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.