New draft scouting tool PCP40 explained


B-Ball is behind the 8-ball and 20 years behind baseball when it comes to meaningful metrics.
Excited at the prospect of OKC’s three first round picks, I decided to create my first NBA draft big board. As a serious draftnik, I've been doing NFL big boards and metrics for more than a decade, but this was uncharted territory.

Tired of trawling through screeds of statistics, and dissatisfied with the current crop of metrics – such as PER,eFG%, TS%, Game Score, NBA Efficiency – I said "stuff it" and decided to create my own.

Metrics need data points – which is why baseball is so suited to stats - so I stripped it back to basics. I examined every common stat and had deep thoughts about what they really actually mean. Life = 42 btw...
Then in the middle of the night, I had an epiphany in terms of how each one could and definitely should be expressed.
I realised I could create a quick and dirty version of baseball’s RC/27 (Runs Created per 27 innings) thus Points Created Per 40 was born.


Each and every common stat has been allocated a points value – mostly standard amongst metrics, apart from a few that I believe were overrated, underrated or just way-off wrong.

I painstakingly entered players every stat from their final season in college -big ups to Basketball Reference - into an excel column, then applied the secret metric formula which gives me the pure PCP we all love and are addicted to...
I then reluctantly take the raw version and age (negative for older) + experience ( a small declining positive per year) adjust it.

So PCP40 captures everything and weighs it up – taking the points you cost from the points you created. So the final RAW number represents the points that player contributed to HIS TEAM total over 40 minutes they are on a COLLEGE court - unfortunately I found that G-League and Foreign stats can be unreliable - and need converting from PER 36 - so those players literally come with an asterisk.
The age/experience adjustment then tailors the raw towards the NBA as a projection of their POTENTIAL impact: PCP40aa


It’s maths. It’s objective. It processes everything - everything readily available that is. Every - good or bad - game. Every - good or bad -stat. It doesn’t play favourites. It doesn’t support a school or coach or have an agenda. Importantly it doesn’t rely on highlight reels or media hype.

It’s a tool that identifies overall college level efficiency. It pops up obscure sleepers that your scout should take a second -if not first - look at.

It also finds flaws on favourites that need further investigation. It often pours cold water on hot takes... So it can get you into arguments...

Some of the strangest results are players who seem to fill the stat sheet properly, but score low because they must be inefficient in a lot of small ways.


It’s a snapshot of a player at the end of HIS college career.
Although age adjusted, it can’t predict actual real-life random growth.

It can’t measure what wasn’t there - such as a guy who never took 3s in college suddenly can in the NBA. Entering the data I found a lot of the famous stars didn’t shoot 3s until after college. Something to bear in mind when scouting.

It can’t predict what situation the player will land in. Where he will be in the pecking order, what role he’ll be put in and how much court time he’ll see. Generally the higher the pick invested the more patient a club will be with you.
Athleticism, length, smarts, defensive prowess etc. should show up in the stats somewhat. But the NBA is a step up and not every player can meet the physical challenge.

The metric doesn’t discriminate, so a lot of the top scorers are (short or slow or old) PGs, who are unfashionable because they don’t have ‘upside’ or can’t guard the 2 or 3 so we can play the beautiful position-less game... Or worse they’re big clunky centers who can only rebound, block and score at 60% but can’t defend the perimeter.
Some players are excellent at everything but have a glaring hole in their game. You can actually see it on the sheet, like a bleeding wound because the columns are colour-banded...

Also, because college is competitive & the metric is a balance of skills and flaws, the majority of results are middling, clustered around the mean (approx 17-20pts) and difficult to differentiate. Thus, the scout’s job is to see if the positives are in the right place, the negative are negligible and the potential for growth is there.
Players still need to be scouted, PCP40 just is an objective evaluation in the room.


The ‘market’ doesn’t value centers, or PGs who are small, slow or old... And it should. Chris Paul - currently in the NBA finals - is all those things. And always has been. The historically successful PGs I entered into the metric, almost always played 3 or 4 years at college honing their craft. And a lot were short.

One of the reasons they're undervalued is the very real points created by a pass-first PG are put on the scoreboard by other players, thereby improving their stats. You only have to look back to last year's Thunder under CP3 to see that...

The 3-point shot can be developed. The rule of thumb is to look at free throw percentage, however some bigs struggle with frees yet can still shoot threes. So, scout players who do everything else well and see if you can develop their three.


The metric is maths in action. I trust maths more than I trust ESPN ‘analysts’. Data is objective; opinions are subjective.
I believe in my raw metric because, with my new mindset, I believe I got the stat values right.
Most players in the NBA aren’t stars or even starters.
I’d rather have a bench loaded with smart efficient players, who may grow even better, than a bunch of projects, who can only grow better. Hope is not a strategy. Your team only needs 2 or 3 stars. Once you have them pick efficiency, generate synergy and compete.

So, with 123 entries for this year’s draft, 630 overall, I’ve made some tiers.
24+ = lottery consideration
19-23 = first round consideration
16-19 = 2nd round consideration

Here is a list of a lot of recent draft steals and undrafted (61) gems and my metric evaluation of them. A good player is an efficient player and that's what PCP40 identifies... Such as our own undrafted gem Kenny Hustle 23.9... Also proves we should have not traded Brandon Clarke for Bambi on Ice.... And kept Alex Caruso...And Sabonis...

Year Pick First # Surname PCP
2009 #18 Ty PG Lawson 34.3
2019 #27 Brandon SF Clarke 29.1
2014 #25 Clint CG Capela* 28.6
2018 #19 John PF Collins 28.3
1995 #61 Brad C Miller 28
2016 #20 Caris CG LeVert 27.8
2016 #11 Domantas PF Sabonis 27.7
2012 #34 Jae SF Crowder 27
2006 #47 Paul PF Milsap 26.9
2001 #19 Zach PF Randolph 26.9
2016 #61 Fred PG VanVleet 26.8
2011 #60 Isaiah PG Thomas 26.8
2018 #14 Miles SF Bridges 26.4
2006 #21 Rajon PG Rondo 26
2005 #61 TJ PG McConnell 26
2012 #35 Draymond SF Green 26.0
2018 #34 Devonte CG Graham 25.6
2009 #61 JJ PG Barea 25
2011 #26 George PG Hill 25.1
2013 #61 Jeremy PG Lin 25
2011 #22 Kenneth PF Faried 25.0
1991 #61 John PG Starks 24
2011 #15 Kawhi SF Leonard 24.4
2003 #51 Kyle SG Korver 24
2006 #24 Kyle PG Lowry 23.8
2007 #40 Marc C Gasol* 23.7
2016 #61 Alex PG Caruso 24
2016 #27 Pascal PF Siakam 22.9
2009 #17 Jrue PG Holiday 22.9
2001 #61 Chris PF Anderson 23
2012 #61 Udonis PF Haslem 23
2004 #38 Chris PG Duhon 22.5
2016 #29 Dejounte PG Murray 22.3
1996 #15 Steve PG Nash 22
2009 #61 Landry SF Fields 22
2009 #46 Danny SG Green 22.1
2009 #20 Darren PG Collison 22.0
2018 #23 OG SF Onunoby 21.9
2009 #19 Jeff PG Teague 21.7
2018 #61 Christian C Woods 21.4
2009 #26 Taj PG Gibson 21.2
2017 #14 Bam C Adebayo 21.2
2002 #61 Reggie SG Evans 21
2011 #38 Chandler SF Parsons 21.0
2011 #16 Nicola C Vucevic 20.7
2001 #40 Earl PG Watson 20.5
2011 #30 Jimmy SF Butler 19.7
2017 #13 Donovan CG Mitchell 19.6
2015 #27 Larry SF Nance 19.4
2001 #25 Gerald SF Wallace 19.3
2010 #61 Joel C Anthony 19
2010 #61 Wesley SF Matthews 19
1991 #61 Derrell PG Armstrong 18
2015 #61 Kent SF Bazemore 17
2011 #61 Aron C Baynes 17