2025 ARTICLE 14: RPI REPORT AFTER WEEK 1 GAMES

In 2025 Article 7 and 2025 Article 8, I described how I assign pre-season NCAA RPI ratings and ranks to teams and then, assuming those ratings and ranks represent true team strength, apply them to teams' schedules to generate predicted end-of-season NCAA RPI ratings and ranks.  Once I have done that, at the end of each week of the season I replace that week's predicted results with games' actual results.  Then, using those actual results combined with predicted results for the balance of the season, I generate new predicted end-of-season NCAA RPI ratings and ranks.  After completing week 5 of the season, I will switch from using assigned pre-season NCAA RPI ratings and ranks as the basis for predicting future results to using the then actual NCAA RPI ratings and ranks as the basis.

Using this process, the predicted end-of-season NCAA RPI ratings and ranks are very speculative at the beginning of the season.  However, as each week passes, they become progressively closer to what the actual end-of-season ratings and ranks will be.  By the last few weeks of the season, they become helpful when trying to figure out what results teams need in their remaining games in order to get particular NCAA Tournament seeds or at large selections.

Today's report shows where things are with Week 1's actual results incorporated into the end-of-season predictions.  The report has a page for teams, for conferences, and for geographic playing pool regions.  You can download the report as an Excel workbook with this link: 2025 Week 1 RPI Report.  The same information also is set out in tables below, but I recommend downloading the workbook as it likely will be easier to use.  (If using the tables below, scroll to the right to see additional columns.)

This year, an emphasis in these reports is on showing why the NCAA RPI, because of how it measures the opponents' strengths of schedule that it incorporates into its formula, discriminates against or in favor of particular teams, conferences, and regions.

TEAMS

This page shows, for each team:

Team name

Geographic playing pool region

Conference

If the team is predicted to be its conference's NCAA Tournament automatic qualifier (AQ)

If the team is predicted to be disqualified from an NCAA Tournament at large selection due to having more losses than wins (1)

Team's 

NCAA RPI rank (based on past history, a key factor in selecting teams that will be in the NCAA Tournament #1 through #4 seed pods)

rank as a strength of schedule contributor to opponents under the NCAA RPI formula

Opponents'

average NCAA RPI rank 

average rank as strength of schedule contributors under the NCAA RPI formula

Conference opponents' 

average NCAA RPI rank 

average rank as strength of schedule contributors under the NCAA RPI formula

[NOTE: Teams have relatively little control over this part of their schedules.] 

Non-Conference opponents' 

average NCAA RPI rank

average rank as strength of schedule contributorsl under the NCAA RPI formula

[NOTE: Teams control this part of their schedules, to some extent.  Geographic factors such as travel expenses, available opponents, and other factors can be limiting considerations.]

NCAA RPI Top 50 Results Score

NCAA RPI Top 50 Results Rank (based on past history, a key factor in NCAA Tournament at large selections and in selecting teams that will be in the #5 through #8 seed pods)

Similar rank and strength of schedule contributor rank numbers under the Balanced RPI

KPI rank if available

Massey rank

 

CONFERENCES

This page shows, for each conference:

Conference name

Conference's NCAA RPI rank

Teams' 

average NCAA RPI rank 

average rank as strength of schedule contributors under the NCAA RPI formula 

 Opponents' 

average NCAA RPI rank 

average rank as strength of schedule contributors under the NCAA RPI formula

Conference opponents' 

average NCAA RPI rank 

average rank as strength of schedule contributors under the NCAA RPI formula

Non-Conference opponents' 

average NCAA RPI rank 

average rank as strength of schedule contributorsl under the NCAA RPI formula

Conference's Non-Conference RPI rank 

Similar rank and strength of schedule contributor rank numbers under the Balanced RPI

KPI rank if available

Massey rank

 

REGIONS

This page shows, for each region:

Region name

Number of teams in region 

Region's NCAA RPI rank

Teams' 

average NCAA RPI rank 

average rank as strength of schedule contributors under the NCAA RPI formula 

Opponents' 

average NCAA RPI rank 

average rank as strength of schedule contributors under the NCAA RPI formula

Region opponents' 

average NCAA RPI rank 

average rank as strength of schedule contributors under the NCAA RPI formula

(NOTE: Due to budget limitations, teams may be compelled to play all or most of their non-conference games against opponents from their own geographic regions.] 

Non-Region opponents' 

average NCAA RPI rank 

average rank as strength of schedule contributorsl under the NCAA RPI formula

Similar rank and strength of schedule contributor rank numbers under the Balanced RPI

KPI rank if available

Massey rank

Regions' proportions of games played against teams from each region (NOTE: This years, the numbers of out-of-region games are down about 30% from past patterns.  This may result in a significant degradation of the NCAA RPI's already impaired ability to properly rate teams from a region in relation to teams from other regions.)

Proportion of in-region games that are ties (as a measure of in-region parity) (NOTE: The NCAA RPI, because of how it measures Strength of Schedule, on average discriminates against teams from regions with higher region parity.)

 

2025 ARTICLE 13: 2025 PRE-SEASON PREDICTIONS AND INFORMATION, PART 6, GEOGRAPHIC REGIONS IN RELATION TO NCAA RPI RANKS AND STRENGTH OF SCHEDULE RANKS

This article, for the geographic regions within which the teams from each state play most of their games, provides information similar to that provided for conferences in 2025 Article 12.  A map showing the four regions is at the RPI for Division I Women's Soccer RPI: Regional Issues page.

As you can see, when averaged across a region, the differences between average NCAA RPI ranks and average Strength of Schedule contributor ranks under the NCAA RPI formula are relatively small.  This makes sense, since each region has an array of strong and weak teams and conferences.  As a generalization, however, looking at the numbers for the regions' teams opponents, overall and on average teams from the West region are discriminated against due to the way the NCAA formula computes Strength of Schedule, the Middle region experiences no impact, and the North and South regions are benefitted by discrimination.

To be clear, there are teams and conferences from all of the regions that the NCAA RPI formula discriminates against and in favor of.  The numbers above simply show the net effect of the discrimination for each region.

A particular concern this year is a significant reduction in out-of-region competition, most likeky due to less funding being available for travel.  The following table shows the extent of the reduction looking at the nation as a whole:

As you can see, the number of out-of-region games will be reduced by 28.1% from what the number historically has been.

A break down of the numbers from the preceding table by region shows reductions in the number of out-of-region games as follows:

Middle  18.3%

North  28.5%

South  30.0%

West  31.7%

These reductions should be a concern for the Women's Soccer Committee.  The NCAA RPI already has a problem ranking teams dispersed among the conferences and across the regions within a single national system.  The reductions in out-of-region play are likely to make the problem worse. 

 

2025 ARTICLE 12: 2025 PRE-SEASON PREDICTIONS AND INFORMATION, PART 5, CONFERENCES IN RELATION TO NCAA RPI RANKS AND STRENGTH OF SCHEDULE RANKS

 In 2025 Pre-Season Predictions and Information, Parts 4 and 4B, for the individual teams I showed the relationship between predicted NCAA RPI ranks and Strength of Schedule Contribution ranks under the NCAA RPI formula, both for the individual teams and for their opponents.  In this article, I will show the same information, but for each conference.  This gives a good picture of how the NCAA RPI discriminates among conferences because of the defective way it calculates Strength of Schedule.

This table has the conferences in NCAA RPI rank order, based on the average rating of their teams.  See below the table for comments.

In the table, the first two green-highlighted columns on the left show, for each conference, the difference between its teams' average NCAA RPI rank and its teams' average Strength of Schedule contributor rank under the NCAA RPI formula.  As you read down the table from the strongest conferences at the top to the weakest at the bottom, you can see the clear pattern: For stronger conferences, the conference teams' Strength of Schedule contributor ranks are poorer than the teams' actual ranks say they should be; and for weaker conferences they are better than they should be.

The next two salmon-highlighted columns look at how this plays out for the conference teams' schedules.  The first of those columns shows the conferences' teams' opponents' average ranks and the second column shows those opponents' average ranks as Strength of Schedule contributors.  The pattern here is the same:  Stronger conferences' opponents' Strength of Schedule Contributor ranks are poorer than the opponents' actual ranks say they should be; and the opposite is true for the weaker conferences.

The next four columns break the numbers for the conference teams' schedules down into conference opponents (green-highlighted) and non-conference opponents (salmon-highlighted).  Given that in conference play, the conferences' teams are playing each other, it is no surprise that the contrasts between the conference opponents' NCAA RPI ranks and their ranks as Strength of Schedule contributors follow the same basic pattern.  For the non-conference opponents, where the individual teams have more control over their schedules, the pattern is similar but less extreme and with a little more variability.

It is important here to point out that coaches in top tier and most coaches in middle tier conferences are aware of these patterns and often take them into consideration in their non-conference scheduling.  They also are aware, however, that in the NCAA Tournament seeding and at large selection processes, good results against highly ranked opponents matter, including against highly ranked non-conference opponents.  Further, coaches of teams with NCAA Tournament aspirations often want to play at least some strong non-conference opponents.  This means that they sometimes decide to schedule opponents whose Strength of Schedule contributions are likely to be poorer than their RPI ranks say they should be, essentially deciding to take a potential RPI "hit" in exchange for the potential of a good result against a highly ranked opponent.

NOTE:  Being aware of the scheduling dilemma I just described, I designed my Balanced RPI, which is a modification of the NCAA RPI, with the specific objective of eliminating the difference between teams' ranks and their ranks as Strength of Schedule contributors.  Thus under the Balanced RPI, if a team has a rank of X, that also is either exactly or very close to exactly the team's rank as a Strength of Schedule contributor.  In other words, if the NCAA were to use the Balanced RPI, coaches no longer would have this scheduling dilemma.   (As an additional benefit, the RPI no longer would discriminate among conferences in relation to conference strength.)

2025 ARTICLE 11: 2025 PRE-SEASON PREDICTIONS AND INFORMATION, PART 4B, TEAMS' SCHEDULES IN RELATION TO OPPONENTS' NCAA RPI RANKS AND STRENGTH OF SCHEDULE RANKS

In Part 4, I discussed and showed the differences between teams' NCAA RPI ranks and their ranks as Strength of Schedule contributors under the NCAA RPI formula.  In this article, I will show predictions for how these differences will affect teams by the end of the 2025 season.

For each team, the following table shows its predicted:

Opponents' average NCAA RPI rank

Conference opponents' average NCAA RPI rank

Non-conference opponents' average NCAA RPI rank

Opponents' average rank as Strength of Schedule contributors under the NCAA RPI formula

Conference opponents' average rank as Strength of Schedule contributors under the NCAA RPI formula

Non-conference opponents' average rank as Strength of Schedule contributors under the NCAA RPI formula

These numbers allow you to see how the NCAA RPI rank versus Strength of Schedule contributor rank differences relate to:

1.  Teams' in-conference schedules, which teams basically can't control;

2.  Teams' non-conference schedules, which teams can control at least to some extent; and

3.  Teams' overall schedules.

If you review the table's numbers with a view to the strength of the teams' conferences, you will see that generally speaking the NCAA RPI formula understates the strengths of schedule of top tier conferences' teams, gets the strengths of schedule of middle tier conferences' teams about right, and overstates the strengths of schedule of bottom tier conferences' teams.  I've arranged the teams by conference so you can better see how this NCAA RPI defect affects teams by conference.  Scroll to the right, if necessary, to see the entire table.

NOTE: The differences in the Conference Opponents Average Rank column for teams from the same conference are primarily due to conference teams not playing full round robins.  The differences in the Non-Conference Opponents Average Rank column for teams from the same conference are due the different teams' non-conference scheduling strategies.

I'll use Baylor, from the Big 12, with a predicted NCAA RPI rank of #66, and Lamar, from the Southland, with a predicted NCAA RPI rank of #55, as examples.  I've chosen these teams because no team ranked poorer than #57 ever has gotten an at large position in the NCAA Tournament.  Thus Baylor is outside the historic at large candidate group and Lamar is within the candidate group.

Baylor (Big 12): 

Conference opponents' average NCAA RPI rank is 81 and conference opponents' average Strength of Schedule contributor rank under the NCAA RPI formula is 119.

Non-conference opponents' average NCAA RPI rank is 110 and non-conference opponents' average Strength of Schedule contributor rank under the NCAA RPI formula is 108.

Overall, opponents' average NCAA RPI rank is 93 and opponents' average Strength of Schedule contributor rank under the NCAA RPI formula is 115.

Thus Baylor's Strength of Schedule component of the NCAA RPI significantly discriminates against Baylor in relation to its conference schedule and only barely offsets that discrimination in relation to its non-conference schedule.  The overall result is that the Strength of Schedule component significantly discriminates against Baylor.

Lamar (Southland):

Conference opponents' average NCAA RPI rank is 212 and conference opponents' average Strength of Schedule contributor rank under the NCAA RPI formula is 185.

Non-conference opponents' average NCAA RPI rank is 153 and non-conference opponents' average Strength of Schedule contributor rank under the NCAA RPI formula is 184.

Overall, opponents' average NCAA RPI rank is 193 and opponents' average Strength of Schedule contributor rank under the NCAA RPI formula is 185.

Thus the Strength of Schedule component of the NCAA RPI significantly discriminates in favor of Lamar in relation to its conference schedule and offsets that discrimination some in relation to its non-conference schedule.  The overall effect, however, is that the Strength of Schedule component still discriminates in favor of Lamar.

Given that Baylor is outside but in the vicinity of the ranking area of teams that historically are candidates for NCAA Tournament at large selections and Lamar is only a little inside that ranking area, this demonstrates the importance of this NCAA RPI defect.  History suggests that Lamar, if not an Automatic Qualifier, would not get an at large selection.  For Baylor, however, being outside the historic candidate area, there is a question whether, if inside the candidate area and considered by the Committee, it might displace one of the "last in" at large teams.  In other words, this NCAA RPI defect may have negative NCAA Tournament at large selection consequences.  (And, by a similar analysis of seeding candidate groups, may have negative seeding consequences.)

The significance of this kind of example is reinforced if you consider Lamar's and Baylor's ranks using my Balanced RPI.  The Balanced RPI is a rating system that builds on the RPI, with modifications that fix the NCAA RPI's defective discrepancy between teams' NCAA RPI ranks and their ranks as Strength of Schedule contributors under the NCAA RPI formula.  The Balanced RPI's predicted rank for Lamar is 110, well outisde the NCAA Tournament at large selection candidate range.  For Baylor, its predicted rank is #57, in other words a candidate for at large selection.

2025 ARTICLE 10: 2025 PRE-SEASON PREDICTIONS AND INFORMATION, PART 4, TEAMS' NCAA RPI RANKS COMPARED TO THEIR RANKS AS STRENGTH OF SCHEDULE CONTRIBUTORS

The NCAA RPI has a major defect, which is the way in which it computes a team's strength of schedule.

As discussed on the RPI: Formula page at the RPI for Division I Women's Soccer website, the NCAA RPI has two main components:  a team's Winning Percentage and its Strength of Schedule.  Within the overall NCAA RPI formula, the effective weights of the two components are approximately 50% Winning Percentaqge and 50% Strength of Schedule.

Within the NCAA RPI formula, in turn, Strength of Schedule consists of two elements: the average of a team's opponents' winning percentages (OWP) and the average of a team's opponents' opponents' winning percentages (OOWP).  And, within Strength of Schedule, the effective weights of these two elements are 80% opponents' winning percentage and 20% opponents' opponents' winning percentage.  Thus for the NCAA RPI's Strength of Schedule component, a team's opponents' winning percentages matter a lot and against whom they achieved those winning percentages matters little.  This is a major defect.

In this and the next two parts of my Pre-Season Predictions and Information, using end-of-season predictions for the 2025 season, I will show how the NCAA RPI's strength of schedule defect plays out for teams (this Part 4), for conferences (Part 5), and for geographic regions (Part 6).

The following table shows, for each team, its predicted end-of-season NCAA RPI rank and its predicted rank as a strength of schedule contributor under the NCAA RPI formula.  In a good rating system, these ranks should be the same or, at least, very close to the same.  As the table shows, however, for the NCAA RPI formula, for many teams, the ranks are not close to the same.

Using some of the top teams in the alphabetical list as examples:

If Team A plays Air Force as an opponent, Team A will have played the NCAA RPI #232 ranked team.  When computing Team A's rating and rank, however, the NCAA RPI formula will give team A credit only for playing the #274 team.

On the other hand, if Team A plays Alabama State, Team A will have played the #340 team.  But when computing Team A's rating and rank, the NCAA RPI formula will give Team A credit for playing the #277 team.

Thus although the NCAA RPI ranks Air Force and Alabama State 108 rank positions apart, when considering each of their strengths for purposes of Team A's strength of schedule computation, the NCAA RPI treats Air Force and Alabama State as roughly equal.

You can scroll down the table and see how this NCAA RPI formula defect plays out for teams you are interested in,  I suggest you look, in particular, at teams in the middle to lower levels of top tier conferences and at teams in the upper levels of middle and bottom tier conferences.  For example:

Look at Alabama:  Its predicted NCAA RPI rank is #37.  But, its predicted rank as a strength of schedule is only #89.

Then look at Bowling Green:  Its predicted NCAA RPI rank is #86 but its predicted rank as a strength of schedule contributor is #26.

These kinds of differences have significant practical implications related to scheduling and the NCAA Tournament.  Teams' NCAA RPI ranks are a key factor in the Women's Soccer Committee's decisions on Tournament seeds and at large selections.  So, if a coach has NCAA Tournament aspirations, from strictly an NCAA RPI perspective, Bowling Green would be a significantly better opponent to play than Alabama.  This would be true for two reasons: (1)  Bowling Green probably is weaker than Alabama, so an easier game in which to get a good result; and (2) Bowling Green, as an opponent, will give the coach's team's NCAA RPI a better strength of schedule contribution than Alabama.

Thus when doing non-conference scheduling, coaches with NCAA Tournament aspirations or with other concerns about where their teams will finish in the NCAA RPI rankings must take this NCAA RPI formula defect into account.  In essence, they are in the position of having to learn how to "trick" the NCAA RPI through smart scheduling -- in the example, choosing Bowling Green rather than Alabama as an opponent. 

2025 ARTICLE 9: 2025 PRE-SEASON PREDICTIONS AND INFORMATION, PART 3, "PREDICTED" CONFERENCE REGULAR SEASON AND TOURNAMENT CHAMPIONS

Continuing with "predictions," using the "results likelihood" method described in 2025 Article 9, my system uses the same "3 points for a win and 1 for a tie" scoring that conferences use for their standings to create team standings within each conference.  It is worth noting that the results likelihoods take game locations into account and that a good number of conferences do not play full round robins.

Using the ACC as an example, here are what its "predicted" end-of-season standings look like:

Although Florida State's and North Carolina's points look the same, that is due to rounding.  Florida State's are slightly higher.  My interpretation of these standings is that it will be very close at the top of the conference among Florida State, North Carolinam Stanford, and Duke, with Virginia also in the mix.

Using the conference standings and the conference tournament formats (as published to date), my system next creates conference tournament brackets.  Then, since it is necessary to have winners and losers to fill out the entire tournament brackets, the system assigns as a game winner any team that has a win likelihood above 50%.  Where neither team has a win likelihood above 50%, the system treats the game as a tie.  For the tiebreaker, the advancing team is the one with the higher win likelihood.

This process results in the following conference regular season and conference tournament champions.  In most cases they are the same, but in two cases they are different.

2025 ARTICLE 8: 2025 PRE-SEASON PREDICTIONS AND INFORMATION, PART 2, "PREDICTED" END-OF-SEASON RANKS AND RATINGS

Once I have assigned pre-season strength ranks and ratings to teams, I combine those with teams' schedules to "predict" where teams will end up at the end of the season following completion of the conference tournaments.  The process to do this requires background work:

1.  For each game, I calculate the game-location-adjusted rating difference between the teams, using their assigned pre-season strength ratings as the base.  Since the assigned strength ratings are based on what the average historic NCAA RPI ratings are for those teams' ranks, my game location adjustments increase the home team's rating by 0.0085 and decrease the away team's rating by 0.0085, for an overall adjustment of 0..0170.  This is the value of home field advantage for the current version of the NCAA RPI with the "no overtime" rule in effect.

2.  For the location-adjusted rating difference between the teams, I calculate each team's expected win, loss, and tie likelihoods.  These likelihoods are based on a study of the location-adjusted rating differences and results of all games played since 2010 (excluding 2020).

[NOTE: For a detailed explanation of how I determine the game location adjustment and the win, loss, and tie likelihoods, go to the RPI for Division I Women's Soccer website's page RPI: Measuring the Correlation Between Teams' Ratings and Their Performance. 

3.  Rather than assigning the opponents in a game either a win, a loss, or a tie result, I assign each team its win, loss, and tie likelihoods since these will give a better picture of what a team's overall record will be given its entire schedule.  As an example, Colorado and Michigan State will play on August 14 at Colorado.  Their location-adjusted rating difference is 0.0333 in favor of Colorado.  For that rating difference, referring to a result probability table for the current NCAA RPI in a "no overtime" world, Colorado's result likelihoods are 53.6% win, 19.9% loss, 26.4% tie (which don't quite add up to 100% due to rounding).  Those numbers go into Colorado's win-loss-tie columns for NCAA RPI computation purposes.  Michigan State's win-loss percentages are the converse.  I assign these likelihoods for all teams' games, add up each team's percentages, and convert them from percentages to numbers .  Thus Colorado ends the season with 12.2 wins, 4.9 losses, and 4.9 ties.  These are the numbers I use for Colorado as its wins, losses, and ties when computing its NCAA RPI.

4.  I then compute all teams' NCAA RPI ratings and ranks.  It is important to understand that these are different than the teams' assigned pre-season strength ratings and ranks.  This is because the NCAA RPI does not measure team strength.  Rather, it measures team performance based on a combination of teams' winning percentages and their strengths of schedule (as measured by the NCAA RPI formula).  Thus two teams with identical strength ratings and ranks will end up with different NCAA RPI ratings and ranks if they have different winning percentages and/or different strengths of schedule.  Below are my computed end-of-season (including predicted conference tournaments) ratings and ranks for teams.  You can compare the ranks to the ones in the preceding post to see the differences between teams' assigned pre-season strength ranks and team' predicted end-of-season NCAA RPI ranks.  (NOTE: I have corrected these rankings since their initial publication to fix a programming error.  The changes are relatively minor.)