Playoff Questions: Does Denver's Home Court Advantage Translate?

curry landry

Hey, all. Aaron here. Both Alex and I have an enormous wealth of statistical expertise on our side -- I've got a degree in statistical science and work as a professional statistician in the banking industry, he has a degree in salamander geography and used a calculator once. Given this, as the 2013 Playoffs soldier on, we're planning to occasionally tackle statistical quirks and curiosities we find interesting or elucidating. Answer the questions that we forgot to ask in the first place. Et cetera, et cetera. Today's topic: Denver's mountain air. Or, more accurately, the diminishing returns thereof.

Entering the playoffs, things looked pretty simple for any garden variety prognosticator. Chalk looked poised to reign -- none of the one-through-three seeds in either conference looked even remotely prime for an upset. Teams had either finished the season strong (DEN), faced opponents that were so depressingly injured that they could solve their late-season struggles (SAS), or were simply in a class completely beyond their opponent (MIA). It just didn't look like there were going to be any upsets on the top-line -- if anything, perhaps there'd be an upset in the 4/5 spot, but those are scarcely upsets at all. Chalk, chalk, chalk. Chalk everywhere.


As we stand, the Warriors are on the verge of a monumental upset. Don't sell this Nuggets team short -- they won 57 games, posted a home efficiency differential that makes lambs bleat, and feature a wealth of talent with an excellent play-calling coach. The Warriors limped into the playoffs with a late season slide that took them from a contender for HCA to the verge of the eight seed -- for a short period of time, it actually looked like they were a threat to miss the playoffs. During the 2013 calendar year, the Warriors posted a regular season record of 26-25, just ONE game above 0.500 -- the Nuggets were 40-10. So you must excuse me if I'm hammering the point home a bit: this Nuggets team is a good team, and what the Warriors are doing is reasonably surprising (even if I wrote several good -- and strangely prescient -- reasons why the Warriors had a good shot at the upset in the Gothic Ginobili series preview).

One of the few things we thought we knew going into the playoffs was this: the Warriors couldn't possibly beat the Nuggets at home. That was part of why many smart analysts chose the Nuggets in 5 -- even if the Warriors match up reasonably well with the Nuggets, there was theoretically no threat of Denver dropping any of their home games in the first round. Simply impossible. The Nuggets were 38-3 at home this season. Entering their first round series, they'd won 23 straight home games. Of course, that ended up being a somewhat silly worry -- the Nuggets were a few errant calls and an Andre Miller explosion away from losing game 1, and they got thoroughly embarrassed in a game two blowout that wasn't as close as the 131-117 score made it seem. Down 3-1 with their backs against the wall, it's tough to figure out how to handicap these Nuggets. They WERE unbeatable at home -- are they, still? Or was the appearance of infallibility bunk to begin with? In our first installment of our stat-based playoff feature, I'll examine that question. Continue reading

All's Quiet on the Eastern Front

east meets west

Hey, all. For today's post, I'd like to present some cross-conference matchup data. A lot of people discuss cross conference games from a perspective of raw wins and losses. I'm amenable to that, in the aggregate -- there are usually enough games that looking at raw intraconference wins/losses can give valuable results. Still, there's generally more insight to be gained by looking a tad bit behind the numbers -- not simply the raw number of wins, but how they came about, what sort of statistical quirks underlie them, and what teams are best against the opposing conference. You know, all that jazz. So come with me, friends, and let's get behind a few of the preliminary factors that drive 2013's main inter-conference trends, and note a few interesting quirks

Full-size table after the jump. Continue reading

Using Talent Right: Title Contenders Force the Tempo

This is part of a two-part series. For observations on the Spurs  and the Thunder's specific matchup, see 48 Minutes of Hell.

As one of my questions in Monday's Statistical Q&A, I fielded a question from the imitable Tim Varner of 48 Minutes of Hell. His query was whether the Spurs stand to gain in OKC smallball lineups by pushing the pace and playing fast. In short? Yes. I covered that today in detail at 48 Minutes of Hell, but there's a lot of interesting tidbits to be had in this table, enough so that I felt a separate post was necessary analyzing the trends and tendencies of the non-Spurs teams. To examine, I've produced this table that shows the W/L record, the offensive and defensive efficiencies, the eFG%, the efficiency differential, and the free throw rates of our four remaining teams in four distinct buckets of possessions. First bucket includes all games with under 91 possessions; the second includes 91-95; the third is 95-100; and the fourth and final bucket includes super-fast games with over 100 possessions. These are roughly quartiles of possessions. I placed in red a team's "worst" pace and in green a team's best.

Looking at this table, some interesting takeaways after the jump. Continue reading

A Very Gothic Ginobili Statistical Q&A: Part 1, Playoffs

This was an idea we had a few weeks back, when I posted the "Last 21 Game" efficiency rating posts both here and at 48 Minutes of Hell. An understated aspect of those posts that I was experimenting with was that I, for the day it was posted, attempted to respond to every twitter question regarding the stats used (or any statistical trend readers wanted to hear about). Probably should've advertised it more.

In a combined sense, I got a lot of great questions from the 48MoH commenters and my twitter followers -- so many, in fact, that I'd like to codify it and make it a feature. As it's memorial day, I have the day off and have a chance to actually spend most of the day sifting through data, and it's a good opportunity to kick this off. To start the discussion, here's a table of team playoff efficiency stats vs regular season efficiency stats. Continue reading

The Last 21 Games: Late Season Offensive and Defensive Rankings

For a more specific look at the surprising Spurs, see today's post at 48 Minutes of Hell.

It was recently brought to my attention that most people aren't quite as ridiculous as I. Let me explain. For much of the season, I've been offhandedly keeping tabs on the overall trends from an offense/defense perspective, through the view of eight-game moving averages, rankings in five-game spans, and a large spreadsheet updated when-I-remember with the latest summary data from Hoopdata. I was asked by my friend -- Tim Varner of 48 Minutes of Hell -- if I'd put together a complete post on the surprising late-season defensive renaissance by the San Antonio Spurs. Compiling my data into an easy-to-share form for the purposes of the post in question led my data to a form where it would be easy to share the whole league picture. Hence, I decided to make a post here about it as well, specifically taking aim at interesting trends (at the most rudimentary, league-wide level) over the last 21 games of the year and sharing the underlying data behind the graphs at 48 Minutes of Hell and the entire ranking. Onward, then. Continue reading

Pau Gasol's Infinite Sadness, by the Numbers.

Due to the crushing unpopularity of the "Gothic Ginobili" brand, we have decided to take the blog in a different direction. Welcome to "Goth Gasol", a blog whose sole purpose is to make you think about death and get sad and stuff. To start things off, we have provided a statistical assessment of Pau Gasol's infinite sadness. Required reading.

I'm gonna be honest. I used to get a little jealous sometimes. Of Zach Lowe, you see. Lowe is something of a wunderkind -- a great writer, a great guy, and generally one of the smartest guys around in the NBA writing game. He also, however, has connections. (Not conniptions, those are different.) For instance, he recently had me run a few numbers for an excellent piece he wrote on defending the corner three. It was a great piece, and I was honored to help out. But had it been the me from months prior, I wouldn't have been able to help being a bit jealous of his access to such interesting numbers. However, that all changed a few months ago. I am jealous no longer. See, I found this one guy. Or rather, he found me. His name shall remain anonymous. This is mostly because I have no idea who he is. He contacted me after my prior piece on Kobe as Stavrogin to send me a detailed spreadsheet of the number of times Kobe has invented a new cuss about wizards during each NBA game in the last four years. I have no idea how he tracked this. Not a clue.

I never asked, and I filed the numbers away for the next time I do an analysis on cusses -- a rare but ever-present option for me. Anyway, long story short, he sends me completely unsolicited spreadsheets every few weeks on things that either make no sense whatsoever numerically or make me wonder who in the world could possibly have these numbers handy. Given our new rebrand around the ongoing sadness of Pau Gasol, though, the data he sent me the other day is of the most paramount value. We now have statistics for the number of Pau Gasol frowns for each game of the 2012 season. Armed with these numbers, we may now examine the relationship between Pau's sadness, egg consumption, and the Lakers' winning ways. Anyways. This is the post you've all been waiting for. It's my big break. So, let's get to it.

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2012 Assist Infographics: Point Gods, Guards, and Quetzalcoatls

So, earlier today, the fantastic folks at Basketball Reference released to the public a marvelous database. It includes a highly interfaced and searchable play-by-play database for the past 10 years. This is, quite frankly, incredible. There's virtually nothing your heart desires that you can't search for -- player performance by the time left in the game, detailed stats of what happens when players make certain plays, team performance in certain situations... I don't think I'm being overly sycophantic when I say that this is among the greatest single advances in easily interfaced, searchable, and public statistical databases in the world of NBA statistics. There may never be any one or two people who mine the database for all the insight you can get from it. Ever. Long story short, it provides an easy way to answer certain questions, and the ability to learn how to raise better ones. To that end, I'll be doing a series of posts where I graphically demonstrate certain things that this database allows one to easily find. I hope that these will be useful to you. They're certainly interesting, if nothing else. Today's introductory topic as I sift through the data for interesting insights: how are assists distributed among the league's best setup men? Who are their most prolific partners? When in the game do they get them -- and what's the score when it happens?

Interesting questions. And it's easier to scratch at answers than ever. Let's dive in.

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Introducing STEVE NASH, our 2012 Projection Model.

Hello, all. Today is going to mark, for us, the official rollout of the Gothic Ginobili preseason projection model. I made the model, Alex came up with the name -- "SRS-Tempered Evaluation of Variable Elucidation; Not A Simple Hyper-Segmented Linear Regression." Which... is an acronym for STEVE NASH, if you hadn't noticed already. Yep. It's either the best model acronym ever, or the worst. Try saying the model name out loud. It's hilarious, and I can't stop laughing. But it's memorable, reasonably descriptive, and honors one of the Gothic's favorite players. So... I guess it'll suffice, for now? Regardless. What is STEVE NASH? It's a model that attempts to use prior data project out what we should expect to happen in the 2012 season. I would never call it a prediction model, for reasons I'll explain in the introduction, but it offers decent projections of what to expect based on prior data. Come with me on a journey through the seedy world of model fitting, setting your priors, and managing expectations. Let's meet STEVE NASH, together.

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Alex Learns Stats: Drawn and Quartered

Aaron's fascinating look into the inherently deleterious effect of the compressed season on injuries (focused on effects wrought solely because more games fit into the same recuperation period) got me thinking. As stat posts are wont to do. What if it weren't the number of games that were compressed, but the games themselves? We've all heard the tired LeBron jokes. I tried to make change with LeBron, but he didn't have a fourth quarter. Well...what if nobody had a fourth quarter? How would we make change then?! What if that was the price of the lockout? What if Commissioner Stern, in a jaw-droppingly flamboyant abuse of power, declared that the cost of a lockout would be felt every night, for 12 missing minutes?

... well, I had some spare time and wanted to try my hand at these public Google Doc spreadsheet posts that Aaron has been using, guess we're going to find out. Follow me hither to the magical world of endless, tedious data entry, where Aaron and I frolic among the sparse statistical flowers of wisdom to be found there. This is kind of a curiosity, but there were a few interesting surprises. Continue reading

Compression Effects: the injuries of 2011, today!

I wasn't planning to write another post about injuries this week, but I was talking with a friend of mine about Chris Paul and a thought came into my head that I didn't want to sit on. One of Chris Paul's most notable traits (unfortunately) is his somewhat sketchy injury history. While he only missed 2 games last season for a scary-but-minor concussion, thinking about all the dings and dents of an NBA season and the possible repercussions on Paul got me to thinking about how those will look this year. To start -- the season is compressed, and every game mathematically matters more. Andrew Bynum was suspended five games for his hit on J.J. Barea in last year's playoffs. In a full season, that's 6.0% of a player's possible games. In a compressed season? 7.5%. Not an insignificant difference, by any means. The effect of individual games being worth more in the overall picture is pretty straightforward. But as for that being the only effect? Not quite.

That's only true for suspensions, which are a designated number of games. What about injuries? When a player gets a hip strain or a sprained ankle, they aren't out some prescribed number of games. It isn't like the NFL, where a concussion means a designated number of games out of action. An injury to a basketball player simply means you're out until you're in playing shape again, whenever that may be. Usually, it takes some set number of days of sitting out and recuperating. Some medical treatment. Some downtime. Some coaches bring players back on less rest, some coaches use more -- my last post on injuries tries to get at the heart of the coaching side of NBA injuries by looking at raw numbers and assigning them to coaches. In this post, I'm more interested in simply translating some player-side numbers from 2011 to the compressed season. This is more like my previous analysis of compression trends, except instead of trends, this involves cold hard facts.

The guiding hypothetical to this post: if players were to go through the exact same injuries in the 2012 season as they went through in the 2011 season, how many more games would they miss? Good question, voice in my head. Let's go find that out. To the spreadsheet, once again.

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