Isaac
Years ago

Best and worst lineups for SF matchups

Thanks to Andrew Price of Spatial Jam:




NZ vs Melb:
NZ best: Sosa, Newbill, Abercrombie, Mika, Pledgehammer.
NZ worst: Sosa, Newbill, Abercrombie, Delany, Loe
Melb best: Casper, CG43, Felix, Barlow, Boone
Melb worst: Casper, CG43, Moller, Wesley, Boone

Adelaide vs Perth:
36ers best: Shorter, Sobey, Moore, Childress, DJ
36ers worst: Moore, Sobey, Creek, Childress, Hodgson
Cats best: DMart, Cotton, Tokoto, Wagstaff, Brandt
Cats worst: Kenny, Cotton, Tokoto, Wagstaff, Brandt

Poor Kenny!

As Andrew notes, small sample size is likely to limit the value of this, but I figured it might provoke some discussion anyway.

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Vodka63  
Years ago

Absolutely no surprise that the momentum killer Kenny is the difference between best and worst line ups for the Wildcats.

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andrewbprice  
Years ago

Just to add further to this, I have just released the full lineup data for all NBL teams over on SpatialJam.com

There are also the individual player on-court metrics in a separate table.

Really interested in hearing people's takes from the data.

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Anonymous  
Years ago

have to take into account for kennys sake that when he gets in to play with that lineup most of them have probly already played most the quarter and are starting to tire...

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D2.0  
Years ago

In addition to small sample size, etc, I just don't think the fundamental analysis is valid?

How is it that the best and worst line-ups from all teams contain mostly the same players?

You can't tell me that the MU lineup is worse that one containing all bench/DPs?

What cut-offs have you used?
Do Kenny and Moller get singled out only because they are good enough to play quality minutes?

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Anonymous  
Years ago

Am I reading it correctly in that it says Perth's "worst" line-up against Adelaide is just as good as Adelaide's best against Perth (both -5)?

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andrewbprice  
Years ago

@D.20 Those tables have a sample size limit, so lineups which have played fewer than 30 possessions together against that team are not shown (as specified at the bottom of the graphic I tweeted). This means that high usage players will be in the same groups together as they're the ones on the court most of the time. If you'd like the full data showing how development players or smaller, less used lineups were played it's over on the website.

Lineup analysis is a very valid and well used process nowadays, although in the case of this and as mentioned by OP the sample size is very small so take this with a grain of salt. It's more for interest's sake than anything.

Reply #673618 | Report this post


Proud  
Years ago

Great work Andrew, glad to see someone do that and I kinda wish I had your job, I love stats!

Reply #673627 | Report this post




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