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Jeff Fogle
Joined: 11 Jan 2011
Posts: 70
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 Posted: Thu Feb 17, 2011 12:26 am Post subject: |
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Updating to 35-20-1 for Philly vs. expectations, 30-14 the last 44 heading into the ASB after Wednesday's win in Houston...
And, 25-31-1 for Utah, 8-20 the last 28 heading into the ASB after Wednesday's home loss to Golden State. |
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DSMok1
Joined: 05 Aug 2009
Posts: 611
Location: Where the wind comes sweeping down the plains
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DSMok1
Joined: 05 Aug 2009
Posts: 611
Location: Where the wind comes sweeping down the plains
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DSMok1
Joined: 05 Aug 2009
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Jeff Fogle
Joined: 11 Jan 2011
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| Posted: Sun Mar 13, 2011 11:43 am Post subject: |
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DSM, would you consider the numbers in your Bayesian column to be a reasonable estimation of the point differences between teams in a 40 minute game? Meaning, Kansas is about 3 points better on a neutral court than Texas because they're 3 points higher?
If not, is there a way to easily convert your output into something that would resemble a point differential scale so readers could compare it to the market prices that go up in the NCAA Tournament?
Living in Austin, it was cool to see Texas so high. Wish they could find a high level of form more consistently. I'm afraid they peaked too early again... |
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DSMok1
Joined: 05 Aug 2009
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Location: Where the wind comes sweeping down the plains
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| Posted: Mon Mar 14, 2011 6:29 am Post subject: |
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| Jeff Fogle wrote: | DSM, would you consider the numbers in your Bayesian column to be a reasonable estimation of the point differences between teams in a 40 minute game? Meaning, Kansas is about 3 points better on a neutral court than Texas because they're 3 points higher?
If not, is there a way to easily convert your output into something that would resemble a point differential scale so readers could compare it to the market prices that go up in the NCAA Tournament?
Living in Austin, it was cool to see Texas so high. Wish they could find a high level of form more consistently. I'm afraid they peaked too early again... |
Those are points per 100 possessions, not per game. To calculate the number of possessions expected in a game, take adjusted pace from Pomeroy for each team, and calculate as PaceA*PaceB/NCAAAvg. to get the expected pace for each game.
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Ilardi
Joined: 15 May 2008
Posts: 265
Location: Lawrence, KS
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| Posted: Mon Mar 14, 2011 8:48 am Post subject: |
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Daniel: great work, as always.
Are you planning to update your numbers through Sunday's games? (Looks like they're current through last Friday, so I suspect the update would only make a minor difference for most teams.) It will be interesting to see how your model fares in predicting upcoming NCAA tourney games. |
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DSMok1
Joined: 05 Aug 2009
Posts: 611
Location: Where the wind comes sweeping down the plains
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| Posted: Mon Mar 14, 2011 10:11 am Post subject: |
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| Ilardi wrote: | Daniel: great work, as always.
Are you planning to update your numbers through Sunday's games? (Looks like they're current through last Friday, so I suspect the update would only make a minor difference for most teams.) It will be interesting to see how your model fares in predicting upcoming NCAA tourney games. |
I will, yes.
Unless, of course, my site is crashed by excessive traffic. It seems to be down now...
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DSMok1
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Jeff Fogle
Joined: 11 Jan 2011
Posts: 70
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| Posted: Mon Mar 14, 2011 11:57 am Post subject: |
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Daniel, thanks for explaining that the Bayesian column represents 100 possessions.
I'm referring to a scale that would show all the teams at once in terms of how they relate in point differential, as Jeff Sagarin's been doing at USA Today for eons for example.
http://www.usatoday.com/sports/sagarin/bkt1011.htm
Would you recommend multiplying the Bayesian column by .667 (eyeballing the midpoint pace factor at kenpom) to approximate 66.7 possessions per game, then using that as a standard for a 68-team comparison in the tourney? I'm aware that it's not as ideal as running each and every conceivable matchup through the algebra ringer...but that's a bit much if you're just trying to see how the teams in a certain regional stack up against each other, etc...
First used the formula you sited for basketball projections back in 1984 with simple game total averages in the NBA. Got the job done well for Over/Unders back then. Oddsmakers didn't realize how much things would blow up or blow down when extremes played each other. Formula captured it very well. When expanded boxscores became widely available, we used half of free throw attempts rather than .44 in the possession estimates. Ahead of the curve I guess, but not quite as exact as it could have been.
Nowadays in the colleges many offshore places reportedly just use kenpom's "Fanmatch" page for posting their over/unders. Saves them a lot of work. Not the same with his game margins though. Less agreement about teams there. Remember kenpom talking in a blog several weeks back about readers being less than enthusiastic about his game/margin predictions. Not able to outperform the prediction markets yet. Maybe soon. Seems very close but the shadings aren't quite there yet I guess. |
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EvanZ
Joined: 22 Nov 2010
Posts: 302
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| Posted: Mon Mar 14, 2011 1:25 pm Post subject: |
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Seems to me like using Daniel's rankings straight up (or something like LRMC) is the way to go to improve chances of getting 2nd or 3rd place in a pool, but may not be the best way to actually win a pool. Above and beyond the "upsets" that the (presumably more accurate) Bayesian rankings predict, there will be upsets that could not possibly be predicted, except by random chance. I don't see someone winning a relatively large sized pool without making some "crazy" picks, if that makes sense.
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DSMok1
Joined: 05 Aug 2009
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Location: Where the wind comes sweeping down the plains
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| Posted: Mon Mar 14, 2011 2:25 pm Post subject: |
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| EvanZ wrote: | | Seems to me like using Daniel's rankings straight up (or something like LRMC) is the way to go to improve chances of getting 2nd or 3rd place in a pool, but may not be the best way to actually win a pool. Above and beyond the "upsets" that the (presumably more accurate) Bayesian rankings predict, there will be upsets that could not possibly be predicted, except by random chance. I don't see someone winning a relatively large sized pool without making some "crazy" picks, if that makes sense. |
Right. The way to maximize your rank, though, in large pools, is not to go with the Bayesian ratings if you know the distribution of selections. Since we know what was picked in the ESPN pool, we can choose using that information. Maximize RoundValue*(Odds% + (Odds%- Chosen%)). In other words, pick against the crowd, but don't pick teams that don't have any chance.
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EvanZ
Joined: 22 Nov 2010
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| Posted: Mon Mar 14, 2011 2:27 pm Post subject: |
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| DSMok1 wrote: | | EvanZ wrote: | | Seems to me like using Daniel's rankings straight up (or something like LRMC) is the way to go to improve chances of getting 2nd or 3rd place in a pool, but may not be the best way to actually win a pool. Above and beyond the "upsets" that the (presumably more accurate) Bayesian rankings predict, there will be upsets that could not possibly be predicted, except by random chance. I don't see someone winning a relatively large sized pool without making some "crazy" picks, if that makes sense. |
Right. The way to maximize your rank, though, in large pools, is not to go with the Bayesian ratings if you know the distribution of selections. Since we know what was picked in the ESPN pool, we can choose using that information. Maximize RoundValue*(Odds% + (Odds%- Chosen%)). In other words, pick against the crowd, but don't pick teams that don't have any chance. |
Rhetorical question...can you come up with a simulation that would tell you how risky to be depending on pool size? That would be pretty nifty.
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Jeff Fogle
Joined: 11 Jan 2011
Posts: 70
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| Posted: Mon Mar 14, 2011 4:39 pm Post subject: |
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For people thinking about office pools...
Kenpom (current offshore line in parenthesis)
Thursday
http://kenpom.com/fanmatch.php?d=2011-03-17
Wisconsin by 3 (4.5)
Cincinnati by 2 (1)
Gonzaga by 1 (St. John's by 1.5)
Utah State by 3 (K-State by 2)
Vandy by 2 (2)
Temple by 1 (2.5)
Michigan State by 1 (1.5)
ODU by 1 (2)
BYU by 13 (8.5)
UCONN by 9 (10)
Kentucky by 12 (13.5)
Louisville by 12 (10)
Florida by 12 (12.5)
San Diego State by 15 (15.5)
Friday
http://kenpom.com/fanmatch.php?d=2011-03-18
Illinois by 1 (UNLV by 1.5)
G. Mason by 1 (1.5)
Marquette by 1 (Xavier by 2)
FSU by 1 (pick)
Michigan by 2 (Tennessee by 1.5)
Washington by 6 (5.5)
Arizona by 8 (6)
Texas by 14 (10)
Syracuse by 13 (11.5)
N. Carolina by 18 (17.5)
Notre Dame by 17 (13)
Purdue by 15 (14)
Kansas by 22 (22.5)
Duke by 27 (22.5)
Many virtual coin flips in the first round, with some disagreement between kenpom and the market in those. Not many methodologies get the coin flip games right when you have to pick them ALL in a pool (lol). As you guys point out, you can create value potential by going opposite the masses if the masses line up on one side of a coin flip...
In some seasons, I think spreads up to as high as 3-4 ish have ended up splitting out as if they were true coin flips. That will happen with this kind of sample size though. Tough to know teams with "certainty" even at this point of the season given strength of schedule issues, injury/suspension issues, young teams getting better as they mature, etc...
Market less enthusiastic about Texas than kenpom is... |
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Jeff Fogle
Joined: 11 Jan 2011
Posts: 70
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