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.)