Aviva Premiership Rugby Predictions ~ Final Standings

Regular readers of this blog know that I do regression analysis for Aviva Premiership Rugby.  It comes out of my RugbyMetrics analysis tool.  Much like MoneyBall, I mine statistics looking for new ratios and performance indicators.

I came up with a new index that takes into account a teams ability to score point versus its ability to defend.  It is an interesting analysis.  Based on performance thus far, I will go out on a limb, and barring any unforeseen circumstances, the following table shows how the teams should finish at the end of the season based solely on the numbers that they have put up:

Scoring Advantage Index	Team
1.438405797	Harlequins
1.034782609	Leicester
0.945652174	Exeter
0.795986622	Northampton
0.785507246	London
0.724637681	Bath
0.688405797	Gloucester
0.617391304	Saracens
0.585507246	Worcester
0.52173913	London Welsh
0.48447205	London Irish
0.194508009	Sale Sharks

This should be a fairly reliable predictor of how the teams will finish.  The numbers show that the Exeter Chiefs should make a big move from their current 8th position to third if they continue to play the way that they do.

A team with a higher scoring advantage index should beat any team with a lower one.

The scoring advantage index can be used as a coaching tool.  If a particular, play, setup, roster change etc creates a better Scoring Advantage Index, then that is a successful strategy.  The next course of action is to tie individual player performance to Scoring Advantage Index.

Read More

RugbyMetrics Queries

I have been getting some queries via comment postings about RugbyMetrics. Some people have even been trying to find a trial download. I will be posting some sample results and white papers here shortly. In the meantime, if you have any queries, please drop me a line at:

rugbymetrics-at-gmx.com (substitute "@" for "-at-").

Read More

Line Formation Elasticity -- RugbyMetrics

A lot of objective information is falling out from the results of my Software tool called RugbyMetrics. While doing extensive statistical data mining on actual professional rugby games in the Aviva Premiership, an incredible statistic fell out of the exercise, and that was line formation elasticity.

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.

Read More

Regress to Success -- RugbyMetrics

So let's suppose that you run a rugby team in the Aviva Premiership or any other professional rugby club. So you haven't qualified for the Heineken Cup and your team is full of journeymen players and you consistently sit in the cellar of the standings table. And let's suppose that you don't have a Daddy Warbucks owner that can buy you a Dan Carter and you want to create a competitive team.

So what are you going to do? You have to find young untried players who will eventually turn into Thomas Waldrom, Schalk Brits, Chris Aston or Tom Wood. How are you going to identify them when they haven't had a chance to prove themselves and amass some statistics to prove that they have the stuff of the egg-chasing gods.

You turn to the geeks, that's how you do it. How so? You regress your way to success. You would use my RugbyMetrics tool (click on this LINK to see all of the articles on RugbyMetrics). Then you would take a game film of your targeted acquisition and using the tool, digitize that player's performance. From there you would use advanced statistics to create a mathematic model (using regression and Bayesian inference) to determine if your player has the right stuff.

How does it work? The seeds of athletic greatness are sown early. However they may not become manifest because the player is not on a team that enhances his skillset, or he is blindside oriented on a team that is predominantly openside oriented. There are many many reasons, however that player will demonstrate the subtle qualities that shows that he has the key performance indicators that tend to greatness.

So what are these KPI's or key performance indicators? They are a new set of statistics that are gleaned from data mining every aspect of the game. These are proprietary knowledge to the users of the system. But as a trivial example, one finds that an Olly Barkley will average x amounts of carries, gaining y amounts of yards, in a certain ratio to the opposition yards gained. This is objective, scientific knowledge of the game of rugby that comes from the field of predictive analytics.

So once you have the three mathematical formulas gleaned from going through mountains of statistics, you can eliminate the pretenders and give yourself a roster of possible stars. This is not meant to replace the years of coaching and scouting, but rather it is meant to give the teams a scientific, valid starting point when scouting for new team members.

The interesting aspect is that the front 8 will have different formulas than the back seven, and each position will have different regression parameters in the models. Also style of play comes into effect as well. If you like a Tom Wood style of play, you would determine the mathematical model by analyzing his performance and looking for players who have similar numbers to him. It sure beats the shot in the dark method of a player that "looks good".

If you have any questions, please leave a comment and I will answer them.

Read More