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.
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.
Where will the Warriors land? Will the Heat flame out? Are the Lakers back?!?
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 goaltender performance is hard. Goalies are subject to the whims of the gods and the players in front of them, whichever is least merciful.
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.
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
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.
The details that are needed to have an intelligent opinion on the matter simply are not present. I don't know whether the claim is right or wrong, but this doesn't bring me any closer to that.