About this database

The main database for each season is the one with the star next to it. Here you will find all of the items listed below plus more. You can select which fields you want to see, the order to present them for, as well as limit it to your favorite conference. Please note that this is all unofficial and that I'm sure there are mistakes in the data. If you just want to see the RPI, you can grab the brief version, also a conference RPI rating s available. Note that this is calculated by averaging the individual team ratings. There is also a Z-Rating brief available if you want to check my power ratings.

Description of Calculated Data

All records are for games against Division I opponents only
RPI
Ratings Percentage Index. This is the one everyone talks about with regard to making the tournament. It is calculated as .25*WIN%+.5*(Opponents WIN%)+.25*(Opponents Oppenents Win%)
RRPI
Road RPI. Same as RPI, but calculated only using the teams road record, your opponents home record, and the opponents opponents road record. Note that the data used has no provision for neutral site games which will show up randomly as home/away games which taints this calculation.
HRPI
Home RPI. See previous, but in reverse
WRPI
Weighted Rating Percentage Index. This uses the same formula as the RPI, except that you are awarded your opponents winning percentage for a win, and 1-oppwin% for a loss. This puts more weight into which teams you beat and which ones you lose to, so losing to Tennessee isn't nearly as damaging as a loss to a 3-8 team. On the other hand, beating Tennessee is worth more that beating the 3-8 team.
DWRPI
Dominance Weighted RPI. This is similar to WRPI, except the win/loss value is scaled statistically based on the margin of victory/loss of the game compared to the other teams which have played your opponent. The idea is, ok, you played that 3-23 team, but you beat them by 60 points, while their average margin of defeat was 10 points, so you get more points for beating them than everyone else. It's not scaled by how much, but compared to everyone else.
RPI2
Second Order RPI. This calculation essentially reruns the RPI again, except that the teams RPI is substituted for their winning percentage.
Z
Z-Rating. This calculation does a statistical analysis using standard deviation of points scored, points allowed, and margin of victory along with a weighted winning percentage (see WRPI). Z scores are also presented for each teams defensive(DEFZ), offensive (OFFZ), and margin of victory (MZ). The unscaled (rawz) score is the sum of DEFZ,OFFZ, and MZ and has no concept of winning percentage for the team in question or the opponent (i.e. no strength of schedule). It is purely a measure of a team's dominance over their opponents. The unscaled Z settles out much more slowly than the other measurements. Sorting by MZ can also provide interesting results.
STD and P-Ratings
These are similar in concept to the Z. STD generates a standardized score for each game a tam plays in for their offensive and defensive efforts (points scored and allowed) based on the opponents other games weighted by the opponents winning percentage. The standard scores are averaged per game for an offensive and defensive rating which are summed for the final power rating. The P system is similar, except the offensive and defensive rankings are assigned based upon (points scored)/(opponents average points allowed) and (points allowed)/( opponents average points scored). The offensive and defensive ranking are averaged per game and summed (off+.75*def) for the final rating. The defensive P rating is scaled down as it tends to inflate rankings. This was originally an arbitrary factor chosen just by eyeballing, but the offensive rankings break pass over 1.000 near team# 150, while the defensive ratings pass 1.000 near team #200, 150/200=.750 :-).
T-Factor
Tournament-Factor. This is a value I came up from analyzing the RPI data versus the teams receiving bids. It is defined as .25*win%/RPI. Studying the RPI data over the last several years, it appears that having a "t-factor" less than 1 is a bad omen for getting an at-large bid. Only one team has gotten a bid at all (automatic or at-large) in the past four years. Florida, with a t-factor of .980, made it in for the 98-99 tournament. A casual study indicates that it should be around 1.333. Below is a graph of the last 4 years (97,98,99,2000) showing the tfactor (x-axis) vs obtaining a tournament berth. It's a bit difficult to see the actual data, but the cutoff at 0 is clear (note: the graph is actually 1-tfactor).
In addition, data is presented for each teams current win/loss streak, record in the last 10 games, record versus the RPI Top 10,25,26-50,51-100, 101-150, and teams below RPI index 150. Also, the percentage of non-conference opponents in the RPI Top 50 (PT50) and RPI bottom 50 (PB150) is listed. These along with overall wins and losses to the top 50 and bottom 150 are believed to be the basis of the "rewards" and "penalties" the NCAA applies to each schools base RPI. Teams playing 50% or more of their non-conference schedule versus top 50 are rewarded, while schools playing 50% or more against the bottom 150 are penalized. (See Jerry Palm's RPI FAQ)

Some of the calculations are also done for non-conference (NC) and conference (C) opponents. The non-conference calculations eliminates bias introduced by teams which play in weak conferences, and also highlights teams which play in strong conferences but play weak non-conference schedules. Some people believe that the selection committee puts more emphasis on your non-conference opponents than on you conference opponents (i.e. who you choose to dance with rather than who you have to dance with).