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

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

P2: Carry out / P4: Produce 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 spearman rank order correlation and on a scatter graph. The results on the spearman rank 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

P2: Carry out / P4: Produce 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

P2: Carry out / P4: Produce 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

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

P2: Carry out / P4: Produce 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

P2: Carry out / P4: Produce 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 6th and finish on the 27th of march. On the 12th I started to collect the heights and win percentages. P2: Carry out / P4: Produce

Literature Review and References Here is a link to my literature review. https://worthingsportscience.wordpress.com/2015/02/26/unit-5-literacy-review-liam-lee/ P2: Carry out / P4: Produce

P2: Carry out / P4: Produce Project Hypothesis At the end of my project I expect to find that teams with taller back five forwards will have a better lineout success rate. P2: Carry out / P4: Produce

Method First create an excel document with all the Aviva premiership teams names down the side. Add the columns 4, 5, 6, 7, 8, total height, average height, lineouts had, lineouts won and finally lineouts won percentage. 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. 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). Copy this excel document four times saving them as round 1, round 7, round 15 and round 18. Using Google, go to the Aviva premiership rugby fixtures and results webpage. 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). 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). Do this for rounds 1, 7, 15 and 18 (putting each rounds results in the appropriate excel document). 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). 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

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

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. I used excel because it is simple to use and allows me to show my data clearly and keeps it organised and presents it in a professional manner. Another reason why I chose excel is because it allows you to put calculations into place which excel will then do for you so this saved me time. With the 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. I chose the scatter graph because it clearly shows if there is a correlation between the two variables and I chose it because it allows you to see the data on a graph instead of it just being as data on the page. After I used the scatter graph I then used the spearman rank correlation measure on excel which again showed me if there was any correlation between the two variables. It also allowed me to see if there was any type of relationship between the two variables I looked at (height and lineout success). Another reason I chose to use it is because it is well known worldwide and is a reliable way to analyse data because it has a method, a calculation and is valid. P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques

Results My results from my scatter graph and the spearman rank correlation measure show that there is a very weak positive correlation(0.20) 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 spearman rank correlation measure using excel and seeing if there was any correlation. 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 support this as the correlation proved to be a weak positive correlation and my scatter graph shows that there is a positive line of best fit which shows as height increases, lineout success will also go up. Overall, my results show that there is a weak positive correlation that means the height of the back 5 forwards and the teams lineout success rate are related but not by much.(see figures and tables 3). P3: Collect and record / M2: Correctly analyse & describe techniques / D1: Correctly analyse & explain techniques

P2: Carry out / P4: Produce Discussion I found that the average height of Aviva premiership rugby teams starting back 5 forwards has a weak positive correlation to them having a better lineout success rate. This finding was what I expected but I thought that the correlation between the two would have been stronger. The trend that I identified was that the average height of the back 5 forwards rarely changed throughout the 4 games that I looked at and was quite consistent throughout my project, this suggests to me that the height of the team which is picked is a tactical decision. One thing that may raise questions about my results Is the sample size because I only looked at 60 players each week. If I increased my sample size by looking at other leagues as well then the results would be more valid and reliable. P2: Carry out / P4: Produce

P2: Carry out / P4: Produce 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, my results did support my hypothesis and were what I expected to find as they showed that there was weak positive correlation (see figures and tables 3) between average height of the back 5 forwards and lineout win percentage. P2: Carry out / P4: Produce

P5: Describe / M3: Explain / D2: Justify Review (1/3) My project conclusion did meet my project aims. My conclusion found weak positive correlation between what I was looking into (as shown on figures and tables 2). Another thing that my conclusion told me is that I should look to further my research in order to make my study more reliable and valid as it shows that in the Aviva premiership there is a weak positive correlation but I would like to see if this is different in other leagues. P5: Describe / M3: Explain / D2: Justify

P5: Describe / M3: Explain / D2: Justify 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

P5: Describe / M3: Explain / D2: Justify Review (3/3) My area to improve would be to learn more about how to use excel and the spearman rank 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 about it and how to use it. P5: Describe / M3: Explain / D2: Justify

Future Recommendations (1/5) One thing I would recommend to look into in the future would be to investigate the affect of standard back 5 forward height and the amount of lineouts won per game in other leagues such as top 14 rugby. I would recommend this because I will then be able to compare the correlation of top 14 rugby and Aviva premiership rugby. The weakness it fixes is the validity of my data as I will be able to compare the results across different professional leagues. My recommendation provides a benefit as it will help people determine what is needed to make a lineout as successful as possible. It would benefit coaches, players and scouts because coaches can pick the taller players, players who are tall are more likely to get picked and scouts can look out for bigger players with potential. P4: Produce / P5: Describe / M3: Explain / D2: Justify

Future Recommendations (2/5) Another recommendation I would make is to measure the results with a percentage instead of a spearman rank correlation. I would recommend this as teams with the best lineout percentage can be put into a rank with their teams heights next to them so you can see if there is a relationship between the best win percentage and the height of the teams. One weakness it fixes is that there will be no confusion when viewing the data because you will be able to clearly see the relationship between the height, lineout success percentage and rank number. This will be a benefit because the data will be much easier to understand and will benefit the researcher and person viewing the data because it will be easier to understand. P4: Produce / P5: Describe / M3: Explain / D2: Justify

Future Recommendations (3/5) Another recommendation would be to change the variable from height of back 5 forwards to height of lifters. I would recommend this because the height of the lifters can have an affect on the height of the lift and the height of the lift can benefit the lineout and could increase the lineout success rate. One weakness this would fix is the validity of the study because before I did not take this into account and the research I did into it I learned that this factor can influence the lineout. This would benefit coaches and scouts as well as players because it could prove that taller lifters are better to have within the team, which will help coaches with making decisions in terms of tactics and squad selection. It will also help scouts when using talent i.d and selection because it will give them an idea of the type of players that they will need to be looking out for. And finally it will help players in terms of opportunities they can be given because again coaches and scouts will be looking for that kind of player to help improve their squads or to help improve the player for future selection. P4: Produce / P5: Describe / M3: Explain / D2: Justify

Future Recommendations (4/5) Another recommendation I would make would be to alter the method I used to collect my data. I would do this by actually watching the games online and collecting the data myself instead of just looking at the games stats, whilst taking into account the weather conditions, the wind, the position of the lineout, the score and the importance of the game, as all of these variables can have an impact on the lineout success rate. One weakness this would fix Is that it would stop the lineout being seen as a closed skill. This would benefit the coaches and players because it would give them a better understanding of the complexity of lineout success. This could also affect the way that coaches coach lineouts because they will have a better understanding of them and will try to come up with new ways to coach lineouts whilst trying to control the less controllable aspects of the lineout stated above. This will then benefit the players knowledge and will also benefit the team because with a higher lineout success rate the team will have more chances to score. P4: Produce / P5: Describe / M3: Explain / D2: Justify

Future Recommendations (5/5) And the final recommendation I could make would be to change the way in which I analysed my data. I would recommend this because I could have done this differently during my project which could have provided a different result. I could analyse my data by using linear regression. One weakness this would fix is that it would reduce the risk of any mistakes made by people looking at my research because the data presentation would be easier to understand. This would be a benefit because the data is easier to analyse and so less mistakes will be made. It would benefit the researcher and other people viewing the research as well as future researchers because they will be able to understand the analysis of the data. This will help them because they can then take what they have seen/learnt and put it into their own work/research. P4: Produce / P5: Describe / M3: Explain / D2: Justify

Research Project Appendices

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

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

Research Project Figures and Tables

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

Figures and tables 3 Here is a photo of my spearman rank correlation

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