All I Want For Christmas Is An Offensive Rebounder

In the spirit of the season, I thought I’d dole out some gifts to 5 teams that have some glaring weaknesses.

PITTSBURGH

Pitt has the #1 offense in the nation (all ranks throughout the article are from the Pomeroy ratings), but ranks only 43rd in defense, which is a pretty big red flag, in terms of contending for a national title.  Their main problems are a lack of turnovers (234th) and steals (246th), 3pt defense (223rd), and few blocks (114th).

Santa’s Bringing Them – Ayron Hardy, Jacksonville.  The  6’5” senior was named A-Sun Defensive Player of the Year in 2008-09.  He averages a steal every 20 opponent possessions (13th), blocks 4.7% of opponent shots (242nd), and helps anchor a Jacksonville defense that holds opponents to only 29.1% from deep (41st).  The fact that he’s an extremely efficient role player on offense (130.2 ORtg, 16.7 %Poss) is a sweet bonus.

Mini-Preview: UNLV-Kansas State

[EDIT: After writing this, Jacob Pullen and Curtis Kelly were ruled ineligible fro Kansas State.  So, umm, never mind.  KSU is no longer a “good” offense, so UNLV should be fine.]

[This post is really just an excuse to use a spreadsheet I whipped up, which takes a year and a school as input, and automatically creates a chart like the one below.]

UNLV tips off against Kansas State in Kansas City a mere hour from now.  The Rebels started off the year with a bit of hype, and lived up to it at first, topping Pomeroy ratings #10 Wisconsin at home by 3, and blowing out some scrubs.  But they’ve stumbled over the last 10 days, following up a very understandable loss at #13 Louisville with a much less forgivable home loss to #100 UC Santa Barbara.  In truth, their problems started to show up 2 games previous to the Louisville loss, when they beat a poor Nevada (#202) team by only 12, but sometimes a W can have a lipstick-on-pig effect.

It’s clear that a 12-point win over #202 isn’t fantastic (Pomeroy had predicted UNLV by 18), but sometimes it’s hard to get a feel for exactly how good/bad a performance is.  We can use opponent ratings to shed some light on the issue by using Pomeroy’s efficiency prediction formula:

Predicted Offensive Efficiency = ([Team Adj Off] + HFA) * ([Opp Adj Def] + HFA) / [Lg Avg Eff]

For each game that a team has played, we can replace the predicted efficiency with the team’s actual raw efficiency in that game, plug in their opponent’s rating, the league average rating, and the appropriate home field advantage (+/- 1.4% for each team, in a normal H/A situation) , and solve for [Team Adj Off].  That gives us the team’s single game adjusted offensive efficiency rating – essentially, this is how efficient a team would have been if they played exactly the same, but were facing an average opponent on a neutral court.  We can do the same for defense, and from those two numbers we can calculate the efficiency margin (which I find more intuitive to use) or Pythagorean rating (which Pomeroy uses to rank teams).  As a last step, we can take the single game Pythagorean rating, pretend that’s how the team has played the whole year, and see where they would rank in the Pomeroy ratings.  That allows us to say, for example, UNLV played like the #232 team in their loss to UC Santa Barbara.  The chart below shows this “played like” rank for each of UNLV’s games so far. (It also shows, from left to right, the location, opponent, opponent ratings, raw game efficiencies, and adjusted game efficiency ratings.)

 

You can see that UNLV played great through the first 7 games, and then has really struggled over the last 5.  They’re still rated #22 by Pomeroy, but taking a close look at the individual game adjusted ratings reveals something interesting.  Their overall defensive rating so far is 89.7, but that’s largely because of ridiculous defense in a few games against poor offensive teams.  Here are the teams they’ve managed to post a sub-90 defensive rating against so far, along with those teams’ offensive ratings (keep in mind, average is 100):

• UC Riverside (90.6)
• SE Louisiana (91.7)
• Illinois State (97.1)
• Southern Utah (90.9)

That’s it.  Their 30th ranked defensive rating comes in large part from really cranking the screws on the little guys.  If they want to compete in the Mountain West this year, that’s going to have to change.  And if they want to win their semi-road game tonight against Kansas State (offensive rating of 106.7), that’s going to have to change.  I’m not saying it definitely won’t, but I’d say KSU has a better chance than UNLV of bettering their Pomeroy prediction (KSU by 1) tonight.

Bruce Pearl Before Swine

As I’m sure anybody reading these words must know, Tennessee managed to achieve “fuhreal” status on Saturday, and fritter it away by Tuesday.  It was an impressive about face, as they played like the #1 team in the country at Pittsburgh, then like UC-Irvine at home against Oakland:

A few messages insinuating that the loss showed Bruce Pearl’s true colors flashed across my Twitter feed last night, and it made me wonder if this kind of massive quality swing was really as rare as people were making it out to be.  So I checked.

I found every instance since the 2002-03 season where a team:

1. Won on the road vs. a top 10 opponent (by Pomeroy rating)
2. In their next game, lost to a team ranked 50 or lower

I figured that would catch only very impressive wins.  And I couldn’t really make the loss section much more strict, because Oakland is a pretty decent team (#71 as I write this).  With the above criteria, there were 9 cases from the last 8 and a half years.

And, wow.  A third of them are Bruce Pearl’s Tennessee teams.  So for non-Tennessee teams, the chances of it happening any given year are around a quarter of a percent.  For one of Pearl’s Tennessee teams, the chances are around 50%. Apparently there’s something to the idea that his teams are prone to a big letdown after a huge win.  Of course, the fact they even have 3 road wins over top 10 teams is something to be proud of.  I wish I would have kept track of how many teams didn’t lose their next game after a top-10 road win, so I could account for opportunities.  But at this point I’m not going to go back through again.  My eyes are already bleeding.

Interaction Effects and Diminishing Returns

BACKGROUND

This continues what’s turned into an ongoing conversation between myself and Nathan Walker (aka @bbstats) of  the basketball distribution, which started with my last post, continued over at Nathan’s site, and has been supplemented on Twitter.  I started by trying to find out the effect that turnovers have had on the Michigan State offense, which I did by calculating what was essentially an opponent-adjusted version of what Nathan later more intuitively converted to:

[Pts/Possession] – [Pts/(Possessions – TO)]

This tells us how much a team’s offensive efficiency would change if their turnovers all magically disappeared.  Turnovers are a very simple case: there can be only 1 or 0 turnovers on each possession; and when there is a turnover, a team never scores on that possession.  Contrast that with rebounds: in theory, a team could gain 20 offensive rebounds in one possession, yet not score; another team could score after every single offensive rebound.  The only way I could think of to track this kind of thing is to look at play-by-play data, which can get extremely time consuming, extremely quickly.

Nathan came up with another way of looking at the efficiency impact of rebounding and the other Four Factors, though.  He published an Excel spreadsheet (in this post) that uses a regression equation to ask, for example, “What would Arizona’s predicted offensive efficiency be if we changed their eFG% to the league average value of 48.5%, and what’s the difference between that value and their actual efficiency?”

How Much Are Turnovers Hurting Michigan State?

[NOTE: Almost the exact same article can be written for Baylor, except that Baylor hasn’t played any tough games, and as a result are undefeated.  But I wanted to choose just one team to refer to throughout.  So, Baylor fans, just Ctrl+H and replace “Michigan State”/”Tom Izzo” with “Baylor”/”Scott Drew”.]

A 6-3 record against a tough schedule certainly isn’t the end of the world, and as The Only Colors pointed out, the Spartans have had plenty of success in the postseason after slow November/December starts.  But Michigan State was ranked #2 in the preseason AP poll, and the team is clearly struggling more than expected.  Taking a look at their stats page on kenpom.com, what jumps out are the big red splotches on the left: they’re ranked 322nd nationally in Turnover%, 293rd in FT%, and 298th in Steal%.  But what are those marks costing Tom Izzo’s team?  Quite a lot of offense, it turns out.

TURNOVERS/STEALS

One way to gauge the effect of turnovers is to look at what happens when a team doesn’t turn the ball over.  To calculate a team’s offensive efficiency on possessions where they managed to hang on to the ball (TOAdjOff), I used a simple formula:

TOAdjOff = Adjusted Offensive Efficiency / (1 – Turnover%)

I then subtracted this from their actual adjusted offensive efficiency, to get what I’ll call turnover cost.  It tells us how much a team’s adjusted offensive efficiency would increase if they somehow never turned it over.  Here’s the top 20 in the country:

In case you’re wondering, that value of 150.6 for TOAdjOff is 3rd in the country, behind Duke and Georgetown.  When the Spartans don’t turn it over, they’re among the best of the best.

Of course, a turnoverless team is a pipe dream; a more reasonable goal for the Spartans is to try to improve their TO% from abysmal to merely average.  This seems doable – the average MSU TO% over the last 8 years has been 21.4%, which is right in line with this year’s national average of 21.2%.  Using the same concept as above, but adjusting TO% to 21.2% instead of 0%, Michigan State ends up with an offensive efficiency of 118.7 (a gain of 6.5 over their current 112.2).  That would bump their offensive rank from 26th to 4th, and their Pomeroy ranking from 14th to 5th.  Couple that with what I can only assume would be a dip in opponent transition points, and they could rise even higher.

FREE THROWS

Michigan State is nearly as poor at free throw shooting as they are at preventing turnovers, but it’s not nearly as important because: A) a missed free throw only costs 1 point, while a wasted possession costs, as we saw above, 1.5 points; and B) there tends to be far fewer free throw attempts than possessions.

The Spartans have a 63.4 FT% so far, compared to a national average of 68.1%.  Over their 202 FTA, that amounts to a difference of 9.6 total points.  Working back from their number of possessions, that works out to 1.5 points per 100 possessions.

ALL TOGETHER NOW

If you add those 1.5 points onto the 6.5 gained from reducing turnovers, Michigan State’s offensive efficiency would rise to 120.2.  However, because the gap between the top 4 teams (Duke, Kansas, Ohio State, and Pittsburgh) and the rest of the field is so large, their overall ranking wouldn’t change.  Still, if Tom Izzo can tighten up (see also: tighten up) his leaky boat, he’ll have a good chance of floating down to Houston, come April.

How much will Kyrie Irving’s Absence affect Duke?

[EDIT: Ken Pomeroy posted a treatise on plus-minus which pointed out there’s a great deal of error inherent in the use of +/- data, something which has also been pointed out to me by John Ezekowitz.  At this point, I’d advise you to take everything I wrote below with a huge grain of salt.  And I doubt you’ll be seeing any more +/- analysis from me.]

There was a great post by the basketball distribution a few days ago about the effect of individual players on a team’s offense.  It ended up being extremely topical, given A) his choice of Duke as one of the example teams, and B) Kyrie Irving’s toe.  He calculated that removing Irving’s production from the Duke team would in theory reduce their offensive efficiency by about 3.1 points per 100 possessions, if the vacated minutes were filled proportionally by the other Duke players according to their share of minutes played so far, and if they all played at the same level.  (Obviously, those assumptions are questionable, but it’s the best that can be done with the available data.)

I thought I’d take a look at it from a different angle, one that is probably on even more tenuous footing, but that is nonetheless interesting.  I thought that perhaps, as a freshman, Irving might not be as advanced as his teammates defensively, so losing him might actually improve Duke’s defense.  The only way I could think of to investigate this was to use StatSheet’s plus-minus data to find out what has actually happened this year when Irving sits on the bench.  Before I get to the numbers, I should strongly emphasize that this data is very dependent on who all the other players in the game are, and we shouldn’t take it to seriously.  But I thought the results were drastic enough, and surprising enough, that they’d be of interest.

The Most Best On The Best [Or: Skillz]

[Apologies to Mrs. Rowland, Mrs. Akers, Mrs. Davis, and all my other English teachers for the mangled title.]

I wanted to do something to celebrate this week’s early Christmas present, the unveiling of tempo-free player stats on kenpom.com, but it’s too early, and the schedules have been too sweet, to draw many meaningful conclusion.  So I decided to do something kind of fun and interesting and statistics-oriented, but totally nonanalytic.  Something Stark-ish.  I went through Pomeroy’s top 25, and for each team, I found the one player who attained the highest ranking in one of the tempo-free stats.  I excluded % of minutes played, offensive rating, % of possessions used, and % of shots taken, because I was looking for specific skills, not overall quality.  For the most part, these turned out to not be the first player you think of when you think of a team, which is what I hoped.  There are some players below who have some potential, and need to build around a skill they already have, some that clearly are on the court only to do that one particular thing, and some who are just great at everything.

[For definitions of the stats below, see “PLAYER SECTION” here.]

1. KANSAS – Markieff Morris, 36.6 DR% (1st)

The Lesser of the Morrii gets the spotlight here.  His DR% last year was “only” 20.5% (152nd), after a 20.0% his freshman year, but both of those Kansas teams had Cole Aldrich in the middle, gobbling up opponent misses.  It stood to reason that his numbers would go up this year, but not this much.  They should drop back down to earth against Big XII opponents.

2. DUKE – Kyrie Irving, 69.6 TS% (36th)

Freshmen are not supposed to be this good.  They’re supposed to force bad shots, settle for jump shots too easily, maybe not practice free throws enough.  But Irving’s shooting percentages are 59%/45%/90% (2/3/FT), and he’s 147th in the country in drawing fouls.  And to top it off, he’s 87th in assist rate, at 31.2%.

3. OHIO STATE – Dallas Lauderdale, 16.9 Blk% (3rd)

Only 8 teams in the entire country block shots at a higher rate than Lauderdale does when he’s on the court.  And it’s a good thing he is so dominant, because the rest of team blocks basically nothing.  He has 69% of tOSU’s blocks, despite only playing 10.4% of the player-minutes.

4. PITTSBURGH – Talib Zanna, 17.0 OR% (24th)

The 2010 season was the first year since Pomeroy started tracking OR% that Pitt didn’t have a monster in the middle (DeJuan Blair 09/08, Aaron Gray 07/06, Chevon Troutman AND Chris Taft 05).  Though Zanna’s not at Blair’s level, he has helped propel the Panthers to 1st in the nation in team OR%.  Cue Radiohead.

5. WASHINGTON – Aziz N’Diaye, 19.8 OR% (7th)

All the 7-foot Senegalese N’Diaye does is block shots, rebound, and miss free throws.  Despite his impressive ranking in OR% and Blk% (35th), the number that practically leaps off the page when viewing his stat line is his 140.9 FTRate. If he had enough minutes to qualify, that would place him 2nd in the NCAA, after Memphis’s Wesley Witherspoon. Unfortunately, he only shoots 42% from the line.  Maybe somebody who watches more UW ball can fill me in – is he getting to the line because it’s so easy for him to receive a pass in close, and teams are forced to play tough defense?  Or is this the result of some Hack-A-Ziz defense?  Seems like the latter could work out pretty well.

6. ARIZONA – Derrick Williams, 75.9 TS% (4th)

Was there any doubt it wouldn’t be Williams listed here?  He’s also 6th in eFG%, 11th in OR%, 11th in FD/40, 47th in FTRate, has a shooting line of 65%/82%/79%, and is, in my book, the frontrunner for National POY.  His only weaknesses are a low assist rate (but really, why should he pass it?) and a high foul rate (5.1 per 40 minutes).  The latter has helped limit him to only 61.6% of possible minutes played, and his 5th foul in the Wildcats’ game against Kansas was the turning point.

7. ILLINOIS – Demetri McCamey, 42.7 ARate (12th)

Combined with his team-high 24.2 %Poss, this means that 66.9% of Illinois possessions (when he is on the floor) end with a McCamey shot, turnover, or assist.  Having not seen an Illinois game yet, I’m curious what happens to the team when he sits.  Do they drift, rudderless, in a sea of frustration?

8. KENTUCKY – Josh Harrellson, 17.5 OR% (21st)

Admittedly, Harrellson is a role player, using only 10.6% of possessions in his 57.9% of possible minutes.  But given that Kentucky is only 146th in the country in 2PFG% (48.7%), it’s a pretty important role.  (Outside of Harrellson, the next highest ranking for any UK player is 59th, for Terrence Jones’s 7.1 FD/40.)

9. WISCONSIN – Tim Jarmusz, 4.1 TORate (3rd)

Pomeroy notes that TORate “can be highly dependent on context.  Players that do little passing or dribbling (i.e. spot-up shooters) will have an artificially deflated TO%.”  Jarmusz has taken 27 shots this year.  24 of them are 3-pointers.  He’s only made 7 of them (29%).  Yuck.  (The next highest ranking for a Badger is Jon Leuer’s 113th in Blk%.)

10. PURDUE – JaJuan Johnson, 6.0 TORate (19th)

In sharp contrast to Wisconsin’s Jarmusz, Johnson is most definitely not a spot-up shooter.  Only 15 of his 116 FGAs have been 3’s, and he draws 5.8 fouls per 40min (244th).  Johnson and E’Twaun Moore have put Purdue on their backs this year, with each having >25 %Poss and >28 %Shots, while still ranking 2nd and 3rd on the team in ORtg.

11. GEORGETOWN – Julian Vaughn, 13.0 Blk% (13th)

Vaughn’s Blk% has nearly doubled from last season’s 7.0%, and his share of possessions has risen from 19.3% to 25.4%.  But that last increase might be bad for the Hoyas, as his ORtg is only 102.3 – only freshman Nate Lubick is lower, among players with %Min >20.

12. BAYLOR – A.J. Walton, 4.5 Stl% (67th)

This one isn’t quite fair, as LaceDarius Dunn would probably be listed for eFG%, TS%, or FD/40, if not for the fact he’s missed time due to suspension.  Walton doesn’t contribute much value beyond his steals; he might subtract value, as he has an ORtg of 93.1 while using a significant number of possessions (22.7%).

13. UNLV – Chace Stanback, 67.3 eFG% (37th)

UNLV has a difficult-to-guard weapon in Stanback, a 6’8” junior who is tall enough to compete down low, but can step out and hit a three (11 for 29 through 8 games).  The Rebels seem to know it – they let Stanback take the most shots when he’s on the flower (26.4%).

14. MICHIGAN ST – Draymond Green, 25.7 DR% (39th)

A poor man’s Derrick Williams, in terms of filling the tempo-free stats sheet: he ranks in the top 309 in everything except %Min, %Shots, TORate, and FC/40.  It’s surprising that he’s the team’s best defensive rebounder, despite being only 6’6”, but what’s more surprising is that he shoots nearly identical from 2, 3, and the FT line: 56%/54%/55% on 45/24/38 attempts.

15. VILLANOVA – Maalik Wayns, 37.0 ARate (32nd)

Can you dish out a ton of assists, avoid turning it over too much, and still have a negative effect on your team’s offense?  Apparently Wayns can.  Eyeballing the numbers, it’s almost certainly due to his 5 for 30 performance from long range.  Last year he only hit 32% on 54 attempts, so there’s no guarantee that will substantially improve.

16. BYU – Jackson Emery, 5.8 TORate (16th)

This was the battle of do-nothings, as Emery’s low TORate (the product of not doing much other than shooting on offense) edges out Jimmer Fredette’s extreme foul avoidance on defense (24th in FC/40 at 1.1).  Another spot-up jumper type (shoots over 7 threes per game), Emery also contributes a 53rd-ranked 4.7 Stl% on the other end.

17. SYRACUSE – Scoop Jardine, 43.9 ARate (9th)

True story: I turned on Syracuse-MSU last night, and the very first play I saw was Scoop Jardine making a beautiful pass to Rick Jackson for an easy dunk.  Guess I had about a 50/50 shot.  He’s Jason Kidd-esque, but that unfortunately includes the terrible shooting range, as he’s 13 of 47 (28%) on threes this year.

18. WEST VIRGINIA – John Flowers, 9.4 Blk% (48th)

If he were an inch shorter, at 6’6”, he’d be the top shot blocker for his height.  Unfortunately, the top swatter in the whole nation happens to be 6’7” William Mosley of Northwestern St, so Flowers is just another guy that’s good at defense.  He’s also a solid rebounder, and gets to the FT line enough that he’s a net positive on offense.

19. TENNESSEE – Brian Williams, 18.0 OR% (15th)

He’s mostly your typical clumsy giant – good at rebounding and blocking shots, doesn’t get very involved in the offense (17.3 %Poss), and bricks a lot of FTs (58% on the year).  But he’s cut his TORate by a third from last year, and ranks 292nd lowest.

20. SAN DIEGO ST – Kawhi Leonard, 26.9 DR% (24th)

The Aztecs’ MVP so far, Leonard’s 122.6 ORtg is 12th in the country among players with a usage rate of at least 26%.  As a freshman last year, he was a high-usage (24.6%), low-efficiency (106.5) player, but two things have changed this year.  He’s cut his turnovers in half, to 9.1% (79th), and he’s improved his 3PFG% from 21% to 38%.  The rebounding was already good, and hasn’t dipped this year.

21. LOUISVILLE – Terrence Jennings, 15.9 Blk% (4th)

According to the height/weight numbers for last year and this year on kenpom.com, Jennings lost 1 inch and 20 pounds in the offseason.  I'm not sure about the inch, but the getting rid of that 20 pounds seems to have given him some extra bounce – his block rate is up over 50%.

22. FLORIDA – Erving Walker, 1.4 FC/40 (64th)

Wow, this is a boring one.  Walker gets plenty of steals (3.4%, 263rd), so it doesn’t seem like his low fouls are due to a swinging door defensive philosophy.  Florida has the 4th lowest opponent FTRate in the country, so I’m going to give nearly all the credit to Florida’s use of zone defense.

23. VANDERBILT – Festus Ezeli, 21.3 OR% (2nd)

Festus Ezeli actually has a higher OR% than DR%, which is weird.  Maybe Festus Ezili is too busy blocking shots (9.6%, 42nd) to worry about grabbing rebounds.  Or maybe Festus Ezeli is getting his defensive boards stolen by equally-awesomely-named teammate Steve Tchiengang (pronounced ChainGang … at least when I say it).  Festus Ezeli.

24. KANSAS STATE – Freddy Asprilla, 15.7 OR% (46th)

Hmm, Asprilla has the exact same rebounding rate on offense and defense.  Maybe this is more common than I though.  The fact that Asprilla has enough minutes to qualify is probably a bad sign for the Wildcats, as he has an ORtg of only 92.9.  And commits 6.3 fouls per 40 minutes, oh dear.  Which probably means Frank Martin would like him to play even more.  This is funny: his 31% FT% is better than 3 other teammates.

25. TEXAS – Tristan Thompson, 108.8 FTRate (13th)

Getting to the line for more FTA than FGA is usually a good thing, since FT’s are usually the most efficient way to score.  But not when you shoot 48% from the FT line, and 53% from the field.  Thompson is just a freshman, so hopefully he will improve.  He’s already a slight positive on offense, even with so many missed opportunities.

Hype Clouds

I’m finding myself a little behind the curve this year, in terms of keeping abreast of this year’s college basketball story lines.  I blame moving cross-country, the beautiful San Diego weather, and the fact that the Kansas City Chiefs still have playoff hopes in November.

Oh, sure, I’ve seen the big headlines:
“Turkish guy ineligible!”
“Bruce Pearl hosts sweet backyard BBQ!”
“Hummel reveals secret identity: Glass Joe!”

But I’ve somehow avoided the usual talking-up of new recruits, revelations that a player spent all summer in the gym, and “in-depth” conference previews that name drop one or two players from each team.  As a result, I don’t know who I am supposed be sick of yet.

To remedy this problem, I thought it would be fun to (1) count how many times a player was name-dropped in various season previews, and (2) make a word cloud out of the results.  Accomplishing #1 was more difficult than I thought, at least if I wanted to do it for more than one or two teams.  So instead, I recorded the number of results found on Google News from October 17 to October 31, with a ton of assistance from Excel and URL Opener.  [I chose October 31 to avoid most exhibition game recaps, and October 17 because Google wouldn’t let me go back any further.  The player names I searched for were simply taken from ESPN.com’s team rosters.]  Then I massaged the numbers a bit (so that end of the bench players would actually be visible), popped them into Wordle, and voila.  Ladies and gentlemen, your Baylor Bears:



A couple of things to note:
1) Scott Drew’s name is ALL CAPS.  I did that with all the coaches, to differentiate them slightly.
2)  The absolute sizes of these names should not be compared to those on other teams.  Each team’s cloud gets scaled so that it is roughly the same size, so the main thing to notice here is how big the biggest names are, compared to the others on the same team.  It should give you a sense of how much of the hype surrounding a team is focused on a specific player or two (or a coach).

Wow, this is already educational for me: I had no idea who Perry Jones is.  A quick glance at the Bears’ roster shows he is a really tall freshman.

Let’s do it for the rest of the 35 teams that I found in at least one major preseason Top 25 ranking.  Clicking each thumbnail should take you to a higher resolution version, so you can read all the tiny names of the unheralded players.  Hopefully you can figure out the teams based on the players and coaches shown, but if not, they’re in alphabetical order, and the images themselves are named after the team.



















Finally, here is a combined cloud, made from all these 35 teams.  This is the one you should use if you’re trying to compare the hype of players on different teams.   (But, remember, hyped players on mediocre teams won’t be included here.)


Wow, that was time consuming, but fun.  Hopefully at the end of the year, I’ll remember to revisit this post, and compare the preseason hype with how much the players actually contributed to their teams.

[NOTE: Feel free to re-use one or two of these, as long as you credit me by linking back to this post.  But please don’t post the whole lot of them elsewhere.  Thanks.]

The Journey Counts

There are articles every year – some by me – that look at qualities that have defined past Final Four teams, champions, upset victims, etc.  These usually use end-of-year stats, because that’s what’s readily available for past teams.  But we obviously don’t have access to end-of-year stats when the tourney begins.  So I thought I’d take a look at how well pre-tourney efficiency numbers match up with the final values, to see how much a team’s performance in the postseason can change our historical perception of them.  I’ll be using Pomeroy’s data from the day after Selection Sunday – after the conference tournaments, but before the NCAA/NIT/CBI/CIT/WTF/ETC.

First, the Final Four teams. Remember, for the changes in defense and rank, negative is good:

Butler’s defense went from very good to elite over the course of 6 games, and I don’t think you’ll find anyone that would argue that the ratings bump wasn’t deserved – and obvious.  What might not have been so obvious was that Duke’s offense switched to a higher gear during the tournament, with their offensive efficiency improving almost as much as Butler’s D.  Michigan State’s offense improved, and their defense declined, and the result was a slightly more balanced team.  West Virginia was basically the opposite of Michigan State.

Now let’s look at aggregate stats for all the NCAA tournament teams (the only ones we really care about):

The average magnitude of change for both offensive and defensive efficiency is about 2/3 of a point, and about 5 ranking spots.  And some teams improved or declined by up to 35 spots in the rankings.  So be careful eliminating someone from your list of contenders just because they’re ranked 15th in something instead of 5th.

Finally, for reference, here are the changes for all teams in the tourney (hope this formats well):

CHANGES IN EFFICIENCY DURING NCAA TOURNAMENT
	BEFORE						AFTER						CHANGE
Team	Off	Rnk	Def	Rnk	Pyth	Rnk	Off	Rnk	Def	Rnk	Pyth	Rnk	Off	Rnk	Def	Rnk	Pyth	Rnk
Duke	121.5	1	85.9	4	0.982	1	123.5	1	85.9	4	0.985	1	2.0	0	0.0	0	0.003	0
Kansas	121.4	2	86.1	5	0.981	2	121.5	2	87.1	8	0.979	2	0.1	0	1.0	3	-0.002	0
Kentucky	115.5	18	87.7	10	0.960	6	116.1	15	86.3	6	0.968	3	0.6	-3	-1.3	-4	0.008	-3
Syracuse	117.9	9	89.1	20	0.962	5	118.0	8	89.0	18	0.963	4	0.2	-1	-0.1	-2	0.001	-1
Ohio St.	119.0	7	89.8	22	0.962	4	118.6	7	90.2	24	0.959	5	-0.3	0	0.4	2	-0.003	1
Baylor	119.6	5	92.7	52	0.949	12	120.4	3	91.7	34	0.958	6	0.9	-2	-1.0	-18	0.009	-6
Kansas St.	115.8	16	88.9	19	0.955	9	116.6	13	88.9	17	0.957	7	0.8	-3	0.1	-2	0.003	-2
West Virginia	117.5	11	90.0	24	0.955	8	117.0	11	89.4	22	0.957	8	-0.4	0	-0.6	-2	0.001	0
Wisconsin	116.5	13	87.3	7	0.965	3	115.6	17	89.1	19	0.952	9	-0.8	4	1.8	12	-0.012	6
Brigham Young	117.4	12	89.6	21	0.957	7	117.1	10	90.7	27	0.950	10	-0.3	-2	1.1	6	-0.008	3
Maryland	119.1	6	91.7	40	0.953	10	119.3	5	92.7	50	0.947	11	0.2	-1	1.0	10	-0.005	1
Butler	109.6	55	88.4	15	0.922	26	110.2	50	86.2	5	0.944	12	0.6	-5	-2.3	-10	0.023	-14
Georgetown	117.6	10	90.9	33	0.951	11	117.4	9	92.6	47	0.938	13	-0.2	-1	1.8	14	-0.012	2
Xavier	115.8	15	92.7	50	0.928	22	116.3	14	92.0	39	0.937	14	0.5	-1	-0.7	-11	0.009	-8
California	121.0	3	95.6	81	0.938	14	120.1	4	95.1	73	0.936	15	-0.9	1	-0.5	-8	-0.002	1
Purdue	109.8	49	86.4	6	0.940	13	108.3	70	85.8	3	0.936	16	-1.5	21	-0.6	-3	-0.004	3
Texas A&M	111.9	39	89.9	23	0.925	23	111.9	38	88.8	14	0.935	17	0.0	-1	-1.1	-9	0.010	-6
Texas	113.4	26	90.2	26	0.933	17	113.5	25	90.2	25	0.933	18	0.1	-1	0.0	-1	0.000	1
Missouri	109.8	50	87.8	12	0.928	21	111.3	43	88.8	13	0.931	19	1.5	-7	1.0	1	0.002	-2
Clemson	110.4	47	87.7	9	0.934	16	110.9	44	88.9	15	0.928	20	0.6	-3	1.2	6	-0.006	4
Villanova	118.7	8	93.9	62	0.937	15	116.6	12	94.0	62	0.923	21	-2.1	4	0.2	0	-0.014	6
Temple	107.5	77	85.7	3	0.931	18	107.8	75	87.0	7	0.922	22	0.3	-2	1.3	4	-0.009	4
Michigan St.	112.0	38	90.3	27	0.923	24	112.9	28	91.1	30	0.922	23	0.9	-10	0.8	3	-0.001	-1
Florida St.	105.2	119	83.9	1	0.931	19	104.6	130	84.5	1	0.921	24	-0.5	11	0.6	0	-0.010	5
Utah St.	116.4	14	93.0	54	0.930	20	115.6	18	93.7	58	0.918	25	-0.9	4	0.7	4	-0.012	5
Georgia Tech	109.5	57	88.7	17	0.919	27	109.1	62	88.6	12	0.917	27	-0.4	5	-0.1	-5	-0.002	0
Tennessee	106.2	99	87.3	8	0.904	35	108.9	64	88.5	11	0.916	28	2.8	-35	1.1	3	0.012	-7
Northern Iowa	107.3	81	87.9	13	0.909	32	109.3	61	88.9	16	0.915	29	1.9	-20	1.0	3	0.006	-3
Washington	112.6	32	91.5	38	0.916	29	112.2	36	91.3	31	0.915	30	-0.4	4	-0.2	-7	-0.001	1
Pittsburgh	111.7	41	90.9	34	0.915	30	111.5	40	90.7	26	0.915	31	-0.2	-1	-0.2	-8	0.000	1
Minnesota	114.1	23	92.1	43	0.922	25	112.9	29	92.1	40	0.912	32	-1.2	6	0.0	-3	-0.010	7
Marquette	114.3	22	92.6	48	0.919	28	114.6	22	93.6	57	0.911	33	0.3	0	1.0	9	-0.008	5
Old Dominion	107.9	72	88.5	16	0.907	33	107.5	82	88.4	10	0.904	34	-0.4	10	-0.1	-6	-0.003	1
Vanderbilt	114.0	25	94.1	64	0.901	36	113.7	24	93.8	60	0.901	35	-0.3	-1	-0.3	-4	0.000	-1
Texas El Paso	107.5	78	88.3	14	0.905	34	107.1	88	89.3	21	0.891	37	-0.4	10	1.0	7	-0.015	3
Notre Dame	119.8	4	99.3	140	0.897	38	118.8	6	99.1	132	0.890	38	-1.0	2	-0.2	-8	-0.007	0
Nevada Las Vegas	109.5	58	90.7	29	0.897	37	110.0	51	91.9	38	0.887	39	0.5	-7	1.2	9	-0.010	2
Oklahoma St.	112.2	37	94.0	63	0.885	44	112.7	31	94.2	63	0.887	40	0.5	-6	0.2	0	0.003	-4
San Diego St.	111.0	43	92.1	42	0.895	40	110.5	48	92.6	45	0.885	41	-0.4	5	0.5	3	-0.010	1
St. Mary's	114.9	19	95.7	82	0.891	43	114.9	20	96.3	88	0.884	42	-0.1	1	0.6	6	-0.008	-1
Louisville	114.9	20	95.3	77	0.896	39	113.8	23	95.4	79	0.884	43	-1.1	3	0.1	2	-0.012	4
Florida	112.3	34	94.9	70	0.874	49	112.6	32	94.9	67	0.877	45	0.3	-2	0.0	-3	0.004	-4
Richmond	108.4	67	91.2	36	0.879	48	108.8	67	91.9	37	0.875	48	0.5	0	0.7	1	-0.005	0
Murray St.	108.3	68	92.3	45	0.863	57	108.2	71	91.7	35	0.870	50	-0.1	3	-0.5	-10	0.007	-7
Cornell	113.3	28	99.2	139	0.822	66	115.9	16	98.5	117	0.867	52	2.6	-12	-0.7	-22	0.045	-14
New Mexico	114.3	21	96.0	87	0.882	47	113.1	27	96.4	90	0.863	54	-1.2	6	0.4	3	-0.019	7
Gonzaga	110.8	44	94.4	67	0.864	56	110.6	47	94.7	66	0.856	57	-0.2	3	0.4	-1	-0.008	1
Wake Forest	106.6	96	90.2	25	0.873	50	106.5	96	91.4	32	0.852	58	-0.1	0	1.3	7	-0.021	8
Siena	108.4	66	93.6	59	0.844	58	107.7	77	93.2	54	0.841	59	-0.7	11	-0.4	-5	-0.003	1
Wofford	102.8	149	93.1	56	0.756	87	102.9	148	92.2	41	0.779	79	0.1	-1	-0.9	-15	0.023	-8
Houston	111.7	40	101.0	165	0.762	85	111.5	41	101.3	174	0.750	89	-0.2	1	0.3	9	-0.012	4
Ohio	105.9	105	97.8	109	0.716	100	107.6	78	98.1	111	0.745	93	1.7	-27	0.3	2	0.029	-7
Sam Houston St.	109.6	56	101.6	178	0.706	102	108.5	68	100.2	154	0.715	101	-1.1	12	-1.4	-24	0.009	-1
Montana	105.7	110	98.4	121	0.696	105	105.2	119	98.2	114	0.686	107	-0.6	9	-0.2	-7	-0.010	2
New Mexico St.	110.5	46	104.1	222	0.665	115	110.6	46	104.1	224	0.669	113	0.1	0	-0.1	2	0.004	-2
Vermont	103.6	140	99.1	135	0.624	129	103.4	141	99.1	135	0.619	132	-0.2	1	0.0	0	-0.006	3
East Tennessee St.	99.0	196	96.1	88	0.583	142	99.4	195	96.5	93	0.583	142	0.4	-1	0.4	5	0.000	0
Oakland	106.2	98	103.1	212	0.584	141	105.5	110	103.1	214	0.567	146	-0.7	12	0.0	2	-0.017	5
UC Santa Barbara	98.6	209	96.9	99	0.551	152	98.2	219	96.7	95	0.544	154	-0.5	10	-0.2	-4	-0.007	2
Morgan St.	104.0	136	102.0	190	0.556	149	103.0	147	101.8	184	0.536	158	-1.0	11	-0.2	-6	-0.021	9
North Texas	102.0	157	101.7	180	0.509	169	102.2	159	101.9	187	0.509	168	0.3	2	0.3	7	0.000	-1
Lehigh	104.2	133	105.1	242	0.474	180	104.9	125	104.8	236	0.503	171	0.8	-8	-0.3	-6	0.029	-9
Robert Morris	98.9	200	100.3	154	0.458	188	98.8	204	99.2	140	0.488	176	-0.1	4	-1.1	-14	0.030	-12
Arkansas Pine Bluff	90.4	307	97.9	111	0.286	238	91.2	302	97.0	97	0.330	222	0.8	-5	-0.9	-14	0.044	-16
Winthrop	90.2	309	94.2	65	0.375	212	89.5	316	95.7	80	0.316	229	-0.7	7	1.4	15	-0.059	17