In the solution to Q8, the concept of the solution is:

if `x`

is the current score, find all scores which are greater than `x`

, average them and then indicate that Sachin would score `(average_of_subset - x)`

.

My doubt is:

Wouldn’t it be appropriate to break the mean into several means based on histogram and then estimate?

E.g.

Sachin’s histogram is:

`(array([99, 36, 28, 16, 11, 17, 8, 8, 1, 1]), array([ 0. , 18.6, 37.2, 55.8, 74.4, 93. , 111.6, 130.2, 148.8, 167.4, 186. ]))`

Thus the average of each bar is:

`[ 9.3 27.9 46.5 65.1 83.7 102.3 120.9 139.5 158.1 176.7]`

So, if Sachin walks in to bat, x = 0, he is likely to score 9 runs

If he crosses, 9 ==> the next mean is 27. So if the input x=10, then Sachin would score 27-10 = 17 more runs.

If he crosses 27 ==> the next mean is 46. So, if input x = 30, then Sachin would score 46-30 = 16 more runs.

Why don’t we use this concept of subtracting the input x from average of the next bar of histogram?

Thanks,