Fobo911
Regular
After reading that "Next WC?" thread, I felt inspired to try to use math functions to predict the winning 3x3x3 average for 2009, since the times have dropped dramatically each year.
(I'm not going to use the 1982 WC results because that would be erratic data.)
In 2003, Dan Knight won with an average of 20.00 seconds.
In 2005, Jean Pons won with an average of 15.10 seconds.
In 2007, Yu Nakajima won with an average of 12.46 seconds.
x
----
2003
2005
2007
2009
y
-----
20.00
15.10
12.46
?????
Using linear regression from my graphing calculator (I tried exponential regression, but that's not working out for me, so linear regression is probably fair enough, for now), the equation for the line of best fit is: y = -1.885x + 3795.278333
Let's predict results for x = 2009.
y = -1.885(2009) + 3795.278333 = 8.313333 = 8.31 seconds
Therefore, according to linear regression, the winner of the 2009 World Championships will average 8.31 seconds!
I won't be surprised at all if that happens. After seeing Harris Chan's 8.79-second average video, anything is possible.


P.S. Is there a way to do this for an exponential function? An exponential function is much more accurate since in this case, the time will never reach 0, unlike linear functions, which will soon make times sub-0.
For example, if we go continue on with the equation...
f(2011) = -1.885(2011) + 3795.278333 = 4.543333 -> 4.54 seconds
f(2013) = -1.885(2013) + 3795.278333 = .773333 -> .77 seconds
f(2015) = -1.885(2015) + 3795.278333 = -2.996667 = -3.00 seconds




Plus, as you get faster, it's much harder to get even faster (much harder to get to 0 seconds, which is impossible), so that models the behavior of an exponential function in the form of f(x) = a^x where 0 < a < 1.
So yeah, there is a huge problem with my calculations, but I don't know how to make it work for an exponential function. Anybody know how?
EDIT:
Vault312's method below this post is much more accurate. 10.42 seconds.
(I'm not going to use the 1982 WC results because that would be erratic data.)
In 2003, Dan Knight won with an average of 20.00 seconds.
In 2005, Jean Pons won with an average of 15.10 seconds.
In 2007, Yu Nakajima won with an average of 12.46 seconds.
x
----
2003
2005
2007
2009
y
-----
20.00
15.10
12.46
?????
Using linear regression from my graphing calculator (I tried exponential regression, but that's not working out for me, so linear regression is probably fair enough, for now), the equation for the line of best fit is: y = -1.885x + 3795.278333
Let's predict results for x = 2009.
y = -1.885(2009) + 3795.278333 = 8.313333 = 8.31 seconds
Therefore, according to linear regression, the winner of the 2009 World Championships will average 8.31 seconds!
I won't be surprised at all if that happens. After seeing Harris Chan's 8.79-second average video, anything is possible.
P.S. Is there a way to do this for an exponential function? An exponential function is much more accurate since in this case, the time will never reach 0, unlike linear functions, which will soon make times sub-0.
For example, if we go continue on with the equation...
f(2011) = -1.885(2011) + 3795.278333 = 4.543333 -> 4.54 seconds
f(2013) = -1.885(2013) + 3795.278333 = .773333 -> .77 seconds
f(2015) = -1.885(2015) + 3795.278333 = -2.996667 = -3.00 seconds
Plus, as you get faster, it's much harder to get even faster (much harder to get to 0 seconds, which is impossible), so that models the behavior of an exponential function in the form of f(x) = a^x where 0 < a < 1.
So yeah, there is a huge problem with my calculations, but I don't know how to make it work for an exponential function. Anybody know how?
EDIT:
Vault312's method below this post is much more accurate. 10.42 seconds.
Last edited: