APBRmetrics

[dsparks]

Alright, I wasn't going to do it, but here's a regression of the interaction of five types at once:

[Mike G]

How about WinChunks?
(WC for short.)

WinPortions? WinParts?
_________________
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39% of all statistics are wrong

[Mountain]

Thanks very much for the past season's type reference list. 5 type regressions and championship examples. I will study them further.

On the lineups shown I understand the caution given about reading too much into the regression results but interesting that Pure shooters are in 6 of the 7 league-wide positive lineups. Pure perimeter, Pure Interior, Interior Scorer, Mixed, Scorer's Opposite all in 5; Perimeter Scorer the lowest at just 4. The prior averages give one story (if you reach for one like I did) but the detail reveals more. All the types can be positive in the right combinations.

Reviewing the most used lineups of teams will be educational about lineups and coaches & front offices. It should be helpful in thinking about trades. Thanks for again going the extra miles David and sharing your work here.


I also got around to viewing the top 50 graphic. I find it a nice cut to help get oriented to the type spectrum and gradations.

[QMcCall3]

[Mountain]

How static (or changing) each type (at a given point) is on average across rest of career would be of interest in addition to for a specific player.

And how type changes or not under different coaches & teams (and for which does a player produce highest MEV or win contribution or both?).

Maybe even a look at type movement (drift toward another type or actual crossing over) in games against counterpart types or team opponent types would help gave more shape to the stat movement. For example, of all Kobe's counterpart or team opponents which half saw him move more toward other neighboring types and when was he taking the right path and increasing win % and was he getting steered and not doing as well as expected?

Comparison of player type from regular season vs playoffs would be another possible research extension that could be quite interesting in some cases.



Welcome Q. I've read a few posts at your blog. Anyone with interest in the WNBA or general "re-thinking of basketball" use link from his screen name and you can go there. A recent post makes note among other things that WNBA-NBA similarity translations can be done using David's graphics.

[QMcCall3]

Thanks Mountain. Any input people have is certainly welcome.

For those interested in an NBA/WNBA comparison, David also created a WNBA spectrum which was posted on my blog yesterday:

http://rethinkbball.blogspot.com/2008/07/visualizing-wnbas-top-player.html

A friend of mine commented that it seemed as though the NBA spectrum seemed to be more "clustered", while the WNBA seemed distributed across the spectrum a little bit more.

I haven't taken yet taken the time to look at the percentage of players within each dimension across the two leagues, but it's an interesting point with regard to the differences in how each game is played.

[Mountain]

This might be a situation where the quantitative tests of quadrant density or mean distance between players or whatever would be better than the eye's impression and I am guessing that in large part because I believe the NBA graphic has many more points and that may be the main reason for the visual interpretation. But there are people around who could address this topic better.

[Mountain]

It is a small sample but championships are the goal so I found the type boxscore averages by players of each type for the 5 championship teams David listed who played over 1200 minutes. The order highest to lowest: Interior Scorer, Perimeter Scorer, Pure Scorer, Mixed, Pure Interior, Pure Perimeter and Scorer's Opposite.

5 of the top 10 boxscores were Perimeter Scorers and no other category had more than 1.

A bigger study would be appropriate but I went with what was here just to see what it showed.

[dsparks]

Mike G and Mountain: Thanks for those suggestions--you have inspired me to think of VC, for Victory Contribution or Victories Contributed. It has the advantage of going well with MEV, PVC and VCR, which I also use, except in the other cases the V is for "Value," which sounds like a slogan for a big-box department store. So, I guess I'm still working on the name. MCW, for Marginal Contribution to Wins? That has the advantage of sounding scholarly and economics-y... Anyway, still an open topic.

Mountain, you had asked for the Spurs team as well, but I failed to post it last time. Here they are, for 2006:

[Mountain]

David, I am glad to you are finding names you like. Once you sketch out your whole system and it stabilizes it will be a little easier for me as a reader to stay on track. I edited some stuff from earlier because I don't think I was using all the labels and type assignments completely accurately.

Of course proceed at your pace, though you are quite prolific. I just suggest extensions when I see them for your consideration and action if/when you can. Thanks for the many direct responses including the data on the Spurs.

Basketball value does have the top 50 most used lineups when you sort the list this way:

http://basketballvalue.com/topunits.php?year=2007-2008&sortnumber=3&sortorder=DESC

With text to columns delimited splitting and some flipping of first names in front of last and cutting extraneous columns this would seem like a good source for your eventual use. If that would work but if would make a difference to have it done for you I'd be willing if you pm'ed me a e-mail address to send it to and any further formatting preferences.

Thanks again.

[Mountain]

Another angle that could be explored is whether player type helps with college to NBA translations more than traditional position assignment does.

Many players must play a different type when they get to the pros and failure to successfully adapt to the type or role might explain a good deal. Were there any indicators in the game records that the player could successfully play another type in certain lineups in the rotation or when a teammate was out or an opponent forced it? Is the player's "talent" flexible enough to adapt to a different type?

I think the type model can guide consideration even if it just one way to slice things. 7 is a pretty good simplified typology but were or are you tempted at all to add a couple more? Just asking.



In the NBA it maybe that too many players are trying to be pure scorers, perhaps repeating their college type. The bottom half may be giving the true NBA pure scorers a worse name than they deserve. Any player in the bottom half of their type I'd scrutinize for any signs he can be successful at that type or signs he could be better at another. I understand teams sometimes need a type even though below average for depth or insurance or type preferences of coach / minutes demands of system but where possible you'd want to upgrade.


The main thrust of the typology was simplification... but you could reverse direction and add back some detail in some circumstances or for some users. Instead of being presented as a Perimeter Scorer because that is the type a player is most often or is most like on average it would be possible to present a player for example as 55% Perimeter Scorer at BXS 10 / 40% Pure Perimeter at BXS 8. The threshold for breaking out a 2nd type might be 30-40% to avoid the mess unless a player is being used or performing as a hybrid. How much is the result of role and how is game to game variance in opportunity and performance will vary and is unclear but this more detailed typing would be intended as an alert system for further study of what is happening and why.


It is also now possible using one of David's recent files giving all player types and performance to summarize a team for a season by SPI7. It is individuals and unique 5 man lineups that actually perform but this summary is another way to see the team as a whole above the often confusing and small sample detail.

Boston was 37% Perimeter Scorer at an average BXS of 8.2, 24% Scorer's Opposite at 8.07, 20% Pure Interior at 3.96, 12% Pure Perimeter at 8.16 and 7% Mixed at 3.24. There was no player predominantly a Pure Scorer or Interior Scorer. That is breadth of contribution but pretty strong type choice.

The Sonics were 9% Perimeter Scorer at an average BXS of 1.54, essentially no Scorer's Opposite, 25% Pure Interior at 1.79, 21% Pure Perimeter at 1.88 and 14% Mixed at 1.45, 20% Pure Scorer at 2.44 and 10% Interior Scorer at 1.84. That is more variety with no big positive contributions.

[dsparks]

Mountain: I dropped the ball on finding the top 50 lineups, thanks for the link. It was a matter of minutes to put together:

http://spreadsheets.google.com/pub?key=pjtolzxemBV6AD_Mu-4i9VA

Recall: 1==SS; 2==SP; 3==PP; 4==PI; 5==II; 6==IS; 7==MM

I've got it sorted by team, I thought that would make for the easiest comparison.
_________________
David

http://arbitrarian.wordpress.com

[Mountain]

Great! Next thing to review.


Looking at teams I see that the 76ers and Cavs were the least diverse teams by type looking at % of total minutes by top 2 types and it was the same 2- II and SP. Both just above and below 80% of all minutes. That is distinctive, presumably intentional.

[Mountain]

Only 8 of the top 50 most used lineups include a type 1 Pure Scorer and the average performance is 60% below the average for these lineups. The best and only one over average was the Hornets and Paul. (Good result but bad neighborhood?)

Teams that started the position progression with 22 did slightly better than average and than 23. 222 did even better, 223 did way better (used by Celtics Suns Nets Wizards Magic). 224 slipped some. 225 and 226 were terrible.

The best interior combo was by far 4, 5. The worst 5, 5 and 6,7.

The most used lineup was 22577 and the performance was 40% above the average.
Magic had 2 variations and Pistons and Nuggets also used it.

The worst lineup in the top 50 was for the team headed for and managed by the group in OKC. The runner-up was the Bobcats. They were both substantially worse than the rest.

[QMcCall3]