Tag Archives: manly

Who will win the 2019 NRL Premiership?

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At this time of year, is there anything else you want to know more than the answer to this question?

For our crystal ball, we turn to Monte Carlo simulations. These simulations work on the principle that if we know the inputs to a complex system and how they relate to each other, then we can test the outcomes of that system using random numbers to simulate different situations.

At its most basic, just imagine if you simulated the outcome of football matches by rolling dice. Numbers one and two might represent a win for the Gold Coast and numbers three through six might be a win for Wests. If you repeat that a couple of thousand times, not only will you be extremely bored but the Gold Coast will “win” about 33% of the time and Wests 66%.

Now take the same approach for the nine finals games, with the winner advancing per the NRL’s system, but instead of using dice, you generate a random number between zero and one and calculate the win probability using Archimedes (form) Elo ratings. Then repeat it 5,000 times over. The number of times that the Storm or Roosters or Broncos or Eels “win” the premiership across your simulations should give you some insight into the probability of that happening in real life. I call this the Finals Stocky and I present its findings.

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

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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)

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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 – Another bloody mid-season review (Part I)

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With the conclusion of round 13, it’s half time in the 2017 NRL season. It’s the ideal time to do what everyone else is doing and look back at the season so far. This week we’re looking at the first eight clubs that come up in alphabetical order.

Part II to come next week.

Benchmarks

There are some important benchmarks to consider when looking ahead to the end of the season.

Firstly, let’s look at the regular season. I’ve tallied up the average number of wins for each position, the average for-and-against and the number of teams with a negative for-and-against for each spot on the ladder. The dataset covers 1998 to 2016, so there are some inconsistencies from seasons which had twenty or fourteen teams and where points penalties were applied to the 2002 Bulldogs, 2016 Eels and 2010 Storm.

The main takeaways are that twelve wins should get you into the finals and eighteen should get you the minor premiership. Six or seven wins will still only get you the bottom spots on the ladder (unless the 2016 Knights are playing).

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Club Report – Manly Sea Eagles

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man-badgeBackground

After an ARL premiership in 1996, Manly entered the NRL in a slightly awkward position. The Sea Eagles took up the NRL’s offer to merge with local rivals North Sydney, forming the Northern Eagles. On-field performances were terrible and in 2003, it was apparent that the marriage was dysfunctional. The venture got a divorce – the Bears never returned to first grade while the Sea Eagles have stuck it out since.

Since the divorce, Manly have been one of the strongest teams in the competition with an unbroken run of finals appearances from 2005 to 2014. Only the salary cap-breaching Melbourne were better and there’s a solid argument that the two runners-up “honours” held by the Sea Eagles could perhaps have been premierships. The last couple of years have not been up to standard but after a patchy start, this year sees them as genuine finals contenders again.

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