NFL and NHL DFS ( DraftKings ) Combinatorics part 2

Part 1 I fixed the NFL script, so the FLEX player is accounted for.
Here's the script:

dk <- read.csv("nflredux")
dk <- lapply(split(dk, dk$Position), function(x) x[sample(15), ])

dk <- dk[c("G","W","C","D","U")]

15*choose(15,3)*choose(15,2)*choose(4,2)*4



rows <- list(t(1:15), combn(15,3), combn(15,2), combn(4,2), t(1:4))



dims <- sapply(rows, NCOL)

inds <- expand.grid(mapply(`:`, 1, dims))



dim(inds)



extract <- function(ind) {

    g <- inds[ind,]

    do.call(rbind, lapply(1:5, function(i) dk[[i]][rows[[i]][,g[[i]]], ]))

}



extract(1)



win <- c(0, 0, 0)

for(i in 1:17000000)

{

    extracted <- extract(i)

    #print(i)

    #print(sum(extracted$Price))

    if(sum(extracted$Price) < 50000){

        if(win[3] < sum(extracted$Points)){

                #print(sum(extracted$Points))

                win <- c(i, sum(extracted$Price), sum(extracted$Points))

                print(win)

  print(extracted)

        }

    }

}



print(win)

Also when I let the hockey script run for less than a few hours the team it gave me did very poorly...like dead last poorly. I would share the results but one of the players had an injury and I subbed him and the UTIL player out based on a best guess. I mean is this telling of this particular use of math to solve this particular problem. Not using the full data set. Not using the best results. Then subbing players out. I don't know, probably not. I'm going to add some filtering and make it run parallel so the results finish faster. As well as take a look at implementing Normal Distribution to determine best outcomes and weight towards those, regarding strong defense and aggression. And again I don't want to steal credit for the most part, currently this is a script from here.

I'll have another update on DraftKings / DFS math on Friday, but I'll have other posts before then as well.