Projections have Twins headed for fourth-straight 90-loss season

[nicksaviking]

In the past five years, Mauer has posted an OBP of .444 and .416.

 

Is it likely he'll post .420 or better in 2014? No. Is it impossible? Definitely not.

 

I'm not even sure his increasing K rate is a concern right now, though that BABIP sure is.

 

I would also think OBP improvements may correspond to any positive development of Sano and Arcia batting behind Mauer.

[twinscowboysbulls]

Why would you project them to be better than 90 losses if you are anyone other than a Twins fan? We have improved 2 rotation spots and that is it.

 

If we add a couple of those hitters that have been talked about, I don't hate our chances of winning 80 games.

[stringer bell]

In the past five years, Mauer has posted an OBP of .444 and .416.

 

Is it likely he'll post .420 or better in 2014? No. Is it impossible? Definitely not.

 

I'm not even sure his increasing K rate is a concern right now, though that BABIP sure is.

 

I think Mauer's K rate and BABIP last year were both aberrations. I sincerely doubt he'll strike out 100 times this year in well over 600 PAs (if healthy all year), but I also doubt he can sustain the otherwordly BABIP. They should balance out to another .300+ BA and .400+ OBP.

[jay]

Why would you project them to be better than 90 losses if you are anyone other than a Twins fan? We have improved 2 rotation spots and that is it.

 

That whole regression to the mean thingie, remember?

 

If we add a couple of those hitters that have been talked about, I don't hate our chances of winning 80 games.

 

If you truly think the Twins are a 72 win team, I think you'd be hard pressed to show how even Drew and Morales/Cruz make them 8 wins better.

[Mike Sixel]

I would put the over under around 75.....based on a healthier Mauer and Willingham, and improvement in pitching.....but they were SO FAR from median in runs scored and allowed that improvement will only get them so far. They need a lot of improvement (defense too) to get to .500.

[Willihammer]

They're certainly not over the moon about Arcia, Dozier, Hicks, Mastro, Pressley, Pinto, or Sano. Significant upside there.

 

Rotation desperately needs another arm IMO.

[ScottyB]

My question is how does Davenport project the Mighty Whities to improve by 16 wins? They've made a couple of moves, but I don't think they've improved by anywhere close to that.

[twinsfan34]

In the last five years only 11 of the thousands of players in baseball have had an OBP of >.420. Mauer's K% has been rising steadily for 4 years straight and even with an absurd BABIP of .383 last season he was unable to even approach .420. I'm curious what you think will be different this year that allows Joe to achieve such a difficult task?

 

I don't have any special formula or regression for this 'guess'.

 

The .420-.440 is Joey Votto territory. I think Mauer can do that. Less prep for hitters (almost none now) compared to doing that as a catcher. And the legs under him. Just my thoughts.

[Maddie]

I don't really know much about the guys they added but I don't think they are going to be very good. I mean we lost Morneau and didn't really add anyone like him to the team. We are a lot worse than we were 3 years ago. I just wish the team would bring in some better players. I remember going to a game last year and I only knew one of the guys in the lineup. I think the rest were all minor league players or something.

[jokin]

My question is how does Davenport project the Mighty Whities to improve by 16 wins? They've made a couple of moves, but I don't think they've improved by anywhere close to that.

 

They've actually made more than a couple of significant moves, but no doubt part of the projection is related to regression to the mean.....The Whities in the previous 5 seasons won anywhere from 79-89 games. A 16 win improvement + last year's total of 63 wins = 79 wins.

[jay]

They've actually made more than a couple of significant moves, but no doubt part of the projection is related to regression to the mean.....The Whities in the previous 5 seasons won anywhere from 79-89 games. A 16 win improvement + last year's total of 63 wins = 79 wins.

 

He pins Jose Abreu, the Cuban 1B, to come in and have a monster season equivalent in value to our 1B, LF, and C combined (Mauer, Willingham, and Pinto/Suzuki) while helping them add 84 runs on offense over last year.

 

He also projects their cobbled #4/#5 pitchers (the likes of Felipe Paulino, Eric Surkamp, etc) to be worth 4 wins... while no two Twins starters total that amount and the Sox shave off 21 runs on the pitching side compared to last year. They subtracted Peavy, Floyd, Santiago, Reed, and Crain from last year... but project to get better?

 

I don't see it.

[jokin]

He pins Jose Abreu, the Cuban 1B, to come in and have a monster season equivalent in value to our 1B, LF, and C combined (Mauer, Willingham, and Pinto/Suzuki) while helping them add 84 runs on offense over last year.

 

He also projects their cobbled #4/#5 pitchers (the likes of Felipe Paulino, Eric Surkamp, etc) to be worth 4 wins... while no two Twins starters total that amount and the Sox shave off 21 runs on the pitching side compared to last year. They subtracted Peavy, Floyd, Santiago, Reed, and Crain from last year... but project to get better?

 

I don't see it.

 

They signed more than just Abreu, and got interesting prospects via the trades of your aforementioned departures, plus he factors in positive regression for certain returning players. Will the Sox reach 79 wins? I highly doubt it, and Abreu's numbers are inflated,

 

but that wasn't my overarching point- which is-

 

the formula Davenport uses seems to overemphasize and exaggerate regressionary bouncebacks.

[TheLeviathan]

That whole regression to the mean thingie, remember?

 

If you truly think the Twins are a 72 win team, I think you'd be hard pressed to show how even Drew and Morales/Cruz make them 8 wins better.

 

This pretty much sums up my opinion. With the fact that we outperformed our Pyth. last year and the offense looks shoddy - I don't think we sniff .500 unless multiple young players make surprisingly big jumps this year.

 

72.5 - that's my prediction for Vegas' number. That would be a 6-7 game improvement over their actual record and an 11-12 game improvement over their expected record last year. That felt overly optimistic as I typed it actually.

[jokin]

This pretty much sums up my opinion. With the fact that we outperformed our Pyth. last year and the offense looks shoddy - I don't think we sniff .500 unless multiple young players make surprisingly big jumps this year.

 

72.5 - that's my prediction for Vegas' number. That would be a 6-7 game improvement over their actual record and an 11-12 game improvement over their expected record last year. That felt overly optimistic as I typed it actually.

 

I'm glad you brought up last year's Pyth-number, which is very contextual in this discussion. I predicted 70-74 after the Winter Meetings brought back nothing (I predicted 66-75 in 2013), which I guess makes my median number @ 72 wins, and at this point, in consideration of how my prediction turned out last year, I feel a little over-optimistic, yet again, myself.

 

Keeping my fingers crossed that a couple more impactful moves are in the works to make this all moot, but for now, the 90-loss projections don't seem all that unreasonable.

[Oxtung]

Win differential from 2013 actual to this 2014 projection...

 

Bottom 5:

1 (t) ATL, BOS, PIT -11

4 KC -9

5 (t) BAL, OAK -8

 

Top 5:

1 HOU +19

2 CWS +16

3 MIA +13

4 SEA +12

5 SF +9

7 (t) MIN, LAA +6

 

Notice any trends here? Teams that were bad or worse than expected last year get better (regress towards the mean). Teams that were good or better than expected last year get worse (regress towards the mean). This highlights what PseudoSABR was referring to. Not exactly an earth-shattering concept.

 

"Regression to the mean" is such a large part of not only baseball but of the world as a whole that it is not surprising that his model is dominated by it. Not everyday can be a good day and not everyday is going to be terrible. In fact most are going to be well, average (however one defines that for themselves). I don't really hear anyone arguing that his model is invalid because he uses regression to the mean but rather people seem to think that he is a little heavy handed with it. That's fine. Is that an accurate assessment with people's problem with his model? If so...

 

I'd like to talk a little bit about the opposite of "regression to the mean". What many of you seem to be saying is that his model, and I'd argue most statistical models, doesn't do a good job of predicting natural variability. That is it doesn't predict enough "spikes" in the way a player plays, whether that is good or bad.

 

It seems to be rather easy to understand some of the triggers for regression to the mean. Getting older. Getting hurt. Having surgery. A huge spike in performance, either good or bad.

 

What about the triggers for natural variability? In order to incorporate that variability into statistical models we must know what the impetus is. Here is another way of asking the question: why was Mauer so much better in 2009 than any year before or since? Then ask yourself, does that data exist publicly?

 

I think that is at the heart of the problem in this model, and really all baseball prognostication. This is a rather esoteric ramble but I thought it worth reflecting on. Enjoy, or not, as one pleases.

[Oxtung]

I think Mauer's K rate and BABIP last year were both aberrations. I sincerely doubt he'll strike out 100 times this year in well over 600 PAs (if healthy all year), but I also doubt he can sustain the otherwordly BABIP. They should balance out to another .300+ BA and .400+ OBP.

Here is a chart showing Mauer's K%, BABIP and batting average on a yearly basis.

 

[TABLE=class: grid, width: 500]

Year

K%

BABIP

Average

2005

11.6

.322

.294

2006

8.9

.364

.347

2007

10.8

.319

.293

2008

7.9

.342

.328

2009

10.4

.373

.365

2010

9.1

.348

.327

2011

11.4

.319

.287

2012

13.7

.364

.319

2013

17.5

.383

.324

[/TABLE]

 

We can see that in the years that his BABIP is high and his K% is low, like 2006, he has a very high batting average. In years that his BABIP is high and his K% is high, like 2012, we can see that his average is still high but not as high as 2006. In years that his BABIP is "low" (yet still above league average) and his K% is high his batting average takes a big hit like in 2011.

 

Now notice the last four years of his K%. It has risen from 9.1% all the way to 17.5% and it is rising very consistently, there doesn't seem to be any spiking. This to me says that Joe is just getting older. His reflexes or perhaps understanding of the strike zone just aren't what they used to be. That doesn't bode well for his batting average. As we can see in 2013 if his BABIP is high enough (somewhere around .300 is league average) it can compensate for his increase in strikeouts.

 

I think that we can all see that if Joe's K% remains high, like the last four years suggest, and his BABIP regresses, like it should, that he is unlikely to continue his impressive batting average and OBP.

 

I think this is part of why all of these models predict that Joe Mauer will not be as productive in 2014 as he was in 2013. Unless he either has another lucky year re:BABIP or his K% trend turns around.

 

Does anyone want to make a pitch as to why either of those should be expected? I'm feeling a little pessimistic at the moment after looking at these trends and could use some hope.

 

On the bright side his BB% is as high as it ever has been. That doesn't seem to be effected. If anyone wants to do some more research into why Joe is striking out more that would be very interesting to know. Is it strikes looking? Called Strikes? Has his "strike zone" gotten larger and so he has to "defend" a larger area? All interesting questions, IMO I just don't have time to look into it currently.

[Oxtung]

They're certainly not over the moon about Arcia, Dozier, Hicks, Mastro, Pressley, Pinto, or Sano. Significant upside there.

 

Rotation desperately needs another arm IMO.

 

I wonder if they don't predict great things for Pinto, Sano, et al. because for every Wil Meyers that enters the league there are two Pedro Florimons or Parmelees. When you're being comped with other first year players I have to believe the numbers of Florimons outweigh the number of Meyers significantly and therefore drag the expectations way down.

[Oxtung]

I don't have any special formula or regression for this 'guess'.

 

The .420-.440 is Joey Votto territory. I think Mauer can do that. Less prep for hitters (almost none now) compared to doing that as a catcher. And the legs under him. Just my thoughts.

 

I think we all can get behind that hope!

[h2oface]

I Keep hearing how Ryan spent money...... but the payroll is virtually the same. He has just replaced the money that went off the books. To me, that is not really spending money, even though many would like to think so.

[jay]

I wonder if they don't predict great things for Pinto, Sano, et al. because for every Wil Meyers that enters the league there are two Pedro Florimons or Parmelees. When you're being comped with other first year players I have to believe the numbers of Florimons outweigh the number of Meyers significantly and therefore drag the expectations way down.

 

Unless you're a Cuban first baseman for the White Sox.

 

What about the triggers for natural variability? In order to incorporate that variability into statistical models we must know what the impetus is. Here is another way of asking the question: why was Mauer so much better in 2009 than any year before or since? Then ask yourself, does that data exist publicly?

 

I think that is at the heart of the problem in this model, and really all baseball prognostication. This is a rather esoteric ramble but I thought it worth reflecting on. Enjoy, or not, as one pleases.

 

I actually couldn't agree with you more. My posts were mostly intended to point out that while these projections are fun to talk about, there is so much unknown reality that will happen. You did a better job articulating that part. Regression is a very real thing, but variation is as well and those causes are extremely unpredictable in a world that we try so hard to predict.

[Oxtung]

Does the model measure what it is supposed to? I can see that 30% of the time the model is off by more than 10 games for team wins. So given the data they have they are grossly inaccurate. With an error bar that large they should lose credibility.

 

This is something that has been bothering me re:statistical modeling both in regards to this application but more in relation to all modeling and metrics with a baseball slant whether that is WAR, SIERA, xFIP, PECOTA, Steamer or whatever.

 

There is a certain segment of the baseball consuming public that latches onto these new mathematical analysis tools and thinks they are great. They are helping to enhance our understanding of the game. On the opposite end of the spectrum there is a segment of fans that seems to feel that these tools weren't needed by the greats of the past and they aren't needed now. There is nothing new that needs to be learned about the game and any attempt to replace RBI's and Wins and Runs Scored and whatever else with a new statistic is anathema. I am not trying to pigeon hole anyone here on this board into either of these groups, certainly not meaning to imply anything about Old Nurse so please don't be offended. There are as many positions on this subject as there are frequnecies in the EM spectrum, surely. These just seem to be the opposite ends of that pendulum.

 

I certainly identify myself with the former group. I love using math as a way to view the world. I find that it helps to clarify things if I can use quantitative analysis. So, I freely admit that I am biased towards these new constructs because of my larger world view. OK enough rambling...to my point.

 

In the quoted post the author states that this model is grossly wrong 30% of the time. For simplicities sake let's assume that means the metric is mostly correct 70% of the time. The implied message being that this is a worthless tool and shouldn't be given much credence.

 

My question is what is the alternative? "Gut feeling"? There is a place in the world for "gut feelings" for certain, but by and large they aren't very accurate. Look no further than houses of gambling, NCAA tournament brackets, the lottery, or the ever popular "how many jelly beans are in this jar" game if you're unsure. In fact my guess is most statistical modeling is derived from a desire to get away from gut feeling guesses.

 

So here are my questions: Is there an alternative to both statistical modeling and gut feelings? Is it more accurate? How accurate is human intuition? How well did Keith Law, Jayson Stark or Gleeman and the Geek do at predicting 2013's final standings? How good is your intuition? If you want me to believe that a computer model is crappy than show me what I should be paying attention to instead, please.

 

Sorry, this turned into much more of a rant than I meant it to be and considerably longer. I obviously don't know what the word concise means and got a double helping of verbose instead. Again Old Nurse and everyone else, this was not directed at you specifically, your post was just the tipping point to something that had been bubbling up within me for a while.

 

TL : DR My point was not to offend with this but rather to say, if you want me to believe you, show some evidence that there is a better alternative. 70% correct is clearly not perfect, but if the alternative is only correct 60% of the time, isn't that a step in the right direction?

[Oxtung]

Unless you're a Cuban first baseman for the White Sox.

 

I wonder if he could be being comped to Puig, Cespedes, Ichiro, etc... My gut (oh god did I just say that after my stupid rant?!?) tells me that imports from other major leagues both succeed more frequently than prospects but also have much better first seasons. I have absolutely nothing to back that up with though.

[Hosken Bombo Disco]

This is something that has been bothering me re:statistical modeling both in regards to this application but more in relation to all modeling and metrics with a baseball slant whether that is WAR, SIERA, xFIP, PECOTA, Steamer or whatever.

 

There is a certain segment of the baseball consuming public that latches onto these new mathematical analysis tools and thinks they are great. They are helping to enhance our understanding of the game. On the opposite end of the spectrum there is a segment of fans that seems to feel that these tools weren't needed by the greats of the past and they aren't needed now. There is nothing new that needs to be learned about the game and any attempt to replace RBI's and Wins and Runs Scored and whatever else with a new statistic is anathema. I am not trying to pigeon hole anyone here on this board into either of these groups, certainly not meaning to imply anything about Old Nurse so please don't be offended. There are as many positions on this subject as there are frequnecies in the EM spectrum, surely. These just seem to be the opposite ends of that pendulum.

 

.....

 

I loved reading this. To be brief in reply, I welcome the new detailed statistical tools, but I don't bury my head in them. I'm grateful that others do, because as you said, they can enhance our understanding, and I guess that makes me a freeloader to some degree, letting the rest of you study in detail what I am happy to know in the abstract. For example the stats tell us that Pelfrey and Hughes are fly ball pitchers with possibly high FB% (verify) and I wouldn't have known that without being on Twins Daily; and won't an outfield of Buxton & Hicks be nice then? And we are just a year away from that. So with this knowledge, I am optimistic Hughes and Pelfrey were good signings. Kudos to Ryan.

 

But in the end, as a casual fan, BABIP doesn't tell me much more than simple batting average already tells me. There is a degree of chance, luck, funny hops and diving grabs that happen in every game that will skew any statistic in any direction.. Same with SIERA and ERA+ for pitchers. I do take heed to the advanced stats, but at the same time, basic ERA is simple, familiar, and usually tells its piece of the story accurately enough.

[jay]

But in the end, as a casual fan, BABIP doesn't tell me much more than simple batting average already tells me. There is a degree of chance, luck, funny hops and diving grabs that happen in every game that will skew any statistic in any direction.. Same with SIERA and ERA+ for pitchers. I do take heed to the advanced stats, but at the same time, basic ERA is simple, familiar, and usually tells its piece of the story accurately enough.

 

I can certainly respect that view. BABIP was greatly misused when it was first bandied about by saying so-and-so was lucky or unlucky with a simple glimpse at the number. It goes much deeper than that and is dependent on the hitter's profile and tools. I do like some of the new pitcher metrics because, frankly, ERA is rotten. The definition of earned runs is convoluted and it can't measure the huge impact of defense outside of that other convoluted measure, errors.

 

My mind was blown when I was introduced to advanced metrics probably 5 years ago. It's amazing how far the studies and measures have come even since then. At some point, a certain level of consolidation and common acceptance needs to occur. There's also lots of work left to do on assigning individual's value. With so many variables, it'll be quite the quest to truly master and measure it all.

[jay]

I wonder if he could be being comped to Puig, Cespedes, Ichiro, etc... My gut (oh god did I just say that after my stupid rant?!?) tells me...

 

Likely so, but it still seems exorbitant. The overall success rate might be higher than a rook from MiLB, but there's been plenty of international imports that flopped... or at least that's what my gut tells me.

[JB_Iowa]

My question is what is the alternative? "Gut feeling"? There is a place in the world for "gut feelings" for certain, but by and large they aren't very accurate. Look no further than houses of gambling, NCAA tournament brackets, the lottery, or the ever popular "how many jelly beans are in this jar" game if you're unsure. In fact my guess is most statistical modeling is derived from a desire to get away from gut feeling guesses.

 

Advanced metrics can enhance understanding of the game but for many fans (myself included), that is all they do --- enhance understanding. Some of us don't aspire to be general managers (even of a fantasy team) or to work for a baseball team in any other manner. Nor do we feel a compulsion to prove that we are the "smartest kid in the class".

 

For me, while I learn something from the advanced stats and dig through the info when someone presents it, I don't particularly want to spend a lot of time or energy making understanding them a highlight of my life. Nor do I want to get drawn into long discussions of statistics.

 

I really just want to sit and enjoy the game. And knowing some advanced stats can enhance that. And things like Parker's analysis of a player (one of my favorite things on TD) can enhance that because I continually learn more about the game. But sometimes just watching and enjoying those "gut feelings" is good enough, too.

 

Baseball (especially in the winter/spring) is about HOPE. The possibility that THIS will be the year. The possibility that a player will be better than we expect (maybe even better than we dream). The possibility that a young player will have a breakout season. The possibility that everything will "go right".

 

It isn't what I expect but can be what I hope for. And its why I kept watching the Twins even when all the losses mounted up the last few years. If I place too much emphasis on advanced stats, I have a "gut feeling" that I would conclude that "all hope is gone".

[big dog]

Using old line that follows the phrase "while the Twins are a bad team on paper", I will sum up my attitude toward projections by saying "but they don't play their games in the projects". Let's hope they surprise.

[old nurse]

This is something that has been bothering me re:statistical modeling both in regards to this application but more in relation to all modeling and metrics with a baseball slant whether that is WAR, SIERA, xFIP, PECOTA, Steamer or whatever.

 

There is a certain segment of the baseball consuming public that latches onto these new mathematical analysis tools and thinks they are great. They are helping to enhance our understanding of the game. On the opposite end of the spectrum there is a segment of fans that seems to feel that these tools weren't needed by the greats of the past and they aren't needed now. There is nothing new that needs to be learned about the game and any attempt to replace RBI's and Wins and Runs Scored and whatever else with a new statistic is anathema. I am not trying to pigeon hole anyone here on this board into either of these groups, certainly not meaning to imply anything about Old Nurse so please don't be offended. There are as many positions on this subject as there are frequnecies in the EM spectrum, surely. These just seem to be the opposite ends of that pendulum.

 

I certainly identify myself with the former group. I love using math as a way to view the world. I find that it helps to clarify things if I can use quantitative analysis. So, I freely admit that I am biased towards these new constructs because of my larger world view. OK enough rambling...to my point.

 

In the quoted post the author states that this model is grossly wrong 30% of the time. For simplicities sake let's assume that means the metric is mostly correct 70% of the time. The implied message being that this is a worthless tool and shouldn't be given much credence.

 

My question is what is the alternative? "Gut feeling"? There is a place in the world for "gut feelings" for certain, but by and large they aren't very accurate. Look no further than houses of gambling, NCAA tournament brackets, the lottery, or the ever popular "how many jelly beans are in this jar" game if you're unsure. In fact my guess is most statistical modeling is derived from a desire to get away from gut feeling guesses.

 

So here are my questions: Is there an alternative to both statistical modeling and gut feelings? Is it more accurate? How accurate is human intuition? How well did Keith Law, Jayson Stark or Gleeman and the Geek do at predicting 2013's final standings? How good is your intuition? If you want me to believe that a computer model is crappy than show me what I should be paying attention to instead, please.

 

Sorry, this turned into much more of a rant than I meant it to be and considerably longer. I obviously don't know what the word concise means and got a double helping of verbose instead. Again Old Nurse and everyone else, this was not directed at you specifically, your post was just the tipping point to something that had been bubbling up within me for a while.

 

TL : DR My point was not to offend with this but rather to say, if you want me to believe you, show some evidence that there is a better alternative. 70% correct is clearly not perfect, but if the alternative is only correct 60% of the time, isn't that a step in the right direction?

 

The question is, of what use is something that is grossly wrong 30% of the time? It does not provide useful material if it is that wrong. The argument that it is better than anything else is hollow. It is not anti statistic argument that I have. It is that the statical method used is grossly flawed. If your analysis of a 40 man roster leaves you that far in error, you have bad analysis. Figure out why you are wrong so often and try again. I said use it for entertainment value. That is all Davenport model is good for. It cannot predict improvements in player. It anticipates declines for individual players that may or may not be there. It has no way to determine the effect of changing teams will benefit/hurt a player. The statistical bias of a design is no different than a gut feeling of an individual that spends a lifetime involved in a game.

[Craig Arko]

The question is, of what use is something that is grossly wrong 30% of the time?

 

Weather forecasting?

[diehardtwinsfan]

This is something that has been bothering me re:statistical modeling both in regards to this application but more in relation to all modeling and metrics with a baseball slant whether that is WAR, SIERA, xFIP, PECOTA, Steamer or whatever.

 

There is a certain segment of the baseball consuming public that latches onto these new mathematical analysis tools and thinks they are great. They are helping to enhance our understanding of the game. On the opposite end of the spectrum there is a segment of fans that seems to feel that these tools weren't needed by the greats of the past and they aren't needed now. There is nothing new that needs to be learned about the game and any attempt to replace RBI's and Wins and Runs Scored and whatever else with a new statistic is anathema. I am not trying to pigeon hole anyone here on this board into either of these groups, certainly not meaning to imply anything about Old Nurse so please don't be offended. There are as many positions on this subject as there are frequnecies in the EM spectrum, surely. These just seem to be the opposite ends of that pendulum.

 

I certainly identify myself with the former group. I love using math as a way to view the world. I find that it helps to clarify things if I can use quantitative analysis. So, I freely admit that I am biased towards these new constructs because of my larger world view. OK enough rambling...to my point.

 

In the quoted post the author states that this model is grossly wrong 30% of the time. For simplicities sake let's assume that means the metric is mostly correct 70% of the time. The implied message being that this is a worthless tool and shouldn't be given much credence.

 

My question is what is the alternative? "Gut feeling"? There is a place in the world for "gut feelings" for certain, but by and large they aren't very accurate. Look no further than houses of gambling, NCAA tournament brackets, the lottery, or the ever popular "how many jelly beans are in this jar" game if you're unsure. In fact my guess is most statistical modeling is derived from a desire to get away from gut feeling guesses.

 

So here are my questions: Is there an alternative to both statistical modeling and gut feelings? Is it more accurate? How accurate is human intuition? How well did Keith Law, Jayson Stark or Gleeman and the Geek do at predicting 2013's final standings? How good is your intuition? If you want me to believe that a computer model is crappy than show me what I should be paying attention to instead, please.

 

Sorry, this turned into much more of a rant than I meant it to be and considerably longer. I obviously don't know what the word concise means and got a double helping of verbose instead. Again Old Nurse and everyone else, this was not directed at you specifically, your post was just the tipping point to something that had been bubbling up within me for a while.

 

TL : DR My point was not to offend with this but rather to say, if you want me to believe you, show some evidence that there is a better alternative. 70% correct is clearly not perfect, but if the alternative is only correct 60% of the time, isn't that a step in the right direction?

 

I think these tools are neat and funn to talk about, but the one thing I don't see a lot of is people going back and looking at what they predicted to how they actually did and compared the tools to each other and to the "experts"... I don't think it's too much to ask that we look at the actual results of said tool before we start placing value in them.