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  The old regime for bonuses for good wins and ties and penalties for poor losses and ties has been replaced by a new, expanded regime.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%

...................................................................... 

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.