2024 ARTICLE 8: EFFECTS OF THE 2022 NO OVERTIME RULE AND THE 2024 CHANGES TO THE RPI FORMULA

[NOTE:  Since initially publishing this article, I have refined the method for computing the relationship between conferences' and regions' ratings (and regions' percentage of tie games) and their actual performance as compared to expected performance.  The article's charts and related tables now are based on the refined method rather than the previous cruder method.]

In 2022, an NCAA rule change did away with overtime games during the regular season, except for conference tournaments.  This year, the NCAA, as proposed by the Women's Soccer Committee, has changed the RPI formula for Division I women's soccer.  The purpose of this article is to show the effects of these changes in relation to (1) NCAA Tournament at large selections and (2) how the RPI functions as a rating system.

The 2024 RPI Formula Changes

There are two basic changes to the RPI formula:

1.  The RPI formula for a team's Winning Percentage has been changed from

(Wins + 1/2 Ties)/(Wins + Losses +Ties)

to

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

Notes: 

(a) Winning Percentage account's for 50% of the RPI rating's effective weight.  Strength of Schedule, made up of Opponent' Winning Percentage and Opponents' Opponents' Winning Percentage, accounts for the other 50%.  In computing Strength of Schedule, the previous valuation of ties at 1/2  remains unchanged.

(b) The NCAA requires that a team have an overall won-lost record of at least 0.500 to be eligible for an at large position in the NCAA Tournament.  If ties are part of the won-lost computation, the previous valuation of ties at 1/2 likewise remains unchanged. 

2.  The bonus and penalty regime, which applies adjustments to the RPI for good wins and ties and poor losses and ties, has changed.  The old regime had high bonuses for wins and ties against teams with Unadjusted RPI ranks from #1 to 40 and lower bonuses for wins and ties against teams ranked #41 to 80.  It had low penalties for losses and ties against the second-to-bottom 40 teams in the rankings and higher penalties for losses and ties against the bottom 40.  In the new regime, there are three bonus tiers: #1 to 25, #26 to 50, and #51 to 100.  There are bonuses for wins and ties in the first two tiers, but only for wins in the #51 to 100 tier.  The penalty tiers are #151 through 250 for the lower penalties and #251 and poorer for the higher penalties.

The bonus and penalty amounts are as follows:

Tier 1, bonuses for wins and ties v opponents ranked #1 to 25:

Wins:

Away 0.0032, Neutral 0.0030, Home 0.0028

Ties:

Away 0.0020, Neutral 0.0018, Home 0.0016

Tier 2, bonuses for wins and ties v opponents ranked #26 to 50:

Wins:

Away 0.0026, Neutral 0.0024, Home 0.0022

Ties:

Away 0.0014, Neutral 0.0012, Home 0.0010

Tier 3, bonuses for wins v opponents ranked #51 to 100:

Wins:

Away 0.0008, Neutral 0.0006, Home 0.0004

Tier 4, penalties for losses and ties v opponents ranked #151 to 250:

Ties:

Away -0.0004, Neutral -0.0006, Home -0.0008

Losses:

Away -0.0010, Neutral -0.0012, Home -0.0014

Tier 5, penalties for losses and ties v opponents ranked #251 and poorer:

Ties:

Away -0.0016, Neutral -0.0018, Home -0.0020

Losses:

Away -0.0022, Neutral -0.0024, Home -0.0026

Ranking Effect of the 2024 Bonus and Penalty Regime

The following tables show the difference between the 2024 Formula's Unadjusted RPI ranks and the Adjusted RPI ranks under the new bonus and penalty regime.  The first table covers all teams.

The next table is for only the Top 57 teams:

These tables compare teams' Unadjusted RPI ranks to their Adjusted RPI ranks and thus are indicators of how much the bonus and penalty regime affects the rankings.  The tables -- especially the one for the Top 57 -- show that the bonuses and penalties have little effect on teams' rankings.  This particularly is noteworthy since small ranking differences within rating systems such as the RPI (or any other accepted sport rating system) are relatively meaningless.

For more detail here are tables that show how the URPI to ARPI rank difference amounts are distributed for All Teams and for the Top 57:

Effect of the No Overtime and RPI Formula Changes on NCAA Tournament At Large Selections

The changes altogether will have a significant cumulative effect on NCAA Tournament at large selections.

In reaching this conclusion, I started with a basic assumption:

The candidates for at large selections will be the teams ranked #1 through #57 in the Adjusted RPI rankings.

This assumption reflects what historically has been the case: Since 2007, all at large selections have come from teams ranked #1 through #57 by the Adjusted RPI.  Although this could change, a change seems unlikely, so this assumption provides a good base for analyzing the effect of the no overtime and RPI formula changes.

The following table shows basic data on the effects of the changes on at large selections.  The table contains the source data for the summary tables that follow.  You can scroll to the right to see the entire table.

The table is based on the seasons from 2010 through 2023, excluding Covid-affected 2020.  The 2024 Formula URPI and ARPI columns are based on applying the 2022 No Overtime rule and the 2024 RPI Formula changes to the data for each season.  The Actual ARPI column is based on applying to each season the overtime rule and RPI formula in effect for that season.  The No OT [Overtime] column is based on applying the current no overtime rule to each season, but using the RPI formula in effect for that season.  Together, these columns allow a determination of the effect of each change on at large selections -- the change to no overtimes, the change to a 1/3 weight for ties in determining winning percentage, and the change in the bonus and penalty regime.

The following table summarizes the information in the above table.  Remember, the numbers are spread over the 13 seasons from 2010 through 2023.

The first three data columns show the numbers of teams that have been brought into the Adjusted RPI Top 57 and thus become candidates for NCAA Tournament at large positions.  As you can see, the No Overtime rule has the greatest effect on this, bringing 32 teams into the Top 57.  Next comes the change to a 1/3 weight for ties, bringing in 8 teams.  Last comes the new bonus and penalty regime, bringing in only 2 teams.  Since the data cover 13 years, this means the changes will bring an average of just above 3 new teams per year into the at large candidate pool.  As the fifth column indicates, one team entering the Top 57 is due to it having had a below 0.500 winning percentage under the actual rules in effect for it but an above 0.500 record if the no overtime rule were in effect.

Of the new teams brought into the at large candidate pool, it is not possible to know whether they would have received at large positions.

Next are three Actual At Large, No Longer ... columns.  These show teams that actually received at large positions but that would not have received them under the new rules, due to their having dropped out of the Top 57 candidate pool.  Of these, 16 dropped out due to the no overtime rule, 6 due to the change in tie weight to 1/3, and 1 due to the new bonus and penalty regime.  Altogether this is just under 2 teams per year that received at large positions but that would not receive them under the No Overtime rule and the 2024 RPI Formula.

Following these are three Not Actual At Large, No Longer ... columns.  These show teams that were Top 57 candidates for at large positions but that did not get them.  Of these, 24 dropped out of the Top 57 candidate group due to the no overtime rule, 4 due to the change in tie weight to 1/3, and 0 due to the new bonus and penalty regime.

Of the teams dropping out of consideration for at large positions, the fourth column shows that 9 of them went from having winning percentages of 0.500 or better under the actual rules in effect for them to having a below 0.500 record if the No Overtime rule were in effect.

(Note: The number of teams moving into the Top 57 and out of the Top 57 do not match, as some teams moving in and out were Automatic Qualifiers, which are not included in the summary numbers.)

As these numbers show, the No Overtime rule has a significant effect on at large selections.  The change in tie weight to 1/3 has a small effect.  The new bonus and penalty regime has very little effect.

Effect of the No Overtime and RPI Formula Changes on How the RPI Functions as a Rating System

The next set of information shows how the No Overtime and 2024 RPI Formula changes will affect the RPI as a rating system: Will they make it better or worse or will they have little effect?

The above table, and the ones that follow, compare how different versions of the RPI perform as rating systems.  In the above table, the first row shows how the NCAA Unadjusted RPI, as in effect in 2023, performs.  The second row is for the NCAA Adjusted RPI as in effect in 2023.  These first two rows use as their data base games played from 2010 through 2021 (about 30,000 games), to show how the systems performed when there were overtime games.

The third row likewise is for the NCAA Adjusted RPI as in effect in 2023, but with games played from 2010 through 2023 as the data base and with all overtime games treated as ties.  By comparing this row to the one above it, you can see the effect of the No Overtime rule.

The fourth and fifth rows are for the NCAA Unadjusted and Adjusted RPI with the 2024 formula changes, with 2010 through 2023 as the data base and with all overtime games treated as ties.

Finally, the sixth row is for my Balanced RPI, with 2010 through 2023 as the data base and with all overtime games treated as ties.  I have included the Balanced RPI to show what can be achieved using the NCAA RPI's architecture as a base but with multiple additional calculations.

The above table shows simply the percentages of games that the higher rated teams (after adjustment for home field advantage) won, lost, and tied for each system.  Since the bottom four systems are based on no overtime games and therefore fewer ties, the best comparison column is the Overall % Correct Disregarding Ties column.  This column looks only at games that were won or lost and shows the percentage of games that the better rated team won.

The most significant information in that column is that with the change to the the No Overtime rule, the RPI became a significantly more accurate measure of teams' performance - -- the increase in accuracy from 81.0% with overtimes to 82,8% with no overtimes is a quite large improvement as rating systems go.  This improvement is not surprising.  With overtimes, about 20% of games when to overtime with about half of those games (~10% of all games) decided by golden goals.  When the NCAA formula considered the golden goal games, it treated them just the same as other win-loss games, thus not recognizing that the two teams' performances were very close to equal.  With overtimes eliminated, the formula now treats the performances as equal, which is a much better measure of the teams' performances than the old win-loss treatment.

This table is similar to the preceding one, but is limited to games involving at least one Top 60 team -- in other words, is related more or less to teams competing for NCAA Tournament at large positions.  It is notable that again, the RPI performs significantly better with no overtimes.

It also is worth noting that under both of the above tables, with the changes to the 2024 formula, the RPI's performance is slightly poorer than under the pre-2024 formula.

The next tables and charts address how the RPI systems do at rating teams from conferences and regions in relation to teams from other conferences and regions and individual teams in relation to other individual teams.

The tables and charts are based on game result probabilities, which in turn, for each rating system, are based on a result probability table unique to that rating system.  A result probability table comes from an analysis of all games played since 2010 and is very accurate, as the following table for the 2024 RPI Formula under a No Overtime rule shows:

On the left are the probable results for teams that are higher rated after adjustments for home field advantage, with the probable results derived using the applicable result probability table.  On the right are actual results.  As you can see, over a large number of game there is almost an exact match.

Looking at how a conference's teams do in non-conference games, if the rating system properly rates the conference's teams in relation to teams from other conferences, then the conference's win-tie-loss likelihoods in those games will match the actual results.  As the following table for the 2024 RPI Formula under a No Overtime Rule shows, however, that is not the case for this rating system.  Scroll to the right to see the entire table.

In the table, the three key columns are on the right.  They show, for a conference, its actual winning percentage, its likely winning percentage using the result probability table, and the difference between the two.  For example, using the ACC at the top, its actual winning percentage is 71.8%, its likely winning percentage based on its teams' and their opponents' ratings is 67.1%, and the difference is 4.7%.  Remember, if a rating system were properly rating the ACC in relation to the other conferences, one would expect the two winning percentages to be the same and the difference to be 0%.  What this means, for the ACC, is that it outperforms its ratings, in other words is underrated, with a 4.7% over-performance being a measure of the extent of the underrating.

By looking at each conference in this way, you can see which conferences the rating system underrates and which it overrates.  In addition, you can see which conference is most underrated, which is most overrated, and the difference between the two.  Thus looking at the above table, the most underrated conference is the ACC at 4.7% and the most overrated is SWAC at -7.4%, with a spread between the two of 12.2% (4.7% + 7.4%, rounded off).  And, looking at all the conferences, you can see the extent by which all of them as a group differ from the ideal 0% difference between their actual performance and their likely performance, in this case a total difference of 64.6%.  These numbers give you measures of the rating system's fairness when it comes to rating conferences' teams in relation to teams from other conferences.  And, when going through this process for multiple rating systems, it allows you to compare them in terms of how the do at rating conferences in relation to other conferences.  For the six rating systems covered by this report, this produces the following table:

The table shows an improvement in the RPI's fairness in relation to conferences when moving to the No Overtime rule.  Then, in the 2024 change to a 1/3 weight for ties when computing Winning Percentage, there is a slight degradation.  The addition of the 2024 bonus and penalty regime slightly reduces the degradation.  As a whole, the table shows that all of the NCAA RPI systems have a conference fairness problem

The next table shows each conference's average rating and the difference between the conference's actual winning percentage and its likely winning percentage using the applicable result probability table.  In addition, it has the teams arranged in order of their average RPI ratings.

The following chart is based on the data in the above table:

In the chart, the upper orange line (actually a series of data points) shows the conferences' average ratings, which are arranged in order from the best average rating on the left to the poorest on the right.  The lower black data points are for the differences between the conferences' actual winning percentages and their likely winning percentages.  The black data points are in the places where they fit in the rating spectrum from the highest rating on the left to the lowest rating on the right.  The black straight line is a computer generated trend line that shows how the conferences' actual versus likely performance differences change as the conferences' average ratings descend.  A simple look at the chart says that for conferences with higher average ratings, their actual results tend to be better than their ratings say they should be and for conferences with lower average ratings, their actual results tend to be poorer than their ratings say they should be.  In other words, the rating system discriminates against stronger conferences and in favor of weaker conferences.

Above the black line is a formula.  This is a formula that describes the black trend line and allows a computation of the extent of the ratings' discrimination.  In the formula, x represents the different data points associated with the trend line.  For the data at the extreme left, where the strongest conference is, x=1.  For the data at the extreme right, where the weakest conference is, x=213.  Thus by using the formula with x=1, one can calculate the actual v likely performance difference at the high point on the trend line at the left, and by using the formula with x=213, one can calculate the difference at the low point on the trend line at the right.  The spread between these two differences is the extent to which the rating system discriminates against strong conferences and in favor of weak conferences.  In the case of the above chart for the 2024 RPI Formula with No Overtimes, the computations produce an overperformance on the left of 4.3% and an underperformance on the right of -6.3%, for a spread between the two of 10.6%.

By going through the processes just described for multiple rating systems, it is possible to compare how they do in rating teams from conferences in relation to conference strength.  The following table shows how the six rating systems covered in this report compare:

As this table shows, the No Overtime rule slightly improved the RPI in relation to conference strength.  The change in tie weight to 1/3 had a small mixed effect, and the 2024 bonus and penalty regime likewise had a small mixed effect.  As a whole, all of the NCAA RPI formulas have a problem fairly rating the conferences in relation to each other and all discriminate in relation to conference strength.

The next table is the general fairness table but for teams categorized by four geographic regions within which teams are located: Middle, North, South, and West:

As the table shows, the NCAA RPI versions all have a general fairness problem in relation to regions.  Again the change to No Overtime resulted in an improvement.  For regions, the 2024 RPI Formula slightly degrades the No Overtime performance from what it was under the pre-2024 Formula.

This table comes from trend charts for regions, looking at performance in relation to the regions' average ratings.  Here, the full NCAA 2024 Formula is a slight improvement over the full 2023 Formula, with the 2024 bonus and penalty regime being responsible for the improvement.  For all of the NCAA RPI systems, however, there is a problem of discrimination in relation to region strength..

For regions, I use an additional metric, which is performance in relation to the percentage of the regions' games that are ties.  I use this metric because the regions have different levels of parity and the percentage of games that are ties is a surrogate for parity -- the higher the percentage of games that are ties, the more likely there is greater parity within the region.  At the RPI for Division I Women's Soccer website, on the RPI: Regional Issues page, I discuss a problem the NCAA RPI has, which is rating teams from regions fairly in relation to each other if the regions have different levels of parity.  There, I show that the West region has a higher level of parity than the other regions, followed by the Middle and North regions, with the South having the least parity.  The following chart is like the one above for conferences, but is for regions and relates regions' performance to their percentages of ties:

The following comparison table comes from charts like this for the six rating systems covered by this report:

As the table shows, unlike for the other metrics above, the change to No Overtimes had a negative impact on how the RPI functions, increasing discrimination among regions in relation to intra-region parity.  Further, the change in tie valuation to 1/3 has increased the discrimination even more.  Both of these effects are as expected, since regions with a higher proportion if ties -- those with more parity -- will suffer more devaluations of their Winning Percentages than regions with lower parity and fewer ties.  As a whole, all of the NCAA RPI systems have a discrimination problem in relation to intra-region parity: they underrate teams from regions with high parity and overrate teams from regions with low parity.

Looking at individual teams produces the following table:

The table again shows an improvement in fairness with the change to No Overtimes, but some degradation in the change to the 2024 Formula.

The last table, below, addresses a different issue.  Because of the way the NCAA RPI calculates strength of schedule, teams' NCAA RPI ranks can be quite different than their ranks as RPI Strength of Schedule contributors to their opponents.  This makes it possible for coaches to "trick" or "game" the RPI by scheduling with a view to opponents' likely Strength of Schedule contributor ranks rather than their likely RPI ranks.  It is one of the reasons basketball stopped using the RPI.

This table shows that the various changes to the RPI over time have slightly degraded the RPI in relation to this problem:  They have slightly increased the differences between teams' RPI ratings and their ratings as RPI Strength of Schedule contributors.  In the table, towards the right, I have blue highlighted the column for Rank v SoS Rank Difference Percent 15 of Fewer Positions, which shows the percentage of teams for which the difference between their RPI rank and their rank as RPI Strength of Schedule contributors is 15 or fewer positions.  I have highlighted this column because, based on my experience assisting coaches with scheduling, there is enough year-to-year variability that when teams' ordinary difference is 15 or fewer positions, it may not be worthwhile to consider that difference as significant for purposes of selecting good future opponents.  As that column shows, for all of the NCAA RPI versions, about 1/3 of teams are in the 15 or fewer group, which leaves 2/3 in the group where it is worthwhile for coaches to consider their likely Strength of Schedule contributions during scheduling so as to artificially maximize their RPIs.  As this column shows, where the higher the percentage the better the system, the bonus and penalty regimes, in particular, degrade the RPI, with the 2024 Formula performing the worst of all the NCAA RPI systems.  The differences among the NCAA RPI systems, however, are relatively small.

Summary as to Effects of Changes on How the NCAA RPI Functions as a Rating System

Looking at the cumulative effects of the No Overtime rule and the 2024 Formula changes as compared to a 2023 Unadjusted RPI formula baseline, they are relatively small.  The changes, however, make the RPI more discriminatory against regions with higher parity and in favor of regions with lower parity.  And, they make the RPI slightly more able to be "tricked."

Conclusion

Of the changes over the past few years, the change to No Overtimes has a significant effect on NCAA Tournament at large selections.  The 2024 change of tie weights to 1/3 for RPI Winning Percentage purposes has a small effect.  The change to the 2024 bonus and penalty regime has a very small effect.  The cumulative effect of the changes is significant.

In terms of how the RPI functions as a rating system, it functions better under the No Overtime rule with two exceptions: it is more discriminatory against regions with higher parity and it makes the system very slightly more susceptible to being "tricked" through smart scheduling.  The 2024 Formula change effects are mixed and small.

2024 ARTICLE 7: POST-WEEK-6 ACTUAL RATINGS, UPDATED PREDICTIONS, AND NEWS

Current Actual RPI Ratings, Ranks, NCAA Tournament Prospects, and Related Information

The following tables show actual RPI ratings and ranks and other information based on games played through Sunday, September 22.  The first table is for teams and the second for conferences.

In this week's teams table, there are color coded columns.  These show which teams, based on past history, are potential NCAA Tournament seeds and at large selections.

For example, the orange column for #1 seeds shows that historically, the poorest ranked team as of this stage of the season to ultimately receive a #1 seed is the currently ranked #22 team.  No team ranked more poorly than #22 has received a #1 seed. 

Also, in the column for at large selections, the color coding covers teams ranked #11 through 149.  This means that as of this stage of the season, those can be thought of as "bubble" teams for at large selection.  They might or might not ultimately get selected.  On the other hand, teams ranked #1 through 10, if not automatic qualifiers, can be considered "locks" for at large positions, meaning that historically teams ranked #10 or better as of this stage of the season always have gotten at large positions.

Note: Scroll to the right to see all the columns. 

Predicted Team RPI and Balanced RPI Ranks, Plus RPI and Balanced RPI Strength of Schedule Contributor Ranks

The following table for teams and the next one for conferences show predicted ranks based on the actual results of games played through September 22 and predicted results of games not yet played.  The predicted results of future games are based on teams' actual RPI ratings from games played through September 22, which makes the predicted results pretty speculative.

Predicted NCAA Tournament Automatic Qualifiers, Disqualified Teams, and At Large Selection Status, All for the Top 57 Teams

The following table has the Top 57 teams in order from the most likely to the least likely to get at large positions in the NCAA Tournament.  It uses the Top 57 because in the past, no team ranked more poorly than #57 as gotten an at large position.

 

 

2024 ARTICLE 6: POST-WEEK-5 ACTUAL RATINGS, UPDATED PREDICTIONS, AND NEWS

 There are several important news items this week:

1.  RPI Valuation of Ties.  With the NCAA having published its initial RPI ratings and ranks for the season, I have verified that in the Winning Percentage portion of the RPI formula, ties now are weighted as one-third of a win rather than one-half.  Thus the new Winning Percentage formula is:

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

For the Opponents' Winning Percentage and Opponents' Opponents' Winning Percentage portions of the RPI formula, however, ties will continue to be weighted as one-half of a win.  NCAA staff has confirmed this.

2.  Bonus and Penalty Adjustments.  The old regime for bonuses for good wins and ties and penalties for poor losses and ties has been replaced by a new, expanded regime.  The new regime is as follows:

Bonuses for Wins and Ties v Teams with Unadjusted RPI Ranks of 1 to 25

Win Away        0.0032

 Win Neutral    0.0030

Win Home        0.0028

Tie Away           0.0020

Tie Neutral        0.0018

Tie Home           0.0016

Bonuses for Wins and Ties v Teams with Unadjusted RPI Ranks of 26 to 50

 Win Away        0.0026

 Win Neutral     0.0024 

Win Home        0.0022 

Tie Away           0.0014 

Tie Neutral        0.0012 

Tie Home           0.0010

Bonuses for Wins v Teams with Unadjusted RPI Ranks of 51 to 100

 Win Away        0.0008 

Win Neutral       0.0006 

Win Home          0.0004

Penalties for Ties and Losses v Teams with Unadjusted RPI Ranks of 151 to 250

Loss Home           -0.0014

Loss Neutral         -0.0012

Loss Away            -0.0010

Tie Home              -0.0008

Tie Neutral            -0.0006

Tie Away                -0.0004

Penalties for Ties and Losses v Teams with Unadjusted RPI Ranks of 251 and Poorer

Loss Home            -0.0026

Loss Neutral           -0.0024

Loss Away              -0.0022

Tie Home                -0.0020

Tie Neutral              -0.0018

Tie Away                -0.0016 

3,  Calculation of 0.500 Minimum Winning Percentage to Qualify for an NCAA Tournament At Large Position.  A team must have at least an 0.500 winning percentage to qualify for an NCAA Tournament at large position.  NCAA staff advises that in calculating winning percentage for this requirement, a tie will be counted as half a win.

Hereafter, my weekly tables will be based on the NCAA RPI formula with these changes.

Teams' Current Actual RPI Ranks and Other Data

The following table for teams and the next one for conferences show actual RPI ranks and other data based on the results of games played through Sunday, September 15.  This corresponds with the NCAA's first publication of ratings and ranks for the season, through September 15.  You will have to scroll to the right to see the entire table.

 

Conferences' Current Actual RPI Ranks and Other Data

Predicted Team RPI and Balanced RPI Ranks, Plus RPI and Balanced RPI Strength of Schedule Contributor Ranks

The following table for teams and the next one for conferences show predicted ranks based on the actual results of games played through September 15 and predicted results of games not yet played.  In predicting results of games not yet played, this week I have changed from using teams' pre-season assigned ratings as the basis for prediction to using teams' actual current NCAA RPI ratings.  Using teams' current RPI ratings as the basis for predicting the results of future games, at this stage of the season, is highly speculative, perhaps even more speculative than using teams' pre-season assigned ratings.  Because of that, I do not take the predictions very seriously.

Predicted Conference RPI and Balanced RPI Ranks, Plus RPI Strength of Schedule Contributor Ranks 

Predicted NCAA Tournament Automatic Qualifiers, Disqualified Teams, and At Large Selection Status, All for the Top 57 Teams

  

2024 ARTICLE 5: POST-WEEK-4 UPDATED PREDICTIONS

This week's predictions are based on the actual results of games played through Sunday, September 8, and win-loss-tie likelihoods of games not yet played.  The predictions assume the change from counting a tie as half a win to one-third of a win will be in effect this year.  They do not, however, include any effects of a changed RPI bonus and penalty system, since we do not yet know what the bonus and penalty amounts will be.  Assuming a change in the bonus and penalty structure also will go into effect this year, I should be able to determine the bonus and penalty amounts next week, once the NCAA has published teams' actual ratings at the RPI Archive.  At that point, I will incorporate the new bonus and penalty amounts into my system.

Team RPI and Balanced RPI Ranks, Plus RPI and Balanced RPI Strength of Schedule Contributor Ranks

Conference NCAA RPI and Balanced RPI Ranks, and Conference NCAA RPI Strength of Schedule Contributor Ranks

This week, for an educational tidbit, take a look at the conferences in the West region: Big Sky, Big West, Mountain West, and West Coast.  (The Summit and WAC have some teams from the West but also teams from other regions.)  You will see that for every one of those conferences, its Balanced RPI rank is better than its NCAA RPI rank, in some cases a lot better.  If you look at those conferences' NCAA RPI Strength of Schedule Contributor ranks and compare them to their NCAA RPI ranks, you will see that for three of those conferences, their Strength of Schedule Contributor ranks are poorer than their RPI ranks.  For a good rating system, those two ranks would not be different, but they are different for the NCAA RPI because of its defective method of calculating strength of schedule.  The Balanced RPI does not have this problem.

When you consider that the West conferences play the great majority of their games against opponents from the West, you can see why the NCAA RPI ranks them more poorly than the Balanced RPI:  The NCAA RPI underrates their strengths of schedule whereas the Balanced RPI does not.

Predicted NCAA Tournament Automatic Qualifiers, Disqualified Teams, and At Large Selection Status, All for the Top 57 Teams

An interesting question came up this week related to teams disqualified from at large selection due to having a winning percentage below 0.500:  In computing winning percentage for at large disqualification purposes, will the NCAA count ties as half a win or a third of a win?  The following table assumes it will count them as a third of a win, which makes a big difference.

2024 ARTICLE 4: POST-WEEK-3 NEWS AND UPDATED PREDICTIONS

 Below are updated predictions based on the actual results of games played through Sunday, September 1 and predicted results of games not yet played.  For background information this week, following the predictions there is some information on the win-loss-tie likelihood part of the prediction process.

Team NCAA RPI and Balanced RPI Ranks, Plus NCAA RPI and Balanced RPI Strength of Schedule Contributor Ranks

Conference NCAA RPI and Balanced RPI Ranks Plus NCAA RPI Strength of Schedule Contributor Ranks

Predicted NCAA Tournament Automatic Qualifiers, Disqualified Teams, and At Large Selection Status, All for the Top 57 Teams

Win-Loss-Tie Likelihoods

To predict the results of games not yet played, over the first part of the season my program bases them on assigned pre-season NCAA RPI ratings for teams.  Those ratings are based on team ranks over the last 7 years.  Using the predicted ratings, the program determines the rating difference between each set of opponents, as adjusted for home field advantage.  For that rating difference, the program then assigns a win, loss, and tie likelihood for the game.

The win, loss, and tie likelihoods are based on a study of all games played since 2010 (over 40,000 games, with pre-2022 game results adjusted to treat games decided in overtime as ties).  The study looks at the location-adjusted rating differences between teams and the actual game results.  It produces a Result Probability Table that shows the win, loss, and tie likelihoods in relation to the location-adjusted rating differences between opponents.  For each of this year's future games, the program determines the location-adjusted rating difference between the opponents and then extracts from the Result Probability Table the win, loss, and tie likelihoods for their game.  The program then tallies teams' actual results for games already played and their win, loss, and tie result probabilities for games not yet played to produce teams' predicted end-of-season records.  From there, the program computes teams' predicted RPI ratings and ranks.

The Result Probability Table is reliable, when applied to a large number of games.  For the more than 40,000 games played since 2010, using the RPI bonus and penalty adjustment regime in effect in 2023, but with tie values changed from 1/2 a win to 1/3 of a win in the RPI computation process as is expected for this year, here is how the the actual results for the better rated team in each game compare to the predicted results using the Result Probability Table:

Higher rated team actually wins:  65.08%

Higher rated team actually ties:   21.12%

Higher rated team actually loses:  13.80%

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Higher rated team predicted wins:  65.06%

Higher rated team predicted ties:  21.18%

Higher rated team predicted losses:  13.76%

This means that, from an overall perspective, when this year's predicted results miss the mark, it is not due to problems with the calculated result probabilities, but rather is due to the pre-season assigned ratings used in predicting game results turning out to not reflect actual team strength.  When this happens, of course, it is not surprising since it is impossible to predict in advance with great accuracy what teams' true strength will be.