Signing off for 2017

I mostly started this blog as something to occupy my brain when work was quiet. Pro tip: spreadsheets are a great way to cover up that you’re not working. I’d actually stopped watching rugby league closely around 2004 but had gone to a few games in the last few years, reigniting a spark somewhere in the back of my head. I went to five games in the last six years and watched a handful on TV but this year, I probably watched well over a hundred games and went to seven.¬†I’ve found it a good distraction from the real world and I’m glad I did it.

Here’s how the year has panned out.

State of the (Northern) Union

The game is a lot faster, more athletic and visually spectacular than I remember. Queensland Cup is pretty good, not withstanding the Hunters’ poor performance last Sunday. Despite the Storm’s dominance, there were plenty of close games in the NRL and, unlike say AFL or NFL, the game is never quite over until the final whistle. In fact, there’s not a lot wrong with the rugby league product in 2017. Fans seem to agree with four of the year’s top six rating programmes being the three State of Origin bouts and the grand final, even if all are down somewhat off their peaks and attendances are sliding.

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Analysis – A deep dive into the 2017 NRL Finals

It’s finals time!

They may be all smiles now but come October, seven of these men will have failed in the quest to win the 2017 NRL premiership, joining the eight that have already been eliminated over the last few months. Let’s have a look at who they might be.

What history tells us

1995 was the first season with a top eight finals structure. Prior to that, it was top five and in 1998 only, it was top ten. Here’s where on the ladder every premiership winner from 1995 to 2016 came from:

gf positions

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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.

Continue reading “Analysis – Stocky vs Reality: Did your team outperform? (Pt II)”

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.

Continue reading “Analysis – Stocky vs Reality: Did your team outperform? (Pt I)”

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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Analysis – Is the NRL getting more competitive?

The short answer is yes and no. Yes, the NRL is more competitive now than when it started but no, it doesn’t seem to be a thing that improves consistently year-on-year.

Let me explain. On this blog, we use Elo ratings to measure teams’ performances and assess each team’s probability of winning a game in advance. Surely we can use our ratings system to assess the competitiveness of each NRL season.

Philosophically, what is a high level of competitiveness? It has to be a situation where the teams are fairly close in performance resulting in a hard to predict outcome. Here’s two ways of measuring that closeness of performance with pros and cons.

  1. You could look at the spread of teams ratings. Pro – makes an assessment based on all teams. Con – if all teams are pretty average but one team is excellent and another awful, then there isn’t a big spread of talent. This would imply a highly competitive season even though there is really only one potential premiership contender.
  2. You could look at the difference between top and bottom ratings. Pro – simple. Con – if one team is truly terrible and three or four are pretty good, then the season is pretty competitive but the difference between top and bottom might be exaggerated due to the crapiness of the bottom team. This would imply a not particularly competitive season despite there being multiple potential champions.

Which is better, measuring the spread or measuring the difference from top to bottom? Neither way of doing this is immediately obvious as a better method. Let’s look in more detail.

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Analysis – Your team and their finals appearances

There was a remark on Twitter a while back that Mitchell Moses had left Wests because he wanted to play finals footy but had chosen to go to the only club that had been without a final appearance longer than the Tigers. That didn’t seem right but I looked into it and it was true.

That got me thinking. How often do teams turn up to the finals? Some, like the Storm and Cowboys, seem to be regular fixtures but how do the rest fare?

Warning: this is going to be one of those “Well, yeah, I knew that” type posts. This is not about showing off some fancy analysis, just a bit of curiosity.

Double warning: Pie charts ahead.

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