Rugby is a game where the defense lines up across the field to defend against a similar line of offence. When a player carrying the egg finds that his forward progress is blocked, he passes left or right down the line to his team mates. If there is a hole in the line somewhere on either the defense or offence, then there is a problem.
So I decided to measure line elasticity -- how quickly the line forms or reforms after it is distorted from a play. This analysis fell out of another analysis where I did a ratio of jersey counts between attackers and defenders at the time of tackle, which had a very interesting result.
What the line elasticity measure showed, was that the more efficient that the line was at reforming, the more successful the play (both in offence and defense). This is especially evident when the team with possession grinds away for a long time with very little field gained. The opposing defensive line is very elastic at reforming and very efficient.
What frame-by-frame video also showed, was the laggards who were late at assuming their position, thus leaving holes in the line. It was very interesting.
From there, when we saw that we could identify the defensive laggards, we saw that we could assign a numeric co-efficient of line efficiency, both at a team level, and at a player level.
From there, it was a short step to rating the roster of a team, and let the results settle into a hierarchy of the best players. There are many developed measures of a players worth coming out of RugbyMetrics. The thought struck me, that if a player is negotiating a raise in his contract, one of the bargaining chips could be a RugbyMetrics analysis to show that he is in the company of the best of the breed in the Premiership. Conversely, a team could use RugbyMetrics to prove that a player asking for a raise tends more to a journeyman than a star.
Its all fascinating stuff, and is opened by the doors of data mining and performance analysis.