Cheating Absorbing Markov Chains in R - r

Question

I am building a Lineup simulator that uses absorbing markov chains to simulate the number of runs that a certain lineup would score. There is a different transition matrix for each different of the 9 players in the lineup and one games is simulated using the following function:
simulate.half.inning9 <- function(P1,P2,P3,P4,P5,P6,P7,P8,P9,R,start=1){
s <- start; path <- NULL; runs <- 0; zz=1;inn=1
while(inn<10){
s <- start; path <- NULL;
while(s<25){
if(zz==1){
P=P1
}
if(zz==2){
P=P2
}
if(zz==3){
P=P3
}
if(zz==4){
P=P4
}
if(zz==5){
P=P5
}
if(zz==6){
P=P6
}
if(zz==7){
P=P7
}
if(zz==8){
P=P8
}
if(zz==9){
P=P9
}
s.new <- sample(1:25,1,prob=P[s,])
path <- c(path,s.new)
runs <- runs+R[s,s.new]
s <- s.new
zz=ifelse(zz==9,1,zz+1)
}
inn=inn+1
runs
}
runs
}
Mat 1-9 are the individual 25x25 transition matrices. And yes I know I should use a list! I then use the next function to simulate 1000 seasons worth of games using this function to try and have it settle towards the "true" number.
RUNS <- replicate(162000,simulate.half.inning9(mat1,mat2,mat3,
mat4,mat5,mat6,mat7,mat8,mat9,R))
R is a matrix that basically tells the function how many runs you get from going from one state to another.
So my question is, is there a way to cheat this system to get "true" numbers for each lineup simulation without running it 1000 times? The goal of this is to see which lineup produces the most "true" runs.
Since there are 9! ways to set a lineup, running 362880 different lineups 1000 times each isn't feasible.
Thank you!