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    Individual Stats Primer

    by Ken Pomeroy on Wednesday, September 21, 2005

    Since there will be more discussion of individual stats on this site this season, I thought I’d throw together a post to let people know what values of each are exceptional. These are the measures I refer to on a regular basis. I’ll add to and adjust this document as events warrant.

    Percentage of possible minutes played (%Min): Self explanatory, I think. La Salle’s Steven Smith (97.3%) led all D1 players in 2005. He missed just 32 of the 1,185 minutes that the Explorers played. Wyoming point guard Jay Straight (96.9%) was second, including a Ripken-esque nine consecutive games without a rest. Few players exceed 90%.

    Offensive rating (ORtg): A measure of personal offensive efficiency developed by Dean Oliver. The formula is very complicated, but accurate. For a detailed explanation, buy Basketball on Paper. Anything over 110 is good, and 120 is excellent for a player that is the…


    Unfinished Business

    by Ken Pomeroy on Sunday, September 11, 2005

    On January 19th, LSU beat Arkansas 66-63 in overtime. It was a particularly fortunate victory for LSU, because the game went to overtime only when officials conferred about a would-be game-winning three-pointer by Arkansas’ Michael Jones at the end of regulation. The shot was ruled a three by one official and a two by the other. Ultimately, replays were inconclusive (can we get some HD monitors, please?), the officials ruled the shot a two, and LSU got an extra chance with the overtime.

    This is one example of how a goofy break can influence the outcome of a contest. Over the course of a season, these breaks are supposed to even out, but they don’t for some squads. For some closure on my earlier look at the unluckiest teams of 2005, here were the 20 luckiest teams in conference play from 2005, based on the difference between expected…


    New Digs

    by Ken Pomeroy on Tuesday, August 30, 2005

    Welcome to 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.

    Regular posting…



    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…


    Feeling Lucky?

    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…


    Points Distribution

    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.


    Four Factors

    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 + DRopp)

    Keep in mind that rebounding percentage is computed from box score data which does not contain team rebounds. Therefore, the figures shown here may differ slightly from calculations made on the rebounding totals provided by a school.

    Finally, free throw rate captures a team’s ability to get to the free throw line.

    FTRate = FTA / FGA

    In Dean’s piece, he mentions the relative importance of each factor. In the NBA, eFG% is easily the most important factor, followed by TO%, OR%, and FTRate. A “RoboScout”-type analysis of games from the 2005 season shows that the importance of each factor is similar in college, with free throw rate being slightly more important in the college game, but still taking a back seat to offensive rebounding. Each team is different though. For instance, Gonzaga’s free throw rate was the second most important contributor to their offensive success. For Michigan State, offensive rebounding ranked second.



    RPI Help

    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…


    College Basketball Data

    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…


    Pomeroy Ratings FAQ

    - 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…


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