Tuesday, December 28, 2004

Flex-ibility

I'd like to start today by giving credit to the Longwood Lancers. Coach Mike Gillian (friend of this blog) certainly got some mileage out of the game against the nation's #1 with a pregame feature on espn.com and an appearance on Cold Pizza. Then the 1-12 Lancers did something that neither Wake Forest nor Gonzaga could do - play a reasonably competitive game against Illinois for 30 minutes, eventually losing 105-79. Priceless trivia tidbit: Longwood and Delaware State are the only teams to shoot better than 50% against Illinois so far. That's the same Delaware State squad that missed its first 16 shots against Northwestern last night, in a 50-43 loss.

I promised some comments on the national efficiency stats I posted yesterday. One thing that jumped out at me involves the participants in the big game tonight, the aforementioned Gonzaga at Oklahoma State (correction: game is at Oklahoma City). It's Mark Few's unstoppable flex offense against Eddie Sutton's impenetrable Iba-style defense. Gonzaga is #1 in adjusted offensive efficiency (OE), having previously torched Georgia Tech's #1 ranked defense.

Over the summer, I declared Gonzaga the most underrated team, or something to that effect, in Andy Katz's preseason top 50. The basis was that even though they lost some guys, including a talented point guard, the Gonzaga offense is as efficient as they come. I calculated efficiency stats for both 2002 and 2004 (I don't know what I was doing in 2003), and Gonzaga led the nation both years.

Two years is a small sample, but I think it says something when a team performs well in the same area with somewhat different personnel. It says something about the system. And that system is working again. With the West Coast Conference schedule coming up, you can expect Gonzaga to finish the season #1 on the raw side of the OE stats again. That fact alone makes the price of $42.95 for tapes of Mark Few explaining his system seem like a bargain.

However...

The Gonzaga defense has been a major disappointment so far. It's weaker than anyone could have expected, ranking dead last among WCC teams. They have a couple of timely wins over highly ranked teams in Georgia Tech and Washington. But if they continue to give up points at the rate they have, then we have seen Gonzaga at their peak. They gave up over a point per possession (roughly the national average) to offensive lightweights Portland State (#203 adjusted OE), Idaho (#229), and Eastern Washington (#271).

They did manage put the clamps on Georgia Tech pretty well, holding them to 40% shooting. Though maybe the travel to Las Vegas and late game time for Georgia Tech had something to do with it, because that performance doesn't fit in with the rest of Gonzaga's portfolio. Gonzaga was able to bully Washington with its offense, beating the Huskies even while allowing them to shoot 50% from both two and three point range. That option won't be available tonight. OSU won't let the 'Zags make 59% of their shots as they did against Washington, even if it means Eddie Sutton has to come on the floor and take a couple of charges himself.

So based on all of this info, we should expect (1) tonight's game will be more high scoring that the Oklahoma State/Syracuse game (74-60 on December 7th) and (2) this game has blowout potential.

Monday, December 27, 2004

National Efficiency

Here's a page where I have computed efficiency stats for the 330 D1 teams.

http://www.kenpom.com/stats.php

It's kind of bulky and awkward, but I think it does give some unique insight into a few teams out there. The columns are sortable nationally. You can sort the adjusted numbers by conference using the T/O/D selector next to the 'Conf' heading.  Unlike the ratings page, this won't be updated daily in the foreseeable future. It will probably be a weekly update until I streamline the process. I'm also thinking of displaying a different "bonus stat" every week.

Any feedback you have would be great, so e-mail me. Also, if you an idea for a bonus stat, I'd love to hear it.

I'll throw out some observations tomorrow. Today, I will do my best to explain these figures. You read on at your own risk. There is no lifeguard on duty.

There are two columns for each statistic: raw and adjusted. First I'll explain the raw numbers.

Tempo/Pace - The number of possessions per 40 minutes. Possessions is not an official NCAA statistic, so it must be estimated. The formula I am using is:

Possessions = FGA-OR+TO+.42*FTA

This is a pretty standard computation that accounts for when possession is lost by a team. The only bit of uncertainty is the free throw portion, because we don't always know how a team got to the line. If they are shooting two, then the two FTAs account for one possession. But if they go to the line for one after making a shot, then the one FTA has no possession attached to it, because the previous FGA accounts for it.

The .42 multiplier estimates how many FTAs equal one possession. It has to be slightly less than one half. John Hollinger in Pro Basketball Prospectus uses .44. Dean Oliver in Basketball on Paper and other work uses .40. I'm splitting the difference. The difference between .4 and .44 means about a 1% change in the efficiency/tempo calculations.

I do a tempo calculation for each team in a game, average those two numbers and apply it to each team for the game, since each team's total possessions should be nearly equal. Then I average the tempo for every team's games-to-date to come up with the figures shown.

Offensive/Defensive Efficiency - This is the number of points scored or allowed per 100 possessions. There are only about 70 possessions for each team in the average college basketball game, so these numbers are higher that the points-per-game statistics you see used by the media.

Like tempo, I average each team's efficency by game. The other way to do this would be to take a team's total points on the season and divide it by total possesions. But this gives some games more weight than others depending on the number of possessions in a particular contest. Also, I only use games involving two D1 teams.

The raw numbers are computed from the data contained in a box score. Over the course of the season, this gives some unintuitive results, such as West Virginia currently having the 2nd most efficient offense in the nation and Texas A&M - College Station having the 2nd stingiest defense. So there's the matter of adjusting for competition - the "adjusted" numbers.

Say team A averages a pace of 62 possessions per game and team B averages 68 possessions per game. And for the sake of this example, let's say the average college game has 70 possessions, a nice round number. For the model I use, the expected possessions in a game involving teams A and B would be 60. This results from the fact that team A averages eight possessions slower than normal and team B averages two possessions slower than normal. The sum is ten possessions slower than normal, or 60.

Why would the game end up being played at a slower pace than either team's average? A team's average pace is a product of how they like to play and how their opponents like to play. A team playing much slower than average, like team A, is more than likely playing opponents that prefer to play faster than them. So team A's average pace on the season is faster that they would really like if they were totally in control.

The adjusted numbers are computed based on this principle. In every game, each team really wanted to play at a certain pace, and my model tries to dig this out of the data. For an example of how this works, take the Georgia Tech-Air Force game from December 11th.

Georgia Tech: season average pace = 71.5
Air Force: season average pace = 53.9
The pace of that game: 62.2

Based on the average national pace of 69.2, we would have expected the game to result in 56.2 possessions. So an adjustment has to be made in the the way each team wanted to play this particular game.

Each team's season average is adjusted upward by the same percentage to produce numbers that would predict the actual game pace of 62.2. In this case, Air Force's pace for the game becomes 56.5 and Georgia Tech's becomes 74.9. That's how each team wanted to play, and that combination produced the game pace of 62.2. (Considering Air Force was playing from behind most of that game, it makes sense that they wanted to play faster than usual.)

All games are examined like this, and a season-long adjusted pace results from averaging a team's adjusted pace for each game played. The computations are repeated until the numbers stabilize.

The efficiency numbers are computed by a similar principle. For exapmle, let's say that team A has an offensive efficiency (OE) of 120 and team B has a defensive efficiency (DE) of 120. Keeping our round number principle, I'll use a national average for OE of 100. For a game between A and B, A's offensive efficiency is expected to be 140. This is arrived at because both teams deviate from the norm by +20. So the sum of the deviations is 40, and that gets added to the nationwide average of 100. This concept was exhibited when Washington State played Oklahoma State. WSU's anemic offense against OSU's renowned defense produced a historically pathetic 29-point outing for Wazzu.

Season-long adjusted numbers are computed in the same manner described for pace above, with each team getting assigned a game OE and DE.

This was very confusing to write, so I am sure it was confusing to read. If you have questions, just e-mail me and I will answer them on the blog.

Wednesday, December 22, 2004

A Very Special Edition of blah blah blah

This will probably be my last post before Christmas. Like many Americans, I find it hard to get motivated on the last day before break. Many other teams find themselves in that predicament tonight, when they will be playing their last game before a holiday vacation. Hopefully, Illinois will be one of those teams tonight in the "Braggin' Rights" game. I'd like to see them look human pretty soon.

Not only have they blown everybody out so far, but four of the teams they beat still have just one loss (Arkansas, Wake Forest, Oregon, and Gonzaga). Of course, with Missouri as the opponent, looking human means being up by five at the half, and winning by 15. But still, it would be reassuring to know they will not be '91 UNLV (whose toughest regular-season game was also at Arkansas. Coincidence? Well, yeah).

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The place I'd go if I could go anywhere tonight? The Pit in Albuquerque. New Mexico hosts Wake Forest. It would be especially useful to be there since the game isn't on TV.

The Lobos are fourth in the nation in shooting percentage. If there's one officially sanctioned NCAA stat you want to be near the top of, it's that one. Of course, New Mexico hasn't played a particularly difficult schedule. In their only loss, at Oregon, they couldn't break 40% from the field, although they did hoist 29 threes in that one. This is New Mexico's weakness offensively - they rely on the three a lot - 40% of their field goal attempts are threes (national average is 32.6%). This is fine when you're playing Arkansas Pine Bluff, but against opponents with quickness on the perimeter, that shiny field goal percentage has a tendency to nosedive.

New Mexico is ninth nationally in offensive efficiency (points per possession) and Wake is 25th. Wake is also 47th in tempo. So this game figures to be high-scoring, and possibly close.

If you want even more about this game, you could read the short preview I wrote five months ago. It's not often I say something that ends up being partially true five months later, so I encourage you to check it out.

Instead of being in Albuquerque, I may end up at the Washington State/Wyoming game, which could be the lowest scoring game on tonight's schedule.

Have a Merry Christmas everybody. When I return, I plan to post adjusted efficiency and tempo data - numbers that take the basic efficiency stats and account for the level of competition - for all 330 teams in the land.