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Sachin Tendulkar better than Don Bradman, new study shows

Howe_zat

Howe_zat

Audio File
Burgey said:
Migara conveniently ignores the WI pace battery not having the benefit of a Bodyline field.
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Also how new and innovative it was at the time. Also that Bradman found himself sacrificing his individual innings totals in that series to focus on faster scoring, to aid his team-mates.
 
Howe_zat

Howe_zat

Audio File
Neil Pickup said:
OK, stats time, what distribution would you use to model batsmen's innings?

What tests would you consider as appropriate to make a call on the significance level of their average after 80 Tests?
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I'm not a statistician, and it's really TC you want to be having this conversation with. I would guess, using my very limited knowledge, that some form of survival analysis could have some merit.
 
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Furball

Furball

Evil Scotsman
I've said it before and I'll say it again.

Viv Richard's batting average places him closer to Chris Martin than Don Bradman.
 
ganeshran

ganeshran

International Debutant
Ikki said:
ganeshran is a member there too it seems. A post in that thread by him:

"Now Bradman fanatics will have to come up with arguments to discredit the study."

:laugh:
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Another post by me in the same thread

Any statistical study that seeks to normalize averages across eras needs to make sort of arbitrary adjustment in the data. Depending on the result that this adjustment gives - people whose idols dont end up on top end up questioning the validity of the study. Tomorrow when a study about Bradman being better than SRT is posted, Bradman fans will treat it as gospel and SRT fans will pick holes in the theory and vice versa.
 
Ikki

Ikki

Hall of Fame Member
You're treating them as equals, as if certain studies are going to put one ahead and others will put another ahead based on some minor tweak. This isn't Tendulkar v Lara, here. Bradman is so far ahead only a terribly flawed/arbitrary one would put Tendulkar in front. The fanatics are mostly on one side here.
 
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ganeshran

ganeshran

International Debutant
No, I am treating responses by their fans as equals. I never compared the players in the first place.
 
ganeshran

ganeshran

International Debutant
Neil Pickup said:
To be fair, anyone capable of basic mathematics can rip that study, and its conclusions, to pieces.
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Any study that normalizes averages needs to have some sort of arbitrary adjustment to the data to adjust for different conditions. Numerous such studies have been done and holes can be picked in all of them.

I was just pointing out that when a statistical study turns up a result that one doesn't like - the first response is to discredit it using various means which is many fans do- regardless of whether that criticism is valid or not..
 
Ikki

Ikki

Hall of Fame Member
ganeshran said:
No, I am treating responses by their fans as equals. I never compared the players in the first place.
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But you're being disingenuous by doing so. Just because they are both arbitrary doesn't mean they'll be equal - equally ridiculous or legitimate. You have to get pretty creative to deduce Sachin > Bradman and the 'arbitrary adjustment' would be laughed at.
 
ganeshran

ganeshran

International Debutant
Ikki said:
But you're being disingenuous by doing so. Just because they are both arbitrary doesn't mean they'll be equal - equally ridiculous or legitimate. You have to get pretty creative to deduce Sachin > Bradman and the 'arbitrary adjustment' would be laughed at.
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Depends on the adjustment itself and the weightages it seeks to apply.
 
ankitj

ankitj

Hall of Fame Member
Howe_zat said:
This thread wasn't actually as bad as a lot of people were making it out to be, even though it was never really going to go anywhere particularly good.

One thing people were discussing that I felt I had to comment on was use of the CLT and normal distribution. I'm afraid that doesn't hold at all, the set of all of a batsman's innings can't be modelled as a normal distribution, because a normal distribution assumes that the population mean and mode are equal, and the distribution is symmetric about the mean.
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CLT has no such requirements, and CLT says or assumes nothing about actual underlying distributions.

A random variable (for example runs scored in an innings) can have any underlying distribution (runs scored in an innings is definitely not a normal distribution, it has a very high right skew). If you take samples of that random variable, and calculate sample mean, this sample mean is itself a new random variable. CLT says that irrespective of the underlying distribution the sample mean will converge to a normal distribution as the sample size goes up.

It is a very, very strong result in statistics, the very basis of all stats actually. You can see it in action in simulations. There is this animation I could find on web that shows it. Underlying distribution is a bimodal distribution (anything but normal). But if you calculate mean of ever larger samples, the distribution of sample mean approaches normal.



EDIT: Some more illustrations

Uniform distribution - http://www.statisticalengineering.com/central_limit_theorem.htm
Triangular distribution - http://www.statisticalengineering.com/central_limit_theorem_(triangle).html
1/X distribution - http://www.statisticalengineering.com/central_limit_theorem_(inverse).html
Parabolic distribution - http://www.statisticalengineering.com/central_limit_theorem_(parabola).html
 
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