George Callil Player Comp Graphics
library(tidyverse)
## ── Attaching packages ───────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.1 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ──────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
data <- read.csv('Callil_Data.csv')
data <- data %>% mutate(BB_Rate = BB / PA)
data <- data %>% mutate(SO_Rate = SO / PA)
data <- data %>% mutate(XBH_Rate = (X2B + X3B + HR) / H)
data
## Name Age G PA AB R H X2B X3B HR RBI SB BB SO BA OBP
## 1 George Callil 21 43 160 131 19 28 6 1 4 17 1 15 37 0.214 0.329
## 2 George Callil 22 16 65 48 9 13 1 0 1 10 1 4 12 0.271 0.446
## 3 Madison Stokes 19 25 86 82 6 17 4 0 0 9 0 3 24 0.207 0.233
## 4 Madison Stokes 20 31 119 102 16 31 5 0 1 12 5 7 29 0.304 0.371
## 5 Madison Stokes 21 47 190 165 18 37 10 0 3 17 3 15 41 0.224 0.312
## 6 Madison Stokes 22 50 220 183 32 59 12 1 11 44 1 29 44 0.322 0.414
## 7 Jordan Rodgers 19 11 30 23 4 3 0 0 0 2 3 5 5 0.130 0.333
## 8 Jordan Rodgers 20 38 114 97 9 27 6 0 0 12 0 8 21 0.278 0.351
## 9 Jordan Rodgers 21 56 243 216 36 61 15 5 4 56 13 15 41 0.282 0.337
## 10 Conner Hale 21 60 226 209 38 64 11 1 4 29 0 10 17 0.306 0.335
## 11 Conner Hale 22 64 288 263 49 86 17 3 4 56 1 18 34 0.327 0.373
## 12 Kyle Farmer 20 65 287 253 39 78 16 0 8 58 3 19 33 0.308 0.364
## 13 Kyle Farmer 21 56 262 245 35 74 19 3 4 41 3 8 26 0.302 0.337
## 14 Kyle Farmer 22 52 237 210 26 61 12 2 3 44 0 12 12 0.290 0.315
## 15 Tyler Smith 19 55 146 113 18 25 4 0 0 11 4 27 23 0.221 0.378
## 16 Tyler Smith 20 58 268 213 46 73 11 2 1 39 9 30 31 0.343 0.434
## 17 Tyler Smith 21 56 260 224 48 69 10 2 2 28 8 24 35 0.308 0.390
## SLG OPS Round MLB BB_Rate SO_Rate XBH_Rate
## 1 0.366 0.696 NA NA 0.09375000 0.23125000 0.3928571
## 2 0.354 0.800 NA NA 0.06153846 0.18461538 0.1538462
## 3 0.256 0.489 10 0 0.03488372 0.27906977 0.2352941
## 4 0.382 0.753 10 0 0.05882353 0.24369748 0.1935484
## 5 0.339 0.651 10 0 0.07894737 0.21578947 0.3513514
## 6 0.579 0.993 10 0 0.13181818 0.20000000 0.4067797
## 7 0.130 0.464 6 0 0.16666667 0.16666667 0.0000000
## 8 0.340 0.692 6 0 0.07017544 0.18421053 0.2222222
## 9 0.454 0.791 6 0 0.06172840 0.16872428 0.3934426
## 10 0.426 0.761 9 0 0.04424779 0.07522124 0.2500000
## 11 0.460 0.833 9 0 0.06250000 0.11805556 0.2790698
## 12 0.466 0.830 8 1 0.06620209 0.11498258 0.3076923
## 13 0.453 0.790 8 1 0.03053435 0.09923664 0.3513514
## 14 0.410 0.724 8 1 0.05063291 0.05063291 0.2786885
## 15 0.257 0.634 8 1 0.18493151 0.15753425 0.1600000
## 16 0.427 0.861 8 1 0.11194030 0.11567164 0.1917808
## 17 0.397 0.787 8 1 0.09230769 0.13461538 0.2028986
ggplot(data, aes(Age, XBH_Rate, color = Name)) + ylab('XBH %') + geom_line() + ggtitle('George Callil XBH% Comparisons')
ggplot(data, aes(Age, SO_Rate, color = Name)) + ylab('SO %') + geom_line() + ggtitle('George Callil SO% Comparisons')
ggplot(data, aes(Age, BB_Rate, color = Name)) + ylab('BB %') + geom_line() + ggtitle('George Callil BB% Comparisons')
ggplot(data, aes(Age, BA, color = Name)) + ylab('BA') + geom_line() + ggtitle('George Callil BA Comparisons')
ggplot(data, aes(Age, OPS, color = Name)) + ylab('OPS') + geom_line() + ggtitle('George Callil OPS Comparisons')
ggplot(data, aes(Age, SLG, color = Name)) + ylab('SLG') + geom_line() + ggtitle('George Callil SLG Comparisons')
ggplot(data, aes(Age, OBP, color = Name)) + ylab('OBP') + geom_line() + ggtitle('George Callil OBP Comparisons')
MiLB Comparison Statistics
MiLB_data <- read.csv('Callil_Comp_MiLB.csv')
MiLB_data <- MiLB_data %>% mutate(BB_Rate = BB / PA)
MiLB_data <- MiLB_data %>% mutate(SO_Rate = SO / PA)
MiLB_data <- MiLB_data %>% mutate(XBH_Rate = (X2B + X3B + HR) / H)
MiLB_data
## Name Highest.Level G PA AB R H X2B X3B HR RBI BB SO
## 1 Madison Stokes A 165 666 612 71 159 35 5 12 67 41 149
## 2 Jordan Rodgers A 214 769 718 66 163 39 5 10 69 27 159
## 3 Conner Hale A 184 701 645 69 150 25 2 8 60 43 143
## 4 Kyle Farmer MLB 505 2081 1896 254 558 145 16 33 279 131 299
## 5 Tyler Smith MLB 564 2313 2021 261 541 106 14 30 199 228 401
## BA OBP SLG OPS Total.Seasons BB_Rate SO_Rate XBH_Rate
## 1 0.260 0.314 0.392 0.706 2 0.06156156 0.2237237 0.3270440
## 2 0.227 0.272 0.337 0.609 3 0.03511053 0.2067620 0.3312883
## 3 0.233 0.280 0.315 0.595 3 0.06134094 0.2039943 0.2333333
## 4 0.294 0.347 0.440 0.787 11 0.06295050 0.1436809 0.3476703
## 5 0.268 0.349 0.379 0.728 8 0.09857328 0.1733679 0.2772643
ggplot(MiLB_data, aes(Name, BB_Rate, fill = Name)) + geom_col(show.legend = FALSE) + ylab('BB%') + ggtitle('G. Callil Comps. MiLB BB%') + theme(axis.title.x=element_blank())
ggplot(MiLB_data, aes(Name, SO_Rate, fill = Name)) + geom_col(show.legend = FALSE) + ylab('SO%') + ggtitle('G. Callil Comps. MiLB SO%') + theme(axis.title.x=element_blank())
ggplot(MiLB_data, aes(Name, XBH_Rate, fill = Name)) + geom_col(show.legend = FALSE) + ylab('XBH%') + ggtitle('G. Callil Comps. MiLB XBH%') + theme(axis.title.x=element_blank())
ggplot(MiLB_data, aes(Name, OPS, fill = Name)) + geom_col(show.legend = FALSE) + ylab('BB Rate') + ggtitle('G. Callil Comps. MiLB OPS') + theme(axis.title.x=element_blank())
ggplot(MiLB_data, aes(Name, SLG, fill = Name)) + geom_col(show.legend = FALSE) + ylab('SLG%') + ggtitle('G. Callil Comps. MiLB SLG%') + theme(axis.title.x=element_blank())
ggplot(MiLB_data, aes(Name, BA, fill = Name)) + geom_col(show.legend = FALSE) + ylab('BA') + ggtitle('G. Callil Comps. MiLB BA') + theme(axis.title.x=element_blank())
ggplot(MiLB_data, aes(Name, Total.Seasons, fill = Name)) + geom_col(show.legend = FALSE) + ylab('Seasons') + ggtitle('G. Callil Comps. MiLB Total Seasons') + theme(axis.title.x=element_blank())
