1. Unit 5 Research Project Worthing College Sports Science Liam Lee 2015

2. ‘To investigate the affect of premiership standard back 5 forward height and the amount of lineouts won per game.’ P2: Carry out / P4: Produce

3. Abstract The aim of my study was to investigate the relationship between the height of the starting back five forwards in elite level male rugby and their lineout success rate. The study had a sample size of 80 male professional rugby players who were involved in the Aviva rugby premiership season between 2014-2015. the data for the study was collected using the Aviva rugby premiership website for games 1,7,15 and 18. The data collected was shown using excel on a spearmint rank order correlation and on a scatter graph. The results on the spearman correlation showed that there was very little correlation between the height and lineout success rate. One thing to look into in the future would be the hookers throwing technique and if it affects accuracy and another would be to look at the height of the lifters in the lineout as they can have a huge part on the height of the lift. P2: Carry out / P4: Produce

4. Contents: General Page 2 – Aim Page 3– Abstract Page 4– General contents Page 5 – Contents appendices Page 6– Contents figures and tables Page 7 – Acknowledgments Page 8 – Introduction Page 9 – Literature review and references 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– Assessment criteria page 19-26 Page 18– Review 1/3 Page 19 Review 2/3 Page 20 – Review 3/3 Page 21– Future recommendations 1/5 Page 22– Future recommendations 2/5 Page 23– Future recommendations 3/5 Page 24- Future recommendations 4/5 Page 25- Future recommendations 5/5 P2: Carry out / P4: Produce

5. Contents: Appendices Page 26 – Research project appendices Page 27 - Appendix 1 Page 28 - Appendix 2 Page 29 - Appendix 3 Page 30 - Appendix 4 Page 31 - Appendix 5 P2: Carry out / P4: Produce

6. Contents: Figures and Tables Page 32 - Research project figures and tables Page 33 - Figures and tables 1/5 Page 34 - Figures and tables 2/5 Page 35 - Figures and tables 3/5 Page 36 - Figures and tables 4/5 Page 37 - Figures and tables 5/5 P2: Carry out / P4: Produce

7. Acknowledgements I would like to thank my peers who have given me advice throughout my project. I would also like to thank my teachers Mark Sambrook and Paul Cox for their advice and guidance throughout my project. I would also like to thank the Aviva premiership website for allowing me to access their information. P2: Carry out / P4: Produce

8. Introduction My project’s aim is ‘To investigate the affect of premiership standard back 5 forward height and the amount of lineouts won per game.’ I chose this aim because I wanted to see if the height of the back five forwards affects lineout success rates in elite level rugby. My timescale for this project was one month. I started my project on the 6 th and finish on the 27 th of march. On the 12 th I started to collect the heights and win percentages. P2: Carry out / P4: Produce

9. Literature Review and References Here is a link to my literature review. https://worthingsportscience.wordpress.com/20 15/02/26/unit-5-literacy-review-liam-lee/ P2: Carry out / P4: Produce

10. Project Hypothesis 1.I expect to find that teams with taller back five forwards will have a better lineout success rate. 2.I expect to find that other factors will also contribute to lineout success within rugby. P2: Carry out / P4: Produce

11. Method 1.First create an excel document with all the Aviva premiership teams names down the side. 2.Add the columns 4, 5, 6, 7, 8, total height, average height, lineouts had, lineouts won and finally lineouts won percentage. 3.Add calculations that will work out the total height for the combined heights of 4, 5, 6, 7 and 8 and calculations that will find the mean for their total combined heights. 4.Add calculations that will work out the lineout win percentage using the calculation: lineouts had divided by lineouts won time 100 = the percentage of lineouts won. To see an example of the excel document see (Appendix 1). 5.Copy this excel document four times saving them as round 1, round 7, round 15 and round 20. 6.Using Google, go to the Aviva premiership rugby fixtures and results webpage. 7.Go to round 1 and look at each game. Take the names of each teams starting back 5 forwards and research their heights on the Aviva premiership rugby website and record them on your excel document (this is where excel will need to calculate the total height for each team and the mean height for each team). 8.Then, on the same website, find each teams stats for the game showing lineouts had and lineouts won. Then record these for each team on your excel document. (this is where excel will calculate each teams lineout success percentage). 9.Do this for rounds 1, 7, 15 and 20 (putting each rounds results in the appropriate excel document). 10.Find the mean height and mean lineout win percentage for each team over the 4 rounds and make a scatter graph and a spearman correlation measure (See figures and tables 1). 11.Write up a conclusion of what you found and your opinions. P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques

12. Data Collection I carried out a desk based research project that consisted of collecting numerical secondary based quantitative data from a reliable source (see appendix 2). It consisted of looking at each teams lineout stats for games 1,7,15 and 18 in the Aviva rugby premiership and recording them in an excel spreadsheet. It also consisted of looking at each teams starting back 5 forwards and researching their individual height using the teams official webpage on the Aviva rugby site. The benefit of using these sites to get my data is that they are a reliable source whereas using a website such as Wikipedia may not have accurate information. P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques

13. Data Analysis I analysed my data using excel by looking at each teams average starting back 5 forwards height and comparing it to their lineout win percentage. I did this for each team over the 4 games looked at. I then had to calculate each teams mean height, lineouts per game and lineouts won per game. With this new information I then made a scatter graph on excel which allowed me to see if there was a correlation between height and lineout win percentage. Then I used the spearman correlation measure on excel which showed me if there was any correlation between the two variables. P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques

14. Results My results from my scatter graph and the spearman correlation measure show that there is a very low correlation between the height of the back 5 starting forwards and the lineout win percentage in the Aviva rugby premiership (see figures and tables 2 and 3). I got my results by entering my data into the a spearman correlation measure using excel and seeing if there was any correlation. I also saw this on my scatter graph which I made on excel to support my results so they were accurate. During my project I also had to do some other research to support my work in which I found that the height of the lifters and the hookers throwing technique can be a vital part of winning the lineout. I found this whilst writing my literature review. My first hypothesis was that I expect to find that teams with taller back five forwards will have a better lineout success rate, my results contradict my hypothesis as the spearman correlation measure indicates that there is little correlation between the two. My second hypothesis was that I expect to find that other factors will also contribute to lineout success within rugby. My results support that as they show that height does not affect the success rate and so there must be other factors to make the lineouts success rate high. P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques

15. Discussion I found that the average height of Aviva premiership rugby teams starting back 5 forwards has no correlation to them having a better lineout success rate. This finding was not what I expected as I thought the opposite and so this result was quite a surprise to me. The trend that I identified was that the average height of the back 5 forwards rarely changed throughout the 4 games looked and was quite consistent throughout my project, this suggests to me that the height of the team which is picked is not a tactical decision. I also found that the height of the lifters and the hookers throw technique has a huge affect on how successful a lineout is. This finding was what I was expecting but I did not know that different techniques when throwing can affect accuracy. P2: Carry out / P4: Produce

16. Conclusion ‘To investigate the affect of premiership standard back 5 forward height and the amount of lineouts won per game.’ My key trends were that all the sources will support my research project. Another is that all my sources are valid and will help me during my project. Another key trend is that my sources are all looking at elite level male athletes. And finally that they are all related to the height and weight of athletes. Overall, half of my results did not support my hypotheses and were not what I expected to find as they showed that there was no correlation between average height of the back 5 forwards and lineout win percentage. Whereas I did expect to find that other factors affected the lineout. P2: Carry out / P4: Produce

17. Review (1/3) My project conclusion did not meet my project aims very well at all. My conclusion found no correlation between what I was looking into (as shown on figures and tables 2). Although my conclusion did meet one of my hypotheses. Another thing my conclusion told me is that I should not continue research on this aim as no correlation was found and so it would be a waste of time. P5: Describe / M3: Explain / D2: Justify

18. Review (2/3) One strength of my research project is the reliability of my data because I used a very accurate and reliable source (see appendix 2). Another strength is the organisation of my data and how it has been presented on excel. This is a strength as the data can easily been seen. (See figures and tables 1,2 and 3). Another strength of my research project is the validity of my data. It is valid because it allows me to clearly see whether height affects lineout win percentage. P5: Describe / M3: Explain / D2: Justify

19. Review (3/3) My area to improve would be to learn more about how to use excel and the spearman correlation measurement as I did not really know about it or how to use it. If I knew how to do it I would have saved time as I had to wait for help. This would allow me to have more time to do research and collect data. It would also have enabled me to have more time to write up my work. I would do this by furthering my knowledge excel using the internet and websites such as YouTube and you can watch tutorials. P5: Describe / M3: Explain / D2: Justify

20. Future Recommendations (1/5) If I was to do this project again I would take into account the hookers technique when throwing more. I would do this as from my research in this project I have learned that different hookers use different techniques when throwing with one of the techniques proving to be the more accurate. I would choose this as I feel that the hookers throw can have a real affect on how successful a lineout is and by doing research on it I would be able to justify that statement further. P4: Produce / P5: Describe / M3: Explain / D2: Justify

21. Future Recommendations (2/5) The benefit of taking the hookers throw into account would be that you would be able to see which technique is more effective in terms of throwing. I would also be able to see if height is more important or the quality of the throw. It would also help support whether this research projects aim was correct or whether it could be questioned with evidence to back it up. P4: Produce / P5: Describe / M3: Explain / D2: Justify

22. Future Recommendations (3/5) If my research was to be done again I would recommend taking different participation levels into account as this may give a more true representation into the relationship into back five height and line out win success. At the lower levels of participation it may be found that because the skill decreases the height of the back five might be more of a factor into success. This would mean having to make the study longitudinal as the data would have to be collected first hand and due the there not being any secondary data. P4: Produce / P5: Describe / M3: Explain / D2: Justify

23. Future Recommendations (4/5) I would then look at the height of the lifters in the lineout. I think this would be a good thing to look into as taller lifters can get more height on the lift which could become a tactical advantage and possibly help win games. This may be down to the fact the arm length may be bigger therefore the extension and height of the line out may be higher which gives a immediate advantage. P4: Produce / P5: Describe / M3: Explain / D2: Justify

24. Future Recommendations (5/5) I would recommend for someone else, who is more experienced to redo my project in order to see if they get the same results. I would advise them to do this as height and size in the forwards is a growing must in the world and this research could help to see whether it is beneficial to have bigger forwards or whether it is just part of the evolution within rugby. P4: Produce / P5: Describe / M3: Explain / D2: Justify

25. Research Project Appendices

26. Appendix 1 Here is a photo of my excel sheet used to show data I collected.

27. Appendix 2 http://www.premiershiprugby.com/

28. Research Project Figures and Tables

29. Figures and Tables 1 Here is a photo of my excel sheet showing my data

30. Figures and tables 3 Here is a photo of my spearmint correlation

31. Figures and Tables 2 Here is a photo of my scatter graph.