How Multinomial Naive Bayes Formula work with real data

The formula taught was:- P(xi | y=c) = ( sum of word xi that occurred in class c ) / ( sum of counts of each word in vocab in class c ).

I have taken a hypothetical example. Can you please tell me if this is how it is supposed to work.
There are 4 features in Vocab - {Cricket, News, Win, Leader} and 2 classes in Y - {Sports, News}.
I take 3 documents as example and there vocab and result is represented by -
V = { C, N, W, L}
D1 = [ 3, 1, 5, 0 ], Y[ 1 ] = Sports
D2 = [ 1, 5, 3, 4 ], Y[ 2 ] = News
D3 = [ 4, 2, 3, 1], Y[ 3 ] = Sports

so how conditional Prob, P( xi=cricket | y=sports ) is calculated??

Is it = (4+3)/{(3+1+5+0)+(4+2+3+1)} = 7/19 ??

If not, please explain how it is supposed to be calculated.
Thank You.

Hey @preetishvij, yes you did it right. Great work done.

Hope this resolved your doubt.
Plz mark the doubt as resolved in my doubts section.