2026 ARTICLE 17: ACTUAL CURRENT RANKINGS AND OTHER DATA AFTER WEEK 8

This Article 17 provides current NCAA RPI, KPI, Balanced RPI, and Massey rankings based on the actual results of games played through Week 8 (through October 4) of the season, for teams, conferences, and regions.

Here is a link to an Excel workbook that contains all the information:

2026 RPI Report Actual Results Only as of 10.4.2026

To download the workbook, use the following steps:

1.  Click on the workbook link.

2.  The link takes you to a Google spreadsheet, which you will not want to use.  On the Google spreadsheet, to the left, click on File, which will bring up a drop down menu.

3.  In the menu, click on Download, which will bring up another drop down menu.

4.  In the drop down menu, click on Microsoft Excel (.xlsx).  This will download the Excel workbook.

The first page of the workbook is for Teams, the second for Conferences, and the third for Regions.  The data so far are based mostly on non-conference games, but also on the first group of in-conference games.  I consider these data preliminary and expect significant changes over the remainder of the season.

On the Teams page, on the left, are color coded columns.  These columns are based on past history.  They show which teams, based on their current ratings, are potential seeds (by seed level) and at large selections for the NCAA Tournament and which already appear assured of getting at least at large selections.  The seed and at large candidate groups still are large and show how unreliable the current rankings are, so far as how the NCAA Tournament bracket will end up is concerned.

Here is what the Teams page shows, so far as potential NCAA Tournament seeds and at large selections are concerned:

This week, here are some data taken from the linked workbook's Conferences page:

This table shows how teams' actual winning percentages so far in non-conference games compare to what one would have expected their winning percentages to be based on their current actual NCAA RPI ratings.  This is an indicator of how well or poorly the current NCAA RPI ratings do at rating teams from each conference in relation to teams from other conferences.  A positive number means the conference's teams have performed better so far in non-conference games than their NCAA RPI ratings say they should have performed (i.e., are underrated) and a negative number means they have performed more poorly (are overrated).

As you can see, the Ivy League's teams currently appear to be significantly underrated.  Next come three of the conferences from the West region mixed together with the Autonomy conferences.  It will be interesting to see if, and how, these numbers change over the remainder of the season.

2026 ARTICLE 16: SIMULATED END-OF-SEASON RANKINGS AND NCAA TOURNAMENT BRACKETS FOLLOWING WEEK 8

This article is based on the actual results of games played through Week 8 (ending October 4) and simulated results of games not yet played, including conference tournament games.  It projects where teams will end up at the end of the season, but before the NCAA Tournament.

Here is a link to an Excel workbook with the simulated full-season NCAA RPI and Balanced RPI ranks -- and other important information -- for teams, conferences, and regions.

RPI Report as of 10.4.2026

2026 Article 8 has an explanation of the method I use to generate the simulated ranks and of the key information in the workbook.  Article 8 also has instructions on the best way to download the workbook.

For purposes of evaluating the usefulness of the ratings the simulation currently uses to predict the results of games not yet played:

The table's reference to Team 1 is to the home team or, if a neutral site game, the team whose name comes first alphabetically.

The first data row in the table, 2026 Team 1 Actual Results, shows Team 1's actual results.  The second data row, 2026 Team 1 Predicted Results, applies teams' simulated end-of-season RPI ratings to games actually played through September 27, to determine what Team 1's win, loss, and tie percentages should be if those ratings are correct.  The third data row -- 2026 Actual Less Predicted Results -- shows the difference between the predicted percentages and the actual percentages.  Thus, for example, the actual winning percentage for all Team 1s was 48.1%.  According to teams' end-of-season simulated ratings, the winning percentage should have been 49.4%.  The Actual Less Predicted Results row shows the difference between predicted and actual was -1.311%.

The last two data rows are similar, but instead of using teams' simulated end-of-season RPI ratings, it uses teams' actual current NCAA RPI ratings to determine what the percentages should be if those ratings are correct.  Here, the Actual Less Predicted Results row shows that predicted results based on actual current NCAA RPI ratings miss the actual results by 1.678%.

In other words, the simulation's current ratings are a better match with actual results than are the actual current NCAA RPI ratings.  Because of this, for now I will continue using the simulation's ratings to predict future game results rather than converting to using the NCAA RPI's current ratings.  In the upcoming weeks, I will continue doing this until the NCAA RPI's current ratings perform better than the simulation's current ratings.

The following table is from the workbook.  If teams end up where the simulation currently says they will end up, it shows the teams -- if historic patterns hold -- that will be candidates for each seed level and for at large selections.  It also shows, in the At Large Bubble column, which teams are at ranking levels that historically always have gotten at large selections (if not Automatic Qualifiers), -- the Top 26 in the NCAA RPI rankings.  Thus the at large bubble teams are #27 through #57.

Following up on my promise last week to highlight some data from the workbook each week, here is a table showing some of the data on the linked workbook's Conferences page:

The key columns in this table are the two salmon highlighted ones on the right.  The first of those columns is for the NCAA RPI.  As I have discussed extensively elsewhere, a major problem with the NCAA RPI is that teams' overall RPI ranks can be very different than their ranks as contributors to the strength of schedule portion of their opponents' RPI formulas.  In the table, I've arranged the conferences in order of those whose teams' opponents have their strength of schedule contributions the most underrated at the top to those whose teams' opponents' strengths of schedule contributions are the most overrated at the bottom.  As you can see, the Big 10's opponents (which includes both conference and non-conference opponents) are underrated as strength of schedule contributors by 32.7 rank positions.  At the other end of the spectrum, SWAC's opponents' strengths of schedule contributions are the most overrated, by 32.8 rank positions.

The column on the right is for the Balanced RPI, showing that its overall ranks of teams and its ranks of them as strength of schedule contributors are essentially the same.  That is not surprising since I designed the Balanced RPI with the purpose of having these two ranks the same.

The yellow highlighted column on the left shows the conferences' ranks when using the NCAA RPI.  The green highlighted column shows the ranks after fixing the NCAA RPI's strength of schedule problem by using the Balanced RPI.

Although the details of the numbers will vary from year to year, the table paints a good general picture of the NCAA RPI's discriminatory patterns due to how it calculates strength of schedule.

SIMULATED NCAA TOURNAMENT BRACKETS USING THE NCAA RPI AND USING THE BALANCED RPI

Here are currently simulated NCAA Tournament brackets, first based on the NCAA RPI and then based on the Balanced RPI.  The simulations use the Women's Soccer Committee's historic decision patterns and likely are very close to what the Committee would decide based on the game results to date and simulated results of games not yet played.

Following the two brackets is a table showing how the two compare. (NOTE: For conference tournaments, I have used simulated conference tournaments based on the NCAA RPI.)

Here is a key to the tables:

The first table shows the projected NCAA Tournament bracket based on the Committee's using the NCAA RPI:

The next table shows the projected bracket based on the Committee's using the Balanced RPI:

The final table compares the projected brackets.  It shows the teams sorted first by region and within regions by conference so you can see how a change to the Balanced RPI would affect regions' and conferences' teams.  Salmon highlighting means the team gets a poorer treatment by the rating system and green means the team gets better treatment.  No highlighting means the treatment is the same.

NOTE:  My weekly Comparison tables are preliminary and evolve throughout the season, so it is not the details that are important but rather the overall impression in terms of the difference between using the NCAA RPI as compared to the Balanced RPI.

This week, I want to draw your attention to the Big 10.  The comparison indicates that using the NCAA RPI, the current prediction is that the Big 10 will have 5 teams in the NCAA Tournament.  But if the Committee were using the Balanced RPI, it would have 10.  That is a huge difference.  Below the table are some thoughts about the difference.

So, why would the Big 10 have only 5 teams in the bracket if the Committee uses the NCAA RPI but 10 if it uses the Balanced RPI?

One major reason shows up in the table in the first part of this report, showing the difference between conference teams' opponents' NCAA RPI ranks and their ranks as strength of schedule contributors.  As the table shows, the Big 10's NCAA RPI rank to strength of schedule contributor rank difference is -32.7 positions.  Looking at the conferences whose teams are bumped out of at large positions using the Balanced RPI, they are the Big East with a difference of -14.4 positions, the Ivy at -13.7, the American at -9.7 positions, the Pac 12 at -4.7 positions, and the Sun Belt at +0.5 positions.  In other words, the Balanced RPI, by eliminating the NCAA RPI's strength of schedule contributor discrimination problem, has moved up Big 10 teams' ranks relative to the ranks of teams from those other conferences.

I believe another reason is the Big 10 playing a 12-game in-conference season:

The effective weights of the three components of the NCAA RPI are 50% Winning Percentage, 40% Opponents Winning Percentage, and 10% Opponents Opponents Winning Percentage.  Thus your opponents' winning percentages are very important (and against whom they achieved those winning percentages is not very important).

If teams were to play only games within their conferences, and assuming each conference played a full round robin, each team's opponents' winning percentage would be 0.5000.  This is because if I play conference opponents A and B, they will have played each other with one winning and the other losing or both having ties.

The way conferences are distinguished from each other thus is not through their in-conference games but rather through their non-conference games -- and most important, by their winning percentages in non-conference games since these winning percentages are by far the primary determiner of the conference teams' values to their conference opponents as strength of schedule contributors.

The following table shows teams' non-conference winning percentages, using the NCAA RPI's Winning Percentage formula:

Next, consider this: The Big 10 plays a 12-game in-conference schedule.  The only other conference to play that many in-conference games is the Metro Atlantic.  Remember, from a strength of schedule perspective, each conference's teams strength of schedule from in-conference play is roughly 0.5000 (but not exactly if the conference doesn't play a full in-conference round robin).  It is the non-conference winning percentages that pull the teams' values as strength of schedule contributors above 0.5000.

The Big 10, by playing a 12-game conference schedule, has reduced its capacity to play non-conference games relative to the capacity of teams from other conferences seeking NCAA Tournament at large positions.  This gives the Big 10 teams fewer non-conference games through which to pull its overall opponents' winning percentages above 0.5000.

The Big 10 could compensate for this by scheduling only very easy non-conference opponents so that its teams' non-conference winning percentages would be higher than those of all the other conferences that play fewer in-conference games.  This, however, would mean foregoing opportunities for "signature" non-conference results, which also are important in the NCAA Tournament seeding and at large selection process.  In other words, the Big 10, by playing a 12-game schedule, has put itself in a disadvantaged position relative to other conferences when it comes to formation of the NCAA Tournament bracket, with no good way out of that position.