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Unit 5 Research Project
Worthing College Sports Science
Jordan Core
2015

Assessment Criteria
Pages 3-17 & 27-38
• P2: carry out sport science or exercise science-
based research
• P3: collect and record data from the research
project conducted
• M2: correctly analyse collected data, describing
techniques used
• D1: correctly analyse data, explaining techniques
used
• P4: produce a full research report using a
standard scientific structure

Does the average height of a
Barclays Premier League
(2013/2014) teams defence,
including players who appeared on
ten or more occasions, affect the
amount of goals they concede per
season?
By Jordan Core
P2: Carry out / P4: Produce

Abstract
I have researched and I am writing about a correlation between the average height of a premier league
defence, featuring only players who have played 10 or more games and whether that affects the
amount of goals their team concedes. This is a pressing issue in football and teams and managers are
always looking to upgrade their players and their defensive coaches, so if they were to look through my
project they may see that ability doesn’t matter and it may well be physiological factors that affect the
goals a defence concedes. My project is trying to take a look at defences and why they concede goals,
whether its due to the amount of defenders they are playing or the ability, I decided to look at the
height, a physiological factor. I am focusing purely on Premier League defenders in the 2013/2014
season and they must have played more then 10 games. I sat down recording average heights of players,
secondary research, from a single website, then using calculations to work out average heights and
recording the goals conceded. Then going to use Excel to help put these into a graph. The Spearman
rank correlation order, however proves that there is no correlation between the height and goals
conceded, my most important source, my only real source was www.Squawka.co.uk and gave me all of
my statistics. My hypotheses wasn’t proved right, due to the that there 2was no correlation and the
general finding was that the height didn’t play a factor at all, in terms of goals conceded. My results are
now very general and wont impact many managers or teams at all, as it shows there is no correlation so
they will continue to employ and buy players based on ability.

Contents: General
Page 1-Title
Page 2- Assessment Criteria
Page 3- Main Aim Title Page
Page 4-Abstract
Page 5-Contents
Page 6-Appendices Contents
Page 7-Figures and Tables Contents.
Page 8- Acknowledgements.
Page 9- Introduction.
Page 10- Literature Review.
Page 11- Project Hypotheses
Page 12-Method
Page 13- Data Collection
Page 14- Data Analysis
Page 15-Results
Page 16- Discussion
Page 17-Conclusion
Page 18- Assessment Criteria.
Page 19- Review 1/3
Page 20- Review 2/3
Page 21- Review 3/3
Page 22- Future Recommendations 1/5
Page24- Future Recommendations 3/5

Contents: Appendices
Page 27- Appendices Title Page
Page 28- Appendices 1- Screenshot of Squawka.
Page 29- Appendices 2- Screenshot of Data
Collection sheet.

Contents: Figures and Tables
Page 30- Figures and Tables Title Page.
Page 31- Figures and Tables 1- Screenshot of
Spearman rank and graph, excel spread sheet.
final scatter graph, with a line of best fit.
Spearman rank correlation order calculations,
taken from excel.

Acknowledgements
I would like to acknowledge Paul Cox, who’s
consistent perseverance with me and effort
towards lesson plans, made my project possible.
Matthew Smith, who I have worked with on a
number of occasions on our projects, aiding
each other when needs be.

Introduction
The aim of my project is to see if there is a correlation, whether it be positive or negative,
between the average height of a Barclays Premier League teams defence, including players
who featured in ten or more games, affect the amount of goals that they conceded that
season.
I chose this aim as I saw it had a lot of potential to explore different avenues with the
research, I also couldn’t find any research that mirrored mine exactly, so as far as I was
aware it was a completely new research project. Football is also a great interest of mine, so
I could be passionate about my research, which helped me to carry it out. The type of data
collection I would’ve had to carry out was all desk research, which is again the type of
collection I am most fond to. So there were just positives in my eyes, to carrying out the
research.
The timescale of the research was over the course of a Premier League season, so I had a
balanced set of statistics to look at, with a balanced number of players, per team to look at.
I chose a past season, due to the fact that the current one is on going and wouldn’t of been
finished. The timescale was February the 16th to the 27th of March, this is the time that I
had to gather my data and come to my conclusion.

Literature Review and References
https://worthingsportscience.wordpress.com/20
15/02/26/unit-5-literature-review-jordan-core/

Project Hypothesis
I believe that after I have gathered all of my data
and I have analysed it in detail, that I will find a
correlation between the average height of a
Barclays Premier League defence, counting
players who featured in over ten games or more
and the amount of goals that their team
concedes. Due to the fact that taller defenders,
are usually stronger and better in aerial battles,
meaning their team will concede less goals due
to this.

Method
1.The only person carrying out this data is me and nobody else, I single handily compiled it and put
together the research project.
2.I began by setting up a data collection sheet, this enabled me to take down all of my research per
team, giving me sufficient room for calculations to take place, players names to be written, heights to be
written and appearances to be written down. So all of it can be written down, in order to analyse.
3.I then made it a priority to gather the necessary equipment that I needed, gathering a pencil, rubber,
pen and a calculator, so I was prepared to take all the information down.
4.After I had collected all the equipment, I started to take down the information after I found a credited
and a reliable website. I settled for Squawka.com, these are trusted and reliable football statistician
throughout football. I then took down all of the results.
5.Starting off with Arsenal and working down in alphabetical order, finding all of the players who
featured in ten or more games over the course of the season. I then took these players and found out
their height in cm.
6.Still on Squawka.com, I looked at the teams themselves, taking down the amount of goals they
conceded, home, away and in total.
7.After this had been completed, I totalled up the averages, by adding all the heights of those who
featured in ten or more games and then divided the sum by how many of them there were.
8.After completing these simple steps I then had the data, however I needed a visual representation of
what I had collected. So I put my findings into excel; with a average height, team and goals conceded
column, using graph wizard to create my graph.
P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques

Data Collection
All of the data that I collected was secondary, as I was using data previously published
on a statistics website and I used it to form rationales, for my research to support or
counter my findings. The website I used was of course, www.Squawka.co.uk, (See
Appendix 1).I was taking statistics from the website in my own way, using it as
research and then decided whether it was going to support my research or counter act
my research. My data is Ordinal, due to the fact that the ranked data gives no
indication of difference between levels, until I further analyse it. It shows who is best
and who is second best etc., in a ranked order, however until further analysis is
conducted I wont have any indication as to why. Its also continuous as it is data that
has numerical data, as its goals conceded in total, at home or away. The collection
method I used was all desk based research. Sitting at a computer, with my data
collection sheet (See appendix 2) , pen and a calculator. Collecting my statistics and
then working out what they confirm. It was mainly mathematic equations and
spending long periods of time taking down all the statistics and working out
formations. Desk research is the research method I tend to find most interesting as it
allows me to apply IT skills with research skills. My data was all quantitative, as it was
purely numerical, in the shape of goals being conceded, total, home and away,
average heights and heights of individuals. This meant when I was sorting my data it
was much easier, as I was able to use ranking and then display it using Spearman rank
order correlation.

Data Analysis
To organise my data I used a rank order distribution which is placing my data into an ordered list, from
lowest to highest in a single column, I did so in excel in preparation for the creation of my graphs. I
collected this data via desk research, looking at two variables, one being the average height of each
Premier League teams defence in the 2013/2014 season and the other being the amount of goals each
team conceded in that time. Then to display my data, after all was put into graphs I used a Spearman
rank-order correlation, which is a non-parametric test. I chose to do this as it gives a clear indication as
to whether there is a correlation or not, which is key for my research project as I was looking at two
variables. Its similar to the Pearson product moment correlation coefficient in its purpose. However, it’s
a non-parametric method of display and is used when data is like mine, ordinal (ranked), as the average
heights and goals conceded are. Its commonly used when trying to find a relationship between two sets
of ordinal data. The first step is two rank the data from highest to lowest, with 1 being the highest.
Then determining the difference between the data and the place in the tournament. (See Figures and
Tables 1). This then enabled me, on excel to create my Spearmans rank order correlation, giving me a
figure which would point in the direction of whether my data did have a link or not. Which would then
help mew to discuss my results, so without doing this I wouldn’t of had a figure and would have had to
look through all my data and come to a conclusion using my brain and pen and paper, which wouldn’t
have been reliable, nor would it have been valid. Using equations on excel, in the form of Spearmans
rank-order correlation was the most reliable and valid way to do so, when looking at quantitative data
of the ordinal kind, especially when there is two sets of it. As I quoted in my acknowledgements earlier,
Paul Cox helped me a great deal when it came to data analysis. As I was fairly uneducated in the field of
excel, he helped me to understand how to use it and why it was best for my research project to do so.
Describing what I had mentioned above, about the clear correlation figure it gives you at the end of the
equations.

Results
After collection and analysis, I was able to look at my results and see, if indeed the average height of a team, including
players who featured in ten or more games, affects the amount of goals a team concedes. After using excel and the
Spearman rank order correlation, the results showed that my correlation was negative (See Figures and Tables 3). My
Spearmans rank-order correlation was -0.181, meaning there is no correlation what so ever in my experiment. Even if
there is a positive it needs to be quite high, as if it shows a weak correlation then there is likely not to be one at all. But
a negative correlation, like my research project has shown means there is no correlation what so ever between the
average height of a Premier League defence and the amount of goals they concede. Neither does the formation, as I
thought the formation would play a part, as teams such as Hull who play five at the back should concede less due to a
greater amount of defenders defending but it appeared to play no part as they conceded 53 goals, placing them 12th on
the goals rank. The only factors that affected it were the ability of the players, as the teams that finished in the top 6,
conceded less goals, due to the ability of the teams and the financial situation of the clubs themselves, as they can
afford better defenders. This looks at clubs such as Chelsea who conceded 27 goals, the least in the league which saw
them 1st on the goals rank on the excel spreadsheet, averaging less than one goal a game and Manchester City who
conceded 37, slightly more than one a game, seeing them at 2nd on the goals rank on the spreadsheet. These teams,
may have conceded less as they had the highest transfer spend in the Premier League that season, meaning they could
afford defenders of a higher quality than say teams like Crystal Palace. The quality of the coaching staff at the club is
also a great deal better, due to the fact that they have the money to employ better staff, so will concede a great deal
less amount of goals. So there is no relationship between the average height of a premier league defence, featuring
players who appeared in ten or more games and the amount of goals a team concedes. Teams such as Fulham also
disprove my hypotheses as they had the 2nd tallest Premier League defence that year but conceded the most amount
of goals, 85, seeing them in 20th on the goals rank, Sunderland were also joint 2nd with Fulham in terms of defensive
height, conceding 60 goals. This however ties in with the theory that the better the financial situation of the club, the
better the coaching staff, the better the quality of player, this should then lead to the team conceding less goals.
Crystal Palace, however disprove that, as they are a team who aren’t financially well off but have a manager who
focus’s on defending a great deal. As they are 15th on the height rank with an average height of 182cm, conceding 48
goals, less than top six clubs Tottenham and Liverpool, being 8th on the goals rank.

Discussion
After I collected my data, for each team in the 2013/2014 season, I had a formation that they fielded all
season long, the amount of goals they’d conceded at home and away, then totalling them, I also had the
height of ever defender who featured in ten or more matches and the average between them. So I was
able to come to a conclusion, that the average height of a premier league defence, of players who
played ten games or more, does not affect the amount of goals they conceded over the course of the
2013/2014 season. I came to this conclusion by analysing all of my results and seeing no noticeable
pattern. There were lots of teams like Aston Villa with an 184cm average height who conceded 61 goals
across the course of the season. Another team like this was Fulham, who had a very high average of
186cm but conceded 85 goals. Other teams that followed this pattern were Stoke, with an average of
186cm, conceding 52 goals and Sunderland with an average of 186cm conceding 60 goals and West
Bromwich Albion who had an average height of 187cm, conceding 59 goals. Then there were teams
with lower average heights conceding far fewer amount of goals, Arsenal with a average height of
183cm, conceding only 41 goals, Everton with an average height of 182cm, conceding just 39 goals over
the course of the season, however their formation could have something to do with this as they play
five at the back, so allowing more defenders in their half defending and defending set pieces could aid
them. But there were a few tall defences in the cases of the Manchester clubs, that conceded few goals,
Manchester City, conceding just 37 with an average height of 184cm and Manchester United with an
average height of 185cm, conceding 43 goals. Its evident that there was no pattern established and its
just down to the ability of the players and the coaching staff, as some tall defences were conceding 50+
goals, some short defences were conceding between 35 and 45 but some tall defences were also
conceding between 35 and 45. It was also evident that the height of the fullbacks of the team dragged
the teams average down, as fullbacks play a very attacking role in modern football so are required to be
short, fast and have attacking ability. The height of the centre back’s was commonly over 185cm with
fullbacks being around 178cm.

Conclusion
My aim was to see if there was a correlation, whether it be positive or negative, between the average
height of a premier league defence, looking at players who featured in ten or more games and the
amount of goals a team conceded in the 2013/2014 season.
The key trends I saw from my literacy review focused around the research that took place into seeing
how physiological factors affected development of players or how players felt and performed in their
sport. There was no trends that looked at linking variables, looking for a correlation, solely with
quantitative research. Also none looked at my specific league, being the Barclays Premier League in the
season that I targeted, 2013/2014. However I was quite lucky to have found a few that look at premier
league football, such as (Michael Bailey 2012) and (Miller 2009). The ones that aren’t looking at football
are looking at physiological factors, I am also looking at a physiological factor, being height like (Maria
Gil 2007) looked at the selection of young players based on physiological factors. However there are
evident differences in the factors that I have chosen for example some look at different sports to
football, such as basket ball. The physiological factors some of the studies look at, are also different to
height, as (Miller) is looking at body composition and the impacts this has.
My results however do not support my hypotheses, as I believed there would be a correlation between
my two variables, however there wasn’t one which was disappointing. After doing the spearman rank
correlation and seeing that there was a negative link and using my eyes on my data collection sheet (See
Appendix 2) to see if I could see evident links. However there were too many teams, that had high
average heights and high amounts of goals conceded, these were often teams that were low ranked in
the league standings at the end of the season. The teams that finished in the top six, often conceded
below 40 goals and had medium average heights. I believe that my results didn’t support my hypotheses
as there were lots more factors that weren’t taken into account, as physiological factors don’t
necessarily make a defence work well, I didn’t take into account the coaching staff, goalkeeper height.

Assessment Criteria Pages 19-26
• P5: carry out a review of the research project
conducted, describing strengths, areas for
improvement and future recommendations.
• M3: carry out a review of the research project,
explaining strengths, areas for improvement
and future recommendations.
• D2: carry out a review of the research project,
justifying future recommendations for further
research.

Review (1/3)
I feel as though my data collection and analysis, in the research project
went very well, better than expected if anything. As before I had no
clue how to use excel but after a brief one to one with Paul Cox, he
taught me how to use Excel. So I was able to put my data into a
Spearman rank correlation order (See Figures and Tables 1). This then
gave me a correlation (See Figures and Tables 3) although it was
negative, meaning that there was no correlation, the way in which my
data was organised and displayed was very good. I also used my own
data collection sheet (See Appendices 1) this was good to create and
helped me organise all my quantitative data in one place, so I could
carry out equations and then transfer it all into excel to create a
Spearman rank correlation order. I thought this went particularly well,
as I carried it out efficiently and whilst I was focused on task. It was all
carried out to a high standard and the results reflect that.
P5: Describe / M3: Explain / D2: Justify

Review (2/3)
I believe that the original aim I chose was a strength to my research
project. As my knowledge of Premier League football is very vast as it
happens, so when researching and collecting data it was only
increasing. The research project aim, was relatively vague also which
gave me lots of avenues to explore, such as formations, league
positions, style of play etc. It also leaves me plenty of room to
investigate next time I conduct it, as I can investigate for longer periods
of time and expand the leagues I am looking at. The study was also
looking at two variables, so I was able to draw comparisons which gave
me more to discuss and more of my knowledge to build on. This
helped when it came to analysing data and transferring it into graphs
(See Figures and Tables 2). I believe if I would have chosen another
question, I could have struggled a great deal. My prior knowledge
made the research project a great deal easier than if I were to chose
another sport all together, as I would have had to educate myself on
the sport prior to carrying out research and collecting data.

Review (3/3)
I believe that my scope was relatively quite limited, as I
constricted it to a specific season, being 2013/2014 and
one league, being the BPL. I could have broadened my
scope, including the main 4 leagues in England, opening it
up to the UK, including some SPL teams. Then going
abroad, looking at the top five leagues around Europe. I
could've lengthened the time period of my data collection
to longer than a season, so its likely to develop more of a
trend and show more of a correlation, than if I were to do
it for the length of a season as I did. So if all variables and
elements were to get larger and longer, the data pool gets
larger which makes my research project better over all.

Future Recommendations (1/5)
If I were to carry out my research project again I would change the way that I
collected my data, not changing the desk research but changing the way that I
recorded my data. As I created my own data collection sheet for this research
project, writing down the data that I collected in free hand, which was time
consuming, recording results from www.Squawka.co.uk and writing them
down and then transferring the written onto a spreadsheet. Where as if I
were to create a spreadsheet prior to my research, that would calculate the
average height of the defence per team. Then I would be able to move this
data into another spreadsheet on the Spearmans rank-order correlation. So I
could get an immediate result. Doing this would save an awful lot of time and
increase the reliability of the research, as when I am writing my own data
down, the margin for error is higher than it would be if I was carrying it from a
website to a spreadsheet, as with paper it has to be transported one less
place, reducing the margin for error and increasing the validity of the
research, as well as the reliability of the research as a whole. This would also
give me more time to analyse my data and look at my results in more detail,
so I would be able to come to a better conclusion.
P4: Produce / P5: Describe / M3: Explain / D2: Justify

If I were to do the research again I would look at the rest of 3 major leagues in
England, the nPower Championship, League 1 and League 2. Seeing if the average
height of defences in these leagues, as well as the Premier League affect the
amount of goals conceded. As the BPL is the highest footballing standard out of the
4, the other leagues rely a lot on physicality of defenders, as they tend to lack
ability. So if I look at the four leagues then I am able to see more of a trend. Seeing if
height affects the amount of goals conceded to a higher degree in the lower 3
leagues, as they rely an awful lot on physiological factors to be an efficient defence.
This would increase the reliability and validity of my research, as I would have taken
into account the major 4 leagues in England, as opposed to just looking at one
league, to draw conclusions from my research. As this is still reliable research but if I
am looking at the question as a whole, seeing if average heights of defences effect
the amount of goals they concede this helps to increase my data pool. If my data
pool is bigger then I have more data to draw my conclusions from and this again,
increases reliability and validity. This helps also when it comes to drawing results, as
I will have more data to talk about, however most of this data is quantitative. So I
will use quantitative data analysis to analyse the vast majority of it.

If I were to carry this research out again I would increase the period of time
that I collected the data from. As I only looked at a season, this could have
effects on my results. As some teams aren’t consistent throughout seasons
and can go through constant changes in a season, such as injuries, managers
coming and going and players being signed and bought. All these factors can
unsettle a team and could affect the performance of the team as a whole and
the defensive unit. For example if a team is buying a new defender from
abroad, its going to take a while for him to integrate, learn the language and
adapt to the game. If I am monitoring my research over 5 seasons, then all
these factors will even out. As most teams go through at least one of the
three described factors a season, so they will all average out and increase the
reliability and validity of my results as a whole. As a team may have their
main two defenders injured for the majority of a season, they are going to
concede more goals than they would say the season after. So increasing the
time period helps to get a more reliable set of data, as all injuries, managerial
changes and signings are averaged out.

If I were to carry my research my research out again, I would look at European
leagues. This gives me an element of geographical diversity in my testing,
which yet again increases my data pool. Again giving me a great source for
comparison, it also allows me to look further into my research, seeing if
different countries and their footballing leagues defences, goals conceded are
affected by the average height of their leagues defences. As different
countries tend to have different styles of play, some require a team that is
very physiologically advanced, whereas some require technically gifted
players in leagues such as Liga BBVA. It gives me a great source for
comparison and justification, when it comes to finding out my results. It gives
the research a greater sense of creditability as I am looking at other leagues
in other countries, not just focusing on the English game, which makes my
study more ethical also. Giving it a wider reach. Increasing the reliability of
my research, as I am taking into account other regions, the over all
acceptance of my research, amongst the footballing world, as they are more
inclined to look at research covering more than one country, rather than just
on in particular.

I would also change the environment I conducted the research in. I would
seclude myself and work intensively, in a quiet environment for the full data
collection of my research project. To ensure that all of my data is correct, as I
feel like this research project, I often worked in the company of people in a
close environment, which I feel that may have compromised the validity and
reliability of my results. If I were to have conducted them with no distractions
what so ever, then they would be perfect. Especially if I was looking at other
leagues, other countries for longer periods of time than a season, it will
benefit my study, research and data if I am doing so not distracted. This would
increase the validity and reliability of the study a great deal. Making the study
more creditable. If I am able to work alone, in an environment where I work
best its only going to increase the reliability and validity of the research as the
quality of the work, as it’ll be at my optimum due to the environment its
conducted in. My concentration is going to need to be at a much higher level
due to the fact that the time period is increasing and the geographical reach
is expanding.

Research Project Figures
and Tables

Figures and Tables 2
The Y axis is the amount of goals conceded, from the lowest amount being, 27 up to
the 85, however I began at 0 and finished at 90, to make the graph measurements
more proffesional.
27
37
39
41
43
46
48
5051 51 5253 54
59 59606162
74
85
0
10
20
30
40
50
60
70
80
90
1.74 1.76 1.78 1.8 1.82 1.84 1.86 1.88
Average heightts of Premier League defences.
Graph showing whether the average height of Premier League defence
affects the amount of goals they concede.
Goals Conceded
Linear (Goals Conceded)

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