RPI broken down by contributions by individual team

I've added another feature to the individual team pages on the site. I've broken down a team's RPI by the contribution made by each team (including the team itself, its opponents, and its opponents' opponents). Remember that the RPI is defined as follows:

0.25*(Adjusted Winning Percentage) + 0.5* Opponents' winning percentages + 0.25 * Opponents' Opponents' winning percentages

This is then sorted and a rank given.

This can be broken down by team if the appropriate weights are used and you can get an idea of how big of an influence an individual team has on any given team's RPI. I've included current weights and end-of-season weights along with current winning percentages and predicted end-of-season winning percentages and an index to gauge the overall influence of a team on the rpi. The winning percentage of the team itself is adjusted according to home/road/neutral games.

For example, I'll choose Utah State and pick the 12 teams with the biggest (current) weights in its RPI:

Team	Curr Wght	fut wght	curr w/l	exp w/l
Utah St.	27.73	26.45	68.63	73.09
Weber St.	8.59	3.67	55.56	58.85
Cal St. Fullerton	4.56	2.01	37.5	38.33
Morehead St.	4.41	1.82	44.44	54.54
Brigham Young	4.37	2.06	100	89.66
Utah	4.37	1.94	36.36	34.61
Cal St. Bakersfield	4.3	2.07	10	17.07
Southern Utah	4.2	1.85	22.22	28.56
Idaho St.	4.14	2	10	30.88
St. Mary's	4.04	1.94	81.82	83.61
Utah Valley	4.02	1.85	28.57	51.66
Northeastern	4.01	1.75	22.22	45.55

Not surprisingly, the biggest influence on a team's RPI comes from the (adjusted) winning percentage of the team itself. Obviously opponents with higher winning percentages help increase an RPI, but this may be diminished if the weight on a team is very small. To get an idea of which teams help or hurt Utah State's RPI the most, we can look at how far above (or below) 50% a team's winning percentage is and multiply that by the weight*. Doing this for Utah State gives us a "net help index". The idea is that anything less than 100% in terms of winning percentage hurts the rpi and on the other hand, things could be very bad so anything greater than 0% winning percentage helps. taking the difference (or net) between this two is like finding the differnce between winning percentage and 50%

Calculating this for Utah State gives the following



*we'll actually multiply by 2* the weight so that this could in theory range between -100% and 100%. In other words, a team with zero winning percentage but a weight of 100% (I know, not possible) would have a net help index of -100%. In other words, it brings what could potentially be an RPI of 100% down to zero.



Sorting by Help Index:

rank	Team	curr wght	fut wgt	curr W/L	exp W/L	curr help index	exp help index
1	Utah St.	27.73	26.45	68.63	73.09	10.33	12.21
2	Brigham Young	4.37	2.06	100	89.66	4.37	1.63
3	St. Mary's	4.04	1.94	81.82	83.61	2.57	1.30
4	Weber St.	8.59	3.67	55.56	58.85	0.96	0.65
5	Kentucky	0.38	0.06	100	83.76	0.38	0.04
6	Nevada Las Vegas	0.54	0.33	84.62	72.04	0.37	0.15
7	Notre Dame	0.37	0.06	84.62	60.27	0.26	0.01
8	Texas	0.19	0.03	100	91.66	0.19	0.02
9	West Virginia	0.19	0.03	100	82.52	0.19	0.02
10	San Diego St.	0.37	0.23	72.73	70.28	0.17	0.09

What's surprising about this sort is that many of the teams that seem to be helping USU's current RPI are not opponents, but opponents of opponents with high winning percentages. However, these are all expected to drop by the end of the season.

By the end of the season, the top ten should look something like this:

Sorting by Expected (end-of-season) Help Index:

rank	Team	curr wght	fut wgt	curr W/L	exp W/L	curr help index	exp help index
1	Utah St.	27.73	26.45	68.63	73.09	10.33	12.21
2	Louisiana Tech	0	4.3	84.62	71.76	0.00	1.87
3	Brigham Young	4.37	2.06	100	89.66	4.37	1.63
4	St. Mary's	4.04	1.94	81.82	83.61	2.57	1.30
5	Nevada	0.14	4.32	58.33	64.41	0.02	1.25
6	Weber St.	8.59	3.67	55.56	58.85	0.96	0.65
7	Boise St.	0.17	4.36	66.67	55.3	0.06	0.46
8	Fresno St.	0.14	4.36	46.15	54.95	-0.01	0.43
9	Long Beach St.	3.85	1.79	33.33	58.91	-1.28	0.32
10	New Mexico	0	0.36	92.31	82.14	0.00	0.23

Here's a look at the ten teams that hurt USU's RPI the most in terms of current help index:

rank	Team	curr wght	fut wgt	curr W/L	exp W/L	curr help index	exp help index
1	Cal St. Bakersfield	4.3	2.07	10	17.07	-3.44	-1.36
2	Idaho St.	4.14	2	10	30.88	-3.31	-0.76
3	Southern Utah	4.2	1.85	22.22	28.56	-2.33	-0.79
4	Northeastern	4.01	1.75	22.22	45.55	-2.23	-0.16
5	Utah Valley	4.02	1.85	28.57	51.66	-1.72	0.06
6	Long Beach St.	3.85	1.79	33.33	58.91	-1.28	0.32
7	Utah	4.37	1.94	36.36	34.61	-1.19	-0.60
8	Cal St. Fullerton	4.56	2.01	37.5	38.33	-1.14	-0.47
9	Morehead St.	4.41	1.82	44.44	54.54	-0.49	0.17
10	Southern	0.3	0.06	10	24.86	-0.24	-0.03

No big surprises. However, some of these ought to reverse by the end of the season.
Sorted by lowest expected (end-of-season) help index:

rank	Team	curr wght	fut wgt	curr W/L	exp W/L	curr help index	exp help index
1	Cal St. Bakersfield	4.3	2.07	10	17.07	-3.44	-1.36
2	Southern Utah	4.2	1.85	22.22	28.56	-2.33	-0.79
3	New Mexico St.	0.37	4.36	50	41.08	0.00	-0.78
4	Idaho St.	4.14	2	10	30.88	-3.31	-0.76
5	Utah	4.37	1.94	36.36	34.61	-1.19	-0.60
6	Hawaii	0.49	4.38	50	43.35	0.00	-0.58
7	Cal St. Fullerton	4.56	2.01	37.5	38.33	-1.14	-0.47
8	Eastern Washington	0.14	0.41	16.67	17.16	-0.09	-0.27
9	Northeastern	4.01	1.75	22.22	45.55	-2.23	-0.16
10	Pepperdine	0.35	0.24	25	25.42	-0.18	-0.12

To see these stats, go to any team's page and scroll down past the graphs.

ESPN BracketBusters projections

As I have done in previous seasons, I've started making projections of what the records and RPIs for all 98 bracket buster teams will be on February 1, which is the day that they are set to announce the matchups. I've broken down by road and home teams. I'll probably update them once or twice a week - maybe more frequently. They can be found here: http://www.rpiforecast.com/bb.html

Bracket Projections

I've started making projections of the field of 65 for the tournament. This is purely a statistical exercise and involves no "gut feeling", however, it is usually more accurate early on in the season than those that DO rely solely on gut feeling (Joe Lunardi, I'm looking at you ;-) ). Anyway, have a look. I use the "Dance Card" methodology along with the simulations to predict who will be in the field. Look through the blog archives to see how I've done in the past. I do not make any projections about seeds, just about who makes the cut.

If you see a team that is in the field that isn't eligible for post-season play, let me know. Here are the teams that I'm giving home court advantage to in the conference tournaments: Mercer (ASun), Siena (Metro Atlantic), UNLV (MWC), and Nevada (WAC). The same is true for those conferences that are held at the higher seed.

Here's the bracket:
http://www.rpiforecast.com/bracket.html

Here's the "Dance Card" site for an explanation of the dance card methodology:
http://www.unf.edu/~jcoleman/dance.htm

New Season!

With 4 new teams and 1 new conference, the 2009-2010 NCAA Basketball conference is underway and I have started making predictions. At some point, I'll add conference tournaments into the forecasts like last season and will probably update them daily.

Daily Updates This Week

I'm going to be traveling much of this week and will not be able to update the site very early in the day starting on Wednesday, but will try to get the updates in when I can.

Podcast of interview

You can hear a podcast of my interview on the radio at this website:

http://usuaggies.com/?p=1008

Media Alert

I will be appearing on KVNU's radio program "Full Court Press" at 610 on the AM dial tonight at 6:00pm Mountain time zone. Those of you that aren't in Northern Utah/Southern Idaho can listen online at http://www.610kvnu.com . Fair warning: I will be focusing on Utah State's RPI/BracketBuster outlook, etc. I think they will be taking phone calls.

Today's update

Today's update will be a little later in the day than usual.

Update on Friday

FYI, Friday's numbers may be posted a little late in the day.

New Features

I have added two features in the past few days:

1. Bracket Busters RPI Projections:

For all 102 Bracket Busters teams, I've started projecting what their RPIs will be on February 1st. That is the day before the first matchups will be announced. This should give you some idea of where teams will likely stand at that point. The address is: http://www.rpiforecast.com/bb.html (bb = bracket busters)

2. Protrade

For all of the NCAA basketball teams on Protrade.com, I have started projecting their end of season fantasy earnings. This involves doing something similar to what I do in the conference tournament simulations, but I include NCAA tournament and NIT games. There is an explanation of the methodology at the website which is here: http://www.rpiforecast.com/protrade.html

Each of these two features will be updated at least weekly, and probably more frequently than that.