“Buy College Basketball Prospectus. Seriously. You’re cheating yourself as a fan if you don’t.” — Andy Glockner
This book brings all of the following characters between two covers: Joey Berlin, Drew Cannon, John Ezekowitz, Asher Fusco, me, Matt Giles, Jeff Haley, Dan Hanner, Jeff Nusser, Kevin Pelton, Ken Pomeroy, Mike Portscheller, Craig Powers, Josh Reed, Nic Reiner, Eddie Roach, Corey Schmidt, and John Templon.
Plus some guy named John Calipari, who chipped in with the Foreword. Seriously, I’ll put this grouping of personnel up against anyone, up to and including the list of stars who’ve guest-voiced on “The Simpsons.” In the last year or two.
And if you prefer your CBP 2012-13 as a snazzy PDF for your iPad, we have that too. Happy shopping, everyone.
People kept asking me when the book was coming out, so I put the book out, even though the PDF did not yet have section links. Now the College Basketball Prospectus PDF offers you that navigation. Pop open the “Table of Contents” button on your viewer or mouse over section titles in our table of contents, and you can glide instantly to the West Coast Conference without having to scroll through 312 pages.
If you’ve already purchased the book, come on back for your free upgrade. If you haven’t bought the book yet, you should. Both types of readers can click here.
Watch the site for an announcement on the paperback, available at Amazon in a few days.
I wrote about this for the NFL at Chase Stuart’s Football Perspective, and I hinted at it for the NBA in this ESPN Insider piece about why an 82-game basketball season is unnecessarily long, so after Kevin wrote today’s article on the reliability of early-season records, I figured I might as well touch on the topic of regressing NBA team records to the mean in-season.
The basic premise is rooted in True Score Theory, which assumes that any observed result is the sum of an underlying “true” skill and a random error component. Any single sporting contest is an imperfect measure of the relative strengths of the two opponents, but over a large enough sample we can assume the random error will subside and we’ll be able to separate signal from noise.
How big of a sample do we need, though?
Glad you asked. The answer to that question comes from this Tangotiger post about true talent levels for sports leagues. From 2005-2011, the NBA had 30 teams and played an 82-game schedule. Over that span, the yearly standard deviation of team winning percentage was, on average, 0.155. Since variance equals the standard deviation squared, this means the NBA’s observed variance of winning percentage, var(observed), is 0.155^2, or 0.024.
The random standard deviation of NBA records in an 82-game season would be sqrt(0.5*0.5/82), or 0.055, meaning var(random) = 0.055^2 or 0.003.
Going back to True Score Theory, we know that var(observed) = var(true) + var(random). Rewriting that in a way that solves for the true variance, we see that var(true) = var(observed) – var(random). In this case, var(true) = 0.024 – 0.003 = 0.021. The square root of 0.021 is 0.145, so 0.145 is stdev(true), a.k.a. the standard deviation of true winning percentage talent in the current NBA.
Armed with that number, we can calculate the schedule length a season would need in order for var(true) to equal var(random) using:
0.25/stdev(true)^2
In the NBA, that number is 12 (more accurately, it’s 11.84, but it’s easier to just use 12). So when you want to regress an NBA team’s W-L record to the mean, at any point during the season, take twelve games of .500 ball (6 wins, 6 losses), and add them to the actual record. This will give you the best estimate of the team’s “true” winning percentage talent going forward.
So far this season, that yields the following set of true WPct talents:
--------------------------------------------------------
Team W L W%(obs) W%(true)
--------------------------------------------------------
San Antonio Spurs 4 0 1.000 0.625
New York Knicks 3 0 1.000 0.600
Milwaukee Bucks 2 0 1.000 0.571
Chicago Bulls 3 1 0.750 0.563
Dallas Mavericks 3 1 0.750 0.563
Miami Heat 3 1 0.750 0.563
Houston Rockets 2 1 0.667 0.533
Memphis Grizzlies 2 1 0.667 0.533
Minnesota Timberwolves 2 1 0.667 0.533
New Orleans Hornets 2 1 0.667 0.533
Orlando Magic 2 1 0.667 0.533
Cleveland Cavaliers 2 2 0.500 0.500
Golden State Warriors 2 2 0.500 0.500
Indiana Pacers 2 2 0.500 0.500
Los Angeles Clippers 2 2 0.500 0.500
Oklahoma City Thunder 2 2 0.500 0.500
Portland Trail Blazers 2 2 0.500 0.500
Atlanta Hawks 1 1 0.500 0.500
Brooklyn Nets 1 1 0.500 0.500
Charlotte Bobcats 1 1 0.500 0.500
Boston Celtics 1 2 0.333 0.467
Philadelphia 76ers 1 2 0.333 0.467
Denver Nuggets 1 3 0.250 0.438
Los Angeles Lakers 1 3 0.250 0.438
Phoenix Suns 1 3 0.250 0.438
Sacramento Kings 1 3 0.250 0.438
Toronto Raptors 1 3 0.250 0.438
Utah Jazz 1 3 0.250 0.438
Washington Wizards 0 2 0.000 0.429
Detroit Pistons 0 4 0.000 0.375
--------------------------------------------------------
It doesn’t really tell you anything about the relative order of teams that you couldn’t already know from regular winning percentage, but it does give a better estimate of future expected winning percentage if you plug the WPct talent numbers into, say, Bill James’ log5 formula.
Another benefit of this process is the ability to compare that “regress-halfway” number to other sports. In basketball, the number is 11.8 in an 82-game schedule (you regress halfway to the mean 14.4% of the way into the season); in the NFL over the same 2005-11 span, the number was 10.2 in a 16-game schedule (64% of the season), and in baseball it was 88.1 in a 162-game slate (54.4% of the season).
You can use those numbers to generate equivalent season lengths between sports. The NFL’s equivalent of an 82-game NBA season? 70.9 games. And baseball’s equivalent? A mind-boggling 610.4-game schedule!
One last way to frame the data. If you picked two teams at random, looked at their records, and knew their “true” talent levels, how often would the team we observed to be better via W-L actually be the better team? In baseball, 79.8% of the time. In the NFL, 78.5% of the time. But in the NBA? 88.4% of the time.
The moral of the story: there’s a lot more certainty in NBA records than other sports, and these types of formulas help us measure that, in addition to regressing a team’s W-L record to the mean for predictive purposes.
I spent the last couple of days revamping the sub-structure on which the Hoops List are compiled, so while I don’t have any pithy comments for each team, I do have my first set of in-season rankings. I’ve changed my methodology from past seasons but rather than point out what is different, I’ll quickly walk through the steps by which these numbers are compiled.
1. I start by compiling each team’s in-season POW, which is a not-so-clever abbreviation for a power rating. These are the numbers that have been included in each of the first four editions of Pro Basketball Prospectus. The metric is meant to capture the “true talent” level of each team, as expressed by the probability that the team will win the championship. POW isn’t a magic bullet, but I’ve found that in most years, it has just the tiniest bit of advantage over point differential in terms of correlating with postseason success. I hadn’t messed with the metric much over the last three years, but I made a couple of slight modifications for this season:
First, I tweaked the way I measure strength of schedule. In my schedule simulator, each team is assigned a probability for winning each game based on team strength (as calculated by a blend of point differential and preseason projection), home court advantage and effects caused by game clustering (back-to-backs, etc.) Now, I am calculating schedule strength as an average of these probabilities. Theoretically, this should give me a more accurate portrait of which teams have played the more difficult slates.
The other components of POW are the same. I start with Pythagorean winning percentage, blend that with an adjusted winning percentage based on home-road success and then factor in the strength of schedule. The results are expressed as wins per 82 games, so a POW of 54.2 means a team has a true talent level of roughly a 54-28 team.
2. The second change I’ve made is that I decided to start giving extra weight to recent performance. My prior research didn’t suggest this was necessary, as long as a team’s roster remained largely stable. However, the change doesn’t harm accuracy either, and will do a better job of evaluating teams that have made a major transaction or suffered a significant, long-term injury to a star player.
3. At this juncture, I’ve got an in-season POW baseline. Next I factor in my NBAPET preseason projection. This is also a new procedure. Before, I only considered in-season results. The preseason projections are gradually phased out as the season progresses and disappear altogether by April 17.
4. My projection-adjusted POW is then fed into my simulation engine. The baseline probabilities for future games are based on current SCHOENE projections. So if a team makes a move or suffers an injury, then this will be reflected in the games yet to be played. Games already played are hard-coded, with the actual winners getting credit for those wins. The remainder of the schedule is played out 1,000 times, generating a new projected win total. This, finally, is the number by which teams are ranked in the Hoops List which, I swear, will normally appear on Monday afternoons. Also, running the sims gives revised playoff and championship odds, which I note.
Ordinarily, the rankings will be presented with 100-200 word snippets of analysis for each team, though as mentioned I’m forgoing that this week. The intent is to provide a weekly snapshot of the league. Also, the rankings provide a kind of narrative for each team’s season when read on a week-by-week basis. I find that handy.
In the few weeks since Pro Basketball Prospectus 2012-13 (and the results of the SCHOENE Projection System) were released to the world, I’ve hammered home the difference between projections and predictions quite a bit, but I’m going to do so again because I think it’s an important distinction.
Aside from the stylistic appeal, where does this collective love for Denver come from? Is it a sincere belief the Nuggets have the necessary tools to mount a guerrilla war in the West and take down the likes of the Thunder or the Lakers or just a desire to see a verdict rendered once and for all that Carmelo Anthony is a bad guy?
I also wonder if the post-Melo Nuggets haven’t become a symbol for those who were repelled by the Anthony saga two years ago. In the era of the superteam, romantics want the Nuggets to prove that a team of non-superstars can compete for an NBA title through sheer effort, athleticism and creativity. A lot of basketball junkies want to live in a world where the 2004 Pistons aren’t a historical outlier and Anthony is the fool. The Nuggets represent their best hope.
Now, I don’t think Kevin was specifically referencing the optimistic statistical projections for the Nuggets (ours and John Hollinger’s), but having the two points so close together reinforces the danger in conflating “the numbers” and our opinions based on them. I’d hate to have anyone think that the reason SCHOENE puts Denver atop the West has anything to do with liking the idea of an elite starless team.
As Kevin has pointed out via Nate Silver’s new book The Signal and the Noise, the numbers never speak for themselves. And it would be disingenuous for me to argue that SCHOENE or any other projection system is completely objective. There are subjective assessments of how basketball works inherently built into the system. At the same time, the projection for the Nuggets–or any other team–has nothing to do with what I think of their players, the team or the narrative. The projections are individually compiled in an unbiased manner, and I do think it’s important for readers to trust that what they see from SCHOENE is strictly where the numbers lead, and if that occasionally leads to some LOLs it’s a small price to pay.
Naturally, this reflects the ongoing discussion about Silver’s more important projections for what’s going to happen next Tuesday. It’s perfectly fair to criticize his method, if you understand it. But simply demeaning Silver (who, it’s worth noting, hired the Basketball Prospectus staff in his old baseball life) because he’s an admitted liberal who happens to have the Democratic candidate more likely to win is silly and demeaning to his work. And I happen to think that’s easier to explain when the numbers and the opinions are explicitly kept separate.
With the 2012-13 NBA season tipping off tonight, I figured I should probably put up some kind of projections. A lot of smart people spend a lot of time and energy working on things like this (see our own SCHOENE system), but I wanted to create a set of projections that are as “dumb” as possible while still generating reasonable results (a la Tangotiger’s Marcel projection system). So consider these the baseline that any credible projection system must beat, because only the bare minimum of information has gone into them:
-----------------------------------------------------
Seed Eastern Conference Avg W Avg L WPct
-----------------------------------------------------
1 Miami Heat 56.2 25.8 0.686
2 New York Knicks 49.7 32.3 0.606
3 Chicago Bulls 47.7 34.3 0.581
4 Atlanta Hawks 47.7 34.3 0.581
5 Indiana Pacers 46.0 36.0 0.561
6 Boston Celtics 46.0 36.0 0.560
7 Brooklyn Nets 40.9 41.1 0.499
8 Milwaukee Bucks 40.5 41.5 0.494
9 Orlando Magic 40.2 41.8 0.490
10 Philadelphia 76ers 39.9 42.1 0.487
11 Toronto Raptors 37.1 44.9 0.452
12 Detroit Pistons 31.7 50.3 0.386
13 Washington Wizards 31.0 51.0 0.378
14 Cleveland Cavaliers 28.3 53.7 0.345
15 Charlotte Bobcats 21.0 61.0 0.256
-----------------------------------------------------
Seed Western Conference Avg W Avg L WPct
-----------------------------------------------------
1 San Antonio Spurs 54.1 27.9 0.659
2 Los Angeles Lakers 50.0 32.0 0.609
3 Oklahoma City Thunder 49.8 32.2 0.608
4 Los Angeles Clippers 48.9 33.1 0.596
5 Denver Nuggets 46.4 35.6 0.566
6 Memphis Grizzlies 45.4 36.6 0.554
7 Minnesota Timberwolves 41.7 40.3 0.509
8 Houston Rockets 39.6 42.4 0.482
9 Utah Jazz 38.9 43.1 0.474
10 Dallas Mavericks 38.6 43.4 0.471
11 Portland Trail Blazers 36.6 45.4 0.446
12 Phoenix Suns 35.6 46.4 0.434
13 New Orleans Hornets 35.1 46.9 0.429
14 Golden State Warriors 35.0 47.0 0.427
15 Sacramento Kings 30.6 51.4 0.373
-----------------------------------------------------
One of the wonders of the modern political process is that, within hours of debates or speeches at the conventions, the presidential candidates’ claims can be thoroughly checked for objective accuracy. In that spirit, I thought something Houston Rockets GM Daryl Morey said during Monday’s press conference introducing James Harden deserved further research.
“I actually can’t come up with any examples of a player of his caliber and age getting traded at the time he was traded – it really has never happened,” Morey said when asked whether he was surprised Harden was available.
Just how unique is that? I set my parameters at players who were 23 or younger at the time they changed teams (Harden reached that age in August) and had posted at least 10+ WARP in a season (Harden had 11.3 last year). Here’s the list I came up with from the last three decades-plus:
Chris Webber, Golden State to Washington (13.0 WARP, age 21)
Stephon Marbury, Minnesota to New Jersey (13.7 WARP, age 22)
Tracy McGrady, Toronto to Orlando (10.4 WARP, age 21)
Elton Brand, Chicago to L.A. Clippers (9.8 WARP, age 22)
Of those four players, McGrady left via free agency under a system long since discarded. Marbury and Webber demanded trades, leaving just one example–the Bulls with Brand–of a team choosing to deal a player with established 10+ WARP track record (Brand had 10.7 as a rookie, before dipping slightly below that number in his final year in Chicago). So we’re certainly talking about something rare, and without precedent in the last decade, though I’d still grade Morey’s comment an exaggeration.
Since the trade, I’ve been surprised by how much more I seem to value Harden than the public at large. I liked Morey’s answer when asked why he felt Harden could be a first option: “I’ve watched him play.” That was a joke, sort of, but Morey continued by saying, “He played well in so many different environments. Obviously playing with Kevin (Durant) and Russell (Westbrook) he played well, but if you really studied the film, and I’d like to think our scouting staff is as diligent as any in the league – I think we are – when he had to carry the load with those guys off the floor he excelled. When there was just one of them on the floor he excelled. Really, frankly, in all environments.”
It’s worth keeping in mind that Harden was a top-three pick before ever playing with Durant and Westbrook. He led the Pac-10 in usage rate as a sophomore at Arizona State before declaring, and did so with above-average efficiency. As Bradford Doolittle pointed out in his analysis of the Rockets going forward, Harden was actually more effective last season with Durant on the bench (and presumably Westbrook, given Scott Brooks‘ tendency to rest both stars at the same time), pushing his usage rate to the stratosphere while increasing his True Shooting Percentage from .641 to .686, resulting in a jump from 16.6 points per 40 minutes to 34.7.
The counter to that stat is that Harden was playing against reserves. Ethan Sherwood Strauss raised a good question (I know, shocking): are second-unit defenders actually worse? The evidence suggests the drop-off is larger at the other end of the floor. Daniel Myers has studied the relationship between regularized adjusted plus-minus and minutes per game and found it much stronger on offense than defense. As in baseball, it appears that replacement level (and reserve level) is much higher on defense than offense.
Now, this does suggest Harden got a bit of a break at the defensive end going against backups, and I have argued that part of his poor NBA Finals performance was due to the extra energy he had to expend defending LeBron James. But I don’t suspect we’re talking about a big effect, and remember that Harden was much more valuable without the stars. If in reality his value to the Rockets is reflected by his overall performance in Oklahoma City, I’m pretty sure Morey would take that.
For anyone with last-minute fantasy drafts, our SCHOENE projections now reflect last night’s trade that sent James Harden to the Rockets. If you’ve already purchased the SCHOENE fantasy projections, you can download a new version by going to Manage Your Profile. If your fantasy draft is still coming up, find out more about our SCHOENE spreadsheet, available for $7.95. And if you’ve already drafted, and you snapped up Harden back when he was a sixth man, this trade should come as excellent news. Harden figures to be more valuable if only because he will play more minutes in Houston.
People have taken note of SCHOENE’s projection that the Denver Nuggets will be one of the top teams in the Western Conference, as seen in Pro Basketball Prospectus 2012-13 and ESPN the Magazine’s NBA Preview edition–including TNT’s broadcast team of Kevin Harlan and Reggie Miller, which discussed the projection during Thursday’s broadcast of the Nuggets-Clippers preseason game. Check it out, courtesy @blazersedge:
The folks at the local Boys & Girls Club will be disappointed Reggie is so dismissive of my playing career.
If you’ve been waiting for a chance to hold the Pro Basketball Prospectus 2012-13 in your old hands to read nearly 400 pages of projections, analysis and insight, wait no longer. Our NBA annual is now available in paperback format via Amazon.com for $19.95. Thanks to our readers for already making us the No. 1 basketball book on Amazon.com!
