Sunday, March 27, 2016

How long does a rebound take?

A deep dive into the minutiae of NBA timekeeping.

I'm working on a post on endgame strategies in the NBA that I hope to roll out soon. As part of that work, I needed to figure out approximately how much time runs off the clock between a missed shot and a rebound. Rather than keep this scintillating information to myself, or perhaps placing it behind a paywall and charging exorbitant sums, I am providing my findings here, free of charge. And for those of you unfamiliar with the concept of a rebound, and what it entails, I refer you to this excellent tutorial from Baylor's Taurean Prince.

Using play by play data from the current 2015-16 season, here are some basic statistics:
  • An average rebound takes 1.15 seconds. This is the average elapsed game time between the missed shot and the rebound (according to the play by play game logs)
  • 19% of missed shots are rebounded in "zero" seconds (i.e. the rebound is recorded in the same second as the miss)
  • 56% of missed shots are rebounded in one second
  • 19% of missed shots are rebounded in two seconds
  • 4% of missed shots are rebounded in three seconds
  • 2% of missed shots are rebounded in four or more seconds

Sunday, March 20, 2016

Gregg Popovich is a Timeout Trendsetter

In a post on the length of NBA games, I noted a trend in the length of each minute. The average length of the 5:00 minute in the 1st quarter (between 5:59 and 5:00 on the game clock) has been dropping steadily, from an average of 2.9 minutes to 2.6 minutes. And on a related note, the average length of the 6:00 minute has been increasing.


Thursday, March 17, 2016

The odds for a perfect bracket this year are 1 in 12 billion

As I did last year, I have used my betting market rankings to calculate an "optimal" NCAA tournament bracket. My ranking system attempts to harness the combined wisdom of the betting market, as revealed by the Vegas point spreads and totals. The rankings can also be used to calculate the odds that this so-called optimal bracket picks every game correctly. Last year, the odds were 6 billion to 1.

This year, the odds are slightly less favorable, at 12 billion to 1, but that is still better than the pre-2015 average of 50 billion to 1. Here is how those odds break down by region and the final four:
  • Midwest: 163 to 1
  • West: 297 to 1
  • East: 191 to 1
  • South: 227 to 1
  • Final Four: 5 to 1
I have created two versions of a populated bracket using my rankings:
  • inpredictable optimal - This bracket picks the best team in each matchup, according to my rankings. It's fairly chalk-y, though it does pick a couple 11 seeds, Gonzaga and Wichita State, to make it further than their seed would suggest. Kansas is the predicted champion.
  • inpredictable upsets - This bracket picks more upsets, but in a strategic way. The lower seed is picked as long as they are expected to be no worse than a two point underdog in the matchup. Michigan State is the predicted champion. 
I have also used my ranking system to enter Kaggle's March Machine Learning Mania contest. Go team boooeee!

Sunday, March 13, 2016

Steph Curry is the MVP even if he doesn't play another game this season

MVP debates across all sports tend to devolve into tiresome semantics. What does "most valuable" mean? Is it the best player? The player most valuable to his team? The best player on the best team? The player you'd most like to build a franchise around?

From my admittedly biased perspective, I think a stat like win probability added is ideally suited for determining a season MVP. It is a narrative stat for what is a narrative award. It explicitly rewards clutch play and ignores garbage time contributions. Last December, I showed how Steph Curry's win probability added pace was well ahead of any recent precedent.

Wednesday, March 9, 2016

Free Throw Deep Dives: Launch Angle

This is the first post in a planned series of deep dives into free throw shooting (and shooting in general). Using SportVU data, which tracks the position of the basketball 25 times per second in all three dimensions, and combining that with a simple physics model, I have built a database of some 250,000+ field goal attempts and 100,000+ free throws from the past three seasons of NBA play. From this database, I can create a variety of new statistics with which to assess shooting. These include:
  • Release angle (vertical) - at what vertical angle the ball leaves the players hand (higher angle = more arc)
  • Release angle (horizontal) - can tell you whether a shot was on target (e.g. wide left, wide right, or on target).
  • Release velocity - how fast the ball is going upon release
  • Release position  - where precisely on the court the player releases the ball. 
  • Release height - how high the ball is when released by the player
  • Approach velocity - how fast the ball is moving when it reaches the hoop
  • Approach angle - At what angle, relative to horizontal, the ball approaches the hoop.
  • Effective hoop area - A function of approach angle, it shows how big the hoop appears to the ball on approach
  • Approach position - where the ball crosses the plane of the hoop (i.e. a PitchF/x view of shooting).
This initial post will focus largely on the first metric: vertical release angle.

Sunday, March 6, 2016

Hero Ball follow up - Inequality and Efficiency

As a follow up on last week's hero ball post, here is how inequality correlates with shooting efficiency. In that original post, I used Gini coefficients to quantify how equally, or unequally, teams share shot attempts in clutch situations. This season, the Cleveland Cavaliers lead the league in hero ball with Lebron James accounting for 71% of the Cavs shot attempts in "double clutch" situations.

But is that necessarily a bad thing? The chart below shows how team shooting efficiency (as measured by effective field goal percentage) correlates with inequality.