Just got some research put up online and thought that some members of the forum would be interested in it. Basically it attempts to calculate the impact of salary cap violations on the probability of a home team win.
Here are some quotes from the article.
The crux of it is this:
Even upon accounting for team specific factors, such as keeping a core player group intact, an additional $1 million AUD of salary cap violations by the Melbourne Storm would have increased the probability of a home team win by between 6.4% and 10.6%. Without these team specific factors the estimate lies between 16.8% and 27%.
The success of the Melbourne Storm between 2006 and 2010 was not solely based on circumventing the salary cap and was also based on the identification of talented players and the ability to keep a certain player group together.
As a result:
The results raise questions on how the salary cap functions with respect to the identification and valuation of player talent. Indeed, the significant success in the period before 2007 predated the largest salary cap violations, but closely aligns with the amount of games played by a core playing group identified in Longden and Kannard (2014). The discussion surrounding Figure 4 of Longden and Kannard (2014) highlights this crucial issue and the timing of the breaches in comparison to home team wins.
Here is the article:
And here is the whole paper that I called Longden and Kannard (2014) so that you can look at Figure 4:
Hope that it passes muster and stands up to the WTF test…
Note: WTF was meant to mean ‘West Tigers Forum’ but it can be interpreted the other way.
Will be interested in the discussion on salary caps and talent identification. Also happy to answer questions as they come…
tom620 - you’ve piqued my interest, how does one get funding and / or publication of NRL-based journal articles and what is the whole Italian connection?
This was basically a side project that I started last year without funding. At the time I was based in Italy and working at FEEM. It took a while to finish as it was done during our spare time and that’s why the data only goes to 2012.
Greg and I are both researchers and the links include the working paper (Note di Lavoro) and a smaller piece (Re3) that attempts to raise interest in the larger piece of work. We have sent the working paper to the Journal of Sports Economics for external peer-review and hopeful publication.
I chose to release it via FEEM as the readership is wide, but figured that nothing would be lost as Australian readers would find it in an online search. It is easy to find with a search on ‘NRL salary cap violation’. I also have sent it to some of the Australian press in case they wanted to base an article on it, but unfortunately haven’t seen anything appear as of yet.
Next up will be an Re3 article on home ground advantage - but I should note that this is in the working paper for anyone keen to see an analysis on the probability of a home team win at a traditional Sydney stadium, such as Leichhardt or Campbelltown.
I will share the link to the Re3 article on that topic once it is online.
One thing we raise is how the salary cap deals with key young players who might have been undervalued. The difficult thing about discussing specific teams is that there is no transparency on who is paid what. In the long run, young players who get paid little but over perform should be hard to keep after periods of success.
The Re3 article states that:
It is of interest that the Salary Cap Auditor indentified three phases of the ‘salary cap rorting’, these being between 2005 and 2007, 2008 and then a final phase which commenced in 2008. These phases are consistent with the success of 2007, large breach amounts after 2007 and the challenge of keeping Premiership winning players together in the period after 2007. Indeed, rather than insufficient valuation of the breaches by the Salary Cap Auditor, the results raise questions on how the salary cap functions with respect to the identification and valuation of player talent.
Also, Canterbury have had large changes in the probability of a home team win that we couldn’t explain based on the data we had. Player movements, injuries and general instability are probably part of the story.
Take a look at this figure and the blue line with squares.
2005, 2007, 2008 and 2010 were all particularly bad years.
Or vice versa, 2006 and 2009 were surprisingly good years during an overall slump.