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.