Final Four Predictions

The Prediction Machine did pretty well on the Sweet Sixteen games.  I think it would have missed many of the Elite Eight games, but I didn't actually run it so we'll never know.  For the first two games of the Final Four:

#1 Florida vs. #7 Connecticut:  Florida by 5.5

I think most people would agree that Connecticut is the weakest of the Final Four teams.  Florida meanwhile has been rolling along quietly taking care of business.  Short of an abnormal shooting night from one or both of the teams, I don't think UConn has much chance in this game.

#2 Wisconsin vs. #8 Kentucky:  Toss-up

Before watching the Kentucky-Michigan game, I thought Wisconsin was playing the best basketball of any of the contenders.  Now I'm not so sure.  Kentucky has been nearly unstoppable on offense throughout the Tournament, and the fabled freshmen have been impervious to the pressure.  Still, the Wildcats may be vulnerable if they get stymied enough on offense (as they did a couple of times this year against Florida), and Bo Ryan's team is certainly capable of applying the defensive pressure.  But even so, Wisconsin is going to have to be very efficient on the offensive end to stay even with the Wildcats.

Adventures in Data Cleansing

According to the ESPN play-by-play data, the American University vs. Penn State game on 12/21/2009 was a blowout -- Penn State won 914-629.

Imagine if it had gone to OT!

Machine Madness Competitors: Monte McNair

Next up in our tour of Machine Madness competitors is Monte McNair.  Monte was in this contest last year as well, under the nom de plume "Predict the Madness". 

Monte attended Princeton but is also a lifelong Stanford fan, so he is enjoying their current Tournament run.  As a UCLA fan I'll try not to hold that against him.  At least he isn't a Cal fan.  Monte blogs (infrequently) about sports at Outside the Hashes.  He also runs a site called Ultimate Bracket Challenge that's worth checking out and bookmarking for next year.

Last year he did a posting over on the Number Crunching Life where he talked about his approach.  He uses a logistic regression based upon the location of the game, metrics for the team's offense and defense, and metrics of the team's opponents' averages for both offense and defense.  Unlike some approaches (like mine) that produce a predicted point spread, Monte's approach produces a confidence number.  Monte finished in the middle of the pack last year but is doing much better this year.  He's currently in second in this contest, and is doing quite well over on Kaggle, where he's currently in eleventh.

Monte has Villanova-Florida-Arizona-Louisville as his Final Four, with Arizona winning it all.  The current leader has Florida for champion, so if Arizona wins it all Monte will likely jump into first and win this contest.

Sweet Sixteen Analysis

Jeff Fogle over on Stats Intelligence has a nice post up analyzing the Sweet Sixteen matchups.  Unlike most analysis you'll see, this is actually grounded in the team statistics instead of some pundits vague intuitions.

Unfortunately for me, Jeff comes to the same conclusion I did about UCLA's chances against Florida:  not very good.  UCLA did beat Arizona (a team very similar to Florida) in the final of the Pac-12 Tournament, but Arizona was a little tired for that game, and UCLA enjoyed a tremendous advantage on the free throw line.  You never know what the officiating will be like in the Tournament, but I'll be very surprised if UCLA ends up with a significant advantage in that category. 

Machine March Madness Competitors: Eric Akers

Our next profile is Eric Akers.

Eric is a software engineer at a small biotech company, so he fits my mental profile of a Machine Madness competitor.  He went to Kansas and his education has focused on robotics, computer vision, and some machine learning.  Needless to say he's a big Jayhawks fan.

Sorry about that Stanford game, Eric.

He's also passionate about baseball (KC Royals fan, naturally) and is contemplating trying to predict baseball games.  It's a slippery slope, this prediction business.  For outside hobbies he's building an ASV (autonomous seasurface vehicle) and is looking at build a quadcopter.

Like many of the competitors, he got hooked into this via Number Crunching Life. One of his work colleagues did some number crunching to try to get an edge in the office pool, Eric got intrigued, Googled around and found Number Crunching Life and got sucked in.  Looking at the competitors from last year I don't see Eric's name, so I think this is his first year competing.

His algorithm uses Danny Tarlow's probabilistic matrix factorization method using the 2D model with a vector for both offense and defense. Data was taken from the Kaggle site. Stochastic gradient decent was used to train the model, but with aging added. After an initial training, the teams were ranked based on the offense and defense vectors, then training continued using a higher learning rate based on the rank of the opponent being faced.

Eric also entered the Kaggle competition and was as high as #7 at one point on the first day, but has since dropped to #224.  If he'd taken the opposite of his predictions he'd be in 25th place :-).

Right now Eric is in 6th place in the Machine Madness competition.  Eric has a Virginia-Louisville final predicted, with Louisville winning it all.  If that happens he'll certainly jump upwards in the standings!

Machine Madness Competitors: Brandon Kling

I thought it would be interesting to get a little insight into some of the Machine Madness competitors, and I'm starting off with Brandon Kling.

I frankly expected all the competitors to be data mining/AI geeks, but Brandon is a Commercial Real Estate broker/investor from Bloomfield, Michigan.  He went to Walsh College of Business, a private school in the suburbs about 1 hour north of Detroit, and -- given that Walsh College doesn't seem to field a basketball team -- is a die-hard University of Michigan fan.  He also plays a little basketball and volleyball himself.

This is his first year entering any sort of automated/algorithm based prediction models. He stumbled upon the Machine March Madness pages while looking for the historical best ways to predict the NCAA tournament brackets and got sucked into doing his own completely automated bracket. 

His method was pretty straightforward:

1. Teams with the higher BPI (Basketball Power Index) advance (unless the difference between seeds playing each other is 3 or less, then see #2)
2. If difference in seeds is 3 or less (i.e. 10v7 or 2vs3 or 1vs1), then disregard BPI and instead advance the team with the lower OPPONENT AVG PPG
3. Championship Game points -  (winning score = Championship Game winner's AVG PPG)  (losing score= Championship Game winner's OPPONENT AVG PPG)

In one sense his algorithm was more advanced than mine -- I just randomly filled in the tie-breaker game points!

Sadly, Brandon's in last place in the pool.   However, he's the only competitor with Virginia winning the Championship, so if that happens he'll move up quite a bit.  And of course his beloved Wolverines are still in the Tournament (at least until they play Tennessee :-) so he has that to be happy about!

Sweet Sixteen Predictions

Thursday Games

#10 Stanford vs. #11 Dayton:   Stanford by 3

The Sweet Sixteen surprise match-up.  My prediction for this game prior to the tournament had Stanford by 4, so Dayton has closed the gap but Stanford is still favored.  My subjective judgement agrees.  (Dayton's biggest fan for this game?  Wojo.)

#2 Wisconsin vs. #6 Baylor:  Wisconsin by 4

The needle hasn't  moved at all on this game.  Baylor has surprised some with their performance, but Creighton was probably over-rated at a three seed.  And let's not discount Wisconsin's beat-down of American and handling of Oregon.  I know many think that Baylor could surprise Wisconsin, but my own expectation is that Wisconsin will win handily.  I think Baylor will quickly get disheartened by the Wisconsin defense.

#1 Florida vs. #4 UCLA:  Florida by 4.5

The machine predictors generally rate Florida as the best team in the country, and after their dismantling of Pittsburgh it's easy to see why.  Prior to 2005 UCLA had never played Florida.  Since then, they've had the misfortune to meet them four times in the Tournament -- every time during a year when Florida was at its best.   As a UCLA fan, I don't like the matchups with the Florida players, and you can't expect them to shoot 55% from the field as they did against Stephen F. Austin.

#1 Arizona vs. #4 SDSU:  Arizona by 8

It feels like both of these teams have been playing well, but they've actually just won their tournament games about as expected.   SDSU was lucky to face North Dakota State instead of Oklahoma, or they might not be in this game.  This should be a straightforward victory for Arizona.

Friday Games

#2 Michigan vs. #11 Tennessee:  Tennessee by 3

The Prediction Machine had Tennessee as one of the most mis-seeded teams this year, and that has certainly borne out.  They've actually played significantly above the predictions, so they're now a 3 point favorite in this game (they were a 1 point favorite before the tourney began).  Michigan has played about as expected.  Tennessee may revert to form, but either way Michigan is facing a tougher challenge here than you'd expect from the seeding.

#3 Iowa State vs. #7 Connecticut:  Iowa State by 3

The Prediction Machine doesn't consider injuries, so this line should probably be a little tighter.  These are both mediocre, inconsistent teams, so I won't be surprised if either team wins big or if it's a 3OT thriller.

#4 Louisville vs. #8 Kentucky:  Louisville by 6

This is the only sub-regional that has played out (so far, anyway) exactly as the PM predicted.   The Kentucky-Wichita State game was a tremendously fun game to watch, but let's not overstate the value of a two point win over a very over-seeded #1.  UK beat Louisville solidly early in the year, but that was on UK's home court.  On the other hand, six points isn't a lock.  It's certainly going to be a hard-fought game, and could be an instant classic.

#1 Virginia vs. #4 Michigan State:  Michigan State by 2

Michigan State has the slight edge here, but Virginia's defense should keep them in the game.  I'll be surprised if this is a blow-out either way.

A Look Back At Some Predictions

Previously on Net Prophet:

Courtesy of the Prediction Machine, here are the five most unpredictable teams in the Tournament:

1. Oklahoma State
2. North Dakota State
3. Harvard

ND State and Harvard certainly did unpredictably well.  OK State may have been unpredictably bad at the wrong time.

Over-seeded teams:
UMass!!  should be a 15, was a 6 (-9)
St. Louis should be a 10, was a 5 (-5)
Colorado should be a 13, was an 8 (-5)
St. Joseph's should be a 15, was a 10 (-5)
New Mexico State should be an "18", was a 13 (-5)
Syracuse should be a 7, was a 3 (-4)

 All of these teams are out.   Only Syracuse and St. Louis even made it to a second game.

Under-seeded teams:
Iowa should be a 4, was an 11 (+7)
Oklahoma St. should be a 2, was a 9 (+7)
Tennessee should be a 5, was an 11 (+6)
Ohio State should be a 2, was a 6 (+4)
Harvard should be a 9, was an 11 (+2) 

Tennessee and Harvard both out-performed their seeds.  Iowa had the misfortune to face Tennessee.  Oklahoma State and Ohio State both lost games they should have won, so the committee might have been right about them.

Three possibly first-round upsets:  Stanford over New Mexico, Providence over UNC, and Xavier over St. Louis. 

Stanford beat New Mexico, and UNC and St. Louis won by a total of 5 points.

Machine March Madness Update

The Machine March Madness competition ended up with 9 competitors.  After 32 games, the current leader is "T.D." with a fairly commanding 9 point lead over perennial competitor Monte McNair.  The winner of the bracket may be determined by the final game, where TD has Florida and Monte has Arizona.  Mark & I (in third place) will be overshadowed by TD and Monte, but if Louisville wins the championship then Tim (currently in fifth place) may pick up enough points to win it all.

Here's a quick summary of the Final Four and Champion predictions from the machines:

Team	Final Four	Champion
#1 Florida	6	3
#1 Arizona	7	2
#4 Louisville	7	2
#1 Virginia	2	1
#2 Villanova	4	0
#4 Michigan State	2	0
#2 Kansas	2	0
#2 Wisconsin	2	0
#1 Wichita State	2	0
#3 Syracuse	1	0
#3 UNC	1	0

Florida-Arizona is the clear favorite for the championship game.  The predictors also seem to agree that the committee under-estimated Louisville and Michigan State, while over-estimating Wichita State and Virginia.

More to come from the competitors in the next few days.

Prediction Recap & Kaggle Contest

Albany vs. Mt. St. Mary's:  Albany by 1.5
NC State vs. Xavier:  Xavier by 4
Cal Poly vs. Texas Southern:  Cal Poly by 2.5
Iowa vs. Tennessee:  Iowa by 2.5

The PM goes 2-2 in the "First Four" which is probably no better than chance.  Interestingly, it got both the 16 seed play-in games correct.   NC State outplayed Xavier, but the Iowa-Tennessee game was more competitive and could have gone either way.

I'm in Vegas with a group of friends to watch the first-round games, so posting will be light, but here's an interesting graphic showing the spread of predictions amongst the Kaggle competitors:

This is a little non-intuitive, but if the caption says "Albany beats Florida" then having the histogram to the left indicates that the predictors don't believe in that hypothesis (and vice versa).

First Four Predictions

The Prediction Machine on the First Four:

Albany vs. Mt. St. Mary's:  Albany by 1.5
NC State vs. Xavier:  Xavier by 4
Cal Poly vs. Texas Southern:  Cal Poly by 2.5
Iowa vs. Tennessee:  Iowa by 2.5

More Grist For Your Tournament Picks

Pando did an article on machines predicting the Tournament.  Quotes from many of my favorite writers on the topic, including my most favorite writer -- me.

The Harvard Sports Analysis Collective put together a nice collection of random Tournament facts, such as:

24. Coming in at the 94th most efficient offense and the 36th most efficient defense in the country, UMASS is statistically the worst 6 seed by a wide margin.

I don't put much stock in those sorts of factoids, but it's an entertaining read nonetheless.

Courtesy of the Prediction Machine, here are the five most unpredictable teams in the Tournament:

1. Oklahoma State
2. North Dakota State
3. Harvard
4. Memphis
5. Massachusetts

 I suspect that much of Oklahoma State's unpredictability stems from the temporary loss of Marcus Smart, so you might want to discount that.  But if you're counting on (say) Harvard to play a great game and beat Cincinnati in the first round upset, you might take this as a positive sign ("They're an inconsistent team, so they have a chance to play over their heads!") or as a negative sign ("They're too inconsistent to count on to pull off the upset!").  In the past, the Prediction Machine has used these numbers to fairly good effect.

Three possibly first-round upsets:  Stanford over New Mexico, Providence over UNC, and Xavier over St. Louis.

Mis-Seedings in the NCAA Tournament

Here's some of the overseeded/underseeded teams, at least according to one analysis by the Prediction Machine.  There's a lot of mis-seeding in the lower seeds because most of the automatic invites down there wouldn't even be 16 seeds in the PM's book, but I'm ignoring those.

UMass!!  should be a 15, was a 6 (-9)
St. Louis should be a 10, was a 5 (-5)
Colorado should be a 13, was an 8 (-5)
St. Joseph's should be a 15, was a 10 (-5)
New Mexico State should be an "18", was a 13 (-5)
Syracuse should be a 7, was a 3 (-4)

On the other end of the stick:

Iowa should be a 4, was an 11 (+7)
Oklahoma St. should be a 2, was a 9 (+7)
Tennessee should be a 5, was an 11 (+6)
Ohio State should be a 2, was a 6 (+4)
Harvard should be a 9, was an 11 (+2) 

The Iowa/Tennessee mis-rating is particularly interesting.  The two most underseeded teams play each other, and then the winner gets UMass, the most overseeded team in the tournament.  Similarly we get underseeded tOSU against overseeded Syracuse in the second round.  Of course, the most egregious mistake might be Louisville as a 4 seed.  And overseeded St. Louis gets them in the second round.

I'm tempted to say that the Committee was intentionally stacking the seeding to create "upsets".

Here are the teams that the Committee got right:

Florida
Arizona
Villanova
Creighton
UCLA
Cincinnati
Oregon
Stanford
Xavier

For whatever reason, the Committee seems to have had the clearest vision of the Pac-12 teams.

UCLA (my team) got a pretty favorable draw for the first two rounds.  Then they run into their old nemesis Florida, a match-up I'm sure the Committee considered when laying out the regions.

Flattening & the Kaggle Contest

Jeff Fogle at Stat Intelligence has another good post up, this time arguing that in the post-season (the NCAA Tournament in the case of college basketball), differences between teams condense.  By his argument, once the best teams are playing each other, they're more able to reduce any differences in strength between the teams.  And one implication of this is that handicapping (prediction) that works for the regular season won't work as well for the post-season.

Putting aside for the moment whether I buy this, how would we test this idea?

One of the big problems with any sort of assertion about the post-season is that it's very difficult to test, simply because of the small sample size.  College basketball is actually the best candidate amongst the major sports, because you have 67 games a year in the post-season -- and arguably more if you're willing to include the NIT and the conference tournaments.  In contrast, the NFL has only 11 games a year in the post-season.

But even for college basketball, 67 games per year is just not that big a sample size.  With five years of data, you still have less than 350 games for testing purposes.  (And give how the rules change in college basketball, going back more than 5 years or so runs the risk of comparing apples to oranges.)  And if we're looking specifically at Jeff Fogle's hypothesis about the best teams playing each other, it isn't entirely clear that some of these games would count -- is a #1 playing a #16 more like a regular season game, or more like a playoff game?

I'm not going to work out the math, but with 350 games in our test set and the known high variance in college basketball, any difference you found between Tournament games and regular season games would have to be huge to significant.

Several other factors make it difficult to assess the difference between the Tournament and the regular season.

One is that the Tournament games are played on neutral courts with mixed officiating crews.  That might well be the cause for any difference we saw between regular season and Tournament games.

Another is that (by necessity) we have to try to predict Tournament games based upon regular season performance.  That will make it more difficult to discern any qualitative difference between regular season and Tournament games.

All that said, in my own experience I haven't identified a qualitative difference between regular season games and Tournament games.  (Or, for that matter, between conference and non-conference games.)  Specifically, if I build a predictor based upon regular season games and a predictor based upon only Tournament games, I find that the regular season version is still the better predictor of Tournament games.  But given the small sample size for building the predictor based on Tournament games, I don't place a lot of confidence in that result.

(I will caveat that preceding paragraph slightly:  Tournament games have different home court advantage numbers in my predictor, but I ascribe that difference to the fact that they're played on a neutral court.)

Interestingly, the Kaggle competition will provide something of an empirical test of this thesis.  Judging by the Phase 1 leaderboard, there are a number of competitors who are specializing their predictors for good performance on the past five Tournaments.  If these predictors generally out-perform the predictors that are optimized for all games (or for regular-season games) it could be taken as some level of evidence that there really are fundamental differences that a predictor can exploit.  (Or not; again, small sample size.)  But at any rate I'm quite interested in seeing the results.

Basketball Power Index

Jeff Fogle over at Stat Intelligence has a new posting deriding ESPN's congratulatory self-coverage of the Basketball Power Index.  I won't comment much on what he says other than to note that as usual he's right on the mark with his criticism.

What I don't like about BPI that he doesn't mention is its "secret sauce" formulation.  Nobody except the stats gurus at ESPN know exactly what the formula is for BPI.  If you chase around the ESPN links trying to find a definition for BPI, you get to this page, which provides this sort of "explanation":

There are a number of small details that we have in our methodology to make it reflective of a résumé for a tournament team -- these are pretty technical and many people won't be interested, so we won't go into detail, but we think they improve how the tool works.

There's no way to check how BPI is calculated, whether all the small details are being applied consistently, whether ESPN is tweaking it weekly to inflate its performance, or to compare it to other methodologies.  (I have the same complaint about Ken Pomeroy, who is similarly vague about his actual calculations.)

Obviously, these folks have every right to keep their ratings formulas secret.  And by all means compare your rating performance to other ratings.  But to my mind, you're not a leader in sports rating systems if you're not willing to expose the details of your rating system and let others test and criticize it.

Top Twenty (3/10) and Predictions

1	Louisville		31.10		0.65%
2	Arizona		30.18		-0.46%
3	Iowa		29.88		-0.57%
4	Duke		29.72		-0.47%
5	Oklahoma St.		29.69		-0.13%
6	Michigan		29.58		0.61%
7	Creighton		29.44		-0.37%
8	Villanova		29.38		0.20%
9	Ohio St.		29.24		-0.03%
10	Florida		29.14		0.83%
11	Michigan St.		29.09		0.28%
12	Kansas		29.01		0.21%
13	Kentucky		28.90		-0.52%
14	Iowa St.		28.80		-0.24%
15	UCLA		28.71		-0.73%
16	Cincinnati		28.62		-0.07%
17	Wisconsin		28.60		-0.35%
18	Gonzaga		28.53		NEW
19	Arkansas		28.49		-0.21%
20	Arizona St.		28.47		-0.32%

The regular season Top Twenty ends with Louisville atop the leaderboard after strong showings against SMU and Connecticut this week.  But the biggest winner was Florida, who jumped four spots after crushing South Carolina and Kentucky.  UCLA took the biggest hit following a bad loss at Washington State.

It's interesting to compare the computer rankings to the AP ranking.  Wichita State is probably going to get a #1 seed for the tournament, so this could be the year that a #16 beats a #1.  And Oklahoma State might not get an invite despite being the in the top twenty of most computer polls.

PREDICTIONS

It's conference tournament week, so the marquee matchups won't happen till later in the week and aren't scheduled yet.  Only a couple of games currently stand out:

Notre Dame vs. Wake Forest (Notre Dame by 1)

An early-round ACC matchup between two teams that are statistically nearly identical.

Utah State vs. Colorado State (CSU by 2)

These two teams are the Notre Dame - Wake Forest equivalents in the Mountain West.

Indiana vs. Illinois (Indiana by 3.5)

These two teams are the Notre Dame - Wake Forest equivalents in the B1G.  They split their previous two meetings (although the Illini needed OT to win in Urbana) so this is the rubber match.

Maryland vs. FSU (Maryland by 3)

After surprising UVa, Maryland gets one more chance at a win on its way out of the ACC.  FSU needs to get on a roll in the ACC Tournament to have a shot at getting into the NCAA Tournament.

Predictions (3/4) Recap

#16 Iowa State @ Baylor:  Baylor by 1 (Baylor by 13)

#11 Louisville @ #18 SMU:  Louisville by 3 (Louisville by 13)

#10 SDSU @ UNLV:  SDSU by 2.5 (SDSU by 9)
#6 Villanova @ Xavier:  Villanova by 6  (Villanova by 7) 

#20 Memphis @ #15 Cincinnati:  Cincinnati by 12.5 (Cincy by 13)
#24 Iowa @ #22 MSU:  MSU by 4 (MSU by 10)
#25 Kentucky @ #1 Florida: Florida by 9 (Florida by 19)
#14 UNC @ #4 Duke: Duke by 12.5 (Duke by 12)
#21 New Mexico @ #10 SDSU:  SDSU by 5 (SDSU by 3)
#19 Connecticut @ #11 Louisville:  Louisville by 14 (Louisville by 33)
Oklahoma State @ #16 Iowa State:  Iowa State by 5 (Iowa State by 4 in OT)

#18 SMU @ #20 Memphis: Memphis by 3.5 (Memphis by 9)

The Prediction Machine goes 12-0 in the final week of the regular season, if not always close on the point spread.  Peaking just in time for the Tournament :-)

Machine March Madness 2014

Obviously the big news this year (in the area of machine prediction of the NCAA Tournament, anyway) is the Kaggle competition for $15,000.  Oh, and there's the Quicken Loans competition for a $1 Billion.  But if that's not enough to keep you busy this March, I'm pleased to announce the continuation of the Machine March Madness competitions.  There's no money at stake, but this is the longest-running machine prediction competition, and let's face it -- if you're going to enter the Kaggle competition you might as well enter Machine March Madness, too.  It's not much more work :-).

 (My thanks to Danny and Lee for letting me keep the competition running.)

The rules are very informal.  Your predictions must be based on a computer algorithm, but you can implement some parts manually as long as they're objective.  For example, your method might include "Take the team with the higher Sagarin rating" which you just handled manually, but please limit these steps and avoid just using your subjective judgement.  You can use any data you can find, including human-generated rankings like the AP poll.

The competition will be run as a Yahoo! Pool called "Machine March Madness" which you can find here.  Scoring will be Fibonacci -- 2-3-5-8-13-21 -- which will make the competition a little bit less dependent on the final round(s) than the traditional scoring.  To get the password to join the pool, email me (srt19170@gmail.com) with the name of your entry and a short description of your approach.  Also, please join the Google Group for announcements and discussion.

Useful data can found in a couple of places.  First, at the Kaggle competition data page.  Secondly, you can look in this Google Group thread from last year for some pointer's to last year's data.  Finally, I have fairly extensive data and will make it available as needed -- email me (or post in the Google Group) what you'd like to see.

Danny Tarlow's starter code from past years can be found here.  A short tutorial I wrote on using RapidMiner to predict games can be found here.  Finally, there have been several useful postings on ratings systems and predictions in the Kaggle forum. 

Tool Posting: Slime and Swank Versions Differ in Emacs

(Another tool posting.  Apologies to the pure basketball types, but this problem and its fix are impossible to find on the Internets so I wanted to document it for others.)

If you use the Superior Lisp Interaction Mode for Emacs (SLIME), you may get an error when starting up Slime that says "Slime and Swank versions differ, continue?"  Answering yes to the prompt usually continues without any problems, but it's still annoying.

The bug is caused by having multiple copies of Slime on your machine.  To fix it, you need to find and remove all older versions of Slime.  (Searching your file system for "slime.el" is a simple way to find all the installed copies of Slime.)  You might also need to clean up your .emacs file to not load (or include on the load-path) those old versions.

However, the problem will persist until the "slime.el" file is recompiled.  To force this, find the "slime.elc" file in the remaining installation of Slime and remove it.  The next time you start Slime in a new Emacs the file will be recompiled and the error should go away.

Top Twenty (3/4) and Predictions

1	NC	Louisville		30.90		-0.48%
2	(+1)	Arizona		30.32		0.43%
3	(-1)	Iowa		30.05		-0.86%
4	NC	Duke		29.86		-0.07%
5	NC	Oklahoma St.		29.73		0.27%
6	NC	Creighton		29.55		-0.27%
7	NC	Michigan		29.40		-0.20%
8	(+1)	Villanova		29.32		0.24%
9	(-1)	Ohio St.		29.25		-0.48%
10	NC	Kentucky		29.05		-0.51%
11	NC	Michigan St.		29.01		-0.38%
12	(+1)	Kansas		28.95		0.00%
13	(-1)	UCLA		28.92		-0.45%
14	NC	Florida		28.90		0.07%
15	NC	Iowa St.		28.87		0.03%
16	NC	Wisconsin		28.70		0.07%
17	NC	Cincinnati		28.64		-0.07%
18	NC	Connecticut		28.58		-0.10%
19	NEW	Arizona St.		28.56		NA
20	NEW	Arkansas		28.55		NA

The big loser this week is Iowa, who drops nearly an entire percentage point thanks to three straight losses.  The big winner is Arizona, who continues to roll through the Pac-12.  Arizona State and Arkansas climb back into the Top Twenty, thanks in part to Syracuse and Pittsburgh looking mortal.

PREDICTIONS

The season is ending with a bang, as this week (and particularly next Saturday) has a large number of marquee matchups.

#16 Iowa State @ Baylor:  Baylor by 1

Baylor probably needs a few more wins to get into the Tournament, this is a good opportunity.

#11 Louisville @ #18 SMU:  Louisville by 3

The PM continues to love Louisville despite its unappealing record against the top twenty.

#10 SDSU @ UNLV:  SDSU by 2.5

A possible stumbling block for SDSU.

#6 Villanova @ Xavier:  Villanova by 6

The Cintas Center has been something of a graveyard for visiting ranked teams (as Creighton can testify), but Xavier also does things like lose to Seton Hall, so overall I still expect Villanova to win this game.

#20 Memphis @ #15 Cincinnati:  Cincinnati by 12.5

A surprisingly large number in this game, but Cincinnati is probably better than the AP thinks.

#24 Iowa @ #22 MSU:  MSU by 4

More B10 on B10 thrashing.

#25 Kentucky @ #1 Florida: Florida by 9

Kentucky has struggled for the last half of February and into March, and this is going to be another frustrating game, I think.

#14 UNC @ #4 Duke: Duke by 12.5

The PM is unimpressed by UNC's recent wins.

#21 New Mexico @ #10 SDSU:  SDSU by 5

Comparing this game to the UNLV game shows the power of the HCA.

#19 Connecticut @ #11 Louisville:  Louisville by 14

Should be an easy win for Pitino's boys.

Oklahoma State @ #16 Iowa State:  Iowa State by 5

There's some talk that OK State won't get into the Tournament without a few more wins.  Crazy talk in my opinion, but what do I know.  A win at Iowa State would probably clinch a spot, but that's going to be a tough road.

#18 SMU @ #20 Memphis: Memphis by 3.5

Both teams are probably a lock for the Tournament at this point, and might not have much to play for in this game.

Prediction Recap (2/24)

#1 Syracuse @ Maryland:  Syracuse by 1
#1 Syracuse @ #14 Virginia:  Virginia by 2

Syracuse is barely favored at mediocre Maryland, which could lead to the spectacle of having the #1 team lose four straight games. 

Syracuse pulls out a 2 point win at Maryland, so we don't get the spectacle of four straight losses, but they do lose to Virginia by 19 (!) points.

 

Virginia Tech @ #5 Duke:  Duke by 31

VT loses by "only" 18.

 

#20 Michigan @ Purdue:  Michigan by 3

A good chance for Purdue to steal a meaningless but satisfying upset.

Purdue loses by 1 point in OT -- but as Feinstein would say, that's what bad teams do.

#7 Cincinnati @ #21 Connecticut:  UConn by 6

UConn by ... 6.

 

#8 Kansas @ Oklahoma State: OKSt by 5

The PM may be the last believer in OK State (although they had a good win this week against Texas Tech).  If the PM is to be believed, they have a good chance to beat Kansas next Saturday.

Yes indeed, you mockers!  OKState wins by 7.

 

#11 Louisville @ #22 Memphis: Louisville by 3

A good illustration of how powerful the HCA is in college basketball. 

Memphis wins by 6 in a game that Louisville led by 8 points or so with just a few minutes to go.  Louisville is now 1-4 against ranked teams this season, which can't be a good sign.

 

#15 Iowa @ Minnesota: Iowa by 4
#11 Creighton @ Xavier:  Creighton by 3.5
#17 Kansas State @ Iowa State:  KSU by 4
#19 Texas @ Oklahoma: Oklahoma by 12
#25 Gonzaga @ St. Mary's:  SMU by 2

We could easily see all the ranked teams lose. 

Five ranked teams losing in the same weekend?  Are you crazy?

Iowa lost, Creighton lost,  Kansas State lost, and Texas lost.  (Gonzaga won fairly easily.) 

Crazy like a fox.  :-)