Complete NBA Player Rankings by WAR

Long time no update – but since the NBA All Star game starters will be announced tonight on TNT, it seemed apropos to step away from the college ratings for a day and get my NBA ratings updated.  So, all 461 NBA’ers who have seen any court time at all rated & ranked in each of my normal metrics (thru 1-21-15 games).  FAQ sheet included for those that may be confused:


Hopefully I’ll be publishing a quick article today over at Sporting News talking about the All Star starters based on my metrics.  I’ll post a link here when that article is up – it will obviously show up on my Sporting News author page as well.


COMPLETE NCAA Player Rankings thru 1-18-15

Click here for my write up about the Top 25 in all the land at the Sporting News.

3121 qualified (played more than 20% of available team minutes) ranked.


Note I added last week’s rank and change in rating (for both HnR & HnI) to the spreadsheet.  There is a FAQ sheet that should answer any questions about any columns – as well as contact info if you have any additional questions or would like to book me at your next birthday party or bar mitzvah.

I will do a hopefully fairly extensive write up about the Top 25 guys with their weekly performances included to help one see the fluidity of the ratings from week to week.  The write up will appear at one of my new alternate gigs at Sporting News within 24 hours.  I’ll Tweet when the article is up, as well as post an update here at Hoops Nerd.

2015 College Player Ratings are here!

About time.  I’ve had some serious site issues trying to upload this file the last couple hours, but it appears to finally be resolved.

Anyway, 3127 D1 players rated & ranked in a spreadsheet to aid in sorting by player, conference, class, etc:


I also added a FAQ sheet to help in understanding a few things about the ratings.  Nothing too complicated – I may add more info (ie ratings broken down into subsets) in upcoming weekly updates, we’ll see. (EDIT 1-13-15, I added player national % rank across 7 rating subsets to the spreadsheet).

Now, as for a write ups – the first should happen later today at a larger sports media site, which I’ll obviously link here when it’s up.  It’ll be my first contribution for them, hopefully it’ll go smoothly.

Thanks to those that have been asking me for these rankings for being patient.  The first college ratings for the season take forever because of so many errors I have to find & cross reference to fix involving the 4700 or so players.  Expect weekly updates (every Monday?) that should appear sooner in the day as the process becomes more streamlined.

Please tweet me what you think (click my name), or comment below – or both.  Let me know what information you’d like to see – or what you’d like to see me write about with my larger sports media partner that I’ll unveil soon.  Thanks.


Future Projects – & a revisit to ol’ > 100 points in a game (twice!) Jack Taylor.

This is actually just a straight copy/paste from a post I just made at the APBRmetrics board in response to the quote below.  Since I’m sure HoopDon isn’t the only guy that feels this way about the ratings he sees here – I figured I should  share my response on the blog.   Plus, I’ve done almost zero writing here for quite a while – I should fill space with more than just WAR rankings.  My rambling in this post gives a good idea on some things I should be tackling in the near future – in addition to the fairly regular NBA WAR player rankings I’ll keep posting & the complete NCAA player rankings that should begin in around 3 weeks – updating weekly I hope.
HoopDon wrote:
HoopNerd, I’ve scrolled through your website and stats multiple times, but I couldn’t find any data on how accurate they were (in terms of correlation to current and future team success), so I ignored them. If your numbers have excellent in-season accuracy, and can predict future performance as well as ASPM/BPM or even (gasp) XRAPM/RPM, I’d use them regularly. If your numbers can do it without using prior-seasons and artificial adjustments (all rookies getting an automatic negative value), I’ll be your #1 fan.More info on how you calculate your numbers will also be appreciated, cheers.

Well, stay tuned, I’ll just have to see how comfortable I am about how much I’ll reveal in terms of calculations. I might just, at the very least, project the next season of every single past season (since 1980) in terms of production per minute (adjusted for pace/blah blah blah) – & since we know the actual minutes played of every guy in their following seasons, plug in the projections tied to actual minutes and see how the compiled projected team results compare to actual results. I haven’t done this because, honestly, no one has ever asked until recently about the predictive nature of the results until the last couple of days – & I don’t know how much people trust someone “testing” their own work & giving results. But obviously it’s something I should do & try my best to do a comprehensive write up to show the honesty behind the results.

Now, the problem with this is that there has to be some trust in my testing my own projections. I think if I give ALL results of every player season projection from the previous season (all the good and all the bad) in a massive spreadsheet – then it’ll be pretty obvious that I’m trying to show full disclosure and not fudging things on the fly. There will be a ton of hits from player to player, but obviously a bunch of misses too. It’ll be interesting to see how close, in general, the projections are & where the standard deviations fall.

Similarly – I plan on projecting the NBA careers EVERY SINGLE D1 player since 1997 & ranking them all in terms of their NBA career projections. I’ll limit the posted results to just guys that project, at some point in a made up NBA career, to be above replacement level. This is a huge project (getting all the DoB’s for all guys that could project above replacement level in a NBA career might be a serious pain – since a good number might not ever appear on basketball-reference) – since I’m not going to try to do what it appears everyone else does & limit my scope to the 80 or so college guys on NBA draft radars (often just for simplicity, but sometimes masking the flaws of their own metric) – but EVERY guy. How much can my methodology be trusted if I pick & choose which college players I apply it to. A good methodology should give solid predictive results on any & every player. If I limit my data set to avoid players (say Javon McCrea last year – I noticed a couple draft projection metrics ignore him, I’m guessing possibly because he measured out too high) who might break the system, then what’s the point? I want to catch that undrafted guy – the Udonis Haslem & the like.

The good thing is, when I finish the above project, I’ll be able to rank every college prospect for every past season – & see if my rankings (without ANY scout, combine, or prospect rank bias) compare to the actual drafts. Since I did already do the college ratings of the last season of every guy that played at least 1 minute the following season as an NBA rookie (in order to create the conversions) – I am pretty confident the projections could very well out perform the actual draft positions. Most “busts” (occasional caveats – Beasley for example projected great) were obvious bust material by the projections. Most sleepers (2nd round studs – Boozer, Blair, etc) were very highly projected – MUCH higher than many of the guys drafted before them (Jarnell Stokes in this last draft would be a comp to those two btw). I can’t get them all right by any means (Bledsoe) – I just think I can do better than the general draft history of the past gms. I wanted to do all this before this last draft – never got anywhere close. Just producing college ratings for every single guy that played in the NBA as a rookie was majorly time consuming (I have to compile all the college ratings at the team level for all those guys). I will have all this done well before the next NBA draft – come hell or high water.

Also, another good thing about the draft project – I could run similarity scores with all past college players since ’97 – see which players who appear most similar to guys that succeeded in the NBA. I could run similarity scores of every projected NBA season to all NBA seasons at every age group since 1980, see if certain player types are being overlooked. This might be a 2nd approach I may eventually merge with the first if it improves the predictability. Have zero idea if I’ll have this fully workable by the next NBA draft. If I were working for a team, it would be because all other projects would probably be back burnered.

So, to answer your point before I went off on a massive tangent, yes HoopDon – I should run all the past projections and show how much they correlate to actual results, & post all the results. I’ll post on this board & on Twitter when I do if you are curious about the results.

If you are curious about how to apply the predictive power of the player ratings (if you trusted them at all) – scroll back to any of my numerous March madness game write ups. I predict the final score of in each write up based on predicted player minutes. Here’s the write up I did for the NCAA championship game:

Note that my general team ratings going into that game had Kentucky winning by 1.1 points. But, when I compiled the optimized lineup player ratings, & with Kentucky without Cauley-Stein – my prediction came out to UConn by 1.3. I surmised going into that game that losing Cauley-Stein cost UK, theoretically, 2.1 points.

Hey – I forgot that I attempted to quantify how good Jack Taylor (the guys that scored over 100 points twice in a college game) really was relative to an average D1 player. I think some of my methodology & how it adjusts for team & pace (that is an EXTREME team in terms of pace) is made a little more evident there. I never saw anyone even dare to try to quantify his play. I noticed this because it was the article before the NCAA championship game. Good times:


Complete NBA Player Rankings by WAR

Every player in the NBA ranked by Wins Above Replacement through yesterday’s games – other ratings included (WAR/48, HnR, & HnI) for your pleasure.  Fully sortable:


For quick reference, the Top 30:

Rank Player Age Tm G M/g P/g R/g A/g S/g B/g T/g TS% ProWAR
1 James Harden 25 HOU 20 36.9 25.2 6.3 6.6 1.9 1.2 4.4 0.566 25.08
2 Stephen Curry 26 GSW 19 32.8 23.2 5.1 7.7 1.9 0.2 3.2 0.625 22.42
3 Chris Paul 29 LAC 19 34.2 17.9 4.2 9.8 1.9 0.3 1.5 0.630 20.70
4 Anthony Davis 21 NOP 19 36.3 25.2 10.7 1.7 2.1 2.9 1.4 0.609 20.39
5 Damian Lillard 24 POR 20 34.8 19.9 5.1 6.3 1.5 0.2 2.6 0.593 17.78
6 Kyle Lowry 28 TOR 20 34.5 20.7 4.9 6.9 1.3 0.2 1.8 0.555 16.93
7 Marc Gasol 30 MEM 20 34.4 19.0 8.1 3.7 1.0 1.6 2.6 0.565 16.86
8 LaMarcus Aldridge 29 POR 19 35.2 22.3 9.9 2.2 0.5 1.3 1.2 0.510 16.04
9 LeBron James 30 CLE 18 38.1 24.6 5.8 7.9 1.4 0.6 3.9 0.556 16.04
10 John Wall 24 WAS 19 35.3 17.6 4.7 10.0 2.2 0.7 3.8 0.505 15.64
11 Kyrie Irving 22 CLE 18 38.0 22.0 3.4 4.8 1.7 0.5 1.7 0.602 14.51
12 Mike Conley 27 MEM 20 32.2 16.7 2.8 6.3 1.2 0.1 2.2 0.567 13.31
13 Jimmy Butler 25 CHI 18 39.9 21.7 5.6 3.4 1.5 0.3 1.7 0.585 12.93
14 Klay Thompson 24 GSW 18 33.3 21.2 3.8 3.5 1.4 0.8 2.1 0.570 12.80
15 Paul Millsap 29 ATL 19 34.1 17.1 7.7 3.2 2.4 0.9 2.4 0.552 12.72
16 Blake Griffin 25 LAC 19 33.7 22.9 7.5 4.0 0.6 0.3 2.3 0.540 12.60
17 DeMarcus Cousins 24 SAC 15 32.0 23.5 12.6 2.4 1.1 1.5 3.7 0.571 12.26
18 Chris Bosh 30 MIA 20 35.3 21.3 8.4 2.3 1.1 0.8 2.4 0.550 12.11
19 Tyson Chandler 32 DAL 22 29.5 11.3 11.9 1.3 0.7 1.5 1.5 0.705 12.07
20 Zach Randolph 33 MEM 20 30.9 15.6 11.0 1.4 1.1 0.1 1.7 0.512 12.07
21 Jeff Teague 26 ATL 19 31.3 17.3 2.4 7.1 1.5 0.4 3.2 0.585 11.82
22 Tim Duncan 38 SAS 18 29.9 13.9 10.3 3.1 0.8 2.1 2.0 0.519 11.58
23 Pau Gasol 34 CHI 17 35.9 20.1 11.9 2.1 0.6 1.8 2.2 0.524 11.25
24 Brandon Knight 23 MIL 22 32.1 17.6 4.7 5.8 1.5 0.1 3.3 0.570 10.96
25 Rudy Gay 28 SAC 18 36.4 21.1 6.3 4.5 1.0 0.4 2.7 0.553 10.51
26 Marcin Gortat 30 WAS 19 30.5 13.5 8.8 1.3 0.7 1.3 1.4 0.560 9.92
27 Kawhi Leonard 23 SAS 19 31.7 14.7 7.6 2.2 1.8 0.5 1.6 0.535 9.73
28 Deron Williams 30 BRK 18 36.6 17.3 3.5 6.2 1.2 0.4 2.7 0.561 9.70
29 Ty Lawson 27 DEN 19 36.5 15.8 3.2 10.3 1.5 0.1 3.0 0.497 9.61
30 Nikola Vucevic 24 ORL 19 35.4 18.6 11.7 2.0 0.7 1.1 2.7 0.536 9.49