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England's kicking options

Indeed, I mentioned wind and surface as important factors in assessing the difficulty of kicks last night and this system doesn't consider them, but how would you factor them qualitatively? If the paper was written in England, maybe more consideration would have been given to accounting for them, but they're much less of a factor in South Africa.

Anyway, excellent stats, thanks @goodNumber10. I'm amazed that the data exists in order to analyse and almost as amazed that anyone has performed the analysis.

Yeah it was written in South Africa: http://academic.sun.ac.za/sajrsper/35.1/Nel.pdf

I know what you're saying about surface etc... but they seem to have reverse engineered the process and eliminated anything that isn't a consistent measurable.

The objective of this study was firstly to derive a formula to estimate the success probability of a particular rugby goal kick and, secondly to derive a goal kicker rating measure that could be used to rank rugby union goal kickers. Various factors that could influence the success of a particular goal kick were considered. A logistic regression analysis was performed to obtain the significant factors that were found to be the most important factors in the estimation of the success probability of a particular goal kick. The estimated success probability of the goal kick was then used to allocate a difficulty rating for the particular goal kick. The result of goal kicks attempted by the kicker along with the difficulty rating of these kicks was used to derive a measure for the ranking of rugby goal kickers. Goal kicker performances during the 2011 Rugby World Cup Tournament were used to illustrate the workings of the ranking system.

I think trying to measure foot stability and stuff would be impossible without access to the pitches at every rugby ground in in the world...
 
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Agreed @goodNumber10, as I said above, how would you quantify wind or footing. It's a shame that only the abstract is available, although I'm not sure I'd have the attention span to read through too long a paper to learn more! Specifically it would be interesting to know what dataset was used to perform the logistic regression analysis (not that I can claim to understand what that is or how it works). It's very hard to imagine that over an English season wind wouldn't appear to be a factor and possibly footing. In South Africa, it seems plausible. Similarly, had this paper been written in England, I suspect altitude wouldn't have made the cut.
 
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It is very hard to quantify wind and underfoot conditions but they are such a big part of goalkicking that these stats are severely limited without them - underfoot conditions I think are particularly overlooked, while everyone knows the effect wind can have on kicking, people forget the vast difference between kicking on a solid dry surface and trying to plant your foot in a quagmire. The trouble is, I really can't see how to do it, because unlike distance and angle each data point is entirely unique - i.e., 15° is 15° on any day, in any stadium, whereas the underfoot condition on one part of the Stade de France pitch 12 minutes into a game on February 28th will never be repeated. The only way I can see would be to have subjective judgements made on each pitch, using a system of protocols which standardises it as far as possible, but that is still very unreliable.

Saying all this, there's no doubt that these types of statistics (assuming the statistical models are sound - I'm no statistician so can't comment on that) are much better than the straight-up percentages which are the only mainstream kicking stats commonly available at present - thanks, @goodNumber10, for drawing my attention to them. I will certainly be referring to them a lot in future, I'm just wary of deferring to them too much without keeping in mind the weaknesses.
 
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