by Ken Pomeroy on Tuesday, October 31, 2006
So, yeah, here it is. The Big 12 preview in all its glory. I can’t guarantee it’s the best preview out there, but it is different from anything you’ve read this season. Much thanks to the Big Ten Wonk for providing the conference-only stats you’ll find in there. Be sure to check out his Big Ten preview in the coming days.
Print this out and pass it around the office. Show it to your friends, especially if they reside in Big 12 athletic departments or work in the publishing business. Some of the figures in here are a smidge different from what you see on my site because I’ve tweaked the way I estimate possessions and calculate Pythagorean winning percentage. More details on that soon.
by Ken Pomeroy on Tuesday, September 26, 2006
It probably goes without saying, but I’m on hiatus until my Big 12 preview is complete. And I know I said it would be October, but don’t expect it until Halloween. I hope it will be worth your wait…
by Ken Pomeroy on Monday, August 21, 2006
It’s time for the annual look at luck. In order to keep things fresh, I’m throwing my old buddy Pythagoras to the curb and leaning on a new pal, Karl Friedrich Gauss, to show us the way to the luckiest and unluckiest teams in the game last season.
You see, past versions of this post used the Pythagorean method to estimate what a team’s winning percentage should have been. But now, I’ll be using the correlated Gaussian method, invented by Dean Oliver. The difference between the two is that Pythagorean averages each of a team’s offensive and defensive efficiency to come up with a winning percentage, while the Gaussian uses the distribution of a team’s game efficiencies. Since most teams have a tendency to play to the level of the competition, the latter method is little more accurate.
by Ken Pomeroy on Monday, August 14, 2006
I think it’s safe to say that normally, when a team loses just one starter and returns its entire bench corps, we can expect that team to improve the following season. Especially when the departing player wasn’t drafted and wasn’t picked on the top two all-conference teams in his conference. (OK, technically, he was second team Big East. But the Big East mysteriously fills its teams with 10 players each. Well, actually 11 were on the first team. Anyway, media covering the Big East basically named this guy no better than the third-best point guard in the league.)
By now, you may have guessed the team in question is the Pitt Panthers. They may well be ranked in or close to the top 5 in the preseason polls. But their new team has to go through a serious adjustment period, despite being nearly identical to the old one.
by Ken Pomeroy on Saturday, July 29, 2006
Let’s just say it wasn’t the most scientific survey ever conducted. It was basically a contest to see who could build the best vote-casting robot. While it was flattering to see the lengths people would go to in order to push their conference to victory, I do realize that about 30 fans cast these votes.
So anyway, congrats to fans of the conference with roman numerals. But don’t despair, Arabic-numeral fans! I’ll make sure this preview appeals to a wide audience.Final Results
Big XII…..10282 A-10…..... 9713 MVC…...... 5872 CAA…...... 2057 ACC…...... 859 Big East…. 254 WAC…...... 157 Pac 10…... 100 SEC…...... 79 MWC…...... 41
by Ken Pomeroy on Thursday, July 20, 2006
I’d like to do a comprehensive conference preview, with the usual objective stats-based analysis. However, I have no preference as to which conference I’ll preview. So please, help me decide among the available choices (OK, I do have a few preferences). Big Ten excluded because others can do it better.
Voting ends Friday, July 28th at midnight MDT. The preview will appear in October. Vote as many times as the system allows.
by Ken Pomeroy on Tuesday, June 27, 2006
Using the principles discussed in the previous post, it may be possible to uncover some trends in player performance for the 2007 season.
First, using the methods shown previously, there are five players that show up as bad 3-point shooters and good free throw shooters based on their 2006 stats. One thing to note is that free throw shooting was better correlated to 3-point shooting (among the top 100 shooters by volume) than the season before, as demonstrated by our oddball groups having fewer members. In the case of this group, there are five qualifiers versus the eight found in 2005. Here are the players in this group:
James Life, rising senior, UMass: 82.3 FT%, 30.2 3P% in 2006. Travis Ford plucked Life from the Juco ranks to give the Minutemen perimeter game some, er, life. James disappointed big time in his inaugural D-I season. His FT% came on only…
by Ken Pomeroy on Friday, June 9, 2006
In no other sport is there such a controlled experiment as the free throw. While statistical analysis of basketball can be more challenging than other sports in numerous ways, at least we have this. And it’s about time we took advantage of it.
Specifically, there is a weak connection between 3-point shooting and free throw shooting. Even though the connection is weak, it does appear that we can use it to our advantage when projecting how a player’s 3-point accuracy will change from one season to the next.
In this exercise, I took the top 100 returning players ranked by their 3-point attempts in 2005. Everyone from Syracuse’s Gerry McNamara (315 attempts in ‘05) to Lipscomb’s James Poindexter (165). I labeled the top 33 by accuracy as “good” 3-point shooters, and the bottom 33 as “bad.” I then sorted them by free throw accuracy in the same manner.
by Ken Pomeroy on Tuesday, May 30, 2006
Apparently, the relationship between rebounding and height didn’t rock the foundation of the basketball community. The connection between shooting percentage and height isn’t going to create any waves, either.
As seen in the chart below, bigger players have a better two-point shooting percentage and shoot fewer 3’s than their smaller counterparts. We didn’t need to do statistical research to figure that out. But like the rebounding chart, this exercise is not without a couple of noteworthy findings. (Note: some additional background on how this chart was developed can be found in the previous post. Those unfamiliar with some of the terms used in these posts should refer to the Glossary of Terms.)
First, players that shoot fewer 3’s tend to have a better eFG%. This is a broad generalization and perhaps a slight mischaracterization of the chart, but take a look at it. As players get taller,…
by Ken Pomeroy on Thursday, May 11, 2006
It’s no secret that height is a factor in rebounding. We’d like to think it’s about desire and hustle, and to some extent it is, but being tall and having some springs is what matters most. What follows is a listing by height of the player that had the best rebounding percentage in the country in ‘06, with a playing time minimum of 40%.
One thing to remember is that schools provide the measurements, and they are under no obligation to give accurate ones. The seemingly abnormal amount of players listed at exactly 6 feet is the best example of height fudging. There are obviously a few 5-10 and 5-11 players being given the benefit of the doubt.
7-2 (1 qualifier):
Defensive: Roy Hibbert, Georgetown…22.0%
Offensive: Roy Hibbert, Georgetown…15.1%
7-1 (2 qualifiers):
Defensive: Luke Nevill, Utah…24.8
Offensive: Luke Nevill, Utah…11.8
7-0 (13 qualifiers):