First 10 Games 17/18 Secondary Market
datA
Seatgeek StubHub Ticketmaster Vivid Other
Total Sold 7368.82 43126.82 5990.50 19785.18 51560.12
Average Price 31.09 42.03 85.58 61.07 41.51
Percent of Market 0.06 0.34 0.05 0.15 0.40
Rest of Games 17/18 Secondary Market
dat <- read.csv("C:/Users/jcowden/OneDrive - LA Clippers/Desktop/dat14.csv")
dat <- dat[complete.cases(dat),]
seatgeek <- subset(dat, dat$Market == "Seatgeek")
stubhub <- subset(dat,dat$Market == "StubHub")
ticketmaster <- subset(dat,dat$Market == "Ticketmaster")
vivid <- subset(dat,dat$Market == "Vivid Seats")
other <- subset(dat,dat$Market == "Other")
datU <- data.frame(
s.geek = round(sum(seatgeek$PerTix),2),
stub.hub = round(sum(stubhub$PerTix),3),
ticket.master = round(sum(ticketmaster$PerTix),2),
vivid = round(sum(vivid$PerTix),2),
other = round(sum(other$PerTix),2)
)
datZ <- data.frame(
s.geek = round(sum(seatgeek$PerTix)/sum(dat$PerTix),2),
stub.hub = round(sum(stubhub$PerTix)/sum(dat$PerTix),2),
ticket.master = round(sum(ticketmaster$PerTix)/sum(dat$PerTix),2),
vivid = round(sum(vivid$PerTix)/sum(dat$PerTix),2),
other = round(sum(other$PerTix)/sum(dat$PerTix),2)
)
datB <- data.frame(
s.geek = mean(seatgeek$PerTix),
stub.hub = mean(stubhub$PerTix),
ticket.master = mean(ticketmaster$PerTix),
vivid = mean(vivid$PerTix),
other = mean(other$PerTix)
)
datA <- rbind(datU,round(datB,2), datZ)
row.names(datA) <- c("Total Sold","Average Price", "Percent of Market")
colnames(datA) <- c("Seatgeek", "StubHub", "Ticketmaster", "Vivid", "Other")
datA
Seatgeek StubHub Ticketmaster Vivid Other
Total Sold 33261.31 129080.76 23182.00 60968.41 162612.52
Average Price 39.64 49.32 69.20 64.38 42.75
Percent of Market 0.08 0.32 0.06 0.15 0.40
Our Data
dat2 <- read.csv("C:/Users/jcowden/OneDrive - LA Clippers/Desktop/ourData.csv")
dat2 <- dat2[,c(1,21,25,27,28,31,36)]
dat2 <- as.data.frame(dat2)
x=str_split_fixed(dat2$Event, pattern = "-",4)
x = as.data.frame(x)
x <- x[,4]
dat3 <- cbind(dat2,x)
dat2 <- dat3[,-2]
colnames(dat2) <- c("Account", "Section","Row", "Seat", "Price.Level", "PerTix", "Date")
dat2$PerTix <- as.numeric(as.character(dat2$PerTix))
## Warning: NAs introduced by coercion
dat2 <- dat2[complete.cases(dat2),]
x <- dat2 %>% group_by(dat2$Section) %>% summarise(round(mean(PerTix),2))
x <- as.data.frame(x)
x <- as.data.table(x)
colnames(x) <- c("Section", "Average Per Tix")
y <- dat2 %>% group_by(dat2$Price.Level) %>% summarise(round(mean(PerTix),2))
y <- as.data.frame(y)
y <- as.data.table(y)
colnames(y) <- c("Price Level", "Average Per Tix")
x[order(-x$`Average Per Tix`),]
Section Average Per Tix
1: 116CT 1306.77
2: 106CT 1298.48
3: 112CT 940.02
4: 110CT 881.34
5: 102CT 699.24
6: 111CT 682.24
7: 119CT 679.68
8: 101CT 644.34
9: 107CT 485.66
10: 115CT 356.50
11: 101 206.35
12: 111 204.46
13: 119 183.87
14: PR13 177.32
15: 102 175.46
16: 110 168.28
17: 112 166.53
18: PR4 156.45
19: 109 136.02
20: 103 131.76
21: PR6 125.50
22: 105 124.56
23: 113 123.75
24: PR1 117.93
25: 114 107.62
26: 104 106.33
27: 108 105.13
28: 118 104.92
29: 117 102.05
30: 106 98.34
31: 115 97.92
32: 107 97.28
33: 116 93.15
34: PR11 82.16
35: 210 77.88
36: PR9 77.86
37: PR3 76.20
38: PR2 75.26
39: 214 74.53
40: 205 74.07
41: PR17 73.97
42: PR12 73.79
43: PR8 72.97
44: 207 72.94
45: PR10 72.64
46: 219 72.07
47: PR16 71.63
48: PR18 70.71
49: PR7 68.42
50: 217 67.73
51: 208 67.42
52: 215 67.08
53: 216 64.84
54: 218 64.32
55: 209 62.24
56: 206 59.83
57: 301 47.90
58: 318 45.76
59: 329 44.90
60: 331 42.23
61: 316 41.91
62: 323 40.59
63: 330 40.39
64: 303 39.87
65: 320 39.27
66: 324 38.41
67: 333 38.22
68: 319 37.85
69: 302 37.67
70: 334 37.49
71: 327 37.30
72: 317 36.94
73: 307 36.28
74: 315 36.25
75: 321 35.72
76: 328 34.75
77: 325 34.70
78: 306 34.09
79: 314 33.57
80: 326 33.30
81: 332 32.95
82: 322 32.73
83: 311 32.41
84: 304 31.87
85: 305 31.87
86: 308 30.69
87: 309 30.69
88: 313 30.53
89: 310 29.86
90: 312 29.45
Section Average Per Tix
y[order(-y$`Average Per Tix`),]
Price Level Average Per Tix
1: A1 - Courtside 2403.94
2: C1 - Courtside 1823.38
3: F1 - Courtside 913.01
4: G1 - Courtside 757.97
5: E1 - Courtside 608.62
6: J1 - Courtside 504.50
7: I1 - Courtside 322.13
8: K1 - Courtside 264.41
9: M1 - Loge Center 216.92
10: O1 - Loge Center 215.99
11: T1 - Loge Center 215.04
12: P1 - Loge Center 201.91
13: H1 - Courtside 201.73
14: V1 - Loge Inside Corner 198.14
15: Q1 - Loge Center 192.02
16: N1 - Loge Center 184.11
17: S1 - Loge Center 177.31
18: L1 - Courtside 170.51
19: R1 - Loge Center 168.01
20: W1 - Loge Center 167.68
21: X2 - Premier 160.37
22: G2 - Loge Corner 145.07
23: D2 - Loge Baseline 142.99
24: B2 - Loge Inside Corner 142.68
25: H2 - Loge Inside Corner 137.53
26: U1 - Loge Center 130.90
27: J2 - Loge Corner 124.51
28: O2 - Loge Corner 123.32
29: E2 - Loge Corner 121.74
30: Z1 - Loge Center 117.14
31: N2 - Loge Corner 115.10
32: I2 - Loge Corner 113.15
33: K2 - Loge 112.79
34: M2 - Loge Corner 105.15
35: V2 - Loge Baseline 103.28
36: Y1 - Loge Inside Corner 102.40
37: A2 - Loge Inside Corner 101.00
38: D1 - Courtside 100.00
39: L2 - Loge 99.68
40: X1 - Loge Inside Corner 99.33
41: Q2 - Loge Corner 99.08
42: F2 - Loge 98.72
43: U2 - Loge Baseline 94.19
44: C2 - Loge Corner 93.79
45: Y2 - Loge Baseline 86.83
46: A3 - Loge Baseline 84.20
47: P2 - Loge Corner 83.21
48: T2 - Loge Baseline 80.13
49: S2 - Loge Baseline 77.19
50: D3 - Mid Level 74.13
51: W2 - Premier 73.75
52: R2 - Loge Baseline 72.85
53: B3 - Mid Level 69.90
54: G3 - Mid Level 69.33
55: Z2 - Mid Level 63.17
56: F3 - Mid Level 55.14
57: J3 - Balcony Level 46.73
58: H3 - Balcony Level 45.98
59: M3 - Balcony Level 39.95
60: E3 - Balcony Level 39.57
61: L3 - Balcony Level 38.91
62: O3 - Balcony Level 38.71
63: C3 - Balcony Level 37.68
64: K3 - Balcony Level 36.33
65: I3 - Balcony Level 35.66
66: P3 - Balcony Level 35.59
67: N3 - Balcony Level 35.03
68: Q3 - Balcony Level 34.85
Price Level Average Per Tix