Sunday, April 24, 2016

New box score feature: Pace and Efficiency Report

I have added a new feature to my NBA Win Probability Graphs and Box Scores. If you click the "pace" tab, you will see a table that looks like this:


Using the play by play data, this table summarizes offensive pace, as measured by seconds per possession. The "season" row shows each team's average offensive pace for the season. The "opponent" row shows how each team's opposing defense has controlled the offensive pace this season. Similar statistics are shown for scoring efficiency, as measured by points per 100 possessions. This is basically a game-specific version of the team summary pace tool I rolled out last season.

The table above is from Game 3 of the 2015 NBA Finals, in which the Cavaliers took a (fleeting) 2-1 series lead over the Warriors. The Cavaliers' success in that game, and the game prior, was often credited to slowing the tempo of the notoriously fast-paced Warriors. But what the table shows is that while Cleveland certainly slowed their own pace on offense (from 15.9 seconds to 17.0), the Warriors were still playing their game on offense, mostly. They averaged 13.9 seconds per possession, just slightly above their season average, and still well below the league average of 15.1 seconds.

Just a note on the possession counts: These do not precisely align with those you find on sites like basketball-reference.com. In general, I am counting more possessions on average than what is usually tallied using the box score, which results in slightly lower efficiencies. The main reason for this, I believe, is that my method counts "end of quarter" possessions that do not result in a typical box score "possession marker", like a made/missed shot or a rebound. If a team gets the ball with 10 seconds left and fails to get a shot off, that will likely not count as a possession using the box score stats, but is counted as such by my method.

Eventually, I hope to add pace and efficiency for the other possession types to this feature: after made shot, after defensive rebounds, and after turnovers.

Saturday, April 16, 2016

The 2015-16 NBA Regular Season Review

Using the various NBA tools I have built for this site over the years (see the sidebar on the top right), here are the top performers, top performances, top games, and team superlatives from the 2015-16 season.

Player awards

For the most part, I'm judging players according to win probability added (WPA), which is an admittedly limited metric (it only accounts for made/missed shots, free throws, and turnovers).
  • Most Valuable Player - Stephen Curry led the league with +10.59 win probability added. Kevin Durant came in a distant second with +8.01, an MVP-worthy total in just about any other season. Here is how Curry's progress compares against the top WPA finishers of the past seven seasons (an update to this post):
  • Least Valuable Player - Rajon Rondo. -4.96 WPA. Granted, this excludes Rondo's other box score contributions, such as assists (where Rondo ranks second in assist WPA) and steals, where he ranks in the top ten.
  • Most Improved Player - The player with the single biggest leap in WPA from 2014-15 to 2015-16 was Kevin Durant, but that "improvement" was due to his injury shortened season last year in which he played just 27 games. If we set Durant aside, the most improved player this year was.......Steph Curry. Curry won the MVP award last year with +5.75 WPA, and nearly doubled down on that mark this season.

Saturday, April 2, 2016

Playoff Seed Probability Motion Charts

With an 82 game season, an NBA team's fortunes then to ebb and flow in increments, rather than huge leaps. This gradual evolution can make for some interesting motion-chart visualizations. For example, there is Aaron Barzilai's animation of the Warriors pursuit of the single season wins record. Or this visualization of the evolution of the win percentage of the NBA's top four teams.

And as I have done the last couple seasons, here are motion charts that show how each team's playoff seed probabilities have evolved and shifted over the 2015-16 season. The probabilities are calculated using my NBA Vegas rankings, which update daily and re-project the remainder of the season and resulting playoff seeds. Time permitting, I will update the chart with the latest results, up until the end of the regular season. Just check back at this same post.

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.