by Ken Pomeroy on Tuesday, August 30, 2005
Welcome to kenpom.com v2.0! I’ve given this place a little bit of a makeover, including the following:
- Additional sortable stats for each team, and 2004 stats have been added as well.
- I hired a highly-respected marketing firm to come up with a new name for the blog, and it paid off big-time.
- RPI data has been added for 1999. Relive the spectacle that was New Mexico getting an at-large bid while ranked 74th in the RPI.
- Pomeroy Ratings pages have been standardized back to 1999.
- More background on what the various tempo-free stats mean, and how they can make your life better. Also, I’m introducing a new free throw multiplier of .475! If you don’t know what that means, don’t worry.
- The blog is a little “cleaner” and it should be more readable, especially with respect to tables and lists.
by Ken Pomeroy on Wednesday, August 17, 2005
For those who missed it, the preseason top 50 for the Wooden Award was announced last week. The list isn't binding in any way - someone not on the list can ultimately win the award in March as the nation's best player. It's strictly a publicity stunt, but it's one I welcome at this time of year. It's a great way to get reacquainted with the top talent in the game. Click here for the complete list.
The preseason list doesn't contain any freshmen and according to a press release, is based on the following criteria...
The list is composed of 50 student athletes who, based on last year's individual performance and team records, are the early frontrunners for college basketball's most coveted trophy.
But that statement isn't true for two of the players that made the list.
One of those is Leon Powe of Cal, who missed all…
by Ken Pomeroy on Tuesday, August 9, 2005
I did this last year, and looking back on it, it was revealing. You can read last year's editions of the luckiest and unluckiest teams from 2004 and judge for yourself. Just ignore my comments about Florida State.
If you don't know what the Pythagorean method is, then you'll have to read the 2004 write-ups. All I am doing is subtracting a team's expected winning percentage from its actual conference winning percentage based on its points scored and allowed during conference play. I have decided to rank the teams by winning percentage difference, instead of by win difference. This is so the system is not biased towards conferences that play a lot of conference games.
These are the teams whose actual conference regular-season record was exceeded by their expected record the most. You could say luck went against these teams the most. The numbers are winning…
by Ken Pomeroy on Monday, August 1, 2005
The points distribution page illustrates where a team’s (and their opponents’) offense in coming from. The numbers in each column indicate the percentage of points scored (or allowed) by each type of shot. Keep in mind that the numbers on this page do not say anything about the quality of a team’s offense or defense. The data provides another piece of the puzzle of how each team plays offense or defense.
The offensive and defensive summary pages are based on Dean Oliver’s four factors concept. You can read Dean’s more detailed explanation of the four factors here. Essentially, the four factors are the building blocks to the efficiency formula.
Efficiency data gives you an idea of the quality of a team’s offense or defense, but the four factors tell you why a team is good or bad when they have or don’t have the ball. Here’s a breakdown of how each statistic on this page is calculated…
Effective field goal percentage is like regular field goal percentage except that it gives 50% more credit for made three-pointers.
eFG% = (.5*3FGM + FGM) / FGA
Turnover percentage is a pace-independent measure of ball security.
TO% = TO / Possessions
Offensive rebounding percentage is a measure of the possible rebounds that are gathered by the offense.
OR% = OR / (OR…
The Ratings Percentage Index (RPI) is the formula used by the NCAA to rate men’s and women’s college basketball teams. The formula is described below.
The RPI is calculated by adding three parts.
Part I (25% of the formula): Team winning percentage. For the 2005 season, the NCAA added a bonus/penalty system, where each home win or road loss get multiplied by 0.6 in the winning percentage calculation. A home loss or road win is multiplied by 1.4. Neutral games count as 1.0. More on the effect of these changes can be found here.
Part II (50%): Average opponents’ winning percentage. To calculate this, you must calculate each opponent’s winning percentage individually and average those figures. This is NOT calculated from the opponents’ combined record. Games involving the team for whom we are calculating the RPI are ignored.
Part III (25%): Average opponents’ opponents’ winning percentage: Basically taking all…
The college basketball game file posted on my site is freely available for anyone to use with three requests on my part. (1) Give me some sort of acknowledgment on your site. (2) Let me know you are using the data and why you are using it. I am always curious. (3) While I do not expect you to actively quality control the data, if you do find errors or somebody reports an error to you, please pass it along to me.
Now a little bit about the format. The home team is listed last. Games not played on a home court are denoted by a letter after the last team’s score. A capital ‘N’ indicates a game played on a neutral court. For a game where the listed home team is not playing on its home court, yet still getting a home court advantage, a lower case ‘n’ is…
- Schedule Strength is computed by averaging the rating of each opponent, factoring in home court advantage as appropriate. For schedule strength purposes only, unrated opponents are given a rating of the worst rated team.
- Data in the ‘LAST 5 GAMES’ column reflects a team’s performance in its last 5 games against rated teams, based on its opponents current ratings, using the same weighting principles that are used to calculate the season ratings.
What is the purpose of your ratings system?
This system is designed to be predictive. One can get a prediction by simply taking the difference in the ratings of two teams and make appropriate adjustments for home site advantage. You can probably save some work by looking at individual team pages. There you can find predictions for future games, along with the chances of winning the game outright. Check out this site to monitor…
Let’s start with the most basic stats to measure the ability of a team’s offense and defense.Offensive efficiency
Points scored per 100 offensive possessions.Defensive efficiency
Points allowed per 100 defensive possessions.
In order to compute efficiency, we need to know how to compute possessions.Possessions
We can estimate possessions very well from box score stats by using this formula.
For each team, possessions are counted for the team and their opponents, and then averaged.
Efficiency gives us broad view of how well the offense or defense functions, but we can break efficiency into what Dean Oliver dubbed the Four Factors. Shooting, rebounding, turnovers, and free throws provide the basic components of efficiency.Effective field goal percentage (eFG%)
(FGM + 0.5*3PM) / FGA
Shooting is measured by effective field goal percentage, which differs from conventional field goal percentage by taking into account the extra value of a made…
The Basketball Shrink finds teams most similar to the team you selected, based on the stats on the page you are looking at. On the summary page, teams are compared by adjusted tempo, adjusted offensive efficiency, and adjusted defensive efficiency. On the other stats pages teams are compared by all the stats listed on the page. The algorithm takes the sum of the percentage difference of each stat and ranks the teams accordingly.
I call this the Basketball Shrink because it’s designed to give the user an idea of the personality of a team by producing a list of teams that play a similar style. For more information, here is my original post on the Shrink from the 2005 season: Basketball Shrink Debuts.