Revisiting the Seeding of the Draft Lottery

I’m supposed to write about the NBA Draft we just had Thursday night – and I will.  I’m doing the research, crunching #’s, racking my brain.  But at the moment, I was inspired (by a question on the APBRmetrics board) to address how the NBA comes up with the seeding of the draft.  The whole idea of “tanking” drives everyone crazy – so I think the obvious solution to get the #1 pick in the draft is simple….

Have them play for it and EARN it.

Yes, I am on board with a NBA lottery tourney (we won’t call it playoff to avoid confusion with the “real” playoff).  Yes, I know it seems counter-intuitive to award the #1 pick to a team that wins a tourney, when it should go to the “worst” team.  But, as we have it now – the #1 pick RARELY goes to the worst team.  There’s just dumb luck that lands many teams the #1 pick.

So, I say TOURNEY.  We can easily set the tourney up to FAVOR the worst teams.  I’m thinking best of 3 game series, 2 home games for the worse record team.  Seed the worst teams together every round (thus also making the better teams knock each other out early).  It doesn’t really matter to me HOW it’s done, just so that I – and the fans of bad teams – can enjoy watching these teams that have languished for months actually  have, finally,  something REAL to play for.  Teams that maybe have been “tanking” better get their best team on the court and start “playing”.  All the extra games would earn the non playoff teams and the NBA some extra $$ to boot!

Based on this last season, I’d maybe set it up like so (top team gets 2 home games, bottom 1):

Bye 1 Bucks
Bye 2 Sizers W1/2
I
3 Magic > W 1-6
4 Jazz W 3/4 I I
> W 3-6 I
5 Celtics W 5/6 I
6 Lakers I
> Champ
7 Kings I
8 Pistons W 7/8 I
> W 7-10 I
9 Cavs W 9/10 I I
10 Pelicans I I
> W 7-14
11 Nuggets I
12 Knicks W 11/12 I
> W 11-14
13 Wolves W 13/14
14 Suns

When it’s all said and done – I’d probably re-seed each bracket (top 1-6) & (bottom 7-14) separately based on results after #1 pick is rewarded to the champ to get the final draft seeding.  So, 1-6 will get shuffled a bit, as well as 7-14 – based on how far the teams get in their bracket (initial seeding obvious tie breaker).  The very worst (and 2nd worst) team going into this tourney would end up – at the very WORST if they lose their first series - the 5th pick.  If they win that first series (again, that game is against the OTHER worst team), the worst pick they’d get is the 3rd pick.

At first glance, maybe it would seem unfair that a “better” team will end up in the finals (seeded 7-14) – BUT they will have a much harder road to get there, AND they will have to WIN the finals to get that #1 pick, otherwise they will end up pick #7 (when we seed the draft from the two brackets separately).  Still a nice reward if the team (say, the Suns last season) went into the tourney at #14.  If the runner up is from the weaker bracket (1-6), they’ll still will have earned the #2 pick – again, not too shabby.

Anyway - only two teams could drop more than 3 spots based on results (#1 could end up with the #5 pick if they lose their first series and a team in the better bracket wins it all, #7 will end up #11 if they lose the 1st round).  No other teams will drop more than 3 spots.

Of course, all future traded draft picks would have to be lottery protected unless the lottery playoff for the pick (ie, that very offseason before that draft) has already been played.  I don’t care how it may complicate trading of future draft picks, I just want the playoff – less talk of “tanking”, more talk of “earning”.

I know the NBA probably will never implement something like this – but man, how great would it be if they did.  How progressive as a pro league would the NBA look if they did something so radically different to “decide” winner of that coveted #1 draft pick – and to give fans of the “lesser” teams something fun to root for after the regular season ends.

Dan

About to live tweet the draft

Here are the prerequisite things I’ll link to over & over.

My draft model rankings based on projected career WAR: 2014DraftRankUpload

A consensus of draft models put together by Nick Restifo that includes mine.

And this: Top18

I am horrible at multi-tasking so I’ll link this post about 100 times on twitter instead of linking different areas.  I will obviously be posting here my draft opinions in a new post, probably starting tommorrow (Friday) morning.

Dan

Hoops Nerd Top 18 Draft Prospects

Only college guys, I will definitely have foreign leagues somehow worked into the model next season – there’s some work to be done on that front.  So, no Exum.

See my previous post for a little exposition on what’s going on here, as well as the spreadsheet with the career WAR projections of the 45 college prospects that project to be above replacement level at some point in their career – broken down season by season.

So, last night I released my draft model results.  When it comes down to it, there are really only 18 pospects the model “sees” as having any realistic chance at a breakout career.  The lower the player is ranked in projected career WAR, obviously the higher my surprise will be if they end up a star during their career.  No doubt there is some real wiggle room there, we are working with limited amount of data (two & sometimes just one college season) – and sometimes players “get it” later than most (the Zach Lavine people are counting on this).

Top18Top18Top18

The guys with peaks and valleys throughout their career (instead of the expected uniform bell curve) are seeing their WAR projections vacillate because of their varying projected playing time season to season.  Different skillsets age differently, and if the player is on the low end of a rating skillset that is closer to being unprecedented at that specific age (within the last 35 years of NBA data), he may see a low playing time projection – limiting his WAR output.  If the WAR bounces back the next season, then he probably wasn’t quite as close to the bottom threshhold in the limiting skillset at that next age group than he was the season before.

The obvious example is Joel Embiid – he projects a very high foul rate as a rookie that lowers slightly as he ages (until 30, then it starts going back up), but always borders on being at the foul rate of a deep backup (big minute players obviously have much lower foul rates).  His foul rate is projected at over twice league rate his rookie season, and never drops below over 50% league rate.  He has an outlier playing time projection dip down to a 36.6% of his team’s playing time as a 27 year old in 2022 because there is no historical precedent of a 27 year old playing more than 36.6% of their team’s minutes with as high a foul rate as he projects.  The next season at 28, his foul rate dips a tiny bit, and the different historical precedent among 28 year olds brings his playing time back up to 53.2%.

Dan

Ranking the 2014 Draft Prospects by Projected Career WAR

2014DraftRankUpload

I’ll probably be writing a ton about this the next 40 hours, but I have to get 6 hours sleep at the moment.  Like, in 5 minutes, I’m BARELY awake. Above is the link to an excel file ranking 45 players who projected – at some point in their career – to be above a replacement level player – broken down by each player year by year as well as career totals.  I had an initial list of 83 guys who had at least the smallest chance of being drafted – but 38 didn’t make the “replacement level” player cut – including notable possible lottery picks Hood & Lavine.  Suffice to say I think a team would be foolish to guarantee 3 years of good $$ to either of those guys – but I digress.

The ratings are based off weighted projections from their 2013 & 2014 college seasons (if they weren’t 1 & done).  The PJ Hairston’s (who played D league last season) projections are off his 12 & 13 season, since I haven’t yet devised my D-League to NBA model – one of my many future projects before the start of next season.

There is obviously absolutely no bias here – I didn’t try to shoe horn things in to try to get Andrew Wiggins into a top 3.  The results are the results, based off the players’ college ratings (adjusted for everything, broken down into 14 subsets), age, skillsets – and then using 19 years of past college to NBA ratings and 35 seasons of NBA age group to age group developement as a base for the year to year projections.

I’ll get more into detail on methodology tommorrow – and hopefully some other very cool stuff.  I dunno - maybe graphs, similarity scores, converting the ratings to general box score stats for the less analytical peeps, whatever I can put together in the very limited time before the draft.

Those who have been expecting this for a number of days – thanks for being patient, there were a few hiccups in compiling (SO MANY past college player stats to fix) as well as in the time I could devote that pushed back the release almost a week later than I wanted.  I wanted to create something different than the rest, and I think I have.  Enjoy, and check back in tommorrow & Thurday for more, as well as follow me on Twitter as I attempt my first time at live tweeting a big event.  I love the draft – should be fun.

Dan

Predicting the Finals

Quick break from my behind-the-scenes but hopefully soon-to-be-posted draft model work – to address one little thing.

I’ve had a couple people mention to me that my “numbers” wouldn’t have predicted the NBA finals.  Well, that is somewhat true – NO metric would have come anywhere close to predicting an average game margin for the Spurs of +14.0 over the Heat.  It was crazy.

However, my metric liked the Spurs before the finals, and liked them even more when looking back and “seeing” the minutes distributions for each team.

I’m not going to do anything crazy or fancy here, I’ll keep it simple – based SOLELY off the regular season, so that no one accuses me of results bias using the finals stats as part of my data.  The regular season HoopsNerd rating for the Spurs as a team was 107.9.  The Heat was at 104.9.  If one were given ONLY the final overall point totals for the series (986 total points scored), the prediction would have been Spurs 500 points over 5 games to the Heat 486 points (in essence, Spurs +2.8 per game).  The formula to figure this is very simple, Spurs projected points = 107.9/(107.9+104.9)*986.  Heat, 104.9/(107.9+104.9)*986.

Now, I suspected (but never posted here, NBA draft focused still) that the Spurs had a bigger “upside” than the Heat going into the finals, considering oft injured (during the regular season) but very important contributor Manu Ginobili would play many more minutes in the finals, thus improving the Spurs team rating.  Well, here’s how it worked out for each team:

Player MP SeaHnI HnI*MP
Tony Parker 176 114.3 20115.52
Boris Diaw 176 102.7 18079.59
Kawhi Leonard 167 119.7 19983.09
Tim Duncan 165 125.6 20718.96
Manu Ginobili 143 118.7 16974.8
Danny Green 106 102.9 10902.25
Tiago Splitter 84 108.6 9122.497
Patrick Mills 76 113.9 8654.303
Marco Belinelli 59 104.8 6185.313
Matt Bonner 27 87.1 2351.183
Cory Joseph 8 99.7 797.7909
Jeff Ayres 7 87.5 612.5584
Aron Baynes 6 76.8 460.5371
Team Totals 1200 134958.4
112.5
Player MP SeaHnI
LeBron James 189 155.0 29297.03
Chris Bosh 181 109.9 19892.12
Dwyane Wade 172 119.7 20588.33
Ray Allen 155 93.7 14524.7
Mario Chalmers 116 98.9 11467.8
Rashard Lewis 114 81.8 9321.015
Chris Andersen 90 106.6 9597.403
Norris Cole 84 83.2 6984.82
Shane Battier 33 78.4 2586.361
Udonis Haslem 22 79.5 1748.494
Michael Beasley 17 102.2 1737.474
James Jones 14 98.7 1381.469
Toney Douglas 10 74.4 743.7049
Greg Oden 3 85.7 257.1778
Team Totals 1200 130127.9
108.4

Again, I am using regular season HnI (HnI, as I always mention, is the best “predictive” rating, ignoring games missed).  One can easily check my work by looking at my 2014 regular season player ratings here.  Anyway, when on the court, my ratings from the regular season (HnI above) had LeBron as easily the star going into this series, followed by Duncan, then Wade & Kawhi at almost a dead heat for 3rd best, then Ginobili-Parker-Mills before you get to Bosh.  The Heat had the STAR, but the Spurs had such great quality DEPTH.

Those not really in the know seem surpised about Kawhi Leonard stepping up and becoming finals MVP – but he was easily a top 30 player this last season – 23rd in WS/48, & 27th in both HnI & HnR.  Only 20 other players were better in all three of those metrics than Kawhi.  In fact, he and Bradley Beal were the next two that fell just outside my current 15 NBA’ers for the future.  His and EVERYBODY ELSES projected future NBA WAR ranking and such can all be found here.

I digress.  Predicting the finals.  Back to the chart above, all one has to do is multiply the players’ HnI rating with his minutes played, compile it all, and then divide by team minutes (in this case 1200) to get a team rating.  The Spurs come out to a 112.5, while the Heat are at 108.4.  So, looking at player playing time allotments, The Spurs have a projected 4.2% increase from their already great regular season rating (112.5/107.9), while the Heat have a 3.4% increase (108.4/104.9).

Using the new team ratings (and total points for the series), we now come out to a 502 points for the Spurs, & 484 points for the Heat.  Not much change, but a 4 point overall swing in 5 games brings the predicted per game outcome from +2.8 for the Spurs to +3.6 – which has some significance for bettors out there.

So, now, when people ask what my ratings predicted for this finals – I have a post to link to right here.  Next season – with a fully formed draft model in tact (not having to create one next season) – I SHOULD be able to do some actual playoff series and projections and such, hopefully something even more in depth than I did for almost every game of the NCAA tourney.

Dan

NBA Player Projections are finally here!

A precursor to my draft model I hope to unveil in the next two weeks, I’ve completed the NBA player projection model.  There’s a ton I should explain – and I will in time – but for now here are the results (two excel files) in the page above.

Looking at the results, there are 15 guys that stand out as for now (based solely on weighted past three year NBA performance combined with age and skill type) appearing to be the impact players of the near AND distant future.  The 15:

Anthony Davis

Kyrie Irving

Kevin Durant

Andre Drummond

Kevin Love

LeBron James

DeMarcus Cousins

Stephen Curry

Chris Paul

Blake Griffin

James Harden

John Wall

Paul George

Russell Westbrook

Brook Lopez

The player projection model projects Anthony Davis as a true superstar level talent.  It loves Kyrie Irving – but part of that is from his projections from his stellar (at such a young age) seasons as a 19 & 20 year old.  His projection will lessen a bit if he doesn’t bounce back from a slightly disappointing 2014.

Drummond projects very strongly for a long time, although he faces playing time skillset limiters – foul rate and assist rate being the two biggest.

The model assumes Brook Lopez will recover from injury and start putting in more complete quality seasons.  That may be a big assumption – the confidence level for him is low (less past data than most others to project from).

As I stated in the player projection page – please comment below or hit me up on twitter if there are any questions about the results or methodology.  I’ll try to cover all those bases in the next few weeks leading up to the draft as best I can.

Dan

The greatest NBA players of the last 35 years (regular season)

In case you missed it – I added an NBA Historical Player Rankings page above, where I will eventually have every NBA player of every NBA regular season & playoffs rated & ranked.  But, for now, I “just” have every player from every regular season ranked from 1980 until 2014.  That’s 17103 player seasons.  Again, all ratings are adjusted for pace, team strength, era (changes in stat frequencies through time – particularly FG% & rebounds), etc.

I also added, for fun, the 22.5/30/150/150 club on that same ratings page.  A player has a season of > 22.5 WAR &/or 30 WAR/48 &/or 150 HnR &/or 150 HnI – and he’s in the club, forever marking his place in history.

Michael Jordan made the club 9 times, 8 of which were 4* seasons (he was above the qualifying threshold in all 4 metrics).  Shaq also made it 9 times, with four 4* seasons.  The rest:

LeBron, 8 seasons (all eight 4* seasons)

David Robinson, 7 seasons (five 4* seasons)

Karl Malone 4 (two 4*)

Kevin Garnett 4 (one 4*)

Dwyane Wade 4 (one 4*)

Charles Barkley 4 (best was a 3* season)

Tim Duncan 3 (two 4*)

Hakeem Olajuwon 3 (two 4*)

Larry Bird 3 (two 4*)

Magic 3 (believe it or not, all three were 2* seasons)

Chris Paul 2 (both 4*)

Kevin Durant 2 (one 4*)

Dirk Nowitzki 2 (one 4*)

Tracy McGrady (4*), Moses Malone (3*), Kobe Bryant (2*), Dwight Howard (2*), and Kevin Love (squeakin’ in w/ a 1* 2014) all with one qualifying season each.  That’s 20 players in total the last 35 seasons – a very select group.  It appears that pretty much any player good enough to make this list twice is a shoo in for the Hall of Fame.  So, Durant probably already has cemented himself a spot (even if he retired today), and Kevin Love got within spitting range.

For fun – here’s what I would put together as a “super team” based on what I see as the best regular seasons these last 35 years (no repeat players obviously)

Last 35 Year Dream Team (best regular season):
Pos Player Year Age Tm G M/g P/g R/g A/g S/g B/g T/g TS%
C Shaquille O’Neal 2000 27 LAL 79 40.0 29.7 13.6 3.8 0.5 3.0 2.8 0.564
PF David Robinson* 1994 28 SAS 80 40.5 29.8 10.7 4.8 1.7 3.3 3.2 0.562
SF LeBron James 2009 24 CLE 81 37.7 28.4 7.6 7.2 1.7 1.1 3.0 0.578
SG Michael Jordan* 1988 24 CHI 82 40.4 35.0 5.5 5.9 3.2 1.6 3.1 0.591
PG Chris Paul 2009 23 NOH 78 38.5 22.8 5.5 11.0 2.8 0.1 3.0 0.587
sub Kevin Durant 2014 25 OKC 81 38.5 32.0 7.4 5.5 1.3 0.7 3.5 0.620
sub Kevin Garnett 2004 27 MIN 82 39.4 24.2 13.9 5.0 1.5 2.2 2.6 0.539
sub Tracy McGrady 2003 23 ORL 75 39.4 32.1 6.5 5.5 1.7 0.8 2.6 0.553
sub Dwyane Wade 2009 27 MIA 79 38.6 30.2 5.0 7.5 2.2 1.3 3.4 0.562
sub Hakeem Olajuwon* 1993 30 HOU 82 39.5 26.1 13.0 3.5 1.8 4.2 3.2 0.567
sub Tim Duncan 2003 26 SAS 81 39.3 23.3 12.9 3.9 0.7 2.9 3.1 0.551
sub Karl Malone* 1997 33 UTA 82 36.6 27.4 9.9 4.5 1.4 0.6 2.8 0.587

Anyway – the pre-1980 regular seasons (including ABA) as well as ALL past NBA & ABA playoff ratings will be done at a later date – hopefully completed by mid July.  I will have complete career ratings & rankings incorporating adjusted playoff WAR & other such things after I finish all the past seasons.

I may give a glimpse of my NBA player career curve model here in a few days (projecting future season by season ratings of every NBA player based on age & skillsets), since I now have my complete historical data set to work with.  We’ll see, time permitting.

Expect during the next month to see complete NCAA player ratings & rankings posted season by season in descending order starting with the final 2014 rankings in a few days.  After all past college seasons player ratings/rankings are  posted (all the way back to the ’96-97 season), I’ll dive head first into my draft model and probably post a TON about the draft.  Expect to see career projections well beyond in scope anything you’ve ever seen before, or even expected to see – as well each current prospect’s spot in ranking the best draft model prospects the past 18 years.  All in June.

Dan

2014 Regular Season NBA Player Rankings

Every NBA Player ranked by WAR (Wins Above Replacement):

2014NBARegularSeason

Same rankings, but sorted by team:

2014NBARegularSeasonByTeam

Players in red played for multiple teams (only the last team played for is listed), while the stats and ratings for these players are based on season totals (combining results from all the teams).

2014 NBA All WAR Team

This team is formed by the highest WAR/48 players, and the roster is completed when their %Min equals 5.000 (a full NBA season for a team).  The roster also has to be above league average in scoring rating, rebound rating, ball handling/passing rating, steal rating, and blocks rating.

Player Age Tm G M/g P/g R/g A/g S/g B/g T/g TS% WAR/48 %Min
Kevin Durant 25 OKC 81 38.5 32.0 7.4 5.5 1.3 0.7 3.5 0.620 34.82 0.788
LeBron James 29 MIA 77 37.7 27.1 6.9 6.4 1.6 0.3 3.5 0.635 32.09 0.730
Chris Paul 28 LAC 62 35.0 19.1 4.3 10.7 2.5 0.1 2.3 0.568 26.51 0.550
Stephen Curry 25 GSW 78 36.5 24.0 4.3 8.5 1.6 0.2 3.8 0.601 24.46 0.718
Brook Lopez 25 BRK 17 31.4 20.7 6.0 0.9 0.5 1.8 1.6 0.614 24.29 0.134
Kevin Love 25 MIN 77 36.3 26.1 12.5 4.4 0.8 0.5 2.5 0.578 23.83 0.704
Russell Westbrook 25 OKC 46 30.7 21.8 5.7 6.9 1.9 0.2 3.8 0.534 23.01 0.356
Anthony Davis 20 NOP 67 35.2 20.8 10.0 1.6 1.3 2.8 1.6 0.569 21.90 0.594
Al Jefferson 29 CHA 73 35.0 21.8 10.8 2.1 0.9 1.1 1.7 0.526 22.26 0.426

Al Jefferson’s %Min was adjusted to get the team to exactly 5.000.  His was adjusted down instead of lower WAR/48 Anthony Davis because Davis’ shot blocking was needed to make this team better than league average in terms of rim protection.

The odd man out is James Harden, who had a better WAR/48 than either Jefferson or Davis – but had to be moved to make room for Davis and his needed shot blocking.

Dan

 

2014 Hoops Nerd All NCAA Tourney Team

(Much of this may look like an exact repost of the article I wrote a week ago just before the Final 4 games, because it is -  I didn’t want to explain all the same things again.  Obviously the info is updated to include all the tourney games.  I did add the All Tourney teams for fun, enjoy!)

To answer this question, I once again do what I do – I rank EVERY player game from this tourney (all 1311 player games) and EVERY player overall (all 718 players) based on their tourney performances – both in sheer dominance (PAOPoints Above Opponent) and in contribution to team wins and losses (PW & PLPlayer Win shares and Player Loss shares).

This isn’t your run-of-the-mill, let me name 10 guys or so that seemed to have some great games (ignoring many others) article – you can find those articles anywhere.  I rank every game, and I rank every guy – it’s what I do.  No stone left unturned.  It’s my thing.

To lead in, if this is your first introduction to my tourney player performance rankings – you might want to read my initial one after the Round of 32, where I explain much of what you’ll see.

Onto the rankings – first the ranking of ALL the NCAA tourney individual player games (PDF files ranking all 1311 player games):

All player games ranked by Player Wins (PW):2014NCAATourneyPlayerGmRankbyPW

I bolded the Top 67 player games in terms of Player Wins (PW), put in italics the top 67 player games in terms of Points Above Opposition (PAO), and put in red the player games that were top 67 in both metrics.  I picked 67 because there were 67 games, seemed logical.

Five different players in this tourney earned over 88% credit for leading their team to victory in a single tourney game: Chassan Randle (0.88 PW in Stanford’s 5 point win over New Mexico in the Round of 64),  Lawrence Alexander (0.91 PW in NDSU’s 5 point OT victory over Oklahoma in the Round of 64), Julius Randle (0.99 PW in Kentucky’s 2 point win over Wichita State in the Round of 32), Shabazz Napier (0.89 PW in UConn’s 6 point win over MSU in the Elite 8), and Frank Kaminsky (0.98 PW in Wisconsin’s 1 point OT squeaker over my beloved Arizona Wildcat’s in the Elite 8).

On the complete other end of the spectrum, Trevor Cooney is ranked #1311, having earned 56% of the blame for Syracuse’s 2 point loss to Dayton.  Cooney played 25 minutes, scored 2 points -  with 2 steals, 1 foul, and a 17% TS%.  #1310 is an Arizona Wildcat in the Elite 8 – combine that with the previously mentioned #2 (a Badger) – that Elite 8 game will haunt me for a while.

 

All player games ranked by Points Above Opponent (PAO):2014NCAATourneyPlayerGmRankbyPAO

These are the guys that just plain put up massive stats – often in blow out wins.

Not surprisingly, Adreian Payne’s 41 point, 8 rebound, 87% TS% performance versus Delaware ranks #1 with a 17.6 PAO.  MSU won that game by 15, so Adreian’s teammates were actually outplayed by Delaware, combining for a -2.6 PAO.

Amazingly, PAO rank #2 AND #3 belongs to players in a losing cause – Aaric Murray of Texas Southern scoring 38 with a 72% TS% in the 12 point play in loss to Cal Poly (Murray was 15.3 PAO, the rest of his teammates combined for a miserable -27.3 PAO), and Bryce Cotton of Providence scoring 36 with 8 assists and a 68% TS% in the two point Round of 64 loss to UNC.  Actually 6 of the top 12 PAO games were in losses – let’s just say Cleanthony Early of Wichita State, Dustin Hogue of Iowa State, Marcus Smart of OK State, and Quinn Cook of Duke shouldn’t be getting any blame for their respective teams’ tourney losses.

While 6 of the top 12 PAO games were in a losing cause – the next 17 top PAO games were in wins.

 

All player games ranked by Player Wins (PW) & sorted by team:2014NCAATourneyPlayerGmRankPWbyTeam

Just to make it easier to find the results of the player games of your favorite team.  Looking at the champs, Shabazz Napier had 5 of the top 6 UConn player games in both PW & PAO - with 4 performances being top 67 in both metrics.  Amazing.

 

Overall NCAA Tourney player rankings by Player Win differential (PW minus PL):2014NCAATourneyOverallRankbyPW

I bolded the Top 32 players in terms of Player Wins (PW), put in italics the top 32 players in terms of Points Above Opposition (PAO), and put in red the players that were top 32 in both metrics.  I picked 32 because there were 32 teams alive in Round “3″.

Shabazz Napier (dominant) and Julius Randle (his rebounding dominance SO integral in UK’s nail biters) easily #1 & #2.

 

Overall NCAA Tourney player rankings by Points Above Opponent (PAO):2014NCAATourneyOverallRankbyPAO

Jarnell Stokes and his 18.0 ppg, 12.8 rpg and 64% TS% in leading his team to the Elite 8 comes in #2 well behind Napier, but just ahead of Kaminsky.

 

The overall Player Win differential rankings – sorted by team:2014NCAATourneyOverallPWRankbyTeam

Again, to make it easier to find all the results of your favorite player(s) and team(s).

 

The 2014 NCAA Hoops Nerd All Tourney Team

1st Team Team G Mn/g Pt/g Rb/g A/g S/g B/g To/g TS%
S. Napier Connecticut 6 36.3 21.2 5.5 4.5 2.5 0.0 3.5 0.645
J. Randle Kentucky 6 32.2 14.8 9.8 1.8 0.7 0.7 1.8 0.539
F. Kaminsky Wisconsin 5 31.2 16.4 5.8 1.2 0.6 1.8 1.0 0.582
B. Dawson Michigan St. 4 32.3 16.3 8.8 1.5 1.3 0.3 1.3 0.663
X. Thames San Diego St. 3 37.7 26.0 1.3 3.7 1.3 0.7 1.7 0.545
2nd Team   G Mn/g Pt/g Rb/g A/g S/g B/g To/g TS%
J. Stokes Tennessee 4 36.0 18.0 12.8 2.0 1.0 0.0 2.3 0.637
A. Gordon Arizona 4 33.8 14.3 9.5 3.3 1.3 2.3 2.0 0.606
D. Daniels Connecticut 6 33.8 16.0 7.2 0.2 0.7 1.3 1.5 0.578
A. Payne Michigan St. 4 30.3 20.5 6.5 1.3 0.5 1.0 1.8 0.636
J. Adams UCLA 3 30.7 19.0 5.7 3.0 1.7 0.3 0.7 0.640
3rd Team   G Mn/g Pt/g Rb/g A/g S/g B/g To/g TS%
J. Richardson Tennessee 4 34.0 19.3 3.5 3.0 0.8 1.0 1.5 0.675
S. Wilbekin Florida 5 34.8 14.2 1.8 2.6 1.6 0.0 1.0 0.530
J. Morgan Michigan 4 29.5 12.8 7.8 1.0 1.0 0.5 1.8 0.739
L. Hancock Louisville 3 30.0 18.7 2.3 3.0 2.7 0.3 3.0 0.667
R. Hollis-Jefferson Arizona 4 27.3 14.0 4.8 1.8 1.0 2.0 0.8 0.727

Shabazz Napier was so good, he had more Player Wins than all other entire teams not named Florida, Wisconsin, & Kentucky (if you sum all their PW’s, you obviously get 4, 4, & 5 respectively – Napier had 3.22 PWs).

I formed these three “teams” subjectively based on their PW/PL differential & their PAO.  Feel free to look over all the complete results I give above and form your own opinions.

As usual, if you have ANY questions  or you like what I’m doing here (and maybe appreciate that I’m trying to offer information and complete results not seen anywhere else, especially from mainstream media) – comment below and/or hit me up a bunch  on Twitter.  Thanks!

Dan