November 9, 2015 3 minute read

Legends of the Fall

The Rugby World Cup (RWC) 2015 is over! I can’t wait until 2019!

This has been a fantastic edition, fully packed with emotion.

I enjoyed the Brave Blossoms resilience, I was inspired by the fighting spirit of “Los Pumas”, I was delighted by the wonderful moves of the All Blacks “golden generation” and I was sad about the wrecking of “Les Bleus”.

In part 1 and part 2 of these blog series, I used SAP Predictive Analytics to create my predictive model based on historical data and tested some scenarios.

I now apply the predictive model to determine the players that will make it to my hall of fame due to their overall RWC performance. The focus is not really on those new talents that emerged across this particular edition, as my data is summing up the performances across the different editions of the RWC.

I reload the model I had created and saved, then I click on Run and Apply Model.

In the Applying a model screen:

The Application Data Set is the data set on which I will apply my model and determine which player should be considered a legend or not. Mine is named RWC 2015 Player List, it contains the figures across the different editions for the players that participated to RWC 2015.
The Generation Options determines the output that is generated from the model. In this case I am selecting the Probability & Error Bars option. If the player is given a probability superior to 0,5 in the resulting file, it should be considered a legend. There are more generation options possible, I find probabilities quite easy to interpret.
The Results Generated by the Model is the place where I output the results. Here I am generating the results into an Excel file.
I click on Apply so that the file gets generated.

I open the Excel file and look into the column D, it is corresponding to the probability of each player being considered (by me) a legend. A probability is a figure between 0 and 1. 0 means that the player is very probably not a legend (for me!), 1 means that the player is very probably a legend.

I loaded the Excel file into SAP Lumira, and selected the players with a probability of more than 0,5:

The list contains 24 players in total.
Most of the players originate from South Hemisphere teams and the All Backs are well represented!

Frederic Michalak is represented in a high position (#26) in the overall list. He is falling a bit short to become one of my legendary players due to a probability equal to 0,42. OK, I’ll give him a bonus because he is a French guy ;-).

Jonathan Sexton or Sergio Parisse are not yet legends for me.

Now I’ll remove the players that I was already considering legends in the RWC history.

My list previously included Dan Carter, Bryan Habana, Richie McCaw, Fourie du Preez, Drew Mitchell, Kieran Read and Victor Matfield. All these legendary players shined this year and through their RWC career!

My final shortlist does include 17 players, from 5 different countries.

10 All-Blacks:

3 Australian players:

2 South-African players:

1 Argentinian player:

Juan Martin “El Mago” Hernandez.

1 Irish player:

Keith Earls

I do agree with most predictions:

This brought my attention over Keith Earls, I did not really know him before.
David Pocock was elected by many as the best player of this World Cup (mine is Dan Carter).
Juan Martin “El Mago” Hernandez is one of my favourite all-time players. He has been a constant threat for French teams over the last years.

As we have seen, it’s very easy to apply a predictive model to generate results on new data samples.

Legends of the Fall

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