I don't fully understand the choice situations from your data. Nevertheless, I tried to compare your data with TravelMode data in this tutorial. It seems that race in your data is similar to individual in TravelMode data, which is the name of the variable that contains the information about the choice situations. So, I assume that race can be assigned to chid.var. Here is my trial:
dat = read.table(text = "race,horseno,place,win,of,ppf,orf,df,jf,tf,wf,af
1,1,4,0,1,0.7,1,0.33,0.13,0.09,0.72,1
1,2,2,0,0.45,0.78,0.99,0.5,0.22,0.2,0.73,0.98
1,3,1,1,0.42,1,0.99,1,0.18,0.1,0.73,0.76
1,4,3,0,0.19,0.27,0.99,0.17,0.22,0.12,0.73,0.47
2,1,2,0,1,1,1,1,0.31,0.16,0.61,0.81
2,2,4,0,0.24,0.88,1,1,0.09,0.07,0.61,0.92
2,3,1,1,0.16,0.03,1,1,0.57,0.29,0.61,0.98
2,4,5,0,0.21,0.47,1,1,0.25,0.05,0.61,0.92
2,5,8,0,0.01,0.3,1,1,0.19,0,0.64,0.92
2,6,7,0,0.01,0.21,1,1,0.2,0,0.61,1
2,7,3,0,0.1,0.34,1,1,0.16,0.04,0.58,0.79
2,8,11,0,0.06,0.03,1,1,0.21,0.16,0.61,0.92
2,9,10,0,0.03,0.03,1,1,0.19,0.16,0.61,0.92
2,10,9,0,0.01,0.29,1,1,0.09,0.05,0.61,0.77
2,11,6,0,0.01,0.25,1,1,0.09,0.05,0.61,0.77", header = TRUE, sep = ",")
x <- mlogit.data(dat, choice = "win", shape = "long",
chid.var = "race", alt.var = "horseno")
x
# ~~~~~~~
# first 10 observations out of 15
# ~~~~~~~
# race horseno place win of ppf orf df jf tf wf af idx
# 1 1 1 4 FALSE 1.00 0.70 1.00 0.33 0.13 0.09 0.72 1.00 1:1
# 2 1 2 2 FALSE 0.45 0.78 0.99 0.50 0.22 0.20 0.73 0.98 1:2
# 3 1 3 1 TRUE 0.42 1.00 0.99 1.00 0.18 0.10 0.73 0.76 1:3
# 4 1 4 3 FALSE 0.19 0.27 0.99 0.17 0.22 0.12 0.73 0.47 1:4
# 5 2 1 2 FALSE 1.00 1.00 1.00 1.00 0.31 0.16 0.61 0.81 2:1
# 6 2 2 4 FALSE 0.24 0.88 1.00 1.00 0.09 0.07 0.61 0.92 2:2
# 7 2 3 1 TRUE 0.16 0.03 1.00 1.00 0.57 0.29 0.61 0.98 2:3
# 8 2 4 5 FALSE 0.21 0.47 1.00 1.00 0.25 0.05 0.61 0.92 2:4
# 9 2 5 8 FALSE 0.01 0.30 1.00 1.00 0.19 0.00 0.64 0.92 2:5
# 10 2 6 7 FALSE 0.01 0.21 1.00 1.00 0.20 0.00 0.61 1.00 2:6
#
# ~~~ indexes ~~~~
# chid alt
# 1 1 1
# 2 1 2
# 3 1 3
# 4 1 4
# 5 2 1
# 6 2 2
# 7 2 3
# 8 2 4
# 9 2 5
# 10 2 6
# indexes: 1, 2
