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    Rugby Statistics

    Numerical analysis of all sports is only going to get more popular since the Moneyball revolution in the US, and more people are starting to get serious about it in rugby too. It's not for everybody, but I think it's another great way to get even more value out of a game I love. Yesterday Murray Kinsella at The Score posted an article about New Zealand Rugby sports scientist Ken Quarrie's kicking analysis, which ranks ROG as 35th best kicker between 2002 and 2011 by a number of different criteria. I'd recommend anyone with this kind of interest having a look. Since Quarrie posted the dataset he used to come to this conclusion, I was able to use it to ask a couple of other questions of the data, and I've posted my analysis on my blog: http://bit.ly/1cgh5N4

    Let me know what you think! And I'd be very interested to see any other similar work anyone else is doing.

    #2
    It was a good article and showed how misleading stats can be.

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      #3
      Interesting stuff.

      "All Blacks first five-eighth Dan Carter was ranked third overall on the list, behind first placed Morne Steyn (South Africa) and second ranked Federico Todeschini (Argentina).

      Springbok Frans Steyn was fourth on the list, thanks to his success with prodigious kicks, for while he only kicked 59% of his 37 attempts, his successful strikes were taken at average 49 metres out, as opposed to 32 to 33 metres for the bulk of kickers.


      Other capped All Blacks on the list were Andrew Mehrtens 13th, Steve Donald 20th, Piri Weepu 21th, Nick Evans 24th, Luke McAlister 27th, Leon Macdonald 41st, Colin Slade 94th, Aaron Mauger 97th and finally Carlos Spencer who was ranked 101."

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        #4
        What level of statistical significance are you using and why? Have you considered the limitations of small sample sizes? What do you consider a statistically significant level of difference?

        P.s. I have many more questions and want to see all the data & not a link to someone elses, please Pm with same if there are confidentiality issues
        My computer thinks I'm gay
        What's the difference anyway
        When all the people do all day
        Is stare into a phone

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          #5
          Sewa - one of the issues with Quarrie's dataset is that, in my opinion, there isn't enough data on many of the kickers. Thus, I haven't applied statistical significance testing - I haven't seen his full paper, so I'm not sure if he ran particular tests, he seems to only have ordered the kickers based on his weighted criteria. I just demonstrated that you can use different criteria. I don't know how to attach files on here, so let me just give you instructions on how to generate the two columns based on Quarrie's data. To arrive at the 'production' column, you start a new column, multiply the first cell in the "Attempts" column by the percentage accuracy in decimal form (e.g. .72 for O'Gara, .88 for Morné Steyn). To multiply one cell be another in Excel, type into a blank cell "=[click on cell 1]*[click on cell 2]. To apply this to all the cells in a column, simply hover the cursor over the bottom right hand corner of the cell into which you have typed the formula, and drag it down to the bottom of the column. This gives you how many kicks each kicker converted during the decade-long run of the study. You can re-order the kickers using the tab at the top of the excel file [order 'descending', so the player with the most conversions, Dan Carter, is at the top], thus giving you the top ten I report. The 'efficiency' numbers are reached by dividing the 'production' numbers by the number of games they played. To divide in Excel, type into a blank cell =[click on cell 1]/[click on cell 2]. Again, you can order descending so that the player with the most conversions per game is at the top. Hope that clears it up - if you PM me your email address I can just attach my amended version of Quarrie's file, but it's good to have the process explained here anyway.

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            #6

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              #7
              It is obvious what type of fan they are aiming for when there is a question about taking "selfies", players' twiiter and who's the best looking on the team

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                #8
                And 38% want to sit with Hook or Wood.

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                  #9
                  Hook is the ultimate in arm candy.

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                    #10
                    Hi lads, a mixture of a preview of today's match with some stats talk on the relevance of point difference as a tool for measuring the quality of teams: http://bit.ly/1ijV6pf


                    Any comments appreciated! Eoin

                    Comment


                      #11
                      The stats of the Heineken cup are interesting. Our fling it wide brought us in with the most passes completed.

                      http://www.ercrugby.com/home.php

                      On a good news front, Ian Keatley was the top conversion scorer. Well done KEats!
                      Tic-Toc. POC and DOC. Stop the clock.

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