(03-01-2013 12:00 PM)Stammers Wrote:  

(03-01-2013 10:39 AM)MemphisCanes Wrote:  

(03-01-2013 10:20 AM)Stammers Wrote:  

(03-01-2013 09:09 AM)MemphisCanes Wrote:  Well, Sagarin's ELO model measures SOS via wins and losses. Everyone starts off tied for first before any games are played, and then teams are spread over the spectrum of 1-344 as they win/lose.

Effectively, both ELO and RPI give "greater value to wins against tough competition", so you can't really mean that. A win against a high RPI team (a team with a good winning pct, good O winning pct and good OO winning pct) jumps your RPI score significantly, thus conveying greater value for defeating a greater opponent. As opposed to playing a very low RPI opponent, winning handily and possibly losing ground in the RPI rankings (this is one of my biggest concerns over the RPI). This is the crux of any SOS weighted system.

This is not true. The RPI gives the same value to a win or a loss with the only variable being the location of the game. Math then determines the value of the win/loss based on data from wins and losses.

ELO assigns an arbitrary value in addition to the mathematical calculation, based on the opponent's strength; which is disproportionally weighted based on the strength of the opponent. In addition, the predictor is biased and is used in tandem with ELO.

If I am wrong in assuming this, please explain why in detail.

Does ELO or a similar weighted system have its place? I think it does. A great example would be with professional golf. The 4 majors are bigger tournaments and victories carry a greater importance. In NCAA basketball, none of the games are more or less important, when considering a team's overall body of work.

Ah, but that's only part of the calculation. Your RPI, inherently, takes into account each opponent's win pct and each of their opponent's win pct. Therefore, playing a tougher opponent (a higher RPI team) is of more value than playing a weak opponent. Each win is not treated equally in the RPI's eyes, when looking past each respective team's win pct.

Just think, if the Tigers, at RPI 18 or so as of today, played and beat Miami tomorrow, our RPI would improve significantly because we just beat the RPI #2 team, who has a great win pct and whose opponents have a great win pct. Conversely, if we play and beat Kennesaw State RPI #341, our RPI score barely increases and because other teams grouped around us in the RPI probably played better teams, our RPI rank will slip.

The RPI is weighted by SOS just like all other metrics.

Also, Sagarin's ELO and Predictor are two separate metrics, the predictor doesn't care what your wins/losses are, it only cares about the scores of your games. He combines both to get his "Sagarin rankings".

The ELO rankings assign no more arbitrary numbers than the RPI with its .25s, .5s, .6s and 1.4s.

First, with the RPI, SOS is weighted solely based on data derived from wins and losses. Second, the values you describe for RPI are not attached to any specific team or any component of performance.

Well, the RPI is weighted on wins and losses, and arbitrary multipliers like .25 for your own win pct, .5 for your opponents, .25 for your opponents opponents, a "road win" is worth 1.4 and a "home win" is worth 0.6. Those numbers are arbitrary, right?

Also, the values in the RPI ARE attached to specific teams. Miami has an RPI score of .6602. Kennesaw State has an RPI score of .3673. Miami essentially has a higher score because of better performance against a stronger schedule. Beating Miami is thus better for an opposing team's RPI score than beating Kennesaw State.

(03-01-2013 02:20 PM)MemphisCanes Wrote:  

(03-01-2013 12:00 PM)Stammers Wrote:  

(03-01-2013 10:39 AM)MemphisCanes Wrote:  

(03-01-2013 10:20 AM)Stammers Wrote:  

(03-01-2013 09:09 AM)MemphisCanes Wrote:  Well, Sagarin's ELO model measures SOS via wins and losses. Everyone starts off tied for first before any games are played, and then teams are spread over the spectrum of 1-344 as they win/lose.

Effectively, both ELO and RPI give "greater value to wins against tough competition", so you can't really mean that. A win against a high RPI team (a team with a good winning pct, good O winning pct and good OO winning pct) jumps your RPI score significantly, thus conveying greater value for defeating a greater opponent. As opposed to playing a very low RPI opponent, winning handily and possibly losing ground in the RPI rankings (this is one of my biggest concerns over the RPI). This is the crux of any SOS weighted system.

This is not true. The RPI gives the same value to a win or a loss with the only variable being the location of the game. Math then determines the value of the win/loss based on data from wins and losses.

ELO assigns an arbitrary value in addition to the mathematical calculation, based on the opponent's strength; which is disproportionally weighted based on the strength of the opponent. In addition, the predictor is biased and is used in tandem with ELO.

If I am wrong in assuming this, please explain why in detail.

Does ELO or a similar weighted system have its place? I think it does. A great example would be with professional golf. The 4 majors are bigger tournaments and victories carry a greater importance. In NCAA basketball, none of the games are more or less important, when considering a team's overall body of work.

Ah, but that's only part of the calculation. Your RPI, inherently, takes into account each opponent's win pct and each of their opponent's win pct. Therefore, playing a tougher opponent (a higher RPI team) is of more value than playing a weak opponent. Each win is not treated equally in the RPI's eyes, when looking past each respective team's win pct.

Just think, if the Tigers, at RPI 18 or so as of today, played and beat Miami tomorrow, our RPI would improve significantly because we just beat the RPI #2 team, who has a great win pct and whose opponents have a great win pct. Conversely, if we play and beat Kennesaw State RPI #341, our RPI score barely increases and because other teams grouped around us in the RPI probably played better teams, our RPI rank will slip.

The RPI is weighted by SOS just like all other metrics.

Also, Sagarin's ELO and Predictor are two separate metrics, the predictor doesn't care what your wins/losses are, it only cares about the scores of your games. He combines both to get his "Sagarin rankings".

The ELO rankings assign no more arbitrary numbers than the RPI with its .25s, .5s, .6s and 1.4s.

First, with the RPI, SOS is weighted solely based on data derived from wins and losses. Second, the values you describe for RPI are not attached to any specific team or any component of performance.

Well, the RPI is weighted on wins and losses, and arbitrary multipliers like .25 for your own win pct, .5 for your opponents, .25 for your opponents opponents, a "road win" is worth 1.4 and a "home win" is worth 0.6. Those numbers are arbitrary, right?

Also, the values in the RPI ARE attached to specific teams. Miami has an RPI score of .6602. Kennesaw State has an RPI score of .3673. Miami essentially has a higher score because of better performance against a stronger schedule. Beating Miami is thus better for an opposing team's RPI score than beating Kennesaw State.

The values are arbitrary but they are identical for each team. The data is identical because it is based on wins and losses. Sagarin, Kenpom etc are different because they include things that I don't consider important. Yes you can say that if Team A beats Team B by 40 points, it is probably better than Team C if it beats Team B by 4 points...over 1 game with common opponents. The reason why I don't think that Sagarin, Kenpom are accurate; is because based on data from thousands of games; margin of victory doesn't matter. Each team is being "protected" by the fact that every team in the NCAA is measured against the same criteria...wins and losses.

When you input so many other variables outside of wins and losses, you are not giving enough credit for wins and losses. Other stuff is given too much importance, when it really has little or no importance.

The other major problem with Sagarin and Kenpom is that it fails to take game in situations into account. A player is 16 minutes late for practice so he sits 16 minutes of a game. A game is out of hand one way or the other so a coach experiments with different lineups. A team is bored and comes out flat. One team scores the last 10 points in a game to turn a tight game into a laugher or a blowout into a close final deficit.

The bottom line is that a win is a win and a loss is a loss. Nothing else really matters. If we were 20-8 with 19 blowout wins and 7 close losses and Sagarin/Kenpom were telling us that we were a great team with a top 25 power ranking, who would really care if you have an RPI of 50 and won't make the tournament?

Well, if wins and losses are all that matter, then why use the RPI? Just look at a team's record for that.

But you keep going back to "margin of victory", this ill-perceived boogey man, when we're not even discussing margin of victory. We'd moved on to Sagarin's ELO rating.

"The bottom line is that a win is a win and a loss is a loss. Nothing else really matters. If we were 20-8 with 19 blowout wins and 7 close losses and Sagarin/Kenpom were telling us that we were a great team with a top 25 power ranking, who would really care if you have an RPI of 50 and won't make the tournament?"

This paragraph alone convinces me that you may be incapable of abstract thought, Stams.

"Who cares, because it is the way it is, no matter how screwed up."

(03-01-2013 04:19 PM)MemphisCanes Wrote:  "The bottom line is that a win is a win and a loss is a loss. Nothing else really matters. If we were 20-8 with 19 blowout wins and 7 close losses and Sagarin/Kenpom were telling us that we were a great team with a top 25 power ranking, who would really care if you have an RPI of 50 and won't make the tournament?"

This paragraph alone convinces me that you may be incapable of abstract thought, Stams.

"Who cares, because it is the way it is, no matter how screwed up."

It isn't screwed up at all; it is 100% results based. What is screwed up is that your system rewards teams for scoring the last 10 points in a game when they are down 15 points. Your system punishes teams when they are bored but in control, and beat bad teams by less than 10 points. [/quote]

How much does this matter? Answer; ZERO.

[/quote]This paragraph alone convinces me that you may be incapable of abstract thought, Stams.[quote]

This paragraph alone convinces me that you may be incapable of grasping simple concepts.

The head of the selection committee was on CBS today saying that he extensively uses KenPom and Sagarin when selecting and seeding teams. Seems as if times have changed.

When someone explains to me how a teams rpi rank doesn't matter yet top 50 rpi wins do then ill believe in it.

A problem with the RPI is how it is used sometime.

Example- Memphis has an 18 RPI. That will be dismissed because Memphis has only 2 wins over top 50 RPI teams.

Oxymoronic.

(03-03-2013 03:47 PM)uomguy78 Wrote:  The head of the selection committee was on CBS today saying that he extensively uses KenPom and Sagarin when selecting and seeding teams. Seems as if times have changed.

John Gasaway ‏@JohnGasaway
"Personally I use [KenPom and Sagarin] fairly extensively." -- Committee chair Mike Bobinski on CBS right now.

That is a wow statement. I think they're better than RPI, but feel RPI has merit as well. Hmmm...

Memphis 36 Kenpom and 30 Sagarin.

Puts us in the 8-10 seed conversation. Interesting... If we win out and get a 10 seed as a 30-4 team, I can't say I'd be very pleased...

All we want is consistency, and we don't get it, not in seeding and not in NCAA investigations and their outcomes.

(03-03-2013 03:47 PM)uomguy78 Wrote:  The head of the selection committee was on CBS today saying that he extensively uses KenPom and Sagarin when selecting and seeding teams. Seems as if times have changed.

So much for the "nobody important gives a crap about Pomeroy or Sagarin" comments.

From RPIForecast.com's website, explaining how the site precdicts future RPI:

"How do you figure out the probability that one team beats another?

I use Jeff Sagarin's "PREDICTOR" to predict the "expected" margin of victory between any two teams. Becuase this is only what you would expect on average, I combinine that with a standard deviation to come up with a probability of victory. Jeff Sagarin's "PREDICTOR" is consistently the best rating available at precicting future outcomes. Thanks Jeff for making the data available. I DO NOT USE THE CURRENT RPI TO FORECAST FUTURE WINS/LOSSES!"

Pretty telling that an RPI site goes to Jeff Sagarin's ratings to predict which team will beat which team, instead of the RPI.

(03-01-2013 07:20 PM)Stammers Wrote:  It isn't screwed up at all; it is 100% results based. What is screwed up is that your system rewards teams for scoring the last 10 points in a game when they are down 15 points. Your system punishes teams when they are bored but in control, and beat bad teams by less than 10 points.

How much does this matter? Answer; ZERO.

This paragraph alone convinces me that you may be incapable of grasping simple concepts.

“You’re throwing away a lot of data when you throw away the score,” Sagarin told me. “Imagine if the stock market went up four days in a row, by a tenth of a percent per day, but today it went down 8%. You could say it had four days that it went up, and one when it went down, so it’s a winner.” That, he feels, is similar to a 4-1 team that wins four games by narrow margins but is blown out in the fifth.

Stams, if you can't wrap your mind around this, there may be no helping you.

By all the metrics mentioned above, UK will miss the NCAAT this season, due to the loss of Noel. The selection committee should discount any quality wins UK amassed with Noel. Makes my day!

(03-04-2013 08:27 AM)4everblue Wrote:  By all the metrics mentioned above, UK will miss the NCAAT this season, due to the loss of Noel. The selection committee should discount any quality wins UK amassed with Noel. Makes my day!

Sure looks like it, unless they can make some noise down the stretch.

For the life of me, I honestly can't see any justification for more than 2 SEC teams in at this point.

(03-04-2013 08:20 AM)MemphisCanes Wrote:  

(03-01-2013 07:20 PM)Stammers Wrote:  It isn't screwed up at all; it is 100% results based. What is screwed up is that your system rewards teams for scoring the last 10 points in a game when they are down 15 points. Your system punishes teams when they are bored but in control, and beat bad teams by less than 10 points.

How much does this matter? Answer; ZERO.

This paragraph alone convinces me that you may be incapable of grasping simple concepts.

“You’re throwing away a lot of data when you throw away the score,” Sagarin told me. “Imagine if the stock market went up four days in a row, by a tenth of a percent per day, but today it went down 8%. You could say it had four days that it went up, and one when it went down, so it’s a winner.” That, he feels, is similar to a 4-1 team that wins four games by narrow margins but is blown out in the fifth.

Stams, if you can't wrap your mind around this, there may be no helping you.

You could also have team A that won 5 games by a narrow margin, and have team B that that played the exact schedule go 3-2 but team B won their 3 games handedly and barely lost their 2, but in the eyes of the Pomeroy method, they are the better team than team A, even though team A went undefeated. Sorry, but he NCAA should not let team B in over team A. Don't get me wrong, I'm not saying we should do away with Pom, but you have to keep a metric in their like RPI fore wins and losses. You can’t just seed teams based on per possession basis like Pomeory, otherwise there would be no need to keep track of team's wins and losses.

(03-04-2013 08:52 AM)memphis mania Wrote:  

(03-04-2013 08:20 AM)MemphisCanes Wrote:  

(03-01-2013 07:20 PM)Stammers Wrote:  It isn't screwed up at all; it is 100% results based. What is screwed up is that your system rewards teams for scoring the last 10 points in a game when they are down 15 points. Your system punishes teams when they are bored but in control, and beat bad teams by less than 10 points.

How much does this matter? Answer; ZERO.

This paragraph alone convinces me that you may be incapable of grasping simple concepts.

“You’re throwing away a lot of data when you throw away the score,” Sagarin told me. “Imagine if the stock market went up four days in a row, by a tenth of a percent per day, but today it went down 8%. You could say it had four days that it went up, and one when it went down, so it’s a winner.” That, he feels, is similar to a 4-1 team that wins four games by narrow margins but is blown out in the fifth.

Stams, if you can't wrap your mind around this, there may be no helping you.

You could also have team A that won 5 games by a narrow margin, and have team B that that played the exact schedule go 3-2 but team B won their 3 games handedly and barely lost their 2, but in the eyes of the Pomeroy method, they are the better team than team A, even though team A went undefeated. Sorry, but he NCAA should not let team B in over team A. Don't get me wrong, I'm not saying we should do away with Pom, but you have to keep a metric in their like RPI fore wins and losses. You can’t just seed teams based on per possession basis like Pomeory, otherwise there would be no need to keep track of team's wins and losses.

You know this is never the case though. No two teams play the same schedule, and teams can't be compared on record alone. The reason we have things like RPI, Pomeroy and Sagarin is to be able to compare two teams that: 1.) Haven't necessarily played one another; and 2.) Have played different opponents.

If we are lucky enough to have two opponents that have played the exact same schedule, even down to home, away and neutral games, then I agree, it may be as simple as looking at their record to see who is more deserving. Until we get that luxury, using a metric such as Sagarin's that takes win/losses along with margin of victory is the optimal way to compare two teams.

(03-04-2013 08:20 AM)MemphisCanes Wrote:  

(03-01-2013 07:20 PM)Stammers Wrote:  It isn't screwed up at all; it is 100% results based. What is screwed up is that your system rewards teams for scoring the last 10 points in a game when they are down 15 points. Your system punishes teams when they are bored but in control, and beat bad teams by less than 10 points.

How much does this matter? Answer; ZERO.

This paragraph alone convinces me that you may be incapable of grasping simple concepts.

“You’re throwing away a lot of data when you throw away the score,” Sagarin told me. “Imagine if the stock market went up four days in a row, by a tenth of a percent per day, but today it went down 8%. You could say it had four days that it went up, and one when it went down, so it’s a winner.” That, he feels, is similar to a 4-1 team that wins four games by narrow margins but is blown out in the fifth.

Stams, if you can't wrap your mind around this, there may be no helping you.

Q: Who wins most of the games?
A: The home team

Q: The majority of home games Vs. top 50 are played at which venue?
A: BCS teams

Q: How many home games do non BCS teams get at home against top 50 BCS teams?
A: Almost none

Scenario A
Team wins 4 close games
Team gets blown out in 1 game
Record of 4-1

Scenario B
Team wins 2 games in blowout
Team loses 2 close games
Team gets blown out in 1 game
Record of 2-3

Sagarin and Kenpom will have almost identical rankings for both scenarios. The problem with this is that winning IS important; it does matter. At the end of the day; the same team has 4 wins versus 2 wins.

Wrap your hillbilly mind around that.

(03-04-2013 09:16 AM)MemphisCanes Wrote:  

(03-04-2013 08:52 AM)memphis mania Wrote:  

(03-04-2013 08:20 AM)MemphisCanes Wrote:  

(03-01-2013 07:20 PM)Stammers Wrote:  It isn't screwed up at all; it is 100% results based. What is screwed up is that your system rewards teams for scoring the last 10 points in a game when they are down 15 points. Your system punishes teams when they are bored but in control, and beat bad teams by less than 10 points.

How much does this matter? Answer; ZERO.

This paragraph alone convinces me that you may be incapable of grasping simple concepts.

“You’re throwing away a lot of data when you throw away the score,” Sagarin told me. “Imagine if the stock market went up four days in a row, by a tenth of a percent per day, but today it went down 8%. You could say it had four days that it went up, and one when it went down, so it’s a winner.” That, he feels, is similar to a 4-1 team that wins four games by narrow margins but is blown out in the fifth.

Stams, if you can't wrap your mind around this, there may be no helping you.

You could also have team A that won 5 games by a narrow margin, and have team B that that played the exact schedule go 3-2 but team B won their 3 games handedly and barely lost their 2, but in the eyes of the Pomeroy method, they are the better team than team A, even though team A went undefeated. Sorry, but he NCAA should not let team B in over team A. Don't get me wrong, I'm not saying we should do away with Pom, but you have to keep a metric in their like RPI fore wins and losses. You can’t just seed teams based on per possession basis like Pomeory, otherwise there would be no need to keep track of team's wins and losses.

You know this is never the case though. No two teams play the same schedule, and teams can't be compared on record alone. The reason we have things like RPI, Pomeroy and Sagarin is to be able to compare two teams that: 1.) Haven't necessarily played one another; and 2.) Have played different opponents.

If we are lucky enough to have two opponents that have played the exact same schedule, even down to home, away and neutral games, then I agree, it may be as simple as looking at their record to see who is more deserving. Until we get that luxury, using a metric such as Sagarin's that takes win/losses along with margin of victory is the optimal way to compare two teams.

I used two teams with the same records because it's easier to get the point across. Teams don't have to have the same records. Say team A had a SOS almost identical to team B. (not the exact same schedule, just same SOS) Team A goes undefeated, and team B losses 3 games. Team B beat their opponents so bad that even though they have 3 losses, Pom ranks them higher than team A because team A didn't beat their opponents as convincingly. That's the problem with Pom and Sag. That's why you need another metric for wins and losses, and the RPI does that.

(03-04-2013 09:23 AM)Stammers Wrote:  

(03-04-2013 08:20 AM)MemphisCanes Wrote:  

(03-01-2013 07:20 PM)Stammers Wrote:  It isn't screwed up at all; it is 100% results based. What is screwed up is that your system rewards teams for scoring the last 10 points in a game when they are down 15 points. Your system punishes teams when they are bored but in control, and beat bad teams by less than 10 points.

How much does this matter? Answer; ZERO.

This paragraph alone convinces me that you may be incapable of grasping simple concepts.

“You’re throwing away a lot of data when you throw away the score,” Sagarin told me. “Imagine if the stock market went up four days in a row, by a tenth of a percent per day, but today it went down 8%. You could say it had four days that it went up, and one when it went down, so it’s a winner.” That, he feels, is similar to a 4-1 team that wins four games by narrow margins but is blown out in the fifth.

Stams, if you can't wrap your mind around this, there may be no helping you.

Q: Who wins most of the home games?
A: The home team

Q: The majority of home games Vs. top 50 are played at which venue?
A: BCS teams

Q: How many home games do non BCS teams get at home against top 50 BCS teams?
A: Almost none

Scenario A
Team wins 4 close games
Team gets blown out in 1 game
Record of 4-1

Scenario B
Team wins 2 games in blowout
Team loses 2 close games
Team gets blown out in 1 game
Record of 2-3

Sagarin and Kenpom will have almost identical rankings for both scenarios. The problem with this is that winning IS important; it does matter. At the end of the day; the same team has 4 wins versus 2 wins.

Wrap your hillbilly mind around that.

Do you understand that:

First, that the RPI suffers from the same "BCS bias" that Kenpom and Sagarin do? Look at the top 100 RPi teams, what percentage of them are Big 6 teams? Big 6 teams get more chances at top RPI teams by convenience of conference. Although, in reality the RPI, kenpom and Sagarin are blind to conference affiliation, the issue you bring up affects all of the metrics.

Second, Pomeroy and Sagarin won't necessarily treat these two teams the same, because they use different variables in their metrics. Who's to say that both metrics wouldn't agree with the RPI that Team A is a better team (notwithstanding the rest of their respective schedules)?

Lastly, stow the name calling. It's too early in the morning for that garbage from you.