0 1

Discussion

Comparing the two different hidden layer sizes, it seems apparent, based on the

error of the predicted values versus the known values, a hidden layer of value one has a

better yield than a layer of 2. This was originally thought to not be the case because of the

twenty-two neuron size of hidden layer one. It was believed that it would need to funnel

down a layer to give appropriate two classification output. However this thought process

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was wrong. Perhaps the three layer MLP is more accurate because it has less varying

weights to manipulate it.

In the game of soccer, there are some variables that are just not predictable such

as ball movement with the feet and injuries. This can be attributed to the poor

calculations of each of the stages. With an error as high as shown, especially with 2

hidden layers, perhaps there is another issue with the program.

One possibility of this, is the set up the training data. This assumption has been

made because the crate after each MLP iteration is 100% having perfect classification. A

problem may be attributed to the fact that undefeated and winless teams compared never

played each other. This could possibly skew the weights when being back propagated.

Before the 2014 FIFA World Cup a trial will be run with training data equal to

undefeated teams and the teams they have played and winless teams and the teams they

have played.

Conclusion

The creation of a three layer multi layer perceptron with back propagation will be

further investigated for the 2014 FIFA World Cup. The improvements to the training data

as well as tweaking the neuronal layers will help improve accuracy of prediction. The

creation of an all out successful program by the World Cup could very well help with

betting purposes but most of all bragging rights.

Data Source

http://www.fifa.com/tournaments/archive/worldcup/germany2006/matches/index.html

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Resource

Huang, Kou-Yuan, and Wen-Lung Chang. "A neural network method for prediction of 2006 World Cup Football Game." Neural Networks (IJCNN), The 2010 International Joint Conference on. IEEE, 2010.

Rotshtein, A. P., M. Posner, and A. B. Rakityanskaya. "Football predictions based on a fuzzy model with genetic and neural tuning." Cybernetics and Systems Analysis 41.4 (2005): 619-630.

"List of football federations." Wikipedia . Wikimedia Foundation, 21 Oct. 2013. Web. 22 Dec. 2013. <http://en.wikipedia.org/wiki/List_of_football_federations

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