In this essay, reprinted from the newly released Pro Basketball Prospectus 2012-13, we answer some common questions readers have about the SCHOENE Projection System, used to generate the projections in the book and in ESPN the Magazine's 2012-13 season preview.
What is SCHOENE? How does it work?
SCHOENE is our player and team projection system. It's named for former NBA forward Russ Schoene, who spent four seasons in the NBA in the 1980s, most prominently playing for my beloved Seattle SuperSonics. Like the Baseball Prospectus PECOTA system, SCHOENE is a backronym, standing for Standardized Comparable Heuristic Optimizing Empirical NBA Evolution.
We first introduced SCHOENE to project the results of the 2008-09 NBA season. Though the player projection aspect is not entirely unique--ESPN Insider's John Hollinger independently developed a similar projection system--SCHOENE goes a step further by beginning to consider team context. For each team, player usage rates are adjusted (along with efficiency) to replicate the interactions between players in divvying up offensive possessions. Passing is incorporated in terms of how it affects teammates' shooting percentages. Another adjustment handles defensive rebounding because of the diminishing returns to individual defensive rebounding at the team level.
While SCHOENE's default output is per-possession or per-shot rate stats, it also incorporates team pace to produce complete, realistic stat lines for each player. This is especially useful for creating fantasy projections, since a player's per-game averages will depend in part upon the pace at which his team plays.
Finally, SCHOENE brings it all together to create team stat lines, unprecedented for an NBA projection system. This gives us an idea not only of a bottom-line projection for each team's win-loss record but also how they will get there and projected strengths and weaknesses.
How do you project player development? What's similarity got to do with it?
At the heart of the SCHOENE system are similarity scores for each player based on 13 statistical categories, standardized for league norms: height, weight, a "shooting" rating (based on 3P%, 3PM/Min and FT%), two-point percentage, "inside" rating (FTA-3PA)/possessions, usage rate, rebound percentage, assist percentage, steal percentage, block percentage, turnover percentage and player winning percentage, the per-minute component of the WARP system.
Like many similarity scores, SCHOENE's are calculated out of 100, that being an identical match. A score of 95 means two highly similar players, while 90 is reasonable similarity and anything below that starts to get dicey. The closest match for any player in this year's projections is Memphis Grizzlies center Marreese Speights and Channing Frye, at 99.5. A handful of players, most of them veterans like Marcus Camby and Jason Kidd, did not have a single match of 90 or better.
In general, at least the 50 most similar players of the same age--within six months of the player's age during the season, as with PECOTA--were used to generate each player's 2012-13 forecast, though the smaller pool of players in the NBA means very young and very old players draw on fewer comps. For six players whose comparable pools were far too small, and for 10 second-year players who did not see regular action in either the NBA or the D-League, an average age adjustment has been applied to their statistics.
In addition to using this group of comparable players to project the improvement or decline in each of 14 statistical categories, we also follow PECOTA's lead in generating summary statistics that reflect the variation in each player's projection. Recreated with each player's projection are the familiar Improve/Breakout/Decline percentages, a breakout or coppage (our term for a steep decline) being defined as at least 20 percent improvement or drop-off.
Only seasons of at least 250 minutes are factored into SCHOENE's projections. Translated statistics from the D-League, Euroleague, EuroCup and in one case the Spanish ACB (for Washington draft pick Tomas Satoransky) are used to generate SCHOENE projections for players who have not played a full NBA campaign within the last three seasons. For rookies, NCAA translations are not run through SCHOENE, though our college database uses the same formula to find player comparisons.
How do you go from player projections to team projections?
After projections are generated for each player, they are put into a team context. Games played are projected for each player using a baseline estimate of 76 games played. From there, players are penalized one game for each six missed last season and one for each 20 missed two years ago, based upon research done by Houston Rockets analyst Ed Kupfer on projecting games played. We also account for preexisting injuries and suspensions. Playing-time projections are strictly subjective based on each team's projected depth chart.
On offense, the first step between individual projections and team totals is the aforementioned usage adjustment. Each player's usage rate is adjusted so that the team as a whole uses only the number of plays projected based on team pace. There is also a corresponding adjustment to the player's shooting percentages and turnover rates to reflect the inverse relationship statistical analysts have found between usage and efficiency. One percentage point of usage is approximately equal to a point of Offensive Rating. The second step attempts to account for the value of passing using team assist rate.
Team defensive performance is projected based on a combination of player statistics (defensive rebounds, steals and personal fouls) and past team performance in forcing turnovers and opponent shooting, regressed to league average.
How has SCHOENE performed?
Honestly, not great. Of the six pure statistical projection systems tracked last year on the APBRmetrics message board, SCHOENE had the largest mean error, pegging teams wrong by an average of 5.0 wins over the 66-game schedule. However, when I studied possible adjustments over the summer, I found nothing that would have improved SCHOENE's results over multiple previous years. So the system remains unchanged from last season, which is essentially the third incarnation of SCHOENE. (The first was used only in 2008-09. The second, rolled out for the first edition of Pro Basketball Prospectus in 2009-10, began incorporating multiple years of past player performance.)
Historically, SCHOENE has proven more effective at pegging the direction teams are heading than their specific win total. So a different measure--which system was closest to each team's final record--showed SCHOENE performing as effectively as any of the other systems. SCHOENE was closest to the pin on six teams; only a set of projections using regularized adjusted plus-minus as tracked by poster EvanZ did better, with seven.
The moral of the story is to temper the most extreme projections. When SCHOENE projects that the Minnesota Timberwolves will be an elite team this year, the appropriate conclusion is that the Timberwolves are closer to contending than conventional wisdom would indicate, not that they are as good as anyone in the Western Conference.
What are SCHOENE's weaknesses?
Historically, there are a few types of teams with which SCHOENE has struggled. The projection system tends to be overly pessimistic about veteran teams, in no small part because players seem to be aging better now than ever before. Limiting the pool of comparables to player seasons since 1989-90 helped this issue, but did not completely alleviate it. SCHOENE also tends to favor offensive-minded teams over those with good defenses because of the historical trend that defense regresses to the mean more than offense.
Additionally, SCHOENE tends to have a tough time going left.
Why does SCHOENE project so few teams as elite or terrible?
This is a feature of any well-calibrated projection system because of the importance of regression to the mean. Keep in mind that we are trying to project the average outcome for each team. Some good teams will overperform and win more games than projected, and vice versa with weaker teams. Essentially, we know that more teams will win at least 55 games than we project. We just don't know exactly which ones will do so.
Why do you often refer to SCHOENE as if it was a sentient being?
You mean it isn't?
No. And I ask the questions around here.
Well, it's kind of funny, as far as stat-geek humor goes.
Does it go beyond that?
It is important to distinguish between what SCHOENE projects and what we, the authors of Basketball Prospectus, actually think will happen. The two are not always the same, and occasionally they can diverge drastically, as the team essays help illuminate. Personalizing SCHOENE is a way of reminding everyone that we do not control the results it produces at anything more than a general level. Sometimes, we're surprised too.
The most important question: How do you pronounce SCHOENE?
SHAY-nee. Think of it like Danke Schoen with an extra e on the end.
Kevin Pelton is an author of Basketball Prospectus.
You can contact Kevin by clicking here or click here to see Kevin's other articles.