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Stat Clusters [Preseason Edition] - Pinwheel Empire

Stat Clusters [Preseason Edition]

submitted 6 years ago by in Stats


This is the first edition of what I hope can be a running series throughout the season. The idea here is to group stats logically for different player types and have a format which can give an overview of a player's vital statistics at a glance and put them in some context.



Positional Breakdown

I have finally relented to the logical breakdown of positions into guards/wings/bigs. I don't accept that only point guards fall outside of the wings definition, so combo guards (any player either a 1/2 or a 2/1) fall into the guards category for me. A wing can be a shooting guard, small forward or in rare cases primarily a power forward. Bigs are always power forwards or centers. For our roster (16 man, pre cut, active players only) this means:

Guards: Lillard, Price, Nolan, Karl 
Wings: Batum, Wesley, Claver, Babbitt, Pavlovic, Barton, Ammo
Bigs: LaMarcus, JJ, Meyers, Freeland, Jeffries 


Statistical Breakdown

I made some executive decisions on which stats would be included in the clusters for guards vs wings vs bigs. All include basic and advanced shooting percentages, PER (calculated using the simplified PER formula), USG% and TOV%. Guards get per game and per 36 minute numbers for points, assists, rebounds, steals and turnovers in that order, as well as an advanced distributing statistic in AST%. Wings have the same for points, rebounds, assists, steals, blocks and turnovers, with an advanced metric for rebounding (TRB%). Bigs' statistical profile ignores steals and assists, but includes personal foul statistics and a deeper emphasis on rebounding, with offensive, defensive and total rebounding percentages, and shot-blocking (BLK%). The goal here is to eliminate presenting less relevant stats for such as blocks for guards or steals for bigs (although whether I've gotten it right in terms of what is unnecessary is something I'd love feedback on)



The way the stats are grouped in the clusters is designed to be simple and logical. PER central as the pre-eminent catch-all stat, eFG% under 3P% to signify the latters' influence on the former, TS% under FT% for the same reason, TS% and USG% next to each other so they can be analysed in concert, per game stats and per 36 stats next to each other for easy reference with advanced stats attached to the appropriate row of raw/per minute numbers. Hopefully that logic makes for something easy to read as a quick reference (any suggestions on changes that might improve the layout would be appreciated).



The way I've looked to add context to the stats provided in these clusters is by adding a colour coding system. If this proves worth continuing with, I'll look to make the comparisons against leagues bests/worsts/averages but for this small preseason data set, I've used team averages to judge performance. The way it works in this post is as follows:

Black squares mark a team worst performance, bronze a below average one, silver above average and finally gold for being best in a category. Fairly simple. The only exception to this rule are where stats are only measured for a single group (bigs particularly with ORB%, DRB%, BLK% and PF stats but also guards with AST%), then the colours are awarded for best/worst/above average/below average within that group.


Preseason Numbers

The purpose of this post is really to see how these stat clusters look in practice and get some feedback, but it also serves as a fun with SSS post for the preseason. All these numbers should be taken with heapings of salt, because although a lot of it looks relatively close to what we might expect in the regular season, the variance possible when looking a 5, 6 and 7 game sets of numbers is quite extreme. With that said, here they are, presented without comment:

NB: Scroll to the bottom and hit the expand button to see the clusters in their full glory 


The Starters








 The Backup Guards






The Backup Wings







The Backup Bigs








If you made it to the point then please let me know what you think of the concept and any ideas for improving it. I plan on expanding these during the season when more comprehensive statistics become available and would quite like to look at them for prior seasons to see if any new insight could be gained. Any help would be invaluable.