Best performed cricketers across all conditions and vs any opponent

[ankitj]

Why is McGrath missing a mention in this thread?

 

[The Sean]

Yeah, I'm done.

Qualifications: 3250+ career runs. 100+ wickets.

Loving this. My only possible amendment might be around the distribution of equivalencies - as it stands (assuming I'm reading it correctly), batting averages of 58-60 match with bowling averages all the way from 16-19, yet all the batting averages from 53-56 are equivalent to a bowling average of 21. Do you think there might be a way of evening out the spread there?

 

[G.I.Joe]

Loving this. My only possible amendment might be around the distribution of equivalencies - as it stands (assuming I'm reading it correctly), batting averages of 58-60 match with bowling averages all the way from 16-19, yet all the batting averages from 53-56 are equivalent to a bowling average of 21. Do you think there might be a way of evening out the spread there?

Partly my fault. I bunched up the columns to fit the entire thing on one screen, which caused a few decimal places to disappear. Mostly, it's a limitation of the data. The relationship between the two sets isn't linear all along the way. I can't imagine the curves if this was plotted on a graph!



Better formatting:

 

[Red]

I'm not sure what I started here!

 

[The Sean]

Partly my fault. I bunched up the columns to fit the entire thing on one screen, which caused a few decimal places to disappear. Mostly, it's a limitation of the data. The relationship between the two sets isn't linear all along the way. I can't imagine the curves if this was plotted on a graph!

Better formatting:

Ha ha, yeah I knew what you'd done and what was causing it. I was talking more just about the fact it was somewhat counter-intuitive that, for example, Marshall (bowling average 20.94) has an equivalent batting average of 56 while McGrath (21.64) has an equivalent of just 52. Or even more perversely, Neil Adcock (21.10) only 53!

As you say though, it's predominantly a case of the data sets not being linear rather than a fundamental problem with the methodology.

I'm not sure what I started here!

Something beautiful.

 

[Migara]

Yeah, I'm done.

Qualifications: 3250+ career runs. 100+ wickets.

Caould you post the above in an Excel sheet? I 'll try to fit the curves if possible using a statitical method.

 

[ankitj]

Players with their batting and bowling flipped, using the equivalent averages (i.e, batting all-rounders converted to bowling all-rounders and vice versa):

I know it is not an accurate representation because it does not take plenty of relevant factors into account. I don't care. It's fun.

Garry Sobers - bowl ave 19, bat ave 32
Jacques Kallis - bowl ave 21, bat ave 36
Aubrey Faulkner - bowl ave 30, bat ave 46

Richard Hadlee - bat ave 51, bowl ave 41
Imran Khan - bat ave 51, bowl ave 32
Keith Miller - bat ave 51, bowl ave 32
Ian Botham - bat ave 44, bowl ave 33
Kapil Dev - bat ave 42, bowl ave 34
Wilfred Rhodes - bat ave 46, bowl ave 35
Vinoo Mankad - bat ave 37, bowl ave 34
Chris Cairns - bat ave 42, bowl ave 33
Andrew Flintoff - bat ave 35, bowl ave 34

Malcolm Marshall - bat ave 56, bowl ave probably 50+
Wasim Akram - bat ave 50, bowl ave probably 50+

Well, that puts Kallis, Miller and Imran roughly even.

Yup. They bring maximum value as all rounders.