Thursday, November 30, 2006

Another Interview with Mike Gillian

It’s a little late, but here’s the annual interview with Longwood head coach Mike Gillian. One of the guys I root for, because, well, he reads my web site. Previous interviews from 2004 and 2005 are available for a little background. In Year 2 of Longwood’s Division I odyssey, the Lancers posted a record of 10-20 after the memorable 1-30 season from Year 1. Without further ado, here is another informative chat with the fourth-year head coach…

Before we talk about Longwood, I have to give you some space here to comment on George Mason’s run last season. You helped recruit and coach Jai Lewis and Lamar Butler as freshmen. You worked for six years under Jim Larranaga. How much did you enjoy watching that team advance to Indy?

Watching Coach Larranaga, the George Mason team, and that whole university community go through their NCAA Tournament run was just incredible.  Bill Courtney (now with Dave Leitao at Virginia) and I were there with Coach L for a long time and we always believed that the players we were recruiting were capable of eventually making a run deep into the Tournament.  You had asked about the GMU memories when we first did this two years ago, and the first round tournament game we played in 2001 against Maryland. We thought that GMU team had the ability to do exactly what last year’s team ended up doing. 

This is where Coach L deserves all of the accolades that have been coming his way over the last 7 or 8 months.  Everyone should understand that this was no fluke George Mason did what they did; the groundwork for an accomplishment of this magnitude had been laid over the course of a long period of time.  Bill Courtney and I did a lot of work together to put together the players that made up a good bit of that Final Four team; Lamar Butler, Jai Lewis, and Tony Skinn in particular.  It’s very gratifying to have seen them accomplish what they did and, more importantly, to show the appreciation they did to all of those who helped them make their Final Four run.

Let me digress here for just a moment and plug the guy who is my best friend in coaching, Bill Courtney.  I feel compelled to do this whenever I get a chance because one day very soon he is going to get an opportunity to be a head coach at this level and, when he does, he’s going to make some athletic director very happy.  After I had left Mason to come down here to Longwood, BC recruited a couple of guys very hard, against high profile programs, and those guys turned out to be the two other stalwarts of last year’s Final 4 team; Folarin Campbell and Will Thomas.  Now that he is at Virginia look for him to have an immediate, very positive impact on all the areas of the Cavalier program.

On a related matter, a big story from the off-season was the push to expand the tournament field. Almost all of the opinions I’ve heard have come from coaches in the multi-bid conferences. What’s your opinion on tournament expansion?

Tournament expansion is a good topic for discussion.  I believe that once something like that comes to the table the way it did, as you referenced, it doesn’t just fade away into the sunset.  Everyone in college basketball was thrilled with George Mason’s accomplishments last year. How could you not be?  Some of those leagues that had members left out because teams like Mason were included are not against including Mason, they just want to be in too.

That being said I think it is just a matter of time before the powers that be eliminate the post-season NIT and just put on one big tournament that includes all of the qualified schools.  This leads to a whole other series of questions that need to be addressed; how many schools should it be, what about expansion for the women’s field as well, what about TV, missed class time, effects on conference tournaments, and then what do you do with the one school from a high profile conference that gets left out because Longwood deserves an at-large bid in the year 2014 - expand the tournament?

There are a few people that believe the tournament at the end of the year should include every Division I program.  I don’t think people realize this (I am fairly certain it is accurate but if not I will gladly be corrected) but the foremost among them is none other than John Wooden.  Who is going to argue with an idea from him, certainly not me!

OK, on to the Lancers. Your program provides a good test for the stats page, because I have to confess to having never seen you play, though I have listened to portions of games thanks to the free radio feed provided on the web site. But just judging from the numbers, the Lancers appear to be aggressive on defense, probably playing almost exclusively man, and gambling more than most. How close am I?

Your assessment of our playing style is fairly accurate.  We are very aggressive defensively and we do play almost exclusively man to man defense.  We will mix it up with a few different trapping defenses and full court defenses as well.

The one thing you are a little off on is in assuming that we may gamble more than most.  It is actually quite the opposite, we reinforce constantly that we don’t want to be gambling on defense, and gambling is exactly the term we use for it.  We equate gambling with “false hustle”, plays that compromise the defense more often than not.  In actuality, what we are trying to do is force the other team into making mistakes with the ball because of the pressure, and putting it in places that we are anticipating, so a play can be made on the ball without having to gamble.

In playing this style we are also trying to get teams to play a little quicker than they are accustomed to on offense and take shots that are out of their normal rhythm.  That all sounds good and works well, until you go up against a team like Villanova last year that is just capable of picking the defense apart patiently.  Fortunately, we are developing some cohesiveness on offense, too, so when those nights happen we are able to keep up by playing well on offense.

In reflecting on the team’s improvement last season, most of it was due to a better offense, which showed an overall improvement, but the area where you improved most was in getting to the free throw line. Last year in this space you expressed a desire to get better in that area, and the improvement was one of the best in the country in ‘06. Some of that was due to playing from ahead more often, but even if we accounted for that, we would still see some serious improvement in getting to the line in the regular flow of the offense. Was there anything special that you stressed to make this happen, or was it just the fact that the offense improved in general making your team harder to guard?

In response to us being better offensively—Our team really began to play well together and trust each other offensively a lot more as we got toward the end of last season.  One of the big factors in that for me was cutting down on the turnovers a bit.  I think that was accomplished in large part by our players understanding the “sequencing” that exists in our offensive scheme much better and having a better feel for where and when their shots were going to come.

Once this happened we got more shots at the basket (logically), our shot selection was better and our shooting percentage went up (logically).  As these things begin to occur you are naturally going to get to the foul line a little more by attacking the basket.  You also are giving yourself a chance to play from out front more often and as a result of this you will get to the foul line a little more often.  Not to be overlooked in all of this is the fact that when your good offensive rebounders can understand where and when their teammates shots are coming, they have a great chance (regardless of size) to get to the offensive glass - which adds to your scoring and free throw opportunities.  Kirk Williams and Clayton Morgan were way up there for much of last season in the Pomeroy stats for offensive rebounding efficiency as a result of these factors.

Let’s stick with the Pomeroy stats for a moment because two things don’t lie, videotape and numbers.  Not that they give you all the answers, but when you look inside the numbers here is what I see.  If you like to play at a high tempo like we do, when you cut your turnovers and get more shots at the basket and run offense better and get to the foul line some more; your offensive efficiency will go up.  Even if your defense remains exactly the same and your offensive efficiency improves, your team will be better and you will have a chance to win more games.

What the stats you compile do for us at Longwood is give us a tool to show the players what we are doing is working, and if they will continue to be diligent in their efforts to trust each other and the scheme - we’ll win.  Make no mistake about it, every coach wants to win.

Thanks, Coach! We look forward to your appearance in the 2014 tournament field.

Wednesday, November 29, 2006

Ratings Explanation

The first thing you should know about this system is that it is designed to be purely predictive. If you’re looking for a system that rates teams on how “good” their season has been, you’ve come to the wrong place. There are enough systems out there that rank teams based on what is “good” by just about any definition you can think of. So I’d encourage you to google college basketball ratings or even try the opinion polls for something that is more your style.

The purpose of this system is to show how strong a team would be if it played tonight, independent of injuries or emotional factors. Since nobody can see every team play all (or even most) of their games, this system is designed to give you a snapshot of a team’s current level of play.

This season, I scrapped the old A-B=C power ratings and went to something that appears a little more complex. It is a little more complex, but it also has the advantage of being based on basketball things. The old system I used wasn’t special for hoops. It could be applied to any sport where a score is kept. Be it the NHL, college lacrosse, or grandma’s bridge league. But now we have the technology to do better. Besides, there are plenty of other power ratings of the old style out there, if that’s what you really prefer. I don’t really do this to imitate what everyone else does.

I would describe the philosophy of the system as this: it looks at who a team has beaten and how they have beaten them. Same thing on the losses, also. Yes, it values a 20 point win more than a 5 point win. It likes a team that loses a lot of close games against strong opposition more than one that wins a lot of close games against weak opposition.

The core of the system is the pythagorean calculation for expected winning percentage. In previous experiments, I found the best exponent for college basketball was between 8 and 9. But for whatever reason, when using adjusted efficiencies, the best exponent is between 11 and 12, probably because previous experiments only included conference games. I am using 11.5 as the exponent.

How did I determine the best exponent? I applied the log5 formula to every game last season and found the exponent with the best fit for expected winning percentages. (A problem here is that I applied the final ratings retroactively to the last season’s results, so it’s a little high for predictive purposes. This will be revisited eventually.) You can get an idea of the chance one team beats another by applying the log5 formula to the two teams’ pythagorean rating. There is a home court advantage consideration, also. More on that, later.

The inputs into the pythagorean equation are the team’s adjusted offensive and defensive efficiencies. Any time you see something “adjusted” on this site, it refers to how a team would perform against average competition at a neutral site. For instance, a team’s offensive efficiency (points scored per 100 possessions) is adjusted for the strength of the opposing defenses played. I compute an adjusted offensive efficiency for each game by multiplying the team’s raw offensive efficiency by the national average efficiency and dividing by the opponent’s adjusted defensive efficiency. The adjusted game efficiencies are then averaged (with more weighting to recent games) to produce the final adjusted offensive efficiency.

While the pythagorean winning percentage is calibrated to the likelihood of winning, the efficiencies are based purely on scoring per possession with no consideration of winning or losing. This allows us to get both a chance of winning and a predicted final score with the system, and makes the system much more predictive than if we ignored scoring margin. It also has the advantage of giving a rating in offensive and defensive terms, and an SOS in those terms, as well. Want to know which team has faced the toughest defenses? Well, with my system you can.

Now let’s do this in Q&A form based on e-mail I’ve received.

How do you cap margin of victory?

This is the most obvious problem with the system - there is no cap on margin of victory. It’s not that I’m particularly comfortable with it, but I’ve looked at quite a few ways to limit the impact of MOV, and I haven’t found one that I like, yet. I’ll find something someday, but until then we have to deal with things like Georgia being ranked 11th and Oklahoma being ranked 17th at this point (12/10/06) in the season. More games will push these teams to their rightful location.

How do you incorporate home court advantage?

I add 1.4% to the home team’s OE and visiting team’s DE, and subtract the same amount from the opposite parameters.

What do all the columns mean?

The new ones are Cons (Consistency) and Luck. The easiest one to understand is Luck, which is the deviation in winning percentage between a team’s actual record and their expected record using the correlated gaussian method. The luck factor has nothing to do with the rating calculation, but a team that is very lucky (positive numbers) will tend to be rated lower by my system than their record would suggest.

Consistency is basically the standard deviation of scoring difference by game for a team. Again, it’s not included in the ratings calculation. It can be an aid in determining which teams are overrated by my system. Highly rated teams that are inconsistent tend to look beatable more often. As of this writing, Georgia is ranked 329 in consistency and Oklahoma is at 334. They’ve played their best games against poor teams, and their worst against good ones.

Ideally, I’d synthesize the consistency and rating into one number, but I haven’t found a way I’m comfortable with. So right now, I’m throwing this system out there with all its warts for everyone to see. The warts tend to decrease as more games are played, but at least I’ve made you aware of them and where they can pop up.

Strength of Schedule now has three columns. It’s potentially more confusing, but worth it in the end. The way I compute SOS is to average the opponents offensive and defensive ratings and to apply the pythagorean calculation to them to rank the overall schedules. So those are the three columns you see, Pyth (Overall SOS), AdjO (Opponents’ average adjusted offensive efficiency), and AdjD (Opponents’ average adjusted defensive efficiency). When comparing the offensive performance of players on different teams, there’s quite a bit of an advantage having their average opponents’ defense quantified. There’s also a column for non-conference SOS which attempts to capture the portion of the schedule under a school’s control. Thus, no postseason or conference games are included in that calculation.

Tuesday, November 28, 2006

Predictions

Contrary to how it may appear, this portion of the site is not quite dead, yet.

I’m feverishly catching up on e-mail from the holiday weekend. One question I’ve been getting a lot has to do with when predictions will be posted. We’re still on the fringe of having meaningful ratings, and hence, meaningful predictions. I’ll probably turn on the predictions portion of the schedule pages beginning with Saturday’s games, but a lot of them won’t look too pretty. Plans for this week include a ratings explanation page, with conference pages and individual data going up next week. Beyond that, I need to get tempo-free stats on an expanded version of the schedule page, as well. Oh yeah, and a plea to stop asking RPI questions is on the way, too.

If you want some more reading, check out the wild game Dominic James had last night.