There's a lot of high quality basketball writing out there these days. We have multiple networks of team blogs, excellent general NBA sites, humor blogs... really everything and then some. Included in that list is the rise of statistically oriented analysis, merging excellent observational analysis with factual data.
Basketball Prospectus' Kevin Pelton has been at the forefront of the NBA statistical movement for many years now. For the second straight NBA season, he (and Bradford Doolittle) have published the Pro Basketball Prospectus. For those not familiar with it, I'd describe it as the ultimate handbook for the basketball junkie. Every team and every player in the league is broken down statistically and analytically. The amount of depth and research found in the Prospectus is just incredible. If you're not a stats guy, you'll still become infinitely more knowledgeable about the game simply by reading the team breakdowns. If you love stats, this is the holy grail. It's simply a perfect mix of analytics and top-notch writing. The 2010-2011 Pro Basketball Prospectus is available as a PDF download for $9.98 or in print for $19.95.
Last week, Kevin Pelton graciously agreed to answer
some many, many questions for At the Hive about the Prospectus and the 2010-2011 New Orleans Hornets season. Since the interview runs rather long, I've decided to split it up into two parts, one today and one Friday. Part 1 is after the jump.
Update: Click here for Part 2.
Kevin Pelton: You can trace SCHOENE back to when I first read about Nate Silver's PECOTA projection system, which was introduced in Baseball Prospectus 2003. That provided an ideal template, but getting there took a long time. The first step was coming up with similarity scores to match players with peers from their past. I came up with those by the summer of 2003 and took a first stab at projecting statistics for a handful of players. By the start of the 2004-05 season, I had developed it to the point where I projected stats for everyone in the league and used them to come up with team projections.
Then everything sat idle for four years, until I started devoting more time to Basketball Prospects in the fall of 2008. I tweaked the similarity scores and player projections, but the key difference was generating team statistics and creating this complete imaginary league where everything balanced out against each other. That took the better part of October 2008. Basketball-Reference.com pointed out that its projections outperformed mine, which offered the impetus to incorporate three years of player statistics instead of just one, which helped improve SCHOENE. Along the way, I've also added translations for college, Euroleague and D-League statistics so we have a projection for just about everyone in the league.
The way I try to sum up what SCHOENE does is like this: We take three years' worth of player stats, then estimate the aging process by looking at how similar players at the same age have developed in the past. Using these player statistics, projections for games missed due to injury, subjective guesses at playing time and a handful of team indicators on defense, we come up with projections for team performance.
KP: I think it is pretty straightforward in terms of comparing the projections to actual results, but there are a couple of complications. The first is, what's the baseline? Basketball-Reference.com's Simple Projection System provided a pretty good baseline at the individual level, since it's much, well, simpler, than SCHOENE. It outperformed us in 2008-09, but using three years of statistics allowed SCHOENE to leap ahead last year. At the team level, we tracked a variety of statistical projection systems last year and SCHOENE finished second of these. The other complication is there is a lot of noise in one-year team projections, so it will probably take a few seasons to really make a definitive comparison between various projection systems.
R: How long and through what age ranges do great point guards sustain elite levels of play, historically?
R: Is there a position that tends to age better than others? Or is aging more a function of player skill-set or reliance on athleticism?
Big guys do tend to hold their value longer, since their size never ages. In general, I'd agree that player type is at least as important as position. Versatile players, for example, tend to age better at any position. The factors I mentioned for point guards (height for position and shooting ability) are also generally important.
R: How do you anticipate Paul aging as he gets to his late 20's and early 30's?
R: One thing we often see in the early season is people overreacting to small sample size induced stats/trends/etc. Is there a time period or general time of year at which we can note that teams are "for real" (or this mostly a function of how much of a surprise a given trend is)?