2026 ARTICLE 20: ACTUAL CURRENT AND SIMULATED END-OF-SEASON RANKINGS AND OTHER DATA AFTER WEEK 10

 Below, I have provide links to two Exel workbooks.  To download a 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 menu, click on Microsoft Excel (.xlsx).  This will download the Excel workbook.

ACTUAL CURRENT RANKINGS AND OTHER DATA AFTER WEEK 10

The first workbook provides current NCAA RPI, KPI, Balanced RPI, and Massey rankings and other data based on the actual results of games played through Week 10 (through October 18) of the season, for teams, conferences, and regions.  Here is a link to the workbook:

2026 RPI Report Actual Results Only as of 10.18.2026

The first page of the workbook is for Teams, the second for Conferences, and the third for Regions.  The data include almost all the non-conference games for the season and the conference games played through Sunday, October 18.  There still is room for significant changes in where teams finally will end up, but the ranges within which teams will finish is narrowing.

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 remain large, so there still is room for significant change in the rankings and how the NCAA Tournament bracket will end up.

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

SIMULATED END-OF-SEASON RANKINGS AND NCAA TOURNAMENT BRACKETS FOLLOWING WEEK 10

The second workbook is based on the actual results of games played through Week 10 (ending October 18) 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, and provides other important information related to teams, conferences, and regions.  Here is a link to an the workbook:

RPI Report as of 10.18.2026

2026 Article 8 has an explanation of the method I use to generate the simulated ranks and of the key information in the workbook.

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 win, loss, and tie percentages in games played through October 18.  The second data row, 2026 Team 1 Predicted Results, Simulated NCAA RPI, applies teams' simulated end-of-season RPI ratings to games played through October 18, 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 actual percentages and the predicted percentages.  Thus, for example, the actual winning percentage for all Team 1s is 47.2%.  According to teams' end-of-season simulated ratings, the winning percentage should be 48.5%.  The Actual Less Predicted Results row shows the difference between predicted and actual is -1.304%.

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 the actual current NCAA RPI ratings are correct.  Here, the Actual Less Predicted Results row shows that predicted results based on actual current NCAA RPI ratings miss the winning percentage actual results by 1.414%.

The comparison suggests that the simulation's current ratings are a slightly 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 (if not AQs).  The table also shows which teams the simulation currently projects as Automatic Qualifiers.

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, depending on which rating system the Committee is using.

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 evolve as the season progresses.  At this point in the season, 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.

Looking at the table, you can see that the ACC and, especially, the Big 10 are hurt by the NCAA RPI relative to the Balanced RPI.  In contrast, the non-Autonomy (i.e., non-Power 4) conferences are helped by the NCAA RPI relative to the Balanced RPI.  This is not surprising since, as I have shown elsewhere, the NCAA RPI discriminates against stronger conferences and regions and in favor of weaker ones whereas the Balanced RPI does not.  In addition, regarding the Big 10, as I showed in 2026 Article 19, its NCAA RPI ratings are hurt by its playing a 12-game in-conference schedule.

It remains to be seen over the rest of the season whether the pattern shown by the comparison table will remain as clear as it is now.  It is worth noting, however, that so far the pattern has been consistent from week to week.

2026 ARTICLE 19: A CASE STUDY ON CONFERENCE SCHEDULING: BIG 10 2026

In this article, I discuss conference scheduling and how it relates to the NCAA RPI.  In particular, the article focuses on strong conferences and their numbers of in-conference as compared to non-conference games.  The article uses the Big 10's 2026 scheduling as a case study.

This article is based on teams' actual results through October 11 and projected results of games not yet played, including conference tournament games.  Because of that, the data here about the current season are "provisional."  At the end of the season, I will update and publish the final data for this case study.

NCAA RPI Background

Because of the way the NCAA constructs its RPI, one should expect that a strong conference with a high proportion of in-conference games will be at a rating and ranking disadvantage, as compared to other strong conferences with lower proportions of in-conference games.  The reasons for this are:

1.  The Importance of a Team's Opponents' Winning Percentages.  The NCAA RPI basic formula is:

(WP + 2*OWP + OOWP)/4

WP is the team's Winning Percentage, computed as:

 (Wins + 1/3*Losses)/(Wins + Losses + Ties)

OWP is the average of the team's opponents' WPs (against teams other than the team itself)

OOWP is the average of the team's opponents' opponents' WPs

In the basic formula, the 2*OWP + OOWP portion is referred to as the Strength of Schedule component.  Thus overall the formula is a combination of two basic components: the team's Winning Percentage and its Strength of Schedule.

Although it is not immediately apparent from the RPI formula, each of the two basic components has an effective weight of 50%.  You can find an explanation of why this is so at the RPI for Division I Women's Soccer website on its RPI: Formula page, under the heading Calculation of RPI.  The NCAA publicly has agreed that these are the effective weights of the two components.

The Strength of Schedule's 50% effective weight is split 40% Opponents' Winning Percentage and 10% Opponents' Opponents' Winning Percentage.  Thus a team's Opponents' Winning Percentage (40%) is almost as important as the teams own Winning Percentage (50%) and its Opponents' Opponents' Winning Percentage (10%) is a relatively minor contributor.  The linked page also cxplains why these are the effective weights of the Strength of Schedule elements.  (The NCAA, so far as I know, has not publicly acknowledged and may not what people to know these are the effective weights, but I am confident the NCAA knows they are.)

The key piece of information here, for purposes of this article, is this: A team's Opponents' Winning Percentage comprises 40% of the effective weight of what makes up its NCAA RPI rating. 

2.   The Effect of In-Conference Games on a Conference Team's Opponents' Winning Percentage.  As a hypothetical, suppose I am in a conference that plays a full round robin.  In relation to my in-conference opponents' winning percentages, suppose I play conference opponents A and B.  If A has beaten B, then when I play A, its Winning Percentage will include its win over B.  Likewise, however, when I play B, its Winning Percentage will include its loss to A.  Or, if the two have tied, each's winning percentage will include the tie.  This means that the effect, from all of my in-conference games, is that my Opponents' Winning Percentages will net out at 0.5000 -- for every conference opponent win there is a matching conference opponent loss and for every conference opponent tie there is a matching conference opponent tie.

Since all conferences do not play full round robins and since there are conference tournaments in which all teams may not participate, the net effect from all in-conference games does not net out at exactly 0.5000.  In general, however, the effect of all of my conference games will net out to an Opponents' Winning Percentage of right around 0.5000.  (This likewise will be the case for the net effect from in-conference competition on Opponents' Opponents' Winning Percentage.)

A result of this is that if all conferences played only conference games, the NCAA RPI would not be able to distinguish the conferences from each other.

3.  The Importance of Non-Conference Games in Distinguishing the Conferences from Each Other.  The way in which the NCAA RPI distinguishes the conferences from each other is through non-conference games.  If I am a conference team with an excellent winning percentage in non-conference games, that excellent winning percentage plus my in-conference winning percentage is what I contribute to each of my conference opponent's Opponents' Winning Percentage.  Thus it is to all of my conference opponents' NCAA RPI advantage for me to have an excellent winning percentage.  The cumulative effect of all of my conference's teams' non-conference winning percentages is by far the main factor that distinguishes my conference from other conferences, from an NCAA RPI perspective.

4.  The Importance of the Proportion of Games That Are Conference and Non-Conference.  Suppose that my conference plays half of its games in-conference and half non-conference.  Then the half that are in-conference will set my Opponents' Winning Percentage at about 0.5000.  The half that are non-conference will cause an adjustment to that 0.5000 based on how good or poor my other conference members' winning percentages are in their non-conference games.  If you assume I am in a strong conference whose members have high non-conference winning percentages, my games against them will cause my Opponents' Winning Percentage to be well above the 0.5000 that comes from my conference games.

As an alternative, suppose again I am in a strong conference whose members have high non-conference winning percentages, but that we play two-thirds of our games in-conference and only one-third non-conference.  The kind of effect of the high non-conference winning percentages will be the same as in the prior example, but because of the lower proportion of non-conference games, the distance my games against my conference opponents will pull my Opponents' Winning Percentage above 0.5000 will be smaller.

Thus if you have two equal-in-strength conferences whose teams have equal non-conference winning percentages, but one conference plays half its games as non-conference whereas the other plays a third as non-conference, the conference that plays half its games as non-conference will have overall higher Opponents' Winning Percentages than the conference that plays a third of its games as non-conference and thus will have higher NCAA RPI ratings.  Although the ratings will make it look like the conference with half its games as non-conference is the stronger conference, that in fact is not the case.  Rather, its appearance as stronger is simply a reflection of how the RPI formula computes strength of schedule combined with the difference between the conferences' proportions of in-conference and non-conference games.

What Do This Year's Data Show?

 1.  What Are the Autonomy (Power) Conferences' Proportions of Conference and Non-Conference Games?  Here are the numbers of in-conference regular season games and the numbers of conference tournament games for the Autonomy (Power 4) conferences:

Big 10: 12 regular season games; 8 tournament participants, 7 games; total of 85 games.  12.78 games average per team.

Big 12: 11 games; 8 participants,7 games; total of 73 games.  11.88 games average per team.

SEC: 10 games; 12 participants, 11 games; total of 66 games.  11.38 games average per team.

ACC: 10 games; 6 participants, 5 games; total of 60 games.  10.59 games average per team.

Here are the numbers of non-conference games:

Big 10:  5.94 games average per team.

Big 12:  7.44 games average per team.

SEC:  7.13 games average per team.

ACC:  7.29 games average per team.

These numbers translate into the following proportions:

Big 10:  68.2% in-conference, 31.8% non-conference.

Big 12:  61.5% in-conference, 38.5% non-conference.

SEC:  61.5% in-conference, 38.5% non-conference.

ACC:  59.2% in-conference, 40.8% non-conference.

Based on these numbers, the Big 10, with its highest proportion of in-conference games, would need the highest winning percentage in its non-conference games, of the four conferences, in order to end up with an overall Opponents Winning Percentage to match theirs.  And, the ACC with its lowest proportion of in-conference games, could get away with the poorest non-conference winning percentage.

2.  What Are the Conferences' Actual Winning Percentages in Non-Conference Games?  Here are the Autonomy conferences' actual winning percentages in non-conference games:

Big 10:  64.5%

Big 12:  65.8%

SEC:  69.6%

ACC:  76.9%

With these non-conference winning percentages and the conferences' proportions of non-conference games, it should be no surprise that the projected end-of-season conference NCAA RPI ranks are ACC #1, SEC #2, Big 12 #3, and Big 10 #4.

3.  What Are the Conferences' Opponents' Strengths?   The most powerful consideration for the Women's Soccer Committee when making its NCAA Tournament seeding and at large selection decisions is teams' NCAA RPI ranks.  Another powerful consideration, however, is teams' results against NCAA RPI Top 50 opponents.  And, within these results are results against non-conference Top 50 opponents.  Here are this year's projected team average numbers of non-conference Top 50 opponents for the four Autonomy conferences:

Big 10:  1.72

Big 12:  1.31

SEC:  1.88

ACC:  1.47

This results in the following proportions of non-conference games against Top 50 opponents:

Big 10:  29.0%

Big 12:  17.6%

SEC:  26.3%

ACC:  20.2%

In addition to this, here are the average NCAA RPI ranks of the conference teams' non-conference opponents:

Big 10:  125

Big 12:  135

 SEC:  129

ACC:  142

Thus according to the NCAA RPI, the Big 10 is playing the most difficult overall non-conference schedule and in addition, of its non-conference games, plays the highest proportion of Top 50 opponents.  The Big 10's playing the highest proportion of Top 50 opponents, of its non-conference games, is not surprising since it plays roughly the same number of Top 50 opponents as the other Autonomy conferences but plays fewer total non-conference games.

What this means, in relation to the above NCAA RPI Background discussion, is that given its 12 game in-conference schedule, the Big 10's non-conference scheduling has been the opposite of what it needed to be in order to offset how the Strength of Schedule component of the NCAA RPI formula works.  Its non-conference schedule needed to be weaker than the schedules of the other Autonomy conferencces and in fact, it has been stronger. 

4.  How Much Does This Affect NCAA RPI Ratings?  Here are how each Autonomy conference's teams' actual performances so far this season relates to their expected performances based on their current NCAA RPI ratings.  The numbers are the difference between the conference teams' actual winning percentages and what the NCAA RPI ratings say their winning percentages should have been if the ratings are correct.  A positive percentage means the conference teams' actual winning percentages have been better than their expected winning percentages:

Big 10:  6.5%

Big 12:  3.8%

 SEC:  5.8%

ACC:  4.6%

As expected, all of the conferences' performances to date are better than their ratings say they should have been.  In other words, the NCAA RPI underrates all of the Autonomy conferences.

Of these, the Big 10 is the most underrated.  In light of the above data and discussion, this is what one should expect.  Because the Big 10 plays the highest proportion of in-conference games, one would expect the NCAA RPI, because of how it calculates Strength of Schedule, to underrate it more than it underrates the other Autonomy conferences.

Putting it All Together

The Big 10, by playing a 12-game in-conference regular season, has reduced the number of non-conference games its teams are able to play.  Given the structure of the NCAA RPI, with its high valuation of opponents' winning percentages, this puts the Big 10 in the position of needing its teams to have higher non-conference winning percentages than the other Autonomy conferences teams, in order to equally compete with them in the rankings.  From this perspective, if the Big 10 wants to play a 12-game in-conference regular season, then from an NCAA RPI perspective it needs to schedule weaker non-conference opponents than the other Autonomy conferences.

On the other hand, if the Big 10 wants its teams to have good Top 50 non-conference results equivalent to those of the other Autonomy conferences, it needs to be scheduling the same number of Top 50 non-conference opponents as the other Autonomy conferences.

In other words, the Big 10's decision to play a 12-game in-conference regular season, coupled with the NCAA RPI's high emphasis on opponents' winning percentages, has put the Big 10 teams in a "damned if you do and damned if you don't" position.  They have to chose between being hurt by playing weaker non-conference schedules than the other Autonomy conferences or being hurt by playing stronger non-conference opponents on a per-non-conference-opponent basis.

In future years, the simple solution for the Big 10 is to stop playing a 12-game in-conference schedule.  In fact ideally, in relation to the Autonomy conferences, they all would play the same number of in-conference games and would have equally sized conference tournaments.  This would put them all on an equal footing from an NCAA RPI formula perspective.

NCAA Tournament Seeding and At Large Selection Effects

In terms of NCAA Tournament seeds and at large selections, does it matter?

Remember, the problem I have described comes from the way the NCAA RPI calculates strength of schedule, specifically its high emphasis on Opponents' Winning Percentage.  The Balanced RPI does not have this problem and is able to eliminate most, if not all, of the effects of conferences having different proportions of in-conference and non-conference games.  Thus if you compare the Women's Soccer Committee's likely NCAA Tournament seeding and at large selection decisions based on the NCAA RPI to what they would be if the Committee were using the Balanced RPI, you can get a picture of how much the Big 10's having a 12-game conference schedule is costing it from an NCAA Tournament perspective.

The second half of my 2026 Article 18 shows the difference between the Big 10's NCAA Tournament prospects under the NCAA RPI and if the Committee were using the Balanced RPI, based on all teams' actual results through Week 9 of the season and their projected results through the balance of the season.  Although the difference shown in Article 18 is provisional and we will not know the actual difference until the end of the season, Article 18 suggests that the Big 10's playing a 12-game in-conference season, given that the Committee uses the NCAA RPI to rank teams, is going to significantly diminish how the Big 10 will fare in the seeding and at large selection process.

This is the first year in which I have done these kinds of calculations.  Given that, I am apprehensive about whether the extent to which the Big 10's NCAA Tournament seeding and at large selection prospects will be diminished due to its 12-game in-conference schedule ultimately will end up as great as the numbers currently suggest.  This will be something to track as we go through the coming weeks.

2026 ARTICLE 18: ACTUAL CURRENT AND SIMULATED END-OF-SEASON RANKINGS AND OTHER DATA AFTER WEEK 9

This week, I'm combining my two regular reports into one.  Later in the week, I'll follow this up with a case study of the Big 10 and how its scheduling this year, as compared to the scheduling of the other Autonomy (Power 4) conferences, appears to be affecting its NCAA Tournament prospects.

Below, I have provide links to two Exel workbooks.  To download a 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 menu, click on Microsoft Excel (.xlsx).  This will download the Excel workbook.

ACTUAL CURRENT RANKINGS AND OTHER DATA AFTER WEEK 9

The first workbook provides current NCAA RPI, KPI, Balanced RPI, and Massey rankings and other data based on the actual results of games played through Week 9 (through October 11) of the season, for teams, conferences, and regions.  Here is a link to the workbook:

2026 RPI Report Actual Results Only as of 10.11.2026

The first page of the workbook is for Teams, the second for Conferences, and the third for Regions.  The data include almost all the non-conference games for the season and a portion of the conference games, about two-thirds of all games that teams will play.  There still is room for significant changes in where teams finally will end up.

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 pretty large, so there still is room for significant change in the rankings and how the NCAA Tournament bracket will end up.

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

SIMULATED END-OF-SEASON RANKINGS AND NCAA TOURNAMENT BRACKETS FOLLOWING WEEK 9

The second workbook is based on the actual results of games played through Week 9 (ending October 11) 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, and provides other important information related to teams, conferences, and regions.  Here is a link to an the workbook:

RPI Report as of 10.11.2026

2026 Article 8 has an explanation of the method I use to generate the simulated ranks and of the key information in the workbook.

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 win, loss, and tie percentages in games played through October 11.  The second data row, 2026 Team 1 Predicted Results, Simulated NCAA RPI, applies teams' simulated end-of-season RPI ratings to games played through October 11, 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 actual percentages and the predicted percentages.  Thus, for example, the actual winning percentage for all Team 1s is 47.9%.  According to teams' end-of-season simulated ratings, the winning percentage should be 49.0%.  The Actual Less Predicted Results row shows the difference between predicted and actual is -1.148%.

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 the actual current NCAA RPI 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 1.341%.

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 (if not AQs).  The table also shows which teams the simulation currently projects as Automatic Qualifiers.

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, depending on which rating system the Committee is using.

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,  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.

Looking at the table, you can see that the ACC and, especially, the Big 10 are hurt by the NCAA RPI relative to the Balanced RPI.  In contrast, the non-Autonomy (i.e., non-Power 4) conferences are helped by the NCAA RPI relative to the Balanced RPI.  This is not surprising since, as I have shown elsewhere, the NCAA RPI discriminates against stronger conferences and regions and in favor of weaker ones whereas the Balanced RPI does not.

It remains to be seen over the rest of the season whether the pattern shown by the comparison table will remain as clear as it is now.  I suspect it will moderate some, but time will tell.

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.

2026 ARTICLE 15: ACTUAL CURRENT RANKINGS AND OTHER DATA AFTER WEEK 7

This Article 15 provides current NCAA RPI, KPI, Balanced RPI, and Massey rankings based on the actual results of games played through Week 7 (through September 27) 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 9.27.2026

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, as we're now early in the conference season.  I consider these data very preliminary and expect significant changes over the remainder of the season, particularly in reation to how conferences and regions compare to each other.

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.

As with Article 14 on the full season simulated rankings, starting this week, each week I'll highlight some data from the "actual games only" workbook that may be helpful to those interested in how the NCAA RPI works.  For this week, I'll cover some data related to regions.

As indicated in Article 13, I have organized the conferences into regions based on their historic non-conference scheduling patterns:

Autonomy (i.e., Power 4): ACC, Big Ten, Big Twelve, SEC

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

MidEast: Atlantic Ten, Big East, Colonial (now Coastal), Ivy

North: America East, Metro Atlantic, Northeast, Patriot

South: American, Southland, Southwestern, Sun Belt

Southeast: Atlantic Sun, Big South, Conference USA, Southern, United

West: Big Sky, Big West, Mountain West, Pac Twelve, West Coast

The following table is from the linked workbook's Regions page:

As you can see, to date teams from the Mideast region, in games against other teams from the region, have played a very high proportion of ties.  On the other hand, teams from the North have played a very low number.  In relation to the 2024 change in the NCAA RPI's Winning Percentage formula, going from treating a tie as half a win to a third of a win, this means the change has affected teams from the Mideast region the most negatively and teams from the North region the most positively.  If you compare this table to the similar table for the full season simulation in Article 14, you will see that the simulation projects that the spread in the above table will narrow.  That will be something to watch for to see if it does.