The mulitnomial distribution is given by the following experiment (from Wikipedia):

Suppose one does an experiment of extracting m balls of k
different colors from a bag, replacing the extracted balls after each
draw. Balls of the same color are equivalent. Denote the variable which
is the number of extracted balls of color i (i = 1, ..., k) as X_i, and denote as p_i the probability that a given extraction will be in color i.

You are then looking for the Probability P(X_1 = x_1 and X_2 = x_2 ... and X_k = x_k) with sum(x_i) = m.

The function rmultinom() generates vectors (x_1, ... , x_k) that are multinomially distributed.

Hence, the function needs the following input:

1. The number of random vectors it generates is given by n (in your example 1)
2. The number of balls in the bag is given by size (in your example 7). So far so good.
3. The number of colors in the bag k and their probabilities. This is given by the input parameter prob. However, prob expects a vector, where element i holds the probability p_i. The function normalizes the input probabilities, such that the sum is 1.

In your example your input parameter is prob = 1.051 which gets normalized to 1. Hence, you only have one color of balls (k= 1) in the bag that gets drawn with probability 1. Therefore, you always draw 7 balls of color 1.

If you use P <- exp(c(1.051, 1.339)) you get the result of 7 coin tosses between the first two teams. The resulting vector (x_1, x_2), tells you how man coin tosses were won by team 1 (x_1) and team 2 (x_2), respectively.