1. Worthing College Sports Science Matthew Smith 2015
Unit 5 Research Project
Worthing College Sports Science
Matthew Smith
2015

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“To investigate if a relationship exists between the acceleration of male, 16-19, participation to performance level footballers, and the ratio of successful 1-on-1s.”
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Abstract
The aim of the study was to investigate if a relationship existed between the acceleration of male, 16-19, participation to performance level footballers, and the ratio of successful 1-on-1s. The study had a sample size of around 15 male footballers, playing in the current season of the Ryman Youth and English School’s Leagues (See Appendix 1). The data collected was shown using a table created in my own style, showing the corresponding player’s acceleration times and their percentage of successful 1-on-1s during match play. The results on the sheet showed that there was a clear correlation between the player’s acceleration and the amount of successful 1-on-1s that were carried out. One aspect to consider for the future, would be looking into the specific player’s dribbling technique, when attempting to get past a player with the ball. As this can also have quite a effect on the success of a 1-on-1 and the retention of the ball.
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Contents: General
Page 2 – Aim Page 3 – Abstract Page 4 – Contents: General Page 5 – Contents : Appendices Page 6 – Contents: Figures & Tables Page 7 – Acknowledgments Page 8 – Introduction Page 9 – Literature Review Page 10 – Project Hypothesis Page 11 – Method Page 12 – Data Collection Page 13 – Data Analysis Page 14 – Results Page 15 – Discussion Page 16 – Conclusion Page 17 – Review 1/3 Page 18 – Review 2/3 Page 19 – Review 3/3 Page 20 – Future Recommendations 1/5 Page 21 - Future Recommendations 2/5 Page 22 - Future Recommendations 3/5 Page 23 - Future Recommendations 4/5 Page 24 - Future Recommendations 5/5
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Contents: Appendices
Page 25 – Research Project Appendices Page 26 – Appendix 1: Screenshot of Ryman Youth Leagues Page 27 – Appendix 2: Photo of Subject (Lee Harding) Page 28 – Appendix 3: Photos of several Subjects on Testing Day
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6. Contents: Figures and Tables
Page 29 – Research Project Figures and Tables Page 30 – Figures and Tables 1: Acceleration Time Table Page 31 – Figures and Tables 2: 1-on-1s Table Page 32 – Figures and Tables 3: Overall Score + Ranking Table
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Acknowledgements
Firstly, I would like to thank my Sport Science classmates, as they gave me advice and aided me during testing and the recording of results. I would also like to thank both Paul Cox and Mark Sambrook for their consistent pieces of advice and overall guidance throughout the project.
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Introduction
“To investigate if a relationship exists between the acceleration of male, 16-19, participation to performance level footballers, and the ratio of successful 1-on-1s.” – I decided this as my primary aim, and basis around my Research Project, as I too, play, and watch a significant amount of football. Yet, as I am only 18 years old, I have still got a lot to learn about the sport itself and the mechanics behind each and every player. With my aim including the acceleration of players, I believe I have done so in this Project. My timescale for this specific Project was 4 weeks.
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9. Literature Review and References
Below, is a link to my Literature Review:
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Project Hypothesis
At the end of my Research Project, I expect to see that wide midfielders i.e. Right and Left Midfielders, will have more success with their 1-on-1s – when looking at the total amount of points in the Final Table of Results - compared to Strikers and Right/Left Backs.
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11. Method
First, set up a 30m Acceleration Test, for the corresponding player to carry out before a match. This must be repeated 3 times, for each and every one of the players, so an average time can be calculated. Also, ensure that the test is carried out before a match, so the player is 100% fit, and the more reliable results can be recorded.
For the test, speed gates or cones can be set 30 metres apart from each other, to signal the start and finish of the sprint.
A table must then be created, to record the times down, and with other headings along the top, ready for the notational analysis.
Then, when the table is ready, the notational analysis can begin. All 1-on1s – both successful and non-successful - between the test subject and an opposition player must be taken down. 1-on-1s as well as personal battles and races for the ball are also considered.
Throughout the notational analysis/match play, the player must be wearing full kit i.e. shin pads, long socks, boots, to make the test as realistic as possible. (See Appendix 2)
After all of the 1-on-1s have been noted, for each of the players, the table can begin to get filled-out. As this is the case, the ratio of successful 1-on-qs can also be calculated. For example, when a player carried out 34 successful 1-on-1s out of a total 120, 34 can be divided by 120. This answer must then be multiplied by 100, and this result is the ‘Ratio’ or ‘Percentage’ number.
P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques

12. Data Collection
The majority of the data that I collected was primary, as I was recording acceleration times using my own tools and in my own time, for my own project. Before I carried out my final data collection method, I did look at the several different methods as to how to collect my data. However, as I had an idea in mind from the first instance, the amount of secondary data/research was minimal. In my opinion, my data falls under the categorisation of ‘Ordinal’, as after the times are recorded, the best and the worst acceleration times will be noted, yet not released, as this is not the aim of my project. Also, the data is continuous, as the data has numerical value; the data is acceleration times, and the number of successful 1-on-1s. The collection methods that I used throughout my project were first, field-based research – carrying out tests on a football field, and the recording of 1-on-1s, with the use of a pen - and desk-based research (calculating the individual player’s ratio of successful 1-on-1s to overall attempted). (See Figures and Tables 1.) Field-based research seemed to be the more interesting method of research for me, as it allowed me to simply watch a clip, or live action, and take notes, as well as watch my own chosen test get carried out infront of me. My data was all quantitative, as all of the data was numerical, in terms of the number of 1-on-1s, as well as the player’s acceleration times.
P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques

13. Data Analysis
To organise my data, I used my own table of ranking, and gave it a brand new scoring system (See Tables and Figures 2.) which involves placing my data into an ordered list, placing the most successful player – in terms of successful 1-on-1s – at the top, and the more unsuccessful players at the bottom. I also used this style of ordering/table for my acceleration times, and it placed the fastest player at the very top of a list, and the slowest player at the bottom. (See Tables and Figures 1). After each player’s average acceleration time had been recorded, the times were put down on the table, which are given to two decimal places. Then, the ranking began. The fastest time recorded, will be inserted at the top of the table, and the slower times, underneath. When the times were ordered, the scoring began. The player with the quickest time was given the maximum 15 points (as there were 15 subjects), and the player with the slowest acceleration time was given 1 point. In my second table, which was created to hold the results for the 1-on-1 data collection, (See Tables and Figures 2) I noted down all of my notational analysis that I had collected when watching the players in-action. Using basic mathematics, I calculated a percentage to show each player’s success rate, in terms of their successful 1-on-1s and their total 1-on-1s. Once the success rate percentage had been calculated, the ranking and scoring system was repeated, which saw the player with a better success rate hit the top of the second table, and given a maximum 15 points to add to their previous points. The player with the lower success rate of course, fell further down the table, and received a smaller amount of points. I decided to use my own scoring and ranking tables, as a method such as Spearman’s Rank Correlation, does not correspond with the results that I gathered, and there was no need to use this method to find a link between two variables.
P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques

14. Results
After carrying out my collection and analysis, I was able to look at my results and see, if indeed the acceleration of a player, of a specific position, affects the amount of successful 1-on-1s that are accomplished. After using my own system of scoring, and my own table of results/ranking, the results showed that the wingers (both RW and LW) had a better overall score, compared to the group of strikers in the group of subjects (See Figures and Tables 3). In the same table, the link between a fast acceleration time and the success rate of 1-on-1s, can be considered. With reference to the table of results, the Top 6 athletes who clocked-in fast acceleration times, finished in the Top half of the table, in the Overall Table. As this is the case, it means that there is a clear correlation between the acceleration of a footballer and the number of successful 1-on-1s they carry out. Looking at the players, and linking to my own knowledge of the players themselves, the players who finish at the top of the Overall Table, have been coached, or are currently being coached, by higher quality managers and staff. This is sure to have an effect on the results, as the player who have received the better coaching, will have been taught the best times to attempt a 1-on-1, and when to retain possession of the ball. Considering my second hypothesis, I needed to look at which position group had the most success with 1-on-1s, and which position had a better acceleration time overall. After my scoring system was incorporated, and the score for each player was calculated, I was able to add up all of the individual scores for the positional groups, to see the most successful position. As the corresponding table shows, the strikers were came out with a higher score, and therefore were more successful with their 1-on-1s and had faster acceleration times. (See Figures and Tables 4).
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Discussion
At the end of my project, I was able to conclude that the acceleration of male, 16-19, participation to performance level footballers, did affect success rate in terms of successful 1-on-1s. I came to this conclusion by analysing all of my results and seeing a clear pattern between the players acceleration times and the amount of successful 1-on-1s that they had carried out during match play. There were players such as Henry Daly, who clocked-in the slowest acceleration time, yet managed to reach 7th place, out of 15, in the amount of successful 1-on-1s. I also noticed that the full-backs within the study, appeared to fall into the bottom half of the Overall Table, after displaying poor acceleration times, and a low number of successful 1-on-1s. This may be because of the fact that they may have been coached to defend first, before attempting to take-on their opposition player. Players like Josh Debayo and Colm Deasy – both full-backs – carried out the acceleration tests at a reasonable speed, yet let themselves down with a low number of successful 1-on-1s. Looking at the top end of the Overall Table – and the players with the better overall score – their age comes in to consideration. Apart from Toby House, in 5th place overall, the Top 6 are all at the top end of the age bracket that is et my project at. Because of this, the older players’ muscle may be more developed and therefore, the could have been possible to clock-in a quicker acceleration time. Overall, my findings were what I expected, as wingers did come out on top of players in other positions like full-backs and strikers.
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Conclusion
Do your results and discussion support your hypothesis or hypotheses? If they do why and not suggest the reasons. My aim was to investigate if a relationship existed between the acceleration of male, 16-19, participation to performance level footballers, and the ratio of successful 1-on-1s. The key trends that I saw from my literacy review focused around the sport of football/soccer. Sources from the likes of Varley et al. (2013), Arpinar-Avsar et al. (2010) and Coverdale (2013), were all based around ‘the beautiful game’. Excluding the research of Berkley (2009), who studied acceleration in American Football, every other source was concentrated on soccer. In another - Acceleration and sprint profiles of a professional elite football team in match play by Ingebrigtsen et al. (2015) – the main aim was to categorise and calculate the acceleration of a group of players, and using technology to record the results. This is similar to the research I carried out in my own Research Project, as I too assessed a group of players, and their acceleration. However there are clear differences in the factors that I have chosen in my own Project, for example, one looks at American Football. Some of the sources also link their acceleration results with some other factors, apart form 1-on1s, like successful crosses. My results do turn out to support my hypotheses, as I believed that the wide midfielders, and wingers would be more successful overall, compared to the full-backs and the strikers in the study. There were some results that seemed to be disappointing for my hypotheses, like the results of Gary Valaydon and Mitchell Hamilton, who were expected to place higher in the Overall Table. I believe that my results did support my hypotheses in a way, yet I believe that I drifted away slightly from the ratio of successful 1-ob-1s, and moved more onto the percentage of successful 1-on-1s.
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Review (1/3)
One of my aims was to find out whether the acceleration of a footballer, had an effect on the amount of 1-on-1s that turned out to be successful. Looking at the conclusions for my project, I believe that I have done so in my work, as there was a correlation between the two variables; the faster the player, the more successful 1-on-1s they carried out. For example, Mark Irvine clocked-in an 50m acceleration time of 6.17secs, and had a success rate in terms of 1-on-1s of 77%. The other aim, was to find out whether a particular position of player attempted more 1-on-1s compared to other positioned-players. Considering my end results, I seem to have found the position that sees more success in terms of 1-on-1s, then any other: left and right wingers. For example, Mark Irvine, Toby House, Lee Harding and Marc Di Lucia all cam in at the top half of the Overall Table, while only 3 strikers came into the top half.
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Review (2/3)
I feel as though my data collection and analysis methods in my research project were the stronger parts of my work. When collecting the data, I used my own, fully-formed data collection sheet (See Tables and Figures 1 and 2). This was a clear strength in my project, as it helped me organise all of my notational analysis/quantitative data in one place, so I was able to complete the suitable equations. Throughout the project, I kept to my deadlines, and kept to my schedule on when to get my testing done, and when to watch the players in matches.
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Review (3/3)
I believe that the players that I assed gave my project quite a narrow scope, as it only assed players aged 16-19, and didn’t address the older players, who would still be playing football to a reasonably high standard in and around the area I live in. Also, due to my access to only two or three teams, it would have been better if I had assed players from different teams in separate leagues, so I could gather a wider range of results. (See Appendix 3) I could've also lengthened the time period of my data collection to longer than just a matter of weeks, as then, I would have been able to asses the player’s 1-on-1s, during poor patches of form, as well as good runs of form. In conclusion, if all of my chosen variables and elements of my Research Project were to widen, the amount of data that could be collected would get larger; making my Project better overall.
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20. Future Recommendations (1/5)
If I were to carry out this research again I would make a number of changes. Possibly looking at changing the time period in which the research took place, perhaps looking across a longer part of the season, or assessing the players in more than just a couple of matches. The possible benefits of incorporating this into future research, would be that the results would be closer to the true amount, in terms of success rate of the 1-on-1s, as the results in my own Project were only taken over a matter of weeks. With the research being repeated, the testing and data collection could be spread out over a longer time period during a season, and more results could therefore be gathered. To carry this out, I could use other researchers, to help me with the workload, and therefore I will not miss any matches for analysing, if I am unavailable to take the notes. I also could have looked at more than the two closest leagues: Ryman Youth, and English School’s League, which would increase the size of my data pool. I would also probably set up an Excel spread sheet, so I would be able to type all of my data into a ready-made table, instead of creating my own table, and a brand new scoring system, in order to rank my players. The benefits of this, would have been that a lot of time would have been saved, and therefore, more time on the concluding of the Project itself could have taken place. I would also carry out the notational analysis part of things, following a clip of the match play, instead of live action. As the match would be recorded, there would be less chance of being distracted by other fans and spectators watching the game, and therefore the researcher would be fully concentrated on the data collection.
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21. Future Recommendations (2/5)
If I was to apply the proposed changes in the previous slide, it would be beneficial to my Project, overall, as it would increase the size of my data, and I would have a wider range of data from leagues which are not just in and around Sussex. I would then be able to draw conclusions regarding the standard of football and seeing if it has an impact on whether the acceleration of a footballer, aged 16-19, affects the number of successful 1-on-1s. I also discussed using Microsoft Excel to put all of my data in, to make it easier to rank, and organise my data as a whole. If I entered all of the correct equations, then the program itself would rank the data and calculate my variables. This would be beneficial for my Project, as this would save time in the entire process of data collection and analysis. The advantages of recording the matches as well as watching the games live, then watching them back at a later stage, would be that it would be nearly impossible to miss any 1-on1s that a player carries out, so therefore, my results will be far more accurate. To make this happen, I would set up a camera on the halfway line, off of the pitch, and on an elevated step, so I could record all areas of the pitch, and get a clear view of any 1-on-1s that did occur.
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22. Future Recommendations (3/5)
If I were to carry out the research project again, I would possibly look at changing the time period in which the research took place, perhaps looking across a longer part of the season, or assessing the players in more than just a couple of matches. As most players would only play a couple of matches per week, it was already difficult to gather all of the information/data needed to make my Research Project happen. As highlighted before, it would be beneficial to my Project, if I was to have the help of some other researchers to help me with getting to games, and watching the action, so more games could be assessed and therefore, more cases of each player could be used. Also, if my timescale was a little bit longer, it would be beneficial to my Research Project, as I would then be able to assess the several different players in the different environments and against a wider rage of opposition within the league. This would have given each player a larger number of total 1-on1s, and therefore giving me a percentage closer to the true value. To carry this out, I would create a plan/calendar, stating all of the games that were going to be played, on each day, so I could organise transport, or ask one of my supporting researchers to watch the player(s) involved in the game, on the particular day.
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23. Future Recommendations (4/5)
If I were to carry my research out again, I also could have looked at more than the two closest leagues to my home: Ryman Youth, and English School’s League, which would increase the size of my data pool. I could look at Leagues within the same performance level, yet in areas that hadn't come into mind before the Project had got underway i.e. County Leagues. This would give me an advantage in terms of my data pool, as more athletes will be assessed this way, and then I will have a greater source of comparison. As different teams within the many different leagues tend to have different playing styles, and team mentalities, this would give me a great source for comparison and justification, when it comes to calculating my results. It would benefit my entire Research Project, as it would give my research a greater sense of creditability, as I will be looking at other leagues, not just focusing on one league.
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24. Future Recommendations (5/5)
I would also carry out the notational analysis, following a clip of the match play, instead of taking the notes during live action. As the matches were live, I was – at times - distracted by other fans and spectators watching the game, and was drawn to different situations on the pitch, other than the specific player’s 1-on-1s. The advantages of having recordings of the games, would be that I wouldn’t miss hardly any 1-on-1s that occurred, and therefore the notational analysis would be more accurate, and more reliable, as I would have had full concentration on the specific player. I feel, that these factors may have compromised the validity and reliability of my results, at times.
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25. Research Project Appendices

26. Appendix 1

27. Appendix 2

28. Appendix 3

29. Research Project Figures and Tables

30. ACCELERATION TIME (SECS)
Figures and Tables 1
ATHLETE / POSITION
ACCELERATION TIME (SECS)
RANKING
SCORE
MARK IRVINE / RW
6.17
1ST 15
OLUFELA OLOMOLA / ST
6.23
2ND 14
DANN PERRY / ST
6.34
3RD 13
LUKE DONALDSON / ST
6.38
4TH 12
TOBY HOUSE / RW
6.42
5TH 11
LEE HARDING / LW
6.45
6TH 10
MARC DI LUCIA / RW
6.49
7TH 9
WILL WOOD / LB
6.51
8TH 8
JOSH DEBAYO / LB
6.59
9TH 7
COLM DEASY / RB
6.89
10TH 6
GARY VALAYDON / LW
MITCHELL HAMILTON / ST
6.92
12TH 4
TABI MOLOI / LW
6.93
13TH 3
ELLIS SOUTH / RB
7.17
14TH 2
HENRY DALY / LB
7.23
15TH 1

31. Figures and Tables 2 ATHLETE / POSITION TOTAL 1-ON-1S SUCCESSFUL
NON-SUCCESSFUL
SUCCESS RATE
RANKING
SCORE
TOBY HOUSE 6 5 1
83%
1ST 15
OLUFELA OLOMOLA
LEE HARDING 4
80%
3RD 13
MARK IRVINE 9 7 2
77%
4TH 12
DANN PERRY 3
75%
5TH 11
LUKE DONALDSON
HENRY DALY
66%
7TH
MARC DI LUCIA
57%
8TH 8
JOSH DEBAYO
42%
9TH
WILL WOOD
37%
10TH
GARY VALAYDON
36%
11TH
TABI MOLOI
33%
12TH
COLM DEASY
28%
13TH
MITCHELL HAMILTON
25%
14TH
ELLIS SOUTH
18%
15TH

32. Figures and Tables 3 ATHLETE POSITION OVERALL SCORE OVERALL RANKING
OLUFELA OLOMOLA ST 29
1ST
MARK IRVINE RW 27
2ND
TOBY HOUSE 26
3RD
DANN PERRY 24
4TH
LEE HARDING LW 23
5TH
LUKE DONALDSON
MARC DI LUCIA 17
7TH
JOSH DEBAYO LB 14
8TH
WILL WOOD
GARY VALAYDON 11
10TH
HENRY DALY 10
11TH
COLM DEASY RB 9
12TH
TABI MOLOI 7
13TH
MITCHELL HAMILTON 6
14TH
ELLIS SOUTH 3
15TH

33. Figures and Tables 4 POSITION OVERALL SCORE RANKING STRIKERS 82 1ST
RIGHT WINGERS 70
2ND
LEFT WINGERS 41
3RD
LEFT BACKS 38
4TH
RIGHT BACKS 12
5TH