Tag Archives: football

A deep dive for each team’s 2019 NRL season

With the first Maori versus Indigenous All-stars game and another edition of the World Club Challenge in the history books, our attention turns to the NRL season ahead.

As with last year, I’m going to do a SWOP – Strength, Weakness, Opportunity and Prospect – analysis for each team. My general philosophy for judging a team’s prospects is that where a team finishes on the ladder the previous year is a more or less accurate reflection of their level, give or take a win or two. If no changes are made, we should see a similar performance if the season was repeated. There are exceptions, e.g. the Raiders pathological inability to close out a game should be relatively easy to fix and the Knights’ managed maybe two convincing wins in 2018 but still finished eleventh, but broadly, if a team finishes with seven wins and they hope to improve to thirteen and make the finals, then we should look at what significant changes have been made in order to make that leap up the table.

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Stats of Six: Is 2018 the worst top eight in NRL finals history?

Using the same format I used during the rep weekend, this is the finals preview-ish post.

I didn’t get to do all the analysis I wanted to because I’ve run out of time. By the time this gets published, I should be somewhere in or around California starting my honeymoon, which I think should probably take priority. I won’t be filing from America (in fact I probably won’t see any rugby league for six weeks) but I will be back in October or November to do some post-season stuff.

This post relies pretty heavily on Elo ratings, so you might want to brush up.

Embed from Getty Images

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Gauging the 2018 State of Origin teams – Game I

For the first time in a long time, it looks like we may have a well balanced Origin season. Indeed, the balance may even be a little Blue for my liking but when three of the last generation’s four best players retire from representative football, and they all happened to play for the same state, then the scales will shift perceptibly.

By now, you would know who’s playing for both Queensland and New South Wales in the first of rugby league’s three biggest games. You might even have formed an opinion as to which side is looking the goods. Consensus seems to have settled on this being the Blues’ year. But why settle for the thoughts of experts who have spent the last forty-eight hours tweeting out the leaked Blues lineup, when I’ve crunched the numbers for you?

Embed from Getty Images

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A deep dive for each NRL team’s 2018 season

The only thing more reliable than March bringing rugby league back is the slew of season previews that each and every media outlet feels the need to produce. I’m no different in this regard and here is what is likely to be the longest post I’ve ever compiled.

This year’s season preview takes a look at each team and is a mix of my usual statistics, a bit of SWOT analysis and some good old fashioned taking a wild punt and hoping it’ll make you look wise come October.

(A SWOT analysis is where you look at Strengths, Weaknesses, Opportunities and Threats. There’s only one threat in the NRL, and that’s the other fifteen teams, so it’s more of a SWO analysis)

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Analysis – Stocky vs Reality: Did your team outperform? (Pt II)

The Stocky is the main forecasting tool driving the analysis on this site. It’s a simulator of the season ahead, using the Monte Carlo method and based on Elo ratings, that gives insight into the future performance of each club. My main interest has been the number of wins, as it determines ladder positions which in turn have a big impact on the finals. The Stocky might not be able to tell you which games a team will win, but it is good at telling you how many wins are ahead.

But how does a computer simulation (in reality, a very large spreadsheet) compare to reality? To test it, I’ve put together a graph of each team’s performance against what the Stocky projected for them. Each graph shows:

  • The Stocky’s projection for total wins (blue)
  • Converting that projection to a “pace” for that point in the season (red)
  • Comparing that to the actual number of wins (yellow)

It will never be exactly right, particularly as you can only ever win whole numbers of games and the Stocky loves a decimal point, but as we’ll see, the Stocky is not too bad at tracking form and projecting that forward.

This week is Part II, from North Queensland to Wests Tigers. Part I, from Brisbane to Newcastle, was last week. Also see this week’s projections update for some errors in the Stocky.

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Analysis – Stocky vs Reality: Did your team outperform? (Pt I)

The Stocky is the main forecasting tool driving the analysis on this site. It’s a simulator of the season ahead, using the Monte Carlo method and based on Elo ratings, that gives insight into the future performance of each club. My main interest has been the number of wins, as it determines ladder positions which in turn have a big impact on the finals. The Stocky might not be able to tell you which games a team will win, but it is good at telling you how many wins are ahead.

But how does a computer simulation (in reality, a very large spreadsheet) compare to reality? To test it, I’ve put together a graph of each team’s performance against what the Stocky projected for them. Each graph shows:

  • The Stocky’s projection for total wins (blue)
  • Converting that projection to a “pace” for that point in the season (red)
  • Comparing that to the actual number of wins (yellow)

It will never be exactly right, particularly as you can only ever win whole numbers of games and the Stocky loves a decimal point, but as we’ll see, the Stocky is not too bad at tracking form and projecting that forward.

This week is Part I, from Brisbane to Newcastle. Part II, from North Queensland to Wests Tigers, will be next week.

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Analysis – The more competitive the season, the more bums on seats

Most rugby league commentators wouldn’t know what a linear regression is or how do one. I’m no different but I do like to compare two variables and see if they’re correlated. A scatter plot with a linear trendline and an R-squared – remember R-squared goes from 0, no correlation, to 1, perfect correlation; I usually need at least 0.2 to raise an eyebrow – is all I need to keep me entertained for hours on end.

Last week, we looked the concept of competitiveness and how to measure it. This week, I want to see if (more or less) competitiveness impacts on other aspects of the game. Using my preferred ratings gap as a proxy for how competitive a season is, this post looks at a few variables to see if they’re correlated.

If you want a specific variable looked at, give me a yell.

Draws

draws vs gap

Surprisingly, there’s no link between the number of draws and how competitive the season is. There’s basically a correlation of nothing with an R-squared of 0.03 . I think draws are more about the specific teams in question and I think golden point may play a role but the overall season competitiveness doesn’t matter.

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