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.

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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-").

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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.

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Toby Flood Reduced To An Equation

Toby Flood is a fly-half for the Leicester Tigers, and a rugby star in the Aviva Premiership. This is his photograph from Wikipedia:


It's almost sad, but true that Toby's running game whilst playing rugby can be reduced to a mathematical equation. If you had to describe Toby's running game performance mathematically, you would do it this way:

Obviously I am not going to tell you what x and y stand for, because it came from digitizing and sifting through mounds of data to come up with the mathematical model using predictive analytics and linear regression.

However, if you wanted to choose a player with Toby's prowess, this formula would be incredibly helpful. It was derived using my software package called RugbyMetrics which adds objective knowledge of the game through data-mining and sifting through mounds of statistics.

Click on the video below to watch Toby kick a conversion after a Tiger try. The fly-half is really good!!

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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.

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RugbyMetrics Performance Analysis Themes

Data mining and predictive analytics is a wonderful thing. It gives objective insight into whatever comes under the data microscope. In this case it is rugby. The insights are fascinating. For example, take a look at this equation:

It's called a Hurst exponent, and it was derived by a mathematics God named Benoit Mandelbrot. It is a Mandelbroatian math element of fractal geometry. It was originally developed to determine how big a dam to build on the Nile River. What does it have to do with rugby?

Let's look at the analogy of the Nile River. In the case of the Nile River, one expects to find ebbs and flows of the amount of water flowing through the river based on rain and drought. The series is infinite as long as the Nile does not run dry. The Hurst exponent is used to estimate variability of the flow over time.

In rugby there are ebbs and flows during the game in terms of meters gained on the pitch by a particular team. Of course, the time is not infinite, but it is 80 minutes. During those 80 minutes the game flows back and forth. If I calculate the variability of meters gained per play during a game using the Hurst exponent, it infers different things about the teams.

The Hurst exponent is defined in terms of the asymptotic behaviour of the rescaled range as a function of the time span of a time series.

Let's suppose that I analyze a video of a rugby game and just for fun, determine the Hurst exponent of the opposing team. Let's suppose that their variability in meters gained on the field is higher than my team. There are a few reasons why a team is highly variable in terms of meters gained in play. Finding that reason shows a vulnerability and something for the opposition to exploit.

If I take this same concept and apply it to a finer degree on granularity at the player level, I can determine by comparative analysis if a player is ready to play or still not up to snuff after an injury.

Analysis and number crunching of this kind yields an amazing amount of objective knowledge about the game that was previously unknown. And this is the type of knowledge that gives teams an incredible advantage over mere human coaching.

As for software, the neat thing about this stuff, is that SQL stored procedures and views are the input from the data mart to determine these things. One needs the game dissected very finely and then non-jagged data for the math transforms to operate on the returned cursors from the data cubes. Data can be made to spill its guts.

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RugbyMetrics Performance Analysis Software

(click on pic for a larger image)

I am about to test my new rugby video and performance analysis software on an Aviva Premiership professional rugby team.

This entire exercise is akin to Sabremetrics in North American professional baseball where one uses data-mining and intense statistical analysis to discover scientific, objective knowledge about the game that was previously unknown.

For example, in baseball a Seattle-based team with a limited budget of $40 million had to compete with a team like the New York Yankees who could afford marquee players with a salary budget of $125 million. It was found that conventional wisdom about assembling a team of good players was neither conventional, nor wisdom.

In the old convention, all of the scouts looked for a young pitcher with a blistering fastball and a high strikeout count. Through data analysis, it was found that not only was a pitcher with a high ground out ratio more valuable, but he was also cheaper on the open market.

This intensive analysis has been done with baseball, football and basketball. No one has yet done it with rugby. Rugby is a very dynamic game and stats abound, but they are all conventional stats that really do not measure what a player does minute by minute and apply that to the greater context of the play. Some software packages give you a minute by minute report, but it is meaningless unless you take it in the context of inputs, outputs and results, both on a micro and macro level. There is tons of minute-by-minute data, but very little pattern knowledge of what that data means.

I developed the piece of video analysis and annotation software that models and digitizes the entire game second by second. It is all put into a database in a proprietary format that allows extensive data-mining and statistical analysis of every second of every game.

There are a few software packages that give you a real wow-factor re-simulation of the game, and a whole pile of individual statistics about every facet of the game, but these are not very helpful in real life. What is helpful is garnering new knowledge -- objective knowledge about the sport and about your team. The way that it is done, is by taking statistics, facts, data and factors, and integrating that data into knowledge by making inferences, testing those inferences and endless cutting and re-dimensioning of data until statistically significant knowledge about the team and the game pops out. Nobody is doing this out there, and I will be the first. Like Sabremetrics, this will add objective knowledge about the sport of rugby on the macro level.

The most important aspect of this, is that the tool can isolate and chart characteristic on-field behaviour of the opposition, thus allowing the planning of a defense for a formidable opponent. Teams that are well coached consist of human beings with ingrained play patterns, and it is a valuable tactical advantage for an opposing team to know this. If you know the attack vector that is coming, you can plan for it. But it is not enough to know the attack vector, you have to know the probabilities and how it will play out, and how it changes with different parameters.

A model is being developed using Bayesian Predictive Analytics that will predict and spot the future Dan Carter superstars of rugby while they are pimply-faced teenagers.

This is a quantum leap forward compared to something like Opta stats, which dissects every game into component parts but just posts flat statistics that do not show why these events that they have dissected happen the way they do. RugbyMetrics is taking rugby performance analysis to the next level.

CLICK HERE to see some of the math behind this tool : http://coderzen.blogspot.com/2011/04/rugbymetrics-performance-analysis_21.html

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