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.