Collecting my notes on sports analytics and other assorted topics

Predicting the Hockey Hall of Fame

Who got in, who shouldn't have, who will, and why

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.

Talking goalies in Vancouver

Summary of my VanHAC talk

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

A hand on the thermostat

Interpretations of inflation evidence

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.

Fetch all the data

Gathering hockey data with cacheblob

Some data rarely changes, such as old boxscores, while some data changes every day, such as game results, and other data may change every time I view it, such as gamelogs for a game in progress. I want to cache my data, but with a concept of staleness and control over data expiry.

NBA win predictions 2016-2017

Building new player and game-level models to project wins for every team

Where will the Warriors land? Will the Heat flame out? Are the Lakers back?!?

Introducing sqloose

On SQL, SQL coding practices, and making life easier

sqloose is a little bit of syntactic sugar to make SQL coding easier. I’ve added ranges and negative indices to GROUP BY and ORDER BY clauses, and added GROUP TO and GROUP THROUGH clauses to make it even easier to write SQL queries.

Predicting NHL goalies with scikit-learn

Code and tutorial on how I modeled goalies

Predicting goaltender performance is hard. Goalies are subject to the whims of the gods and the players in front of them, whichever is least merciful.

How to be a little wrong everywhere

NHL skater and goalie projections 2016-17

So there you have it, some no-holds-barred nothing-to-hide projections. Full tables are in my github link. I'll use them as a starting point, but by no means an ending point, on my opinions and pool draft this year.

In which Pew spews and The Atlantic eats it

Please critique rather than regurgitate

The point I want to make is that attending church more often in the past, and attending church less often in the past, are not mutually exclusive

In which there are other ways for Trump to exit

The Upshot avoids discussing awkward scenarios

Even if your opinion is that this is highly unlikely (or a distasteful thing to bet on), that doesn't mean that others feel the same way.