Tag Archives: player ratings

Rating the 2019 State of Origin teams

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It’s that time of the year again. The weather has just turned cold and the NRL season has built just enough momentum to be interesting and has now been brought to a screeching halt. It’s State of Origin time, the world’s only all-star game that the players actually care about. Naturally, the first question anyone needs to consider is: which team looks stronger on paper?

When it comes to assessing representative games, we don’t have access to the usual team rating tools and, even if we did, the gaps between matches and changes to the teams are so significant that Elo ratings aren’t particularly useful. This year, we can evaluate the Origin teams using Production Per Game (PPG), which is a player rating tool.

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Primer – PPG and WARG

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Turns out that StatScore didn’t pan out the way I had hoped. There were some conceptual errors but the biggest was that I wanted a measure of rate and a measure of volume and you can’t have one statistic that does both. It’s like having one number that meaningfully states that a boxer is both the best in the world pound-for-pound but also the best boxer in the world who can beat anyone irrespective of weight classes. The world doesn’t work like that. As a result, there was some okay, but not great, player analysis. Unfortunately, the creation of a new tool requires that you use it for a while on new scenarios in order to evaluate it’s usefulness. Sometimes it doesn’t pan out as well as you would have hoped.

Also, the name sucked.

So I went back to the drawing board. There were some salvageable concepts from StatScore that have been repackaged, with corrections made for some fundamental mistakes, and repurposed into new player rating systems: PPG and WARG.

moneyball

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Primer – StatScore and Win Shares: rating NRL players

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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.

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