UNIT 5 RESEARCH PROJECT - OSCAR GAMBLE

Unit 5 Research Project

Worthing College Sports ScienceOscar Gamble

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

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

AbstractI 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.


Contents: GeneralPage 1-Title Page 2- Assessment CriteriaPage 3- Main Aim Title PagePage 4-AbstractPage 5-ContentsPage 6-Appendices ContentsPage 7-Figures and Tables Contents.Page 8- Acknowledgements.Page 9- Introduction.Page 10- Literature Review.Page 11- Project HypothesesPage 12-MethodPage 13- Data CollectionPage 14- Data AnalysisPage 15-ResultsPage 16- DiscussionPage 17-ConclusionPage 18- Assessment Criteria.Page 19- Review 1/3Page 20- Review 2/3Page 21- Review 3/3Page 22- Future Recommendations 1/5Page 23- Future Recommendations 2/5Page 24- Future Recommendations 3/5Page 25- Future Recommendations 4/5Page 26- Future Recommendations 5/5


Contents: Appendices

Page 27 – Appendices Title PagePage 28 – Squawka Stat CentrePage 29 – PremierLeague.com Player Profile examplePage 30 – BMI calculatorPage 31 – PremierLeague.com Assists Data


Contents: Figures and Tables

Page 32 – Figures and Tables Title PagePage 33 – Original Stat Collection SheetPage 34 – Spearman's Rank Order Correlation table Page 35 – Scatter Graph


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.


IntroductionThe 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.


Literature Review and References

My literature review can be found below:

http://tinyurl.com/ofuo3qs




Project Hypothesis/Hypotheses

1. At the end of my research project I expect to see a positive relationship between a healthy fit level of BMI and number of assists made.

2. 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.


Method1. 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

http://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmicalc.htm

http://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmicalc.htm

Data CollectionThe 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.


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.


ResultsAfter 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 very weak. This as a result also gives very weak support to my first hypothesis where I stated that a healthier BMI will result in more assists being made on the pitch, and as a result a positive relationship between the two variables. However the very weak correlation does support greatly my second hypothesis stating that there will no relationship between the two variables.Although the results show a weak correlation, looking at my scatter graph (see Figures and Tables 3) there are some supporting factors showing that there is something there however it is far too small to make any categorical conclusions that BMI has an affect on number of assists. Perhaps any further research could look into this more to try and find a stronger correlation however with my results it is simply not enough to claim a strong relationship between the two.


DiscussionI found that there was a very weak 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 did expect to see some kind of 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.


ConclusionTo 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 very weak support to my hypothesis that the healthier the BMI the more assists the BPL midfielder will make. This suggests that there is only a very small link in my data 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 correlation between the two original variables. I think the reason I found such a weak correlation was simply because a skill like assist making is effected too much by other factors such as skill level. If I was to look at another variable, passing accuracy for example then I know the correlation would be more stronger as all professional players are able to pass and it doesn't’t require too much talent, however with assist making, some players are hugely impressive and some simply aren't.


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)

The conclusions of the project is that there is a very weak 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 very strong one, I believe that there is a relationship present. Although I have not finished my project with a strong 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

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).


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.


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


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.



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.




To investigate if a relationship exists between BMI and pass accuracy per game over a whole season for Premier League Centre midfielders.




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.


Research Project Appendices

Appendix 1

Appendix 2

Appendix 3

Appendix 4

Research Project Figures and Tables

Figures and Tables 1



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BMI and its Relationship with Assists made

Assists

Linear (Assists)

BMI

Assit

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Date post:	06-Aug-2015
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