Put adjusted plus minus into the search engine at ESPN and you get back 14 references
in 8 years.
http://search.espn.go.com/adjusted-plus-minus/
If perhaps 20 teams have it and are using it, I'd think there would more questions to ask, more things to discuss even if teams are not cooperating. But if teams are using it, why not acknowledge it? I recall far more team officials saying they don't think much of APM or don't use it all than anyone who will admit to using it or valuing it.
Since I have relatively little expectation any team will actually inquire with me privately about other APM variations, I guess I share the ones that came quickly to mind briefly here:
RAPM Splits for:
First / second half of games
First / second half of season
First groups of plays in the first 4 minutes played by a player, then 4-8, 8-12+ minutes in a stint
For top 1 or 5 team lineups vs the rest
When with or against a top 10 player or PG or shot blocker; or a “real center”, a great 3 pt shooter, etc.
When against a top 20 lineup in the league
When against a top 5-10 opponent overall, on offense, defense, 3pt frequency, inside shot / FT frequency, etc.
Against top, middle, bottom top pace teams or for actual top, middle and bottom pace games
Home / away
Playoffs only
When against “small ball” / big lineups in general and when specific players have x advantage / disadvantage on height, weight, wingspan, NBA experience, etc.
East / west
In games with above, below and near normal foul calls
For plays made in less than 8 seconds, 8-16, and 17- 24+ seconds
In games when main star is performing above, below average or near normal
With three or more starters or 3+ bench players
On weekends vs during week (to catch party impact?)
At high altitude
On plays with more or less than x passes
On plays with offensive rebounds
Starting / off bench
By position played
At high, low and near normal usage or touches
Against man to man / zones
When up or down by x pts
On play after a turnover (on offense or defense)
When they use the possession and receive the first pass past half court or by location of their first reception or the ball ever goes into the post vs not
In lineups with low, middle and high minutes of pair or whole lineup experience together
(In the absence of RAPM splits for these things, one could check the raw play by play data but the signal would not be as clean.)
Other RAPM variations
For refs down to RAPM factor level
For coaches down to factor level
League-wide average RAPM results for “similar” players or lineups as a group on any of many different criteria (to possibly get clues from much larger samples than given for one individual or lineup)
RAPM shot impact broken down to impact on specifically the 3 pt game, inside shots and FTs.
Of course these splits reduce sample size, but you could and probably should use multiple seasons of data to increase sample size back up some (might as well run every year separately too). Findings might not be statistically significant but some very high, low or just surprising for that player results may raise questions that prompt further view of tape and more thinking. Could also run scenarios that involve of two+ of the above ideas.
Some of these may not be considered that important by everyone, but I think there is a rationale for checking all or almost all of these. If you have the capacity to run the data, I'd suggest that an organization with revenue that might touch 9 figures and reaching for a title in a highly competitive arena might want to look at at least some of them to see what additional detail they might learn about players and lineups and what other questions they might uncover along the way. It might not take too long in most cases to produce the numbers with the right technical staff. There is probably far more time to be spent in the analysis of the results than the production. There is also value in proposing what to look at next. How many teams have previous thought of or run and used any of these 3 dozen variations? How many even look at factor level RAPM? I wish I knew. If the number is low, then all the low or mid-level fruit is not yet picked clean and there is still plenty of room for information and analytic advantage.