Lost in Translation
Over the past year, I’ve dabbled a bit in the realm of what I like to call “translating” stats — that is to say, the process of taking a player’s numbers out of one context and plopping them down in another context. Now, this doesn’t necessarily mean the usual “what would Player W from Year X have averaged had he switched places with Player Y in Year Z?” strain of time-travel fantasizing, but more like, “given that Player A’s averages were worth B wins in Year C, what would have had to average in Year D to create the exact same number of wins?” The difference is a nuance, a shade of meaning, but still very important, because typically we’re in the business of making value judgments in the latter sense, and we leave the former to the alternate-history crowd.
This time, though, I’ll make an exception. Why? Because it’s fun, and it’s the offseason, and besides, what else is there to talk about in August of a non-Olympic year (aside from the dregs of the UFAs and what could be an impending NBA financial crisis)? But just the same, apply these translations at your own risk.
What’s the method? Well, in the past we simply adjusted for pace, then league. Trouble is, pace adjustments assume a linear increase across the board — 20% more possessions equals 20% more rebounding chances equals 20% more turnovers… you get the idea. And worse yet, we can’t calculate pace accurately for seasons prior to 1973-74, due to a galling lack of statistical tracking on the NBA’s part, so we often end up having to make assumptions and estimations that tend to make everyone uncomfortable. So what’s the solution, then? Well, Bill James once suggested that instead of getting hung up on park factors, why don’t we worry about the player’s actual context — his team’s stats and those of his opponents?
As James writes in the Historical Baseball Abstract:
“My original thinking was that by using team data, rather than league data, we would be approximating the effect of park illusions. If the park in which Del Pratt played in 1916 tended to diminish offense, then Pratt’s team would tend to score or allow fewer runs than the league average; if it was a hitter’s park, they would tend to score and allow more. So I would just place Del Pratt for 1916 in the context not of the American League, but in the context of the St. Louis Browns.”
“But after I had been doing this for a few hundred players, I realized that it was not only an acceptable substitute [for park factors], but actually a preferable alternative. Why? Because the team is the only thing that is truly relevant to the player.”
“…The construct of the ‘league’, in fact, has nothing at all to do with the value of what the player has accomplished. The ‘league’ is simply some other teams playing some other games that are utterly unconnected with the activities of this particular player.”
“Though those games may be important to him, they should not be used to inform the evaluation of what he does. If Tony Pena is in St. Louis driving in three runs in a game in an attempt to help St. Louis win the pennant, he might be vitally interested in the score of a game in Chicago — but whether the score of that game is 1-0 or 16-12 has nothing at all to do with the won/loss impact of Tony Pena’s performance in St. Louis.”
All of which is to say, I’m not going to deal with the league at all when making these translations. What if, instead, each team had their own FG/48 min. environment, their own TRB/48 environment, and so on? Then we could simply adjust each category by its own individual context (for instance, the 1973 Celtics’ rebounding environment, or the 1971 Phoenix Suns’ free-throw attempting environment), and not have to worry about pace or possessions at all.
The only catch? If we want to include all of NBA history, we have to pretend the 3-point shot never existed, especially if we’re translating backwards to a season before the bonus sphere was introduced in 1980. Such is life. Still, let’s put ourselves in a time machine and travel back 40 years…
The year is 1969, and the Boston Celtics are on their way to their 11th NBA championship, capping a run of 10 in 11 years. Wes Unseld is the league’s MVP, while Elvin Hayes led the league in scoring with 28.4 PPG. Wilt Chamberlain snagged an NBA-best 21.1 rebounds per game, and Oscar Robertson paced the Association is assists with 9.8 a night. But what if we transported our modern players from 2009 back to 1969, dropping them into a neutral environment across the board? The new leaders might look like this:
Player PPG -------------------- Dwyane Wade 35.9 LeBron James 34.0 Dirk Nowitzki 30.0 Kobe Bryant 29.7 Kevin Durant 28.5 Elvin Hayes 28.4 Chris Paul 28.1 Al Jefferson 27.4 Tony Parker 27.4 Chris Bosh 27.4 -------------------- Player RPG -------------------- W. Chamberlain 21.1 Nate Thurmond 19.7 Bill Russell 19.3 Jerry Lucas 18.4 Dwight Howard 18.3 Wes Unseld 18.2 Elvin Hayes 17.1 Troy Murphy 15.2 David Lee 15.1 Al Jefferson 15.0 -------------------- Player APG -------------------- Chris Paul 12.8 Deron Williams 11.0 Steve Nash 10.0 Oscar Robertson 9.8 Jason Kidd 9.6 Rajon Rondo 9.1 Jose Calderon 9.0 Dwyane Wade 8.5 LeBron James 8.5 Lenny Wilkens 8.2 --------------------
Obviously, once again this is more of a fun, frivolous exercise than anything else, but it is informative insofar as it tells us more about the ways the game has changed over the past half-century. You can even download the Excel spreadsheet here, and feel free to play around with different contexts, seeing how the player stats change when you copy stats from the “Leagues” tab and paste into “RefLg”.