Football Club Player Performance & Fee Analysis

This assignment involves a statistical analysis of football club player data, focusing on the relationship between brand value, fee paid to players, and their performance measured by goals scored. The report employs hypothesis testing and correlation analysis using R-squared values, revealing insights into the impact of fees and brand image on player performance.

BUSINESS INTELLIGENCE

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
Problem or opportunity statement for football club........................................................................1
Theoritical framework to link problem statement with business intelligence and analytics
systems.............................................................................................................................................1
Evidence of knowledge of key business intelligence and analytics................................................3
Excel dashboard...........................................................................................................................3
Data import code..........................................................................................................................4
SAS output...................................................................................................................................4
Coorelation................................................................................................................................12
Critical analysis and justification of effective and efficient use of BI and DA Systems
implemented..................................................................................................................................13
Conclusion and recommendation for successful implementation and use of BI and analytics
solution..........................................................................................................................................14
REFERENCES..............................................................................................................................15

INTRODUCTION
Buisness analytics is one of the growing field and in current time period it is widely used
by sports academy and associations to make decisions. In the current report dashboard is
prepared and regression as well as coorelation analysis is done in SAS. On basis of analysis of
results useful decisions are taken in the current report. At end of research study, conclusion
section is prepared in the report.
Problem or opportunity statement for football club
Soccer is the one of the most popular game in the UK and every year number of
tournaments happened in the mentioned nation. It can be observed that football clubs
consistently add members in their group and due to this reason it become very important to
identify number of factors so as to ensure that suitable candidate is included in the football club
so that it become to compete with rival clubs (Alamar, 2013). Usually, it is observed that football
clubs pay million of amount to football players but sometimes they does not receive that support
or performance from the players which they expect from them. Thus, one of the major problem is
to replace those players time to time which do not perform well even million of amount was paid
to them. Major problem associated with football club is that many of its players failed to make a
goal in most of matches. Thus, in order to solve this problem analytics will be used and under
this dashboard is prepared in Excel which will reflect entire performance of the players on single
sheet (Newcastle limited, 2017). Apart from this, in respect to problem statement analytical tools
will be applied on analytics platform or software like SAS.
Theoritical framework to link problem statement with business intelligence
and analytics systems
Buisness intelligence is the one of the field that is gaining wide popularity now a days.
This is because in business intelligence huge chunk of dataset is analyzed by preparing charts on
variables and by aligning them in proffesional manner (Travassos and et.al., 2013). In advanced
softwares like Tableau attractive charts are prepared on dashboard and trend lines are prepared
on them to make prediciton. Apart from this, descriptive analysis results can also be depicted on
them. Business intelligence is nothing but a tool to analyze large data set thart encompass
multiple variables and is related to the previous years time period. On other hand, analytics
1 | P a g e

systems are another option that are also available to the firms. This is because in analytics
software or systems data can be analyzed by using multiple tools like regression analysis, cluster
analysis, decision tree analysis and RFM analysis etc. All these methods have different
properties and can be used to find out answers of different questions. For example regression
analysis method is the one of the tool which reflect the relationship between two variables in
terms of significent difference that exist between them (Mondello and Kamke, 2014). On other
hand, cluster analysis tool reflect grouping of variables on basis of similarity that is identified by
using proximity and pattern matrix. Apart from this, decision tree is another tool which reflect
that if certain condition occur then what may be outcome.
In order to solve problem that is faced by football association dashboard is prepared.
Major problem faced by football association is that its few players are performing well and some
are struggling to make even a single goal in match. Higher amount of contract is signed with
such kind of players and when they does not perform it prove costly football association. In order
to solve this problem dashboard is prepared which reflect that how many goals are made by
single player in last two years. On viewing chart it can be clearly identified that which are the
players that perform worst in most of matches they played for football club. Apart from this, in
second chart that is on left side is indicating million of fee that is paid to each player. By
comparing both charts it can be identified that which are those players to whom higher amount is
paid but they failed to perform well for football club. By doing so answer of problem statement
can be easily identified by using dashboard. Further, brand value chart is also prepared which if
compared with goals that is made by players will reflect that which are those players that have
good brand value but failed to deliever performance in line to expectation (Miller, 2015). In the
chart position of the players of football club is also reflected. Hence, it can be said that through
charts deep analysis of players performance is done and in terms of fee, brand value as well as
goals they made it is identified that which players need to be retained and which one must be
excluded from squad.
Statistical tools like regression analysis will be applied on data set specifically on
variables like fee paid and brand value. By doing so it will be identified whether due to change in
brand value fee also changed or brand value factor play significent role in determination of fee
2 | P a g e

paid to the players of the team. Apart from this, coorelation tool will be applied to identify
relationship between multiple variables like position and goals. By doing so it will be identified
whether position play any role in increasing or decreasing number of goals from player side.
3 | P a g e

Evidence of knowledge of key business intelligence and analytics
Excel dashboard
4 | P a g e

Data import code
/** Import an XLSX file. **/
PROC IMPORT DATAFILE="/folders/myfolders/sasuser.v94/A53064 New.xlsx"
OUT=WORK.A53064_New
DBMS=XLSX
REPLACE;
RUN;
/** Print the results. **/
PROC PRINT DATA=WORK.A53064_New; RUN;
SAS output
Regression analysis
PROC REG;
MODEL Feepaid=Brandvalue;
RUN;
5 | P a g e

Number of Observations Read 1
2
Number of Observations Used 1
2
6 | P a g e

Analysis of Variance
Source DF
Sum of
Squares
Mean
Square F Value Pr > F
Model 1 43.648
86
43.648
86
27.94 0.000
4
Error 10 15.620
31
1.5620
3
Corrected Total 11 59.269
17
Root MSE 1.2498
1
R-Square 0.736
5
Dependent Mean 4.6416
7
Adj R-Sq 0.710
1
Coeff Var 26.925
94
Parameter Estimates
Variable Label DF
Parameter
Estimate
Standard
Error t Value Pr > |t|
Intercept Intercept 1 2.00066 0.61626 3.25 0.008
8
Brandvalue Brandval
ue
1 0.49457 0.09356 5.29 0.000
4
7 | P a g e

Fit Diagnostics for Feepaid
0.7101Adj R-Square
0.7365R-Square
1.562M SE
10Error DF
2Parameters
12Observations
Proportion Less
0.0 0.4 0.8
Residual
0.0 0.4 0.8
Fit–Mean
-2
0
2
4
6
-4 -3 -2 -1 0 1 2 3 4
Residual
0
10
20
30
40
50
Percent
2 4 6 8 10 12
Observation
0.0
0.2
0.4
0.6
Cook's D
2 4 6 8 10
Predicted Value
2
4
6
8
10
Feepaid
-1 0 1
Quantile
-2
-1
0
1
2
Residual
0.2 0.4 0.6 0.8
Leverage
-2
-1
0
1
2
RStudent
2 4 6 8 10
Predicted Value
-2
-1
0
1
2
RStudent
2 4 6 8 10
Predicted Value
-2
-1
0
1
Residual
8 | P a g e

0 5 10 15
Brandvalue
-2
-1
0
1
Residual
Residuals for Feepaid
0 5 10 15
Brandvalue
0
5
10
15
Feepaid
95% Prediction Limits95% Confidence LimitsFit
0.7101Adj R-Square
0.7365R-Square
1.562M SE
10Error DF
2Parameters
12Observations
Fit Plot for Feepaid
9 | P a g e

10 | P a g e

Number of Observations Read 1
2
Number of Observations Used 1
2
Analysis of Variance
Source DF
Sum of
Squares
Mean
Square F Value Pr > F
Model 1 129.030
03
129.030
03
4.14 0.069
4
Error 10 311.969
97
31.1970
0
Corrected Total 11 441.000
00
Root MSE 5.58543 R-Square 0.292
6
Dependent Mean 5.50000 Adj R-Sq 0.221
8
Coeff Var 101.553
22
11 | P a g e

Parameter Estimates
Variable Label DF
Parameter
Estimate
Standard
Error t Value Pr > |t|
Intercept Interce
pt
1 12.34865 3.73366 3.31 0.007
9
Feepaid Feepai
d
1 -1.47547 0.72551 -2.03 0.069
4
12 | P a g e

Fit Diagnostics for Goals
0.2218Adj R-Square
0.2926R-Square
31.197M SE
10Error DF
2Parameters
12Observations
Proportion Less
0.0 0.4 0.8
Residual
0.0 0.4 0.8
Fit–Mean
-5
0
5
10
-15 -5 5 15
Residual
0
10
20
30
40
Percent
2 4 6 8 10 12
Observation
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Cook's D
0 5 10 15 20
Predicted Value
0
5
10
15
20
Goals
-1 0 1
Quantile
-10
-5
0
5
10
Residual
0.1 0.2 0.3 0.4 0.5
Leverage
-2
0
2
4
RStudent
-2.5 0.0 2.5 5.0 7.5 10.0
Predicted Value
-2
0
2
4
RStudent
-2.5 0.0 2.5 5.0 7.5 10.0
Predicted Value
-5
0
5
10
Residual
13 | P a g e

2 4 6 8 10
Feepaid
-5
0
5
10
Residual
Residuals for Goals
2 4 6 8 10
Feepaid
-20
-10
0
10
20
Goals
95% Prediction Limits95% Confidence LimitsFit
0.2218Adj R-Square
0.2926R-Square
31.197M SE
10Error DF
2Parameters
12Observations
Fit Plot for Goals
14 | P a g e

Coorelation
PROC CORR DATA=A53064_New;
RUN;
Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum Label
Goals 1
2
5.5000
0
6.3317
4
66.000
00
1.00000 23.00000 Goals
Feepaid 1
2
4.6416
7
2.3212
3
55.700
00
1.00000 9.90000 Feepaid
Brandvalue 1
2
5.3400
0
4.0277
4
64.080
00
1.00000 16.65000 Brandval
ue
Position_1 1
2
2.1666
7
0.9374
4
26.000
00
1.00000 3.00000 Position_
1
15 | P a g e

Pearson Correlation Coefficients, N = 12
Prob > |r| under H0: Rho=0
Goals Feepaid Brandvalue Position_1
Goals
Goals
1.00000 -
0.54091
0.0694
-0.27049
0.3952
-0.59732
0.0403
Feepaid
Feepaid
-
0.54091
0.0694
1.00000 0.85817
0.0004
0.20541
0.5219
Brandvalue
Brandvalue
-
0.27049
0.3952
0.85817
0.0004
1.00000 -0.07729
0.8113
Position_1
Position_1
-
0.59732
0.0403
0.20541
0.5219
-0.07729
0.8113
1.00000
16 | P a g e

Critical analysis and justification of effective and efficient use of BI and DA
Systems implemented
Excel dashboard
From dashboard it can be observed that in case of Dwight Gayle 23 goals are made in
releavnt duration. Apart from this, Ayoze perez make 12 goals and thereafter on third position in
terms of performance there are Aleksandar Mitrovic and Mohamed Diame who make 6 goals
individually. Remaining players make 1 to 3 goals in last two years. It can be observed that most
of players failed to perform well in past couple of years and there are only 2 to 3 players that are
performing well and playing very important role in success of any team. Highest amount is paid
to Aleksandar Mitrovic 9.9 million and thereafter on second place is Mikel to whom 6.3 million
is paid by football club. It can be observed that mentioned player give worst performance. On
other hand, Perez make 12 gaols but only 1 million amount is paid to it. This reflect that that
there are many players to whom payment of good amount is paid but they failed to perform well.
On other hand, there are few players that perform well even low amount of fee is paid to thme.
Interesting fact is that those players that have good brand value failed to give expected
performance. For example brand value of Aleksandar Mitrovic is 16.65 but it make 6 goals
which can be consdiered as moderate performnace. Dwight Gayle make 23 goals and have brand
value of only 1 million. Apart from this Perez brand value is 2 which is low. It can be said that it
does not mean that if football club purchase player that have high brand value then it will
deliever good performance. It is also observed that in football club most of respondents are
acting as mid fielder which means that club is following specific strategy which playing each
game in specific tournament.
Regression analysis
H0: There is no significent mean difference between fees paid and brand value
H1: There is no significent mean difference between fees paid and brand value
There is significent difference between both variables as value of level of significence is
0.0004<0.05 which means that with change in brand value signficent change comes in fee paid to
players. This also means that brand image is the factor that play an important role in payment of
17 | P a g e

fee to players R square value is 0.73 which means that with change in brand value 73% change
comes in fee amount that is paid to the players for football club.
H0: There is no significent difference between performance of players (goals) and fee paid to
them.
H1: There is no significent difference between performance of players (goals) and fee paid to
them.
Value of level of significence is 0.06>0.05 which reflect that there is no significent
difference between both variables which means that with change in amount of fee paid to players
big difference will not be observed in performance of players. R sqaure value is 29% which
indicate that with change in fee amount that is paid to employees only 29% change can be
observed in their performance. Hence, fee amount can not bring any big change in players
performance.
Coorelation
Goals and fee paid is negatively coorelated as its value is -0.54 which means that with
change in fee paid negative change comes in goals that are made by players. Means that if fee
increased then number of goals decrease in dataset. Position and brand value both are negatively
coorelated to goals which is -0.59 and -0.27. It can be said that brand value if increase the
number of goals decrased in case of players.
Conclusion and recommendation for successful implementation and use of BI
and analytics solution
On basis of above discussion it is concluded that players brand image and fee does not
mean that they will suerly perform well at the ground. It is recommended that firms must
implement flexible fee paying system under which payment must be made and should be change
time to time with change in the performance of the football players. By doing so players of team
can be motivated to work hard for football club and to earn more money. In order to successfully
use BI and analytics system firms can make use of Tableau software instead of Excel in order to
prepare dashboard. Moreover, instead of SAS R open source software can be used because it
18 | P a g e