This is based on code and data in golf simulation.r and material in GHIN Cap org 2014 version 2021 update.xlsm The workbook has 2014 handicap data for about 40 HRYCC guys and some categories based on other research on MidAm results for 300 plus contestants. These results provide the ‘probabilities’ of scoring for the various handicap categories.
The first objective here is to show and document the calculations from ‘golf simution.r’
There are a few lints that are not explained but seem to be ‘scratch pad’ stuff as follows
Next there is code to build a scoring matrix relative to par and also for two different stableford scoring schemes. Changes have been made to the original to improve clarity
toPar<-c(-3,-2,-1,0,1,2,3) #relative to par score
scores<-c("dblEag","eagle","birdie","par","boggie","dbl","other")
stableford<-c(5,4,3,2,1,0,0)
mod_stableford<-c(12,6,4,2,1,0,0)
pointMat<-data.frame(scores,toPar,stableford,mod_stableford)
print(pointMat)Next is par for the Golden Marsh Course in 2014. Looks like we didn’t get to RR par yet.
gmPar=c(4,5,4,3,4,4,4,3,5,4,3,4,5,3,5,4,4,4)
print(gmPar) [1] 4 5 4 3 4 4 4 3 5 4 3 4 5 3 5 4 4 4Sunday April 4 Recalculated the odds of scoring in excel and have the following
par_and_under =c(0,0.0088,0.1901,0.6667,0.1345,0.0000, 0.0000)
plus1_5 = c(0,0.0036, 0.1047, 0.6368, 0.2251, 0.0249, 0.0050)
plus6_10 =c(0,0.0009, 0.0703, 0.5341, 0.3087, 0.0694, 0.0166)
plus11_15 = c(0,0.0000, 0.0385, 0.4203, 0.3880, 0.1192, 0.0341)
plus16_and_over = c(0,0.0000, 0.0299, 0.3226, 0.3889, 0.1774, 0.0812)
rev_Probs = data.frame (scores,par_and_under,plus1_5,plus6_10,plus11_15, plus16_and_over)
rev_ProbsJust to understand what sample does here is one with and without probability weights This shows the par_and_under golfer had 4 birds, 1 boggie and the rest pars with the weighting compared to the unweighted sample with an 1 double eagle,1 eagle, 4 birds,1 par, 3 boggies,5 doubles and 4 tripples
sample(toPar,size=18,prob=par_and_under,replace=TRUE) [1] -1 -1 0 0 0 0 0 1 0 0 0 0 0 -1 0 0 -1 0sample(toPar,size=18,replace=TRUE) [1] -3 -1 1 0 2 -1 -1 3 1 3 2 3 3 -1 -2 1 2 2Now going back we also created variables with par for River Ridge and Golden Marsh and a function to return the handicaps for each as follows
HRGMCap<-c( 7,11,3,15,1,13,9,17,5,4,18,14,12,16,2,8,10,6)
HRRRCap<-c(9,15,5,1,11,7,17,3,13,12,6,18,8,16,2,10,4,14)
#we can use an ifelse test to get the strokes by handicap
getStrokes<-function(HRcaps,IndCap){
strokes<- ifelse(HRcaps<=IndCap,-1,0)
}
cap = getStrokes(HRGMCap,7)
print(cap) [1] -1 0 -1 0 -1 0 0 0 -1 -1 0 0 0 0 -1 0 0 -1We can then get the scores on each hole add the par and the strokes if any and get the net scores.
Netscores=function(prob1,par,nbrballsCount,Course_Strokes){
p1name <- deparse(substitute(prob1))
v=rep(0,nbrballsCount)
# cat ("Par ", par, "\n")
for (i in 1:nbrballsCount){
res<-sample(toPar,size=18,prob=prob1,replace=TRUE) #rp is relative to par score
gross = res + par
# cat ("Gross ", gross,"\n")
# cat ("Stroke ", Course_Strokes, "\n")
res<-res + par + Course_Strokes
net = res
# cat ("Net ", res, "\n")
# cat (" ","\n")
#print (paste((res),"Total=",sum(res)))
#cat(res,"total=",sum(res),"\n")
v[i]=sum(res)
# if (i==nbr){
# cat("Results for ",p1name, " with ", nbr, " Balls counting", "\n")
# cat("Net scores ", v, "\n")
# cat("Total ", sum(v),"\n")
# cat("Low ",min(v), "\n")
# cat("High ",max(v),"\n" )
# cat("Avg ", mean(v),"\n")
# cat("StdDev",sd(v),"\n")
# cat(" ","\n")
#
#
#
# }
}
return(v)
}
Now run NetScores for the plus1_5 with a handicap of 3
t1 <- Netscores(plus1_5 ,gmPar,4,getStrokes(HRGMCap,3))Par 4 5 4 3 4 4 4 3 5 4 3 4 5 3 5 4 4 4
Gross 3 5 4 4 4 4 6 3 5 5 2 3 5 3 5 4 4 5
Stroke 0 0 -1 0 -1 0 0 0 0 0 0 0 0 0 -1 0 0 0
Net 3 5 3 4 3 4 6 3 5 5 2 3 5 3 4 4 4 5
Gross 4 5 4 2 4 3 5 3 5 4 3 4 6 2 5 4 4 4
Stroke 0 0 -1 0 -1 0 0 0 0 0 0 0 0 0 -1 0 0 0
Net 4 5 3 2 3 3 5 3 5 4 3 4 6 2 4 4 4 4
Gross 4 6 4 4 4 5 7 3 6 6 4 4 5 3 6 4 5 4
Stroke 0 0 -1 0 -1 0 0 0 0 0 0 0 0 0 -1 0 0 0
Net 4 6 3 4 3 5 7 3 6 6 4 4 5 3 5 4 5 4
Gross 4 5 4 3 4 5 4 3 5 4 2 3 4 3 6 4 4 5
Stroke 0 0 -1 0 -1 0 0 0 0 0 0 0 0 0 -1 0 0 0
Net 4 5 3 3 3 5 4 3 5 4 2 3 4 3 5 4 4 5
t1[1] 71 68 81 69Now the higher cap players - use 13
t2 = Netscores(plus11_15 ,gmPar,4,getStrokes(HRGMCap,13))Par 4 5 4 3 4 4 4 3 5 4 3 4 5 3 5 4 4 4
Gross 5 5 5 5 5 6 4 4 5 4 4 5 5 3 6 4 4 4
Stroke -1 -1 -1 0 -1 -1 -1 0 -1 -1 0 0 -1 0 -1 -1 -1 -1
Net 4 4 4 5 4 5 3 4 4 3 4 5 4 3 5 3 3 3
Gross 5 6 4 4 4 4 5 5 8 4 3 5 6 3 6 4 5 4
Stroke -1 -1 -1 0 -1 -1 -1 0 -1 -1 0 0 -1 0 -1 -1 -1 -1
Net 4 5 3 4 3 3 4 5 7 3 3 5 5 3 5 3 4 3
Gross 5 5 4 4 6 4 4 4 6 4 6 4 7 3 6 6 5 5
Stroke -1 -1 -1 0 -1 -1 -1 0 -1 -1 0 0 -1 0 -1 -1 -1 -1
Net 4 4 3 4 5 3 3 4 5 3 6 4 6 3 5 5 4 4
Gross 4 6 5 4 6 5 6 3 5 5 4 4 6 4 6 4 4 6
Stroke -1 -1 -1 0 -1 -1 -1 0 -1 -1 0 0 -1 0 -1 -1 -1 -1
Net 3 5 4 4 5 4 5 3 4 4 4 4 5 4 5 3 3 5
t2[1] 70 72 75 74RunNetScores = function(plus1_5,plus11_15, par,cnt_Nbr_balls,nbrTrials,Team1Cap,Team2Cap){
#prob1 and prob2 are values like plus11_15 or plus1_5
#par is the var like gmPar
#cnt_Nmb_balls - how many of the team of 4 will count -
#nbrTrails - how many matches are to be simulated eg 100, 20,000
#
Team1name <- deparse(substitute(plus1_5))
Team2name <- deparse(substitute(plus11_15))
v=rep("NA",nbrTrials) # holder for results
cntTeam1 = 0
cntTeam2 = 0
tied = 0
for (i in 1:nbrTrials){
# # use 4 as all balls count - change later if needed
team1_Res = Netscores(plus1_5,par,cnt_Nbr_balls,getStrokes(HRGMCap,Team1Cap))
team2_Res = Netscores(plus11_15,par,cnt_Nbr_balls,getStrokes(HRGMCap,Team2Cap))
t1Sum = sum( team1_Res)
t2Sum = sum(team2_Res )
res = t1Sum - t2Sum
#cat (i ," Team Net Diff is ", res, "\n")
if (res < 0 ){
winner = paste (i," ",Team1name , t1Sum , " vs " , t2Sum , "\n")
cntTeam1 = cntTeam1 + 1
}
if (res > 0 ){
winner = paste (i," ",Team2name , t2Sum , " vs " , t1Sum , "\n")
cntTeam2 = cntTeam2 + 1
}
if (res == 0 ){
winner = paste (i," ","Tied " , t2Sum , " vs " , t1Sum , "\n")
tied = tied +1
}
v[i]=winner
}
cat ("Final Results for ",nbrTrials," trials with Low cap of : ",Team1Cap, "vs High Cap of " , Team2Cap, "\n")
# cat( v, "\n")
cat (Team1name, " wins =", cntTeam1,"\n" )
cat (Team2name, " wins =", cntTeam2 ,"\n" )
cat ("Ties = " ,tied ,"\n")
} Test the function
testRes = RunNetScores(plus1_5,plus11_15,par = gmPar,cnt_Nbr_balls = 4,nbrTrials = 1000,Team1Cap = 3,Team2Cap = 11)Final Results for 1000 trials with Low cap of : 3 vs High Cap of 11
plus1_5 wins = 721
plus11_15 wins = 251
Ties = 28 Look at the combinations of the plus1_5 from 1 to 5 versus the plus11_15 from 11 to 14 (caps are maxed at 14)
lowcaps = c(1,2,3,4,5)
highcaps = c(11,12,13,14)
for (i in 1:length(lowcaps)){
for (j in 1:length(highcaps)){
#cat ("lowcaps =", lowcaps[i], " highcaps = ", highcaps[j],"\n" )
RunNetScores(plus1_5,plus11_15,par = gmPar,cnt_Nbr_balls = 4,nbrTrials = 1000,Team1Cap = lowcaps[i],Team2Cap = highcaps[j])
cat(" ","\n")
}
}Final Results for 1000 trials with Low cap of : 1 vs High Cap of 11
plus1_5 wins = 322
plus11_15 wins = 637
Ties = 41
Final Results for 1000 trials with Low cap of : 1 vs High Cap of 12
plus1_5 wins = 204
plus11_15 wins = 763
Ties = 33
Final Results for 1000 trials with Low cap of : 1 vs High Cap of 13
plus1_5 wins = 105
plus11_15 wins = 869
Ties = 26
Final Results for 1000 trials with Low cap of : 1 vs High Cap of 14
plus1_5 wins = 41
plus11_15 wins = 946
Ties = 13
Final Results for 1000 trials with Low cap of : 2 vs High Cap of 11
plus1_5 wins = 502
plus11_15 wins = 453
Ties = 45
Final Results for 1000 trials with Low cap of : 2 vs High Cap of 12
plus1_5 wins = 370
plus11_15 wins = 590
Ties = 40
Final Results for 1000 trials with Low cap of : 2 vs High Cap of 13
plus1_5 wins = 198
plus11_15 wins = 772
Ties = 30
Final Results for 1000 trials with Low cap of : 2 vs High Cap of 14
plus1_5 wins = 119
plus11_15 wins = 860
Ties = 21
Final Results for 1000 trials with Low cap of : 3 vs High Cap of 11
plus1_5 wins = 680
plus11_15 wins = 289
Ties = 31
Final Results for 1000 trials with Low cap of : 3 vs High Cap of 12
plus1_5 wins = 551
plus11_15 wins = 414
Ties = 35
Final Results for 1000 trials with Low cap of : 3 vs High Cap of 13
plus1_5 wins = 339
plus11_15 wins = 620
Ties = 41
Final Results for 1000 trials with Low cap of : 3 vs High Cap of 14
plus1_5 wins = 205
plus11_15 wins = 765
Ties = 30
Final Results for 1000 trials with Low cap of : 4 vs High Cap of 11
plus1_5 wins = 805
plus11_15 wins = 160
Ties = 35
Final Results for 1000 trials with Low cap of : 4 vs High Cap of 12
plus1_5 wins = 670
plus11_15 wins = 305
Ties = 25
Final Results for 1000 trials with Low cap of : 4 vs High Cap of 13
plus1_5 wins = 513
plus11_15 wins = 442
Ties = 45
Final Results for 1000 trials with Low cap of : 4 vs High Cap of 14
plus1_5 wins = 343
plus11_15 wins = 610
Ties = 47
Final Results for 1000 trials with Low cap of : 5 vs High Cap of 11
plus1_5 wins = 889
plus11_15 wins = 92
Ties = 19
Final Results for 1000 trials with Low cap of : 5 vs High Cap of 12
plus1_5 wins = 818
plus11_15 wins = 159
Ties = 23
Final Results for 1000 trials with Low cap of : 5 vs High Cap of 13
plus1_5 wins = 663
plus11_15 wins = 302
Ties = 35
Final Results for 1000 trials with Low cap of : 5 vs High Cap of 14
plus1_5 wins = 541
plus11_15 wins = 421
Ties = 38
Now look at the same Simultion as above but use 2 and 3 balls counted since the previous run counted all 4 balls MAY HAVE TO CHANGE THE LOGIC TO COUNT THE LOWEST 2 OR 3 BALLS PER HOLE
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