library(tidyverse) library(binom) Someone had a relatively straight-forward question: They had sets of binary outcomes for different response variables, and wanted to display them all in a simple way that highlighted both the probability of success and amount of data they had for each observation. There are more than a few ways to do it, and it can be hard to determine which is best without seeing them, so let’s look at a few examples and see which we like!

Both Economics and Statistics share a peculiar failure mode: Many critical results in both rely on “large sample”/“long run average” proofs.
The Central Limit Theorem is fundamental to much of classical statitics, including most (if not all) of the fundamental approaches that people are exposed to in their first few courses. The Efficient Market Hypothesis underpins much of the economic theory on which Western economies are based. Both are powerful tools for explaining common phenomena and often make complex problems simpler to understand and model.

So we’ll call that break a “summer hiatus”.
But now we’re back, and coming recently from the Joint Statistical Meetings (2019) in Denver, I’ve got Thoughts.
This year’s JSM was different for me, because I spent most of my time on recruitment, speaking with potential applicants during many of the sessions. As a result, I attended many fewer talks that I normally do. By happenstance, the topic of the p-value came up repeatedly in the talks I was able to attend.

As a newly-minted PhD Statistician, I was hired by a company that didn’t have a lot of native statistical expertise because they wanted to change that. As a result, I felt empowered to give lots of opinions on topics within my domain to anyone who happened to be in the room, including the head of the division. One of those opinions was that pie charts were the worst.
I viewed pie charts as the scarlet letter of bad analysis: Having one in your analysis should get you shamed and shunned.

This is an update to my Analysis Philosphy page, which is still working towards completion
Nonlinearity is a commonly-misunderstood problem when it comes to data analysis, mostly because our profession has once again managed to find a way to use a simple-sounding term in a way that’s counterintuitive to lay audiences. (See also Artificial Intelligence is Dumb.) When people think about nonlinear response variables, they think of functions that have non-linear relationships.

Previously on DIY Metircs… Last time in the DIY Metrics series, we had reached the point where we could extract a host of individual metrics from our data set using a function we’d named add_simple_stat_indicators:
add_simple_stat_indicators <- function(tb){ tb %>% mutate( gotblk = (description == "BLOCK"), gotstl = (description == "STEAL"), gotast = (description == "ASSIST"), gotreb = map_lgl(description, str_detect, "REBOUND"), tfoulu = map_lgl(description, str_detect, "T.FOUL"), tfoull = map_lgl(description, str_detect, "T.

With the recent success of the Rockets, people are trotting out that old saw about analytics nerds ruining sports. With the Houston Rockets specifically, the question is a combined referendum on the numbers-based approach of GM Daryl Morey and the foul-drawing proclivities of Houston’s two stars, James Harden and Chris Paul. Of course, the latter is linked with the former, since analytics shows us that drawing shooting fouls is extremely efficient offense.

Disclaimer: This post is at least tongue-half-way-in-cheek. I acutally like the article I’m lampooning.
A recent publication by academics and AI researchers titled “Data Sheets for Datasets” calls for the Machine Learning community to ensure that all of their datasets are accompanied by a “datasheet.” These datasheets would contain information the dataset’s “motivation, composition, collection process, recommended uses, and so on.” The authors, Gebru, et al., would you like to include more data about your dataset.

Alrighty! This post got delayed a bit due to real life as well as some challenges with the data. But it’s also an exciting post because we’re finally on the road to generating player-level counting statistics!
Simple Statitistics This post is focused on simple counting stats or box score statistics that were basically the standard way to discuss NBA players until quite recently. So aggregate numbers of rebounds, assists, steals, etc.

As promised, today we’re going to talk about normalizing by possession instead of time on court.
First, a but of motivation. Different teams play at different paces. Some teams try to score a lot in transition, some teams try to slow the ball down and make sure they get good shots in the half-court. Part of this is related to a team’s defense and how quickly they get rebounds in the hands of players who can push the ball.