I'm not here to gloat. Or at least, not about the Raptors. I'm here to gloat about my predictions.
I love how this literature, and our entire understanding of probability, has either been about predicting rainfall of profiting through gambling.
A lot of these teams are really dragged down by having a few weak players, especially if they're expected to play significant minutes.
Next year could go in many directions, since there are no sure-fire NHL candidates. There's no Martin Brodeur or Teemu Selanne. The most accomplished player might not be an NHLer, since it looks like Hayley Wickenheiser is eligible. This gives the committee a lot of room to reconsider players it overlooked in the past.
Some of these teams will be much better than expected, and some will be much worse, but right now we don't know which.
We can fit the data perfectly, but we shouldn’t. This is what’s known as the bias-variance trade-off. The more precise we try to be in fitting the data at hand, the more likely we memorize effects that are due to random variation in the training data that do not generalize to other datasets.
I love lists, and I love arguing, so here are five players who should be on the top 100, and who they could take off.
All this talk about the NHL's Greatest 100 got me thinking about what goes into hockey greatness. I always lamented how there wasn't a Hockey Hall of Fame model like basketball-reference's model. So I thought I would go about predicting both things.
There are two primary challenges we face when judging goalies: The number of goals, especially as we try to account for confounding factors, is relatively small, and we have trouble isolating goalie performance from team effects
If we wanted to test the impact of unemployment and expectations on inflation, we could study data from a regime where inflation is not a goal of policymakers.