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CU/Pittsburgh Preview (more of a scouting report)

And that's where I think KenPom gets it wrong.

Whose metrics does he use to determine SOS?

Because if he's using his own stats to evaluate relative team strength and then using those same stats to adjust the stats for SOS... those are circularly reasoned metrics. It doesn't work.

He uses points per possession offensive and defensive data adjusted for tempo - he's not using anybody's metrics or calculations other than raw PPP data. He then averages all of the adjusted off eff and def eff of opponents for they year. Then he adjusts each teams raw efficiency numbers for the opponent to get the adjusted #'s.
 
He uses points per possession offensive and defensive data adjusted for tempo - he's not using anybody's metrics or calculations other than raw PPP data. He then averages all of the adjusted off eff and def eff of opponents for they year. Then he adjusts each teams raw efficiency numbers for the opponent to get the adjusted #'s.

Yes. So raw is generated against any opponent regardless of the quality. At that point they are all equal. Then, based on how teams look after that round of statistical analysis, he determines how to adjust for SOS. So it's circular. This is why you need to base SOS on something other than your own metrics. For instance, RPI with your D1 record, the D1 record of the teams you beat, and the D1 records of their opponents - modified by whether all those games were played on home, away or neutral courts. To modify your metrics for quality of opponents, you need to base the quality of the opponents on something other than your own metrics or it's circular.

That's an inherent flaw with all of the metrics guys and their systems. It's why teams like Louisville that play absolute **** schedules end up rated so high on those relative to their RPI.

It's one thing to rate oR and dR based on adjusted pace and oR and dR of opponents. But it can't stop there to tell the true story. It doesn't tell the actual quality of the opponent -- just how well their data looks against whatever teams they are playing. 30 games against Arizona are going to give you a very different oR and dR and rR than 30 games against Loyola-Marymount.
 
he's adjusting off of raw average data, not his metrics, at no point is he ever introducing a "kenpom algorithm"
 
he's adjusting off of raw average data, not his metrics, at no point is he ever introducing a "kenpom algorithm"

So there's no true SOS built in. It's just a set of relative ratios based on simply playing basketball games against whomever. (Which was what I think I'd first posted.)
 
it's based on playing whomever based on how good their offense and defense is.

Clearly i'm doing a poor job of explaining this, he does a better job here.

http://kenpom.com/blog/index.php/weblog/entry/ratings_explanation

From right in the beginning, KP says exactly what I'm trying to point out:

"If you’re looking for a system that rates teams on how “good” their season has been, you’ve come to the wrong place."

My annoyance with KenPom and other metrics ranking teams on "how good they are" and even using that as a means of predicting favorites in games or scores is not what the data is designed to do.

It is designed to tell you a team's style of play and how well it plays it relative to its competition.

I am tired of people trying to tell me things like, "But KenPom says that they're the 18th best team in the country and the Buffs are #47." That's not what his data is capable of showing. He admits that. But then he goes on to provide that. To me, it looks like he and other stat geeks have realized that the money is in appealing to the masses who make up the vast majority of fans and who really want to be told in simple terms how "good" each team's season is.
 
My annoyance with KenPom and other metrics ranking teams on "how good they are" and even using that as a means of predicting favorites in games or scores is not what the data is designed to do.

if you read his first sentence that's exactly what it's designed to do

The first thing you should know about this system is that it is designed to be purely predictive.
 
if you read his first sentence that's exactly what it's designed to do

The first thing you should know about this system is that it is designed to be purely predictive.

So on the one hand he's saying that his data can't tell you how good teams' seasons are... but on the other hand he thinks he can tell you which team will win and by how much if two teams play? That doesn't strike you as a strange and questionable thing?
 
So on the one hand he's saying that his data can't tell you how good teams' seasons are... but on the other hand he thinks he can tell you which team will win and by how much if two teams play? That doesn't strike you as a strange and questionable thing?

The idea is that good is driven by metrics, not perception, I think your misinterpreting what he's saying. It's like saying a team that has gone 0-10 sucks, they won't be rated in any AP poll ever, but if they're playing a team that is 10-0 the 10-0 is obviously favored right? Well what if the 0-10 team has played the hardest SOS, and the 10-0 the easiest, his ratings are meant to be predictive of who will win that 0-10 vs 10-0 matchup, not tell you that the 0-10 team sucks.

i get not everybody loves KenPom, margin of victory is confrontational (even if it's been proven to matter) among other things. But it's better than everything else out there, there is a very small deviation between KenPom predictions and vegas lines, it's been shown to be the best predictive system out there in determining ncaa tournament games, so there's clearly some value. If you blatantly dismiss it, that's fine. But kenpom is working as a consultant for numerous ncaa teams, so there are a lot of people who do believe in what he is doing.

The best thing he's done is help move the narrative from simple raw number statistics and into rebounding %, TO%, FT/fGA and such.
 
The idea is that good is driven by metrics, not perception, I think your misinterpreting what he's saying. It's like saying a team that has gone 0-10 sucks, they won't be rated in any AP poll ever, but if they're playing a team that is 10-0 the 10-0 is obviously favored right? Well what if the 0-10 team has played the hardest SOS, and the 10-0 the easiest, his ratings are meant to be predictive of who will win that 0-10 vs 10-0 matchup, not tell you that the 0-10 team sucks.

i get not everybody loves KenPom, margin of victory is confrontational (even if it's been proven to matter) among other things. But it's better than everything else out there, there is a very small deviation between KenPom predictions and vegas lines, it's been shown to be the best predictive system out there in determining ncaa tournament games, so there's clearly some value. If you blatantly dismiss it, that's fine. But kenpom is working as a consultant for numerous ncaa teams, so there are a lot of people who do believe in what he is doing.

The best thing he's done is help move the narrative from simple raw number statistics and into rebounding %, TO%, FT/fGA and such.

I love KenPom.

But there are simply things that numbers can't capture and can never capture. What he calls "Luck" I call "Competitive Fire". There is a reason why teams are consistently "Lucky" and it's not because they're rubbing rabbit feet. Similarly, there's a reason why teams are consistently "Unlucky". It's my same issue that I have with metrics in baseball. I get that BABIP. There's a norm and a standard deviation. It works in the aggregate. But when I see a pitcher or batter who is consistently, season-to-season, outperforming the norm... it's something that guy is doing, not simple good fortune.

Some teams and players are winners and some teams and players are losers.

I was listening to an NBA guy on the radio today (may have been Reggie Miller) and his comment that of the 10 guys who are on the court in a close game, only 3 of them want the ball in their hands to take the last shot... and you just hope your team has 2 of them. This type of mental toughness and competitiveness and thriving on pressure is what separates some coaches from others and it permeates the culture of a team. It is why when KenPom's numbers tell me that Michigan State and Tennessee are equivalent teams this year I roll my eyes (or that Stanford is #35 and Colorado is #64). By necessity, he's got to write overperformance or underperformance as "Luck" because it's probably impossible to statistically account for. But when teams consistently find a way to win, even if it's winning ugly and winning close, that means something. That "Luck" becomes "Fact".

As much as the metrics are useful tools for uncovering tendencies, revealing team strengths and weaknesses, giving insight into styles of play, and pointing out which factors have greater relevance in leading to wins... it's still sports. It's still competition. The metrics may also be useful as predictors. But they don't know how to quantity the "Competitive Fire" as a modifier.

Last, as you brought up, MOV is a highly questionable statistic. In many cases, it might point to a team having more offensive firepower so when they have things going they really get out of hand... especially against bad teams.

The other side of that is when you have a coach like Tad who plays things very conservatively in endgame situations. If he's up by 7 with 4 minutes left, he's going to look at it from the point of view that if he can take 30 seconds per possession there are only 4 possessions per team. Even if his team is taking difficult shots that might lower CU's PPP to a 0.8, he's still at a +10. He'll take that and bet that his defense isn't going to give up a 2.5 PPP to the opponent. And if the other team decides to extend the game by fouling, that plays into his hands too. Because it virtually guarantees that the Buffs will be over a 1.0 PPP and he trusts his defense (even if the opponent is making miracle shots) not to give up so much above that to cause a loss. Will it yield more 1-5 point wins than 10-15 point wins? Sure. But that doesn't make the Buffs a worse team than if they'd gone for the blowout. At the end of the day, all that matters is the W or L. Tad gets that, but his Buffs get penalized in the metrics and rated the #64 team. Buffs aren't 10-2 in games decided by 7 points or less by accident or "Luck". To accomplish that with one of the youngest teams in the country shows a winning culture. Likewise with Pitt only being 7-7 in those types of games even with a veteran team. I couldn't give a **** less whether Pitt beat Savannah State by 30 or 15.

P.S. I don't gamble any more, so the predictive measures from this stuff are inherently less meaningful to me. I only care about the W or L, not the style points. But I would assume that over the course of most seasons that Tad's teams are a solid bet against the spread since they win games they "shouldn't" and rarely lose games they "should win". I'd think that would usually outweigh the fact that they're not always delivering the margin they should or, in a season like this one, where a young team let some scores get away from them.
 
Also -- someone please hit JG with some rep for me. He brought a great discussion to the board, as usual. YMSSRA
 
I have been using KenPom's site for at least 5 years. I use it for the stats jgisland mentioned (+ so many others) as well as his accuracy on predicted margin of victories. I recall him mentioning on several occasions that one should not take the overall rankings as being definitive. I might be not be remembering correctly but I feel like even he has said that his opinion on teams vary from what his model bears. Like Nik said is Stanford really the 35th best team in the country--30 spots ahead of us? Miami is rated one spot behind CU and they have 4 losses to teams rated 166 or worse on KenPom & 3 of those 4 came on their home court. They're a young team too but so is CU and CU doesn't have 1 loss outside of teams in his top 100. It's incredibly hard to rank hundreds of teams and so like I said, I take them with the same grain of salt as any other rankings. Ultimately, it's not what KenPom is about anyway.
 
Like CVille said, they were tremendously lucky to get the Clemson W. Would be interested to know where KenPom ranks them. That said, really appreciate your offerings in these threads. Always thought Patterson was the PG & got thrown off when I saw him listed as a 3. Playing positions 1-4 makes more sense. Any thoughts on Zanna's propensity to get into foul trouble? Doesn't seem that big, but my eyes lit up when I saw he fouled out 2 of last 4

These last two games have been an outlier to what he's done all year. He's a very smart big and rarely gets in foul trouble. He averages under 3 fouls per game in over 30 minutes of action. He's only fouled out 4 times all year. I'm excited it should be a good match-up down low!
 
These last two games have been an outlier to what he's done all year. He's a very smart big and rarely gets in foul trouble. He averages under 3 fouls per game in over 30 minutes of action. He's only fouled out 4 times all year. I'm excited it should be a good match-up down low!


Surprisingly Scott only averages 1.8 PF's per game @ 31.6min/game. Should be fun to see if either can get the other in to any foul trouble.
 
These last two games have been an outlier to what he's done all year. He's a very smart big and rarely gets in foul trouble. He averages under 3 fouls per game in over 30 minutes of action. He's only fouled out 4 times all year. I'm excited it should be a good match-up down low!

Should be interesting for sure, both don't foul much while drawing quite a few fouls. And both have incredibly high FTA per FGA (both top 100)

Scott average 2.3 fouls per 40 minutes while drawing 5.5 fouls per 40.
Zanna averages 3.5 fouls per 40 minutes while drawing 5.5 fouls per 40
 
this could be the most informative thread ever in the history of allbuffs. thanks for the work, guys...
 
Its nice when we get these kind of threads when some of the big eastern programs turn their attention this way.
 
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