2026 ARTICLE 13: ACTUAL CURRENT RANKINGS AND OTHER DATA AFTER WEEK 6

This Article 13 provides current NCAA RPI, KPI, Balanced RPI, and Massey rankings based on the actual results of games played through Week 6 (through September 20) 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 9.20.2026

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 almost entirely on non-conference games.  A way to think about the data at this point in the season is that they are most reliable in showing how the regions stack up against each other, are reasonably reliable in showing how the conferences stack up, and are not very reliable in showing how the individual teams stack up.  How the individual teams stack up will be sorted out during conference play.

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.  As the color coding shows, teams with NCAA RPI ranks of #65 or better are potential #1 or #2 seeds.  Teams ranked #192 or better are potential candidates for all other seeds and for at large selections.  Only the top 5 teams appear assured of at large selections, which means the current at large bubble is teams ranked #6 through #192.  These numbers show how unreliable the current rankings are, so far as how the NCAA Tournament bracket will end up is concerned.

Mostly for illustration purposes, here are the current NCAA RPI Top 65 teams -- i.e., the potential #1 and #2 NCAA Tournament seeds, with some comments below:

Comment 1:  You may notice that some of the Balanced RPI ranks for teams are significantly different than the other systems' ranks.  The NCAA RPI formula, at this point in the season, weights teams' winning percentages at 42.9% and their opponents' strength of schedule at 57.1%.  This is different than what its weights will be at the end of the season, which will be approximately 50.0% and 50.0%.  The Balanced RPI, on the other hand, currently weights teams' winning percentages at 50.0% and their strength of schedule at 50.0% and will do so throughout the season.  (The systems calculate strength of schedule differently.)  Most of the significant differences are accounted for by the NCAA RPI's current de-emphasis on teams' winning perentages.

Comment 2:  Massey's rating formulas take into consideration teams' ratings over a number of past years.  As the season progresses, the weight of prior years' ratings decreases.  This accounts for some of the differences between his ratings and the other systems' ratings.

Comment 3:  Although there will be significant differences among the rating systems at the end of the season, particularly between the NCAA RPI and KPI systems on the one hand and the Balanced RPI and Massey on the other, the differences are likely to be significantly narrower than what you see today.

Actual Less Predicted Winning Percentages

This year, I have added data for each of teams, conferences, and regions, showing their actual winning percentages as compared to their expected winning percentages based on their current NCAA RPI ratings.  For Teams, these data are in the far right columns.

Using currently #1 rated Michigan State as an example, its Actual Winning Percentage so far is 74.1%.  Based on its current NCAA RPI rating and the current ratings of its opponents, however, its Expected Winning Percentage Based on NCAA RPI Rating is 80.8%.  The difference is -6.7%.

Compare this to North Carolina.  Its Actual Winning Percentage is 90.0%, its Expected Winning Percentage Based on its NCAA RPI Rating is 75.9%, and its difference is 14.1%.

These numbers suggest that currently, the NCAA RPI is overrating Michigan State and underrating North Carolina.

For Conferences, the Actual less Expected numbers are in column H and how conferences rank in terms of this difference is in column I.  For example, using the ACC as an example, the difference between its teams' Actual and Expected winning percentages is 4.5%, meaning the conference overall is underrated.  And, it is the 6th most underrated conference.

For Regions, the numbers likewise are in columns H and I.  Using the Autonomy region (all Power 4 teams) as an example, the difference between its teams' Actual and Expected winning percentages is 7.7% and it is the most underrated region.

Percentage of In-Region Ties

Of particular interest this week, column Q on the Regions page shows the proportion of each region's in-region games that are ties.  This likely is a measure of in-region parity.  Most notably, the MidEast region has had 30.2% of its in-region games to date end as ties.  That is extremely high, especially in relation to an historic norm around 21%.  This is significant because the NCAA in 2024 changed its RPI Winning Percentage formula to treat ties as only 1/3 of a win rather than the 1/2 of a win it had used in previous years.  As the percentage of in-region ties numbers show, this is having a disproportionate negative impact on MidEast region teams this year.

The MidEast region consists of teams from the Atlantic Ten, Big East, and Colonial (now Coastal) conferences and the Ivy League.

2026 ARTICLE 12: SIMULATED END-OF-SEASON RANKINGS AND NCAA TOURNAMENT BRACKETS FOLLOWING WEEK 6

This week I begin posting two articles a week.  This Article 12 is based on my simulated end-of-season rankings.  Article 13 is based only on teams' actual games played through Week 6 and includes current NCAA RPI, Balanced RPI, KPI, and Massey ranks and other information.

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Here is a link to an Excel workbook with simulated full-season NCAA RPI and Balanced RPI ranks -- and other important information -- for teams, conferences, and regions.  The ranks are based on the actual results of games played through Week 6 (ending Sunday, September 20) and simulated results of games not yet played.

RPI Report as of 9.20.2026

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 second data row in the table, 2026 Team 1 Predicted Results, applies teams' simulated end-of-season RPI ratings to games actually played through September 20, to determine what Team 1's win, loss, and tie percentages should be if those ratings are correct.  The third data row then shows the difference between those predicted percentages and the actual percentages.  Thus, for example, the actual winning percentage for all Team 1s was 49.1%.  According to teams' end-of-season simulated ratings, the winning percentage should have been 50.3%.  The Actual Less Predicted Results row shows the difference between predicted and actual was -1.272%.

The last three 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..

As you can see, 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 use of 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.

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.  Following those 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 in region order and in conference order 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:  The Comparison table is preliminary, 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.  If you review carefully the data in the above linked RPI report, you will see that the NCAA RPI has discriminatory patterns in relation to regions and conferences, whereas the Balanced RPI eliminates these patterns.  Thus the table below shows the effect of eliminating the NCAA RPI's discriminatory patterns.

2026 ARTICLE 11: SIMULATED END-OF-SEASON RANKINGS AND NCAA TOURNAMENT BRACKETS FOLLOWING WEEK 5

Here is a link to an Excel workbook with simulated full-season NCAA RPI and Balanced RPI ranks -- and other important information -- for teams, conferences, and regions.  The ranks are based on the actual results of games played through Sunday, September 13, and simulated results of games not yet played.

RPI Report as of 9.13.2026

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 table indicates that although individual teams' results may not be consistent with the ratings used for predictions, results of all the teams together are reasonably consistent with those ratings.  In other words, missed single game predictions tend to even out over the number of games already actually played.

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.  Following those 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 in region order and in conference order 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.

In reviewing the table, it may help to consider it together with the weekly RPI report linked in the first paragraph of this article.  If you review the data in that report, particularly the details of the data for conferences and regions -- the Autonomy region, in particular -- you will see that the NCAA RPI discriminates against the Autonomy conferences because of the way it measures Strength of Schedule.  The essence of the Balanced RPI is that it fixes this problem.  Thus the above table shows the effects of the discrimination and the effects of fixing it.

NOTE:  The Comparison tables are very preliminary, so it is not the details that are important but rather the overall impression in terms of the effects of the NCAA RPI's discrimination.  As a check against assigning too much weight to the details:

In 2025, the actual Top 57 in the end-of-season NCAA RPI rankings included 37 Autonomy teams and 20 teams from other conferences.  In 2024, the numbers were 39 and 18; in 2023, 34 and 23; and in 2022, 40 and 17.  Thus one could reasonably expect the number of Autonomy teams in the Top 57 to be between 34 and 40 and the number of other conference teams to be between 17 and 23.

In this week's 2026 projection, the NCAA RPI Top 57 include 32 Automony teams and 25 teams from other conferences.  Thus the number of Autonomy teams is below the range of past distributions.

And, in the above 2026 projection, the Balanced RPI Top 57 include 41 Autonomy teams and 16 teams from other conferences.  Thus the number of Autonomy teams is above the range of past distributions.

Comparing the NCAA RPI and Balanced RPI projected brackets, the NCAA RPI excludes from at large consideration 9 autonomy teams that the Balanced RPI says should be considered and instead substitutes for consideration 9 teams from other conferences.  In terms of at large selections, the projections indicate that under the NCAA RPI, 4 fewer Autonomy teams would receive at large selections than under the Balanced RPI.  Although I think the numbers may shrink some at the end of the season, I also think this is an indicator of the effect of the NCAA's persistence in using the NCAA RPI. 

2026 ARTICLE 10: SIMULATED END-OF-SEASON RANKINGS AND NCAA TOURNAMENT BRACKETS FOLLOWING WEEK 4

Here is a link to an Excel workbook with simulated full-season NCAA RPI and Balanced RPI ranks -- and other important information -- for teams, conferences, and regions.  The ranks are based on the actual results of games played through Sunday, September 6, and simulated results of games not yet played.

 2026 RPI Report as of 9.6.2026

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 table indicates that although individual teams' results may not be consistent with the ratings used for predictions, results of all the teams together are reasonably consistent with those ratings.  In other words, missed single game predictions tend to even out over the number of games already actually played.  Perhaps of significance, although it may be too early to tell, there appears to be a little higher proportion of wins and lower proportion of ties than in the past.  It is possible, however, that this will change as conference play begins.

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.  Following those 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 in conference order so you can see how a change to the Balanced RPI would affect 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.

In reviewing the table, it may help to consider it together with the weekly RPI report linked in the first paragraph of this article.  If you review the data in that report, particularly the details of the data for conferences and regions -- the Autonomy region, in particular -- you will see that the NCAA RPI discriminates against the Autonomy conferences because of the way it measures Strength of Schedule.  The essence of the Balanced RPI is that it fixes this problem.  Thus the above table shows the effects of the discrimination and the effects of fixing it.

NOTE:  The Comparison tables are very preliminary, so it is not the details that are important but rather the overall impression in terms of the effects of the NCAA RPI's discrimination.  As a check against assigning too much weight to the details:

In 2025, the actual Top 57 in the end-of-season NCAA RPI rankings included 37 Autonomy teams and 20 teams from other conferences.  In 2024, the numbers were 39 and 18; in 2023, 34 and 23; and in 2022, 40 and 17.  Thus one could reasonably expect the number of Autonomy teams in the Top 57 to be between 34 and 40 and the number of other conference teams to be between 17 and 23.

In this week's 2026 projection, the NCAA RPI Top 57 include 35 Automony teams and 22 teams from other conferences.  Thus this is in the range of past distributions.

And, in the above 2026 projection, the Balanced RPI Top 57 include 45 Autonomy teams and 12 teams from other conferences.

Comparing the NCAA RPI and Balanced RPI projected brackets, the NCAA RPI excludes from at large consideration 10 autonomy teams that the Balanced RPI says should be considered and instead substitutes for consideration 10 teams from other conferences.  Although I think the number will shrink some at the end of the season, I also think this is an indicator of the effect of the NCAA's persistence in using the NCAA RPI. 

2026 ARTICLE 9: SIMULATED FULL SEASON RANKINGS AND PROJECTED NCAA TOURNAMENT BRACKETS (NCAA RPI-BASED AND BALANCED RPI-BASED) FOLLOWING WEEK 3

SIMULATED FULL SEASON RANKINGS

Here is a link to an Excel workbook with simulated full-season NCAA RPI and Balanced RPI ranks -- and other important information -- for teams, conferences, and regions.  The ranks are based on the actual results of games played through Sunday, August 30, and simulated results of games not yet played.

 2026 RPI Report as of 8.30.2026.

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 table indicates that although individual teams' results may not be consistent with the ratings used for predictions, results of all the teams together are almost exactly consistent with those ratings.  In other words, missed single game predictions even out over the number of games already actually played.

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

New this year, I have done some programming so that my Balanced RPI ratings have a range and value levels that match the range and value levels of the NCAA RPI ratings.  I have done this so that the Balanced RPI ratings would look to the Women's Soccer Committee just like the NCAA RPI ratings look, if the Committee were to use the Balanced RPI ratings as a basis for its NCAA Tournament seeds and at large selections.

My bracket formation program uses teams' NCAA RPI ratings and other inputs, and the Committee's historic decision patterns related to the ratings and other inputs, to produce predicted NCAA Tournament seeds and at large selections.  The program's seeds and at large selections come very close to matching the Committee's decisions.  Since my Balanced RPI ratings and other inputs would look to the Committee just like those using the NCAA RPI, it is reasonable to believe that the bracket formation program would be just as good at matching the Committee's decisions if the Committee were using the Balanced RPI.

With that in mind, here are currently simulated brackets, first based on the NCAA RPI and then based on the Balanced RPI.  Following those 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 following 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 in conference order so you can see how a change to the Balanced RPI would affect conferences' teams.

In the table, if the NCAA Tournament and Balanced Treatment cells are white, the two rating systems result in the same Committee decision for the team.  If the NCAA Treatment cell is green and the Balanced Treatment cell is salmon, the NCAA RPI produces a better treatment for the team than the Balanced RPI.  Conversely, if the NCAA Treatment column is salmon and the Balanced Treatment column is green, the Balanced RPI produces a better treatment.

In reviewing the table, it may help to consider it together with the weekly RPI report linked in the first paragraph of this article.  If you review the data in that report, particularly the details of the data for conferences and regions -- the Autonomy region, in particular -- you will see that the NCAA RPI discriminates against the Autonomy conferences because of the way it measures Strength of Schedule.  The essence of the Balanced RPI is that it fixes this problem.  Thus the above table shows the effects of the discrimination and the effects of fixing it.

NOTE:  The Comparison tables are very preliminary, so it is not the details that are important but rather the overall impression in terms of the effects of the NCAA RPI's discrimination.  As a check against assigning too much weight to the details:

In 2025, the Top 57 in the end-of-season NCAA RPI rankings included 37 Autonomy teams and 20 teams from other conferences.  In 2024, the numbers were 39 and 18; in 2023, 34 and 23; and in 2022, 40 and 17.  Thus one could reasonably expect the number of Autonomy teams in the Top 57 to be between 34 and 40 and the number of other conference teams to be between 17 and 23.

In the above 2026 projection, the NCAA RPI Top 57 include 32 Automony teams and 25 teams from other conferences.  Thus this probably understates what the final number of Autonomy teams will be and overstates the number of other conference teams, so far as the NCAA RPI is concerned.

And, in the above 2026 projection, the Balanced RPI Top 57 include 44 Autonomy teams and 13 teams from other conferences.

2026 ARTICLE 8: SIMULATED FULL SEASON RANKINGS FOLLOWING WEEK 2 (THROUGH AUGUST 23)

This is the first report for the 2026 season showing simulated end-of-season ratings for teams, conferences, and regional playing pools.  The report is based on the actual results of games played through Sunday, August 23, and simulated results of all scheduled games to be played after August 23 as well as simulated conference tournament games.

As a starting point for determining simulated results, I used the ranking method described in the preceding article (2026 Article 7) to rank all teams.  I then assigned ratings to teams based on the historic average rating for each rank level.

Next, I assigned result probabilities to each game -- a win, a loss, and a tie probability.  To do this, I (1) identified each team's rating, (2) determined the difference between the two teams' ratings, (3) adjusted the difference to take home field advantage into consideration, and (4) used a history-based result probability chart to assign a win, loss, and tie probability to each team.  I used these probabilities as the game result for purposes of the simulation.  Thus for a game, Team A would show X.XX% win, Y.YY% loss, Z.ZZ% tie as its game result and opposing Team B would show Y.YY% win, X.XX% loss, Z.ZZ% tie.

I then ran NCAA RPI calculations for all teams based on the actual results of games played and simulated results of all games not yet played.

Once I had done this, I had simulated full-season NCAA RPI ratings (as well as Balanced RPI ratings) for all teams.  I then used those NCAA RPI ratings to re-determine the results of games not yet played (thus replacing the initially used ranks and ratings described in Article 7).  This gave me current full season simulated ratings and rankings following week 2, and related data.

As a test, for the 549 games already played I compared how well the predicted results using these ratings and rankings compare to the actual results of the games.  I used the results of home teams (or for the few neutral site games, the teams whose names are first in the alphabet) as the basis for evaluation.  The home teams actually won 51.2% of their games, lost 29.4%, and tied 19.4%.  This compared to predicted results of 50.2% wins, 29.3% losses, and 20.6% ties.  Thus although some individual game results may have varied significantly from predicted results, overall predicted results came very close to matching actual results.  This suggests that over a significant number of games, this is a reasonable method for producing simulated full season rankings.

The results of this process are in the Excel workbook 2026 RPI Report as of 8.23.2026.  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 following explanations are for the material in the workbook.

TEAMS

The first page of the workbook is for Teams.  I will use the top 10 teams in the simulated full season rankings to illustrate the workbook's material on teams.

This shows the first group of columns on the Teams page.  This year, I have revised the way I have divided teams among regions.  A main reason for this was to recognize that the Autonomy (Power 4) conferences to a great extent no longer are regional and that their teams tend to compete against each other.  As a result, I have created the Autonomy region, consisting of the ACC, Big 10, Big Twelve, and SEC.  I also have created the Middle, Mideast, North, South, Southeast, and West regions, with each conference placed in one of those regions.  I show the conference assignments below in the section on Regions.

The simulation includes simulated conference tournaments; and the NCAA Tournament Automatic Qualifier column shows the teams that the simulation projects as the conference tournament winners.

For the NCAA Tournament, teams with more losses than wins cannot receive at large positions.  The NCAA Tournament Disqualified Due to Losses Greater Than Wins shows, with a "1," the teams disqualified from an at large position based on the simulation.

The  NCAA RPI Rank column shows teams' simulated ranks.  Next to it, the NCAA Strength of Schedule Contributor Rank column is important to understanding the RPI.  Using Alabama as an example, its NCAA RPI rank is #10.  Thus you would think, if you were to play Alabama, that within the strength of schedule portion of your own NCAA RPI's calculation, you would get credit for playing the #10 team.  You won't.  As the NCAA Strength of Schedule Contributor Rank column shows, you only get credit for playing the #39 team.  This is a result of the way the NCAA RPI formula computes Strength of Schedule and is a formula defect about which I have written extensively.

I developed the Balanced RPI specifically to fix that NCAA RPI formula defect.  The Balanced RPI Rank and Balanced RPI Strength of Schedule Contributor Rank columns show that the Balanced RPI does not have this problem.  They also show how the fix changes the ranks.

Continuing to the right on the Teams table, the next columns look at each team's opponents' average strength:

The first two columns bunch conference and non-conference opponents together.  Using Stanford as an example, its Opponents Average NCAA RPI Rank is 58.  As above, one would think its Opponents Average NCAA RPI Strength of Schedule Contributor Rank likewise would be 58, but it isn't, instead it is 88.  In other words, the NCAA RPI formula, because of how it computes teams' strengths of schedule, significantly understates Stanford's opponents' strength when computing Staford's strength of schedule.  If you use the workbook itself, you can scroll down through all the teams and see which teams this hurts and which it helps.

The next two columns look only at a team's conference opponents and the two after that look at a team's non-conference opponents.

The next columns look at teams' good results against Top 50 opponents:

Apart from the NCAA RPI itself, results against Top 50 opponents one of the significant factors the Women's Soccer Committee considers in its NCAA Tournament decision-making process.  I have developed a system for scoring good Top 50 results that is highly skewed towards good results against very highly ranked opponents.  The above two columns show the teams' scores under my system and their ranks based on those scores.

Continuing to the right:

This part of the Teams table is similar to the above table showing the strength of the teams' opponents, but is based on the Balanced RPI rather than the NCAA RPI.  As you can see, teams' opponents' Balanced RPI Ranks and their Balanced RPI Strength of Schedule Contributor Ranks are essentially the same.  When comparing teams, this gives a much better picture of each team's strength of schedule Overall, in-conference, and non-conference.

CONFERENCES

The second page of the workbook is for conferences.

The first columns, on the left of the Conferences page, shows the conferences and the number of teams in each conference.  Next, it shows the average NCAA RPI of each conference's teams and how the conferences rank based on their average NCAA RPIs.  And next, it shows how each conference's teams rank based on their average Balanced RPIs.  As you can see, although the NCAA RPI and Balanced RPI have the same ranks for the Autonomy conferences, they have some pretty big differences for other conferences.

Continuing to the right on the Conferences page:

The next two columns (columns 2 and 3 in the above table) show the average NCAA RPI rank of each conference's teams followed by the average NCAA RPI rank as strength of schedule contributors of each conference's teams.  As you can see, some conference's teams are significantly underrated as Strength of Schedule contributors, some are rated about right, and some are significantly overrated.

The next two columns (columns 4 and 5 in the above table) show the average NCAA RPI rank of each conference's teams' opponents followed by the opponents' average NCAA RPI rank as Strength of Schedule contributors.  Again, there are significant differences between the two.

The next four columns (columns 6 through 9 in the above table) show similar conference opponents information broken down between in-conference opponents and non-conference opponents.

The last column shows conferences' ranks using the NCAA Non-Conference RPI.

Continuing more to the right:

These columns show information similar to that in the preceding table but for the Balanced RPI.  As you can see, unlike the NCAA RPI, conferences' teams' Balanced RPI average ranks and their average ranks as strength of schedule contributors are essentially the same.  They also are essentially the same for conferences' opponents.

And, continuing further to the right:

These columns show the difference between each conference's opponents' average ranks and their ranks as strength of schedule contributors, first for the NCAA RPI and then for the Balanced RPI.  A negative number is the extent to which a conference's opponents are underrated as strength of schedule contributors and a positive number is the extent they are overrated.

REGIONS

I have assigned conferences to regions within which their teams tend to play their games, as follows:

Autonomy: ACC, BigTen, BigTwelve, SEC

Middle: Horizon, MidAmerican, Missouri Valley, Ohio Valley, Summit

Mideast: AtlanticTen, BigEast, Colonial, Ivy

North: AmericaEast, MetroAtlantic, Northeast, Patriot

South: American, Southland, Southwestern, SunBelt

Southeast: AtlanticSun, BigSouth, ConferenceUSA, Independent, Southern, United

West: BigSky, BigWest, MountainWest, PacTwelve, WestCoast

I first will show columns from the Regions page that match those on the Conferences page, without re-explaining what they show.  Then I will show some additional information from the Regions page with explanations.

The next columns show how each region's games are distributed among opponents, by region:

Of particular note, the West region's teams play a very high proportion of their games in-region and except for games against Autonomy opponents play almost in isolation from the rest of the country.  The North region's teams are in a similar situation with the Mideast, although to a lesser extent.

The next table relates to the proportion of each region's in-region game that are ties:

This shows the proportion of each region's in-region games projected to be ties, which one can consider as a measure of in-region parity.  For the last few years, the NCAA's RPI formula has treated tie games as 1/3 of a win when calculating a team's Winning Percentage, rather than the 1/2 of a win it used previously.  The table suggests that this change will hurt the Autonomy teams the most, followed by the Mideast teams.  This is something the Women's Soccer Committee is monitoring to see whether it wants to continue with the 1/3 of a win part of the RPI formula or revert to 1/2 of a win.

Finally, on the far right of the table, this is similar to the right of the Conferences table.  Again, a negative number means that the rating system, within its formula, underrates the region's teams' opponents as Strength of Schedule contributors and a positive number means it overrates them.

2026 ARTICLE 7: A NEW WAY TO DO EARLY SEASON TEAM RANKS

In doing some work recently in cooperation with the new Intercollegiate Women's Soccer Organization for Coaches (IWSOC), I developed a new, easily understandable way to do early season rankings of all teams.  I'll start with an explanation of the basic method and then will show how it ranks the Top 75 teams.

Summary:  The early season rankings, as a starting point, use last year’s NCAA RPI Selection Rankings -- not the individual teams’ rankings but rather the ranking positions occupied by teams’ conferences.  Thus each conference will have the number of national pre-season ranking positions assigned to it equal to the number of teams in the conference and the particular national pre-season ranking positions assigned to it will be the ranking positions its teams occupied last year.  (The Selection Rankings are the NCAA RPI rankings following completion of the regular season including conference tournaments.  These are the rankings the Women’s Soccer Committee uses when seeding and making at large selections for the NCAA Tournament.)

The second step is to look for how "experts" have ranked teams within each conference.  For example, each conference's coaches do a pre-season poll that ranks the teams within the conference.  Using those rankings as an example, when a conference’s coaches have done their pre-season rankings for the teams in their conference, the conference’s teams will be assigned in order to that conference’s ranking positions in the national early season ranking list.

For example, last year (2025), ACC teams occupied the #1, 2, 3, and 10 positions in the NCAA RPI Selection rankings.  This year, the ACC coaches' pre-season poll had Notre Dame, Stanford, Duke, and Florida State in the top 4 in-conference positions, in that order.  The method I described therefore assigns ranks to those teams of #1 Notre Dame, #2 Stanford, #3 Duke, and #10 Florida State.

Thus once I have all the conference's coaches pre-season polls, I can put all the teams in pre-season rank order, depending on how each conference's coaches have placed the teams within their conference and on which ranking positions the conferences' teams occupied last year.

Rationale for Using Last Year’s NCAA Selection Rankings as a Baseline Starting Point:  Individual teams’ ranks, with very few exceptions, vary from year to year.  Conference strength, on the other hand, is quite consistent from year to year.  In particular, the rank positions a conference’s teams occupy, although not identical from year to year, are reasonably consistent.  This is particularly true in the area of the rankings that include the teams that will receive consideration for NCAA Tournament seeds and at large positions.  The following table shows this, with an explanation below the table.  (Scroll to the right to see more of the table.  Alternatively, click on the table to see the entire table.)

Using the ACC at the top of the table as an example, this table shows in the first green highlighted column – headed Number in 2024 NCAA RPI Top 5 – that in 2024, the ACC occupied 3 of the top 5 rank positions.  In the next green highlighted column – headed Number in 2025 NCAA RPI Top 5 – the table shows that the following year, the ACC again occupied 3 of the top 5 rank positions.  Moving on to the next two columns, in 2024 the ACC occupied 5 of the top 10 rank positions and in 2025 it occupied 4 of the top 10.  Continuing all the way to the last two columns on the right, in 2024 the ACC occupied 11 of the top 57 positions and in 2025 it occupied 10 of the top 57 positions.  (The table includes the Top 57 positions because historically #57 has been the poorest NCAA RPI ranked team to get an NCAA Tournament at large position.)

In the table, the gold highlighted cells are for reading convenience, simply marking where the different conferences had teams.

At the bottom of the table is a row for Matches.  If you look down the Top 5 columns, you will see that the ACC had 3 teams in the top 5 each year, the SEC had 1 team in the top 5 each year, and the Big 10 filled the other spot in 2024 but the Big 12 filled it in 2025.  Thus from a conference perspective, 4 of the 5 top positions had conference matches over the two years.  This is represented by the 4 in the Matches row below the Top 5 columns.

Thus looking at the Matches row across the table, there are 4 conference matches for the Top 5, 7 for the Top 10, 11 for the Top 15, and so on across the table, to 50 for the Top 57.

The color coding in the Conference column on the left shows with green highlighting the conferences for which using 2024 as a base for 2025 pre-season ranks would have assigned more Top 57 positions to the conferences than they actually ended up with in 2025.  The orange highlighting shows the conferences for which using 2024 as a base would have assigned fewer Top 57 positions than they actually ended up with.  As the two right-hand columns show, the maximum difference for any conference is only 1 position.  Further, if you compare the green and orange highlighted conferences, there does not appear to be any patterned discrimination between to the two groups of conferences.

As a whole, the table shows that in the end-of-season ranking area of teams that are in the ranking range for NCAA Tournament at large positions, using this method of assigning rank positions by conference is a reasonable way to predict where conferences’ teams are likely to end up.

At the very bottom of the table, under the cell Matches, is the number 271.  This is the total of the matches across the table in the Matches row.  I also have done tests using the average of the prior two years’ rankings and the average of the prior three years’ rankings as a baseline starting point.  These resulted in 255 total matches using the prior two years’ rankings and 257 using the prior three years.  Thus using only the prior year’s rankings for the assignment of baseline rank positions to conferences produces the best match with where the final rankings will end up.

The following table shows in more detail how using the prior one, two, and three years’ rankings as the baseline compare:

NOTE: In assigning teams’ ranking positions to conferences, this method treats teams as being in the conferences they will be in in the year for which the predictions are being made.  Thus, for example, Texas State’s 2025 rank was #57.  In 2026, Texas State will be in the re-constituted Pac 12 conference.  The method therefore assigns the #57 rank position to the Pac 12 for purposes of the 2026 pre-season rankings.

Rationale for Using the Coaches’ Conference Pre-Season Rankings to Fill the Pre-Season Assigned Conference Rank Positions:  In the past, there have been three sets of pre-season rankings of the teams in each conference.  The coaches in each conference rank their teams.  Chris Henderson uses a series of detailed metrics to rank the teams within each conference.  And I use historic ranking data to rank the teams within each conference.  All three ranked teams from 2022 through 2024.  The following table shows how each’s ranks compared to teams’ actual end-of-season ranks within their conferences:

As the table shows, on average the coaches’ pre-season rankings came within 2.2 positions of teams’ actual final conference ranks.  Chris Henderson’s were next in accuracy at 2.3 positions and mine were at 2.4 positions.  Thus the coaches’ ranks, with the coaches’ detailed knowledge of their conference opponents, roster changes, and other information, are as good a set as you can get of pre-season rankings of teams within their conferences.  Chris Henderson tweaks his ranking method from time to time and I consider his rankings also to be excellent.

EARLY SEASON RANKS OF TOP 75 TEAMS

With that background, the methodology produces the following early-season ranks of the Top 75 teams, based on the conference coaches' pre-season polls.  The table also includes the IWSOC ranking committee's ranks of the Top 25 teams, for comparison.

I will be updating the Top 60 every other week for IWSOC's publication, most likely keeping the early-season rank positions assigned to conferences but possibly changing the teams' positions within their conferences if results indicate that would be appropriate.  You can follow these and other coach-oriented information on X at https://x.com/theIWSOC.