StatScore, a player rating tool I developed over the off-season, feels like something powerful but I haven’t had a real chance to use it yet. Thanks to Fox League posting individual player stats, I can at least keep updating the scores through 2018 which I was worried I wouldn’t be able to.
But here we are, about one-fifth of the way into the 2018 campaign, and it’s time to start assessing performances. StatScore gives a lot of props to players in the halves, so much so that three of the four top players from the last four seasons have worn the number 7. During the off-season, a lot of the halves shuffled around, including the top line names of Maloney, Cronk, Pearce, Green, Hunt and Marshall. Let’s have a look at how that’s panned out.
Last week, we took a look at estimating the number of premierships we expected NRL teams to have won versus the number of premierships they actually won. This was spurred by a Twitter query from AFL fan, Bill (whose name I don’t remember and won’t be searching for), who asked the same question of someone else but about AFL.
Well Bill, this week, I did it for you.
Two weeks ago, you may have (but almost certainly did not) read my Complete History of the NRL: Nerd Edition.
Actually, there wasn’t much to read in the way of words and for those who aren’t so inclined to dealing with Elo ratings, Pythagorean expectation and counting premierships, possibly because you’re an Eels fan and don’t remember what victory feels like, I’ve prepared a simpler edition of the Complete History of the NRL.
The history is presented in a series of colourful graphs. The graphs track each team’s win per season and are helpfully annotated to remind you of great moments in NRL history.
And if you’re familiar with the work of Jon Bois, you’ll recognise this as eerily similar to his History of Every NFL Team video, which I have shamelessly ripped off for content.
Last year, I did a report on each NRL club featuring a bit of history, a few statistics and some graphs. The series didn’t do super well in terms of clicks but also didn’t take a lot of effort to produce.
One thing I did enjoy putting together were the class graphs. These use a slow moving Elo rating system called Eratosthenes to track the long term performance of clubs. You can see a full listing of all current ratings here.
If you’ve got the right kind of stuff between your ears (that is, if you’re a massive nerd), each picture tells each team’s thousands of words history in the NRL. To that end, I’ve updated all sixteen clubs’ graphs to the end of the 2017 season for your nerdy consumption.
This headline (the actual article is fine) from Tim Gore typified the attitude when the draw was released in November last year:
Not only were the NRL never going to redo the draw, it speaks to the tendency that the they can do no right in the fans’ eyes (“Release the draw earlier! No, not that one, a different one”).
Looking at last year’s ladder and having a whinge that your team has to play three top four teams twice is peak gronkery. It’s fuelled by emotion and we can do better. So here you go: a quantitative analysis of who has the toughest draw in the NRL.
Can we predict a premiership winner from their Elo ratings?
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Obviously, yes. That’s what the Stocky is for and this site would pointless if this was not true. But what if we wanted to look into the future before a single game has been played? I think that the Elo ratings of premiership winning teams might have a common pattern to them that show up if we take a closer look at their long term performance, or class, ratings with Eratosthenes.
We’ll need some premiership winners to review. To do this analysis I’ve tried to pick one premiership per club (to avoid autocorrelation) and pick a premiership that stands on its own. That eliminated a number of
premierships years for Melbourne and multiple premierships for Manly and Brisbane. I also biased it towards more recent premiers where possible. I was left with the following list:
If you’d like to skip the explanation and see the full list of StatScores and Win Shares, you can go to this Google Sheet.
The biggest off-season story in the NRL was the transfers of Cooper Cronk from Melbourne to Sydney and then Mitchell Pearce from Sydney to Newcastle. From the Roosters’ perspective, for two players likely on similar pay packets, how did the Roosters decide one was better than the other? Then I wondered if it were possible to work out a way of judging value for money in player trades. It’s big in baseball, so why not rugby league? This led me to develop StatScore and Win Shares as ways to numerically evaluate rugby league players.