1. Unit 5 Research Project
Worthing College Sports Science
Oscar Gamble
2015
2. 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
3. To investigate if a relationship exists
between BMI and the number of
assists per game over a whole
season for Premier League Centre
midfielders.
By Oscar Gamble
P2: Carry out / P4: Produce
4. Abstract
I have researched and conducted this project to see whether a relationship
does exist between BMI and the number of assists per game over a whole
season for premier league centre midfielders, who have played in 20 or more
games. To determine this I went about trying to find a correlation between
the variables and to see how strong the relationship will be. I thought that
this was an interesting task to look into as midfielders have to be fit to get
into good positions on the pitch and I expected that the ones with the best
BMI ratings would therefore get into better areas and as a result get more
assists. My project is therefore trying to take a look at midfielders and
determine why some players can make plenty of assists and some zero. After
collecting all the necessary data from the 2013-14 Premier League season,
using spearman's rank I found my data had a very very weak correlation of -
0.5. This showed a weak support for one of my hypothesis stating a healthy
BMI will have a positive relationship with number of assists, however strong
for another stating a healthy BMI will have no relationship with the number
of assists. My results are not yet useful and at this current time and will not
impact anyone including managers or players. In fact if anything it supports
the idea of managers buying players because of ability and talent.
P2: Carry out / P4: Produce
6. Contents: Appendices
Page 27 – Appendices Title Page
Page 28 – Squawka Stat Centre
Page 29 – PremierLeague.com Player Profile
example
Page 30 – BMI calculator
Page 31 – PremierLeague.com Assists Data
P2: Carry out / P4: Produce
7. Contents: Figures and Tables
Page 32 – Figures and Tables Title Page
Page 33 – Original Stat Collection Sheet
Page 34 – Spearman's Rank Order Correlation
table
Page 35 – Scatter Graph
P2: Carry out / P4: Produce
8. Acknowledgements
Firstly I would like to acknowledge Paul Cox who
was constantly there to help me with different
aspects of the project. Without his help I know
that this project would not have been possible. I
would also like to acknowledge Scott Goodman
who has helped me with parts of my project I
was unsure of.
P2: Carry out / P4: Produce
9. Introduction
The aim of my project was to investigate if a relationship exists between BMI and the
number of assists per game over a whole season for Premier League Centre
midfielders. It was based on statistics from the 2013-14 season and the centre
midfielders had to have at least played 20 league games to be included in the project.
The reason I chose this aim is because I have a lot of general interest in this area as it
focuses on my preferred sport of football. It also includes data from the league I have
most interest in and follow as a fan, the Barclays Premier League. This helped me feel
passion for my project and I allowed me to really enjoy working on it. As well as this, I
was also unable to find any research that looked at exactly the same as mine or even
similar to an extent. This potentially meant I could find some new findings which could
cause to be quite influential and I liked the idea of this very much. The type of data
collection I carried out was all desk research which I preferred as I feel I am very handy
with the internet and knew the best places to find the most accurate data.
The timescale of the research was over the course of the 2013-14 Premier League
season. The reason I chose a past season was because the current 2014-15 premier
league season is still on going and the data would not be sufficient for my project. The
project timescale therefore lasted from the 23rd January to the 27th March.
P2: Carry out / P4: Produce
10. Literature Review and References
My literature review can be found below:
http://tinyurl.com/ofuo3qs
P2: Carry out / P4: Produce
11. Project Hypothesis
1. At the end of my research project I expect to
see no relationship between a healthy fit level of
BMI and the number of assists made.
P2: Carry out / P4: Produce
12. Method
1. I carried out the research by looking online for data which I then plotted on an excel table.
2. The excel table had headings for 6 different sub-headings. Team, Player name, height(cm), weight(kg),
BMI and number of assists.
3. One website I used for collecting my data was squawka.com. In their very impressive stat centre I was
able to do specific searches to discover how many midfielders from each team had played 20 games or
more in the 2013/14 season (see Appendix 1). I then noted these players and which team they played
for in my excel table.
4. I then moved onto another website to collect the height and weight of each player. For this I used the
premierleague.com and found each players specific profile. All that was necessary was to Google
premierleague.com and the players name (see Appendix 2). All the data was then placed into my excel
table.
5. From there I simply had to make the calculations on the BMI calculator from each players height and
weight. For this I consistently used the same website which was called
http://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmicalc.htm (see Appendix 3.) When a BMI
rating was received I placed it into my excel table.
6. For the final part of data collection I then had to return to the premierleague.com where I went about
finding all players number of assists. I went to the player index table, looked by statistic of assists and
searched each individual player (see Appendix 4.) I then noted all data into my excel table. The final
excel data collection table was then complete (see Figures and Tables 1)
7. I then used the information in this table and inputted it into another table, one which allowed me to
rank the data and also calculated the spearman coefficient rank of my results (see Figures and Tables 2).
8. Then still using excel I was able to create a scatter graph showing the correlation between BMI and
number of assists (see Figures and Tables 3).
P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques
13. Data Collection
The type of data I will be collecting is secondary data as all information will be taken
from statistic websites online. The data is secondary because I have not gone out and
collected the data myself but instead taken other peoples primary data from certain
websites. From these sites I will be discovering which centre midfielders have played
20 games or more in the Barclays Premier League, each centre midfielders BMI from
their height and weight and the number of assists they made in that whole 2013/14
season. All data I am collecting is quantitative data meaning all of my data is
numerical. I will be noting down all data onto an excel file before transferring into a
ranking table where I will create graphs and other diagrams. When calculating the BMI
of the players I will use the same BMI calculator (see Appendix 3) so that all
calculations are the same and as accurate as possible. The design of my research
project is experimental as I am looking at the effects of an independent variable on a
dependant variable. In my project my independent variable is BMI and I am trying to
find out whether it has an effect on the number of assists.
One issue that is vital to take into account when collecting research from online
sources is that not all results are accurate and reliable, however I have ensured that all
sites used correct and reliable. As well as this it is important to not breach any ethical
or legal laws which I made sure I did not do.
P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques
14. Data Analysis
To arrange my data appropriately I used a rank
order distribution which allowed me to place my
data into an ordered list. I then also used a
spearman's rank order correlation which allowed to
me see if there was a clear correlation present
between my two variables (see figures and tables
2). The correlation could be one of five, being very
weak, weak, moderate, strong and very strong. The
My results were then displayed in a scatter graph
(see figures and tables 3) to visibly show the
correlation between the two variables. A line of
best fit was also drawn.
P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques
15. Results
After collection and analysis was complete I was then able to look at my
results to see whether centre midfielders in the BPL with healthier BMI does
effect the number of assists made in each season. Due to the use of
spearman's rank order correlation when collecting my data in excel, I was able
to see that the correlation between my two variables was negatively
moderate. This as a result gives moderate support to my hypothesis where I
stated that there will be no relationship between the two variables of BMI
and the number of assists made.
Due to the results showing a negatively moderate correlation and after
looking at my scatter graph (see Figures and Tables 3) it is clear that my
results actually state there is more of a relationship that BMI as a variable has
no affect on the number of assists made. However this negatively moderate
correlation is still not particularly strong enough to make any categorical
conclusions that BMI doesn't’t have an affect on number of assists. Perhaps
any further research could look into this more to try and find a stronger
correlation and whether BMI has absolutely no effect on the number of
assists, however with my results it is simply not enough.
P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques
16. Discussion
I found that there was a negatively moderate correlation between BMI of BPL midfielders and the
number of assists made in a single season. I did not expect to find the most significant correlation
between these two variables as I know that many other factors such as skill level and playing
team would have played a part, however I was expecting to see perhaps a slight positive
relationship as I expected the more fit players with the healthier BMI ratings to get into better
areas on the pitch and consequently make more assists. It is important to mention the other
factors which would have played a part in my data. Some midfielders are just simply better
players than others, meaning they are more able to pick out a pass and as a result achieve more
assists. Another factor is the team they are playing in. There may be a very talented midfielder
who plays lots of great passes through to his strikers however they may not be as talented and
struggle to finish their shots. This means that the midfielder is then considered to have a bad
number of assists. These are just some of the factors which play a part and would've effected my
project. There were not many trends in my results mainly due to there being a very weak
correlation in my findings. For example Steven Gerrard of Liverpool FC has got one of the highest
BMIs in the whole project however is the highest assister in the whole project. This is because as
we know Steven Gerrard is one of the highest rated midfielders in the world and his technique is
different class, just showing the problems other factors can make. As well as this Gerrard was
playing with one of the top strikers in the world in Luis Suarez, meaning that the majority of the
time when Gerrard would make a pass to him in on goal it would be scored. Showing another
factor effecting my results.
P2: Carry out / P4: Produce
17. Conclusion
To investigate if a relationship exists between BMI and the number of assists per game over a
whole season for Premier League Centre midfielders.
A key trend in my literacy reviews is that when looking at BMI and football players they
commonly looked at all three positions of defence, midfield and attack. Bloomfield and the
International research journal of Applied and Basic sciences both did this which differs to my
project of just focusing on midfielders. The fact my research only focuses on midfielders means
that results for each player are realistic to another however cannot be compared to see how
different positions differ regarding BMI.
My results give a moderate amount of support to my hypothesis that at the end of my research
project I expect to see no relationship between a healthy fit level of BMI and the number of
assists made. This suggests that there is a moderate link in my data between the BMI ratings and
number of assists however not enough to make any assumptions. However it could still be the
basis for further research to be done looking at far more factors and variables to try and establish
a stronger positive or negative correlation between the two original variables. I think the reason I
found such a negatively moderate correlation was simply because a skill like passing to create an
assist is effected way too much by other factors such as skill level. If I was to look at another
variable, passing accuracy for example then I know a correlation would more likely to be positive
and much stronger as all professional players are able to pass and it doesn't’t require too much
talent.
P2: Carry out / P4: Produce
18. 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.
19. Review (1/3)
The conclusions of the project is that there is a negatively
moderate correlation (see Figures and Tables 3) however it
still does meet my project aim as I was only looking to
investigate whether a relationship exists, and although not a
positive or particularly strong relationship was found, there is
definitely a negatively moderate relationship present.
Although I have not finished my project with a strong or
positive correlation between the two variables I am still very
happy with the way my project has turned out as the data is
all reliable and accurate as well as organised and displayed
very well in tables and graphs. I also enjoyed it very much
discovering and learning about BMI effecting assists in the BPL
and I felt that I carried out the task very efficiently.
P5: Describe / M3: Explain / D2: Justify
20. Review (2/3)
I believe that one strength of my research project is my
aim as it investigates a sport I enjoy to both play and
follow, especially the Barclays Premier League. This
meant that I already had plenty of knowledge already
even before my project began as well as obvious
excitement and passion about undergoing this task.
Another strength of my research project was the use of
spearman's rank order correlation which allowed me to
display all my results in a clear very professional way as
well as receive accurate and reliable results regarding the
correlation strength. As well as this I was able to make an
impressive graph to help interpret my final findings and
results (see Tables and Graphs 3).
P5: Describe / M3: Explain / D2: Justify
21. Review (3/3)
What were the areas for improvement of the research
project? (include evidence and specific examples)
One very big and clear issue in my project was that other
factors effected the data I had collected too much. Skill level
of the players for example effected the number of assists
rather than the BMI. I think if I changed the scope of my
project and perhaps looked at the top four teams in all
European leagues my data would therefore look slightly
different as they will all be at a similar level of football and
data will be much more representative to BMI effecting the
number of assists. It will then also not include the teams with
less skill. I believe that this would result in a much stronger
correlation between the two variables than before.
P5: Describe / M3: Explain / D2: Justify
22. Future Recommendations (1/5)
If the project was to be completed again I would make a
number of changes. Firstly I would have changed the scope of
my project. Instead of looking at the whole of the Barclays
Premier League I think I would've looked at the top 4/6 teams
in the top European leagues, such as the French, Italian,
German and Spanish leagues. This would ensure that skill level
has less of an impact on number of assists as all players will be
at similar skill level, rather than a midfielder playing for top of
the league compared to a midfielder playing for bottom of the
league. It would hopefully allow BMI to show whether it
effects the number of assists more than before and hopefully
strengthen the correlation between the two variables.
P4: Produce / P5: Describe / M3: Explain / D2: Justify
23. Future Recommendations (2/5)
Secondly I would have liked to look at data over
a larger period of time, for example 2/3 seasons
long. This data of 3 seasons over 1 will give
much more accurate and reliable measures and
will rule out any unusual performances and give
a fair representation of each players ability. As a
result also it will hopefully strengthen the
correlation between the two variables.
P4: Produce / P5: Describe / M3: Explain / D2: Justify
24. Future Recommendations (3/5)
State a proposal for further research
To investigate if a relationship exists between
BMI and the number of assists per game over a
whole season for the midfielders in the top 4
sides of the English, Spanish, German, Italian
and French leagues.
P4: Produce / P5: Describe / M3: Explain / D2: Justify
25. Future Recommendations (4/5)
State a proposal for further research
To investigate if a relationship exists between
BMI and pass accuracy per game over a whole
season for Premier League Centre midfielders.
P4: Produce / P5: Describe / M3: Explain / D2: Justify
26. Future Recommendations (5/5)
State a proposal for further research
To investigate if a relationship exists between
BMI and successful challenges made per game
over a whole season for the midfielders in the
top 4 sides of the English, Spanish, German,
Italian and French leagues.
P4: Produce / P5: Describe / M3: Explain / D2: Justify
