Overview

The Players

Alex Kaechele
Jerry Albrecht
Jerrod Castro
Nikki Savastano

The Game

Bracket Challenge 2019! Each game is worth 320 divided by the number of the games in the round. Fill out a bracket at the beginning of the tourney and hope for the best.

Standings Overview

Here’s a look at the standings and possible points before the Sweet 16…

# Loading Packages
require(data.table)
library(knitr)
library(kableExtra)
library(dplyr)

# Table
data.table(`Bracket Name` = c("Alex Kaechele2", "Probably going to win", "Alex Kaechele1", "Nikki T Savastano2", "Nikki Savastano", "PROBS NOT BUT LESGO", "Upset City Bish", "THEBESTONE"),
           `Creator` = c("Alex", "Jerry", "Alex", "Nikki", "Nikki", "Jerrod", "Jerry", "Jerrod"),
           `Current Points` =         c(540, 530, 520, 510, 490, 440, 420, 370),
           `Sweet 16 Possible` =      c(320, 320, 320, 240, 240, 200, 120, 200),
           `Elite 8 Possible` =       c(320, 320, 320, 160, 320, 80,  160, 240),
           `Final 4 Possible` =       c(320, 320, 320, 160, 320, 160, 160, 320),
           `Championship Possible` =  c(320, 320, 320, 0,   320, 0,   0,   320),
           `Max` =                    c(1820, 1810, 1800, 1070, 1690, 880, 860, 1450),
           `Place` =                  c(1, 2, 3, 4, 5, 6, 7, 8)) %>%
  kable(table.attr = "class='dtable'") %>%
  kable_styling("striped", full_width = F, position = "center") %>%
  column_spec(c(2), bold=T) %>%
  row_spec(0, bold = T, color = "white", background = "#B22222")

Bracket Name	Creator	Current Points	Sweet 16 Possible	Elite 8 Possible	Final 4 Possible	Championship Possible	Max	Place
Alex Kaechele2	Alex	540	320	320	320	320	1820	1
Probably going to win	Jerry	530	320	320	320	320	1810	2
Alex Kaechele1	Alex	520	320	320	320	320	1800	3
Nikki T Savastano2	Nikki	510	240	160	160	0	1070	4
Nikki Savastano	Nikki	490	240	320	320	320	1690	5
PROBS NOT BUT LESGO	Jerrod	440	200	80	160	0	880	6
Upset City Bish	Jerry	420	120	160	160	0	860	7
THEBESTONE	Jerrod	370	200	240	320	320	1450	8

Future Point Outlook

Prediction Data

To get a sense of who is winning. I web-scraped data from FiveThirtyEight to get probabilities of each bracket’s pick moving on. The expected points by round are added to the current points to get each bracket’s expected points.

Here is a table showing how many points each bracket can expect to get

data.table(`Bracket Name` = c("Alex Kaechele2", "Probably going to win", "Alex Kaechele1", "Nikki T Savastano2", "Nikki Savastano", "PROBS NOT BUT LESGO", "Upset City Bish", "THEBESTONE"),
           `Creator` = c("Alex", "Jerry", "Alex", "Nikki", "Nikki", "Jerrod", "Jerry", "Jerrod"),
           `Current Points` =         c(540,   530,   520,   510,  490,   440, 420,  370),
           `Sweet 16 Expected` =      c(207.2, 187.2, 192,   162,  169.2, 88,  25.6, 92.8),
           `Elite 8 Expected` =       c(108,   92,    122.4, 36.8, 109.6, 4,   13.6, 66.4),
           `Final 4 Expected` =       c(68.8,  72,    105.6, 20.8, 73.6,  3.2, 9.6,  68.8),
           `Championship Expected` =  c(44,    64,    64,    0,    57.6,  0,   0,    64),
           `Expected` =                    c(sum(c(540, 207.2, 108, 68.8, 44)),
                                             sum(c(530, 187.2, 92, 72, 64)),
                                             sum(c(520, 192, 122.4, 105.6, 64)),
                                             sum(c(510, 162, 36.8, 20.8, 0)),
                                             sum(c(490, 169.2, 109.6, 73.6, 57.6)),
                                             sum(c(440, 88, 4, 3.2, 0)),
                                             sum(c(420, 25.6, 13.6, 9.6, 0)),
                                             sum(c(370, 92.8, 66.4, 68.8, 64))),
           `Expected Place` =                  c(2, 3, 1, 5, 4, 7, 8, 6)) %>%
  kable(table.attr = "class='dtable'") %>%
  kable_styling("striped", full_width = F, position = "center") %>%
  column_spec(c(2), bold=T) %>%
  row_spec(0, bold = T, color = "white", background = "#B22222")

Bracket Name	Creator	Current Points	Sweet 16 Expected	Elite 8 Expected	Final 4 Expected	Championship Expected	Expected	Expected Place
Alex Kaechele2	Alex	540	207.2	108.0	68.8	44.0	968.0	2
Probably going to win	Jerry	530	187.2	92.0	72.0	64.0	945.2	3
Alex Kaechele1	Alex	520	192.0	122.4	105.6	64.0	1004.0	1
Nikki T Savastano2	Nikki	510	162.0	36.8	20.8	0.0	729.6	5
Nikki Savastano	Nikki	490	169.2	109.6	73.6	57.6	900.0	4
PROBS NOT BUT LESGO	Jerrod	440	88.0	4.0	3.2	0.0	535.2	7
Upset City Bish	Jerry	420	25.6	13.6	9.6	0.0	468.8	8
THEBESTONE	Jerrod	370	92.8	66.4	68.8	64.0	662.0	6

Sweet 16 Evaluation

Here’s who people chose to advance out of the sweet 16 along with their win probabilities and how many points brackets can expect to add to their totals.

teams <- data.table(tibble::tribble(~team,           ~s16,  ~e8,  ~f4,  ~f2,
                                    "Virginia",      .87,   .53,  .33,  .18,
                                    "Duke",          .75,   .55,  .33,  .20,
                                    "Michigan_St",   .74,   .26,  .13,  .07,
                                    "Gonzaga",       .74,   .48,  .25,  .14,
                                    "UNC",           .62,   .38,  .18,  .09,
                                    "Kentucky",      .56,   .25,  .10,  .04,
                                    "Tennessee",     .51,   .23,  .12,  .05,
                                    "Texas_Tech",    .51,   .20,  .08,  .04,
                                    "Michigan",      .49,   .20,  .09,  .05,
                                    "Purdue",        .49,   .21,  .11,  .05,
                                    "Houston",       .44,   .17,  .06,  .02,
                                    "Auburn",        .38,   .20,  .08,  .03,
                                    "Florida_State", .26,   .12,  .05,  .02,
                                    "LSU",           .26,   .05,  .02,  .005,
                                    "Virginia_Tech", .25,   .14,  .06,  .02,
                                    "Oregon",        .13,   .03,  .01,  .003))

alex1_16_nam <- c("Duke", "Michigan_St", "Florida_State", "Michigan", "Virginia", "Tennessee", "UNC", "Kentucky", "Max", "Expected")
alex1_16_num <- c(teams[match(alex1_16_nam, teams$team),]$s16[1:8], 40*sum(alex1_16_nam %in%teams$team), 40*sum(teams[match(alex1_16_nam, teams$team),]$s16, na.rm = TRUE))

alex2_16_nam <- c("Duke", "Michigan_St", "Gonzaga", "Texas_Tech", "Virginia", "Tennessee", "UNC", "Houston", "Max", "Expected")
alex2_16_num <- c(teams[match(alex2_16_nam, teams$team),]$s16[1:8], 40*sum(alex2_16_nam %in%teams$team), 40*sum(teams[match(alex2_16_nam, teams$team),]$s16, na.rm = TRUE))

jerry1_16_nam  <- c("Duke", "LSU", "Gonzaga", "Michigan", "Virginia", "Tennessee", "UNC", "Houston", "Max", "Expected")
jerry1_16_num <- c(teams[match(jerry1_16_nam, teams$team),]$s16[1:8], 40*sum(jerry1_16_nam %in%teams$team), 40*sum(teams[match(jerry1_16_nam, teams$team),]$s16, na.rm = TRUE))

jerry2_16_nam  <- c("Virginia_Tech", "Yale", "Florida_State", "Florida", "Oregon", "Iowa", "Kansas", "Ohio_St", "Max", "Expected")
jerry2_16_num <- c(teams[match(jerry2_16_nam, teams$team),]$s16[1:8], 40*sum(jerry2_16_nam %in%teams$team), 40*sum(teams[match(jerry2_16_nam, teams$team),]$s16, na.rm = TRUE))

jerrod1_16_nam <- c("Virginia_Tech", "LSU", "Florida_State", "Buffalo", "Virginia", "Villianova", "Kansas", "Kentucky", "Max", "Expected")
jerrod1_16_num <- c(teams[match(jerrod1_16_nam, teams$team),]$s16[1:8], 40*sum(jerrod1_16_nam %in%teams$team), 40*sum(teams[match(jerrod1_16_nam, teams$team),]$s16, na.rm = TRUE))

jerrod2_16_nam <- c("Duke", "LSU", "Vermont", "Buffalo", "Oregon", "Villianova", "UNC", "Kentucky", "Max", "Expected")
jerrod2_16_num <- c(teams[match(jerrod2_16_nam, teams$team),]$s16[1:8], 40*sum(jerrod2_16_nam %in%teams$team), 40*sum(teams[match(jerrod2_16_nam, teams$team),]$s16, na.rm = TRUE))

nikki1_16_nam  <- c("Duke", "Michigan_St", "Gonzaga", "Texas_Tech", "Virginia", "Villianova", "UNC", "Iowa_St", "Max", "Expected")
nikki1_16_num <- c(teams[match(nikki1_16_nam, teams$team),]$s16[1:8], 40*sum(nikki1_16_nam %in%teams$team), 40*sum(teams[match(nikki1_16_nam, teams$team),]$s16, na.rm = TRUE))

nikki2_16_nam  <- c("Duke", "Michigan_St", "Gonzaga", "Texas_Tech", "Virginia", "Villianova", "Kansas", "Houston", "Max", "Expected")
nikki2_16_num <- c(teams[match(nikki2_16_nam, teams$team),]$s16[1:8], 40*sum(nikki2_16_nam %in%teams$team), 40*sum(teams[match(nikki2_16_nam, teams$team),]$s16, na.rm = TRUE))



data.table(`Name` = alex1_16_nam,   `Prob` = alex1_16_num, 
           `Name` = alex2_16_nam,   `Prob` = alex2_16_num,
           `Name` = jerry1_16_nam,  `Prob` = jerry1_16_num,
           `Name` = jerry2_16_nam,  `Prob` = jerry2_16_num,
           `Name` = jerrod1_16_nam, `Prob` = jerrod1_16_num,
           `Name` = jerrod2_16_nam, `Prob` = jerrod2_16_num,
           `Name` = nikki1_16_nam,  `Prob` = nikki1_16_num,
           `Name` = nikki2_16_nam,  `Prob` = nikki2_16_num) %>%
  kable() %>%
  add_header_above(c("Alex 1" = 2,   "Alex 2" = 2,
                     "Jerry 1" = 2,  "Jerry 2" = 2,
                     "Jerrod 1" = 2, "Jerrod 2" = 2,
                     "Nikki 1" = 2,  "Nikki 2" = 2), bold = T, color = "white", background = "#5e78d6")%>%
  kable_styling("striped", full_width = F, position = "center", font_size = 11) %>%
  column_spec(c(1,3,5,7,9,11,13,15), bold=T) %>%
  row_spec(0, bold = T, color = "white", background = "#5e78d6") %>%
  row_spec(c(9, 10), bold = T, background = "#E5E5E5")

Alex 1		Alex 2		Jerry 1		Jerry 2		Jerrod 1		Jerrod 2		Nikki 1		Nikki 2
Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob
Duke	0.75	Duke	0.75	Duke	0.75	Virginia_Tech	0.25	Virginia_Tech	0.25	Duke	0.75	Duke	0.75	Duke	0.75
Michigan_St	0.74	Michigan_St	0.74	LSU	0.26	Yale	NA	LSU	0.26	LSU	0.26	Michigan_St	0.74	Michigan_St	0.74
Florida_State	0.26	Gonzaga	0.74	Gonzaga	0.74	Florida_State	0.26	Florida_State	0.26	Vermont	NA	Gonzaga	0.74	Gonzaga	0.74
Michigan	0.49	Texas_Tech	0.51	Michigan	0.49	Florida	NA	Buffalo	NA	Buffalo	NA	Texas_Tech	0.51	Texas_Tech	0.51
Virginia	0.87	Virginia	0.87	Virginia	0.87	Oregon	0.13	Virginia	0.87	Oregon	0.13	Virginia	0.87	Virginia	0.87
Tennessee	0.51	Tennessee	0.51	Tennessee	0.51	Iowa	NA	Villianova	NA	Villianova	NA	Villianova	NA	Villianova	NA
UNC	0.62	UNC	0.62	UNC	0.62	Kansas	NA	Kansas	NA	UNC	0.62	UNC	0.62	Kansas	NA
Kentucky	0.56	Houston	0.44	Houston	0.44	Ohio_St	NA	Kentucky	0.56	Kentucky	0.56	Iowa_St	NA	Houston	0.44
Max	320.00	Max	320.00	Max	320.00	Max	120.00	Max	200.00	Max	200.00	Max	240.00	Max	240.00
Expected	192.00	Expected	207.20	Expected	187.20	Expected	25.60	Expected	88.00	Expected	92.80	Expected	169.20	Expected	162.00

Elite 8 Evaluation

Here’s who people chose to advance out of the elite 8 along with their win probabilities and how many points brackets can expect to add to their totals.

alex1_8_nam <- c("Duke", "Michigan", "Virginia", "Kentucky", "Max", "Expected")
alex1_8_num <- c(teams[match(alex1_8_nam, teams$team),]$e8[1:4], 80*sum(alex1_8_nam %in%teams$team), 80*sum(teams[match(alex1_8_nam, teams$team),]$e8, na.rm = TRUE))

alex2_8_nam <- c("Michigan_St", "Gonzaga", "Tennessee", "UNC", "Max", "Expected")
alex2_8_num <- c(teams[match(alex2_8_nam, teams$team),]$e8[1:4], 80*sum(alex2_8_nam %in%teams$team), 80*sum(teams[match(alex2_8_nam, teams$team),]$e8, na.rm = TRUE))

jerry1_8_nam  <- c("Duke", "Michigan", "Tennessee", "Houston", "Max", "Expected")
jerry1_8_num <- c(teams[match(jerry1_8_nam, teams$team),]$e8[1:4], 80*sum(jerry1_8_nam %in%teams$team), 80*sum(teams[match(jerry1_8_nam, teams$team),]$e8, na.rm = TRUE))

jerry2_8_nam  <- c("Virginia_Tech", "Florida", "Oregon", "Kansas", "Max", "Expected")
jerry2_8_num <- c(teams[match(jerry2_8_nam, teams$team),]$e8[1:4], 80*sum(jerry2_8_nam %in%teams$team), 80*sum(teams[match(jerry2_8_nam, teams$team),]$e8, na.rm = TRUE))

jerrod1_8_nam <- c("LSU", "Buffalo", "Villianova", "Kansas", "Max", "Expected")
jerrod1_8_num <- c(teams[match(jerrod1_8_nam, teams$team),]$e8[1:4], 80*sum(jerrod1_8_nam %in%teams$team), 80*sum(teams[match(jerrod1_8_nam, teams$team),]$e8, na.rm = TRUE))

jerrod2_8_nam <- c("Duke", "Buffalo", "Oregon", "Kentucky", "Max", "Expected")
jerrod2_8_num <- c(teams[match(jerrod2_8_nam, teams$team),]$e8[1:4], 80*sum(jerrod2_8_nam %in%teams$team), 80*sum(teams[match(jerrod2_8_nam, teams$team),]$e8, na.rm = TRUE))

nikki1_8_nam  <- c("Michigan_St", "Texas_Tech", "Virginia", "UNC", "Max", "Expected")
nikki1_8_num <- c(teams[match(nikki1_8_nam, teams$team),]$e8[1:4], 80*sum(nikki1_8_nam %in%teams$team), 80*sum(teams[match(nikki1_8_nam, teams$team),]$e8, na.rm = TRUE))

nikki2_8_nam  <- c("Michigan_St", "Texas_Tech", "Villianova", "Kansas", "Max", "Expected")
nikki2_8_num <- c(teams[match(nikki2_8_nam, teams$team),]$e8[1:4], 80*sum(nikki2_8_nam %in%teams$team), 80*sum(teams[match(nikki2_8_nam, teams$team),]$e8, na.rm = TRUE))



data.table(`Name` = alex1_8_nam,   `Prob` = alex1_8_num, 
           `Name` = alex2_8_nam,   `Prob` = alex2_8_num,
           `Name` = jerry1_8_nam,  `Prob` = jerry1_8_num,
           `Name` = jerry2_8_nam,  `Prob` = jerry2_8_num,
           `Name` = jerrod1_8_nam, `Prob` = jerrod1_8_num,
           `Name` = jerrod2_8_nam, `Prob` = jerrod2_8_num,
           `Name` = nikki1_8_nam,  `Prob` = nikki1_8_num,
           `Name` = nikki2_8_nam,  `Prob` = nikki2_8_num) %>%
  kable() %>%
  add_header_above(c("Alex 1" = 2,   "Alex 2" = 2,
                     "Jerry 1" = 2,  "Jerry 2" = 2,
                     "Jerrod 1" = 2, "Jerrod 2" = 2,
                     "Nikki 1" = 2,  "Nikki 2" = 2), bold = T, color = "white", background = "#5e78d6")%>%
  kable_styling("striped", full_width = F, position = "center", font_size = 11) %>%
  column_spec(c(1,3,5,7,9,11,13,15), bold=T) %>%
  row_spec(0, bold = T, color = "white", background = "#5e78d6")  %>%
  row_spec(c(5, 6), bold = T, background = "#E5E5E5")

Alex 1		Alex 2		Jerry 1		Jerry 2		Jerrod 1		Jerrod 2		Nikki 1		Nikki 2
Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob
Duke	0.55	Michigan_St	0.26	Duke	0.55	Virginia_Tech	0.14	LSU	0.05	Duke	0.55	Michigan_St	0.26	Michigan_St	0.26
Michigan	0.20	Gonzaga	0.48	Michigan	0.20	Florida	NA	Buffalo	NA	Buffalo	NA	Texas_Tech	0.20	Texas_Tech	0.20
Virginia	0.53	Tennessee	0.23	Tennessee	0.23	Oregon	0.03	Villianova	NA	Oregon	0.03	Virginia	0.53	Villianova	NA
Kentucky	0.25	UNC	0.38	Houston	0.17	Kansas	NA	Kansas	NA	Kentucky	0.25	UNC	0.38	Kansas	NA
Max	320.00	Max	320.00	Max	320.00	Max	160.00	Max	80.00	Max	240.00	Max	320.00	Max	160.00
Expected	122.40	Expected	108.00	Expected	92.00	Expected	13.60	Expected	4.00	Expected	66.40	Expected	109.60	Expected	36.80

Final 4 Evaluation

Here’s who people chose to advance out of the final 4 along with their win probabilities and how many points brackets can expect to add to their totals.

alex1_8_nam <- c("Duke", "Virginia", "Max", "Expected")
alex1_8_num <- c(teams[match(alex1_8_nam, teams$team),]$f4[1:2], 160*sum(alex1_8_nam %in%teams$team), 160*sum(teams[match(alex1_8_nam, teams$team),]$f4, na.rm = TRUE))

alex2_8_nam <- c("Gonzaga", "UNC", "Max", "Expected")
alex2_8_num <- c(teams[match(alex2_8_nam, teams$team),]$f4[1:2], 160*sum(alex2_8_nam %in%teams$team), 160*sum(teams[match(alex2_8_nam, teams$team),]$f4, na.rm = TRUE))

jerry1_8_nam  <- c("Duke", "Tennessee", "Max", "Expected")
jerry1_8_num <- c(teams[match(jerry1_8_nam, teams$team),]$f4[1:2], 160*sum(jerry1_8_nam %in%teams$team), 160*sum(teams[match(jerry1_8_nam, teams$team),]$f4, na.rm = TRUE))

jerry2_8_nam  <- c("Virginia_Tech", "Kansas", "Max", "Expected")
jerry2_8_num <- c(teams[match(jerry2_8_nam, teams$team),]$f4[1:2], 160*sum(jerry2_8_nam %in%teams$team), 160*sum(teams[match(jerry2_8_nam, teams$team),]$f4, na.rm = TRUE))

jerrod1_8_nam <- c("LSU", "Kansas", "Max", "Expected")
jerrod1_8_num <- c(teams[match(jerrod1_8_nam, teams$team),]$f4[1:2], 160*sum(jerrod1_8_nam %in%teams$team), 160*sum(teams[match(jerrod1_8_nam, teams$team),]$f4, na.rm = TRUE))

jerrod2_8_nam <- c("Duke", "Kentucky", "Max", "Expected")
jerrod2_8_num <- c(teams[match(jerrod2_8_nam, teams$team),]$f4[1:2], 160*sum(jerrod2_8_nam %in%teams$team), 160*sum(teams[match(jerrod2_8_nam, teams$team),]$f4, na.rm = TRUE))

nikki1_8_nam  <- c("Michigan_St", "Virginia", "Max", "Expected")
nikki1_8_num <- c(teams[match(nikki1_8_nam, teams$team),]$f4[1:2], 160*sum(nikki1_8_nam %in%teams$team), 160*sum(teams[match(nikki1_8_nam, teams$team),]$f4, na.rm = TRUE))

nikki2_8_nam  <- c("Michigan_St", "Kansas", "Max", "Expected")
nikki2_8_num <- c(teams[match(nikki2_8_nam, teams$team),]$f4[1:2], 160*sum(nikki2_8_nam %in%teams$team), 160*sum(teams[match(nikki2_8_nam, teams$team),]$f4, na.rm = TRUE))



data.table(`Name` = alex1_8_nam,   `Prob` = alex1_8_num, 
           `Name` = alex2_8_nam,   `Prob` = alex2_8_num,
           `Name` = jerry1_8_nam,  `Prob` = jerry1_8_num,
           `Name` = jerry2_8_nam,  `Prob` = jerry2_8_num,
           `Name` = jerrod1_8_nam, `Prob` = jerrod1_8_num,
           `Name` = jerrod2_8_nam, `Prob` = jerrod2_8_num,
           `Name` = nikki1_8_nam,  `Prob` = nikki1_8_num,
           `Name` = nikki2_8_nam,  `Prob` = nikki2_8_num) %>%
  kable() %>%
  add_header_above(c("Alex 1" = 2,   "Alex 2" = 2,
                     "Jerry 1" = 2,  "Jerry 2" = 2,
                     "Jerrod 1" = 2, "Jerrod 2" = 2,
                     "Nikki 1" = 2,  "Nikki 2" = 2), bold = T, color = "white", background = "#5e78d6")%>%
  kable_styling("striped", full_width = F, position = "center", font_size = 11) %>%
  column_spec(c(1,3,5,7,9,11,13,15), bold=T) %>%
  row_spec(0, bold = T, color = "white", background = "#5e78d6")  %>%
  row_spec(c(3, 4), bold = T, background = "#E5E5E5")

Alex 1		Alex 2		Jerry 1		Jerry 2		Jerrod 1		Jerrod 2		Nikki 1		Nikki 2
Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob
Duke	0.33	Gonzaga	0.25	Duke	0.33	Virginia_Tech	0.06	LSU	0.02	Duke	0.33	Michigan_St	0.13	Michigan_St	0.13
Virginia	0.33	UNC	0.18	Tennessee	0.12	Kansas	NA	Kansas	NA	Kentucky	0.10	Virginia	0.33	Kansas	NA
Max	320.00	Max	320.00	Max	320.00	Max	160.00	Max	160.00	Max	320.00	Max	320.00	Max	160.00
Expected	105.60	Expected	68.80	Expected	72.00	Expected	9.60	Expected	3.20	Expected	68.80	Expected	73.60	Expected	20.80

Champ Evaluation

Here’s who people chose to win it all along with their win probabilities and how many points brackets can expect to add to their totals.

alex1_8_nam <- c("Duke", "Max", "Expected")
alex1_8_num <- c(teams[match(alex1_8_nam, teams$team),]$f2[1], 320*sum(alex1_8_nam %in%teams$team), 320*sum(teams[match(alex1_8_nam, teams$team),]$f2, na.rm = TRUE))

alex2_8_nam <- c("Gonzaga", "Max", "Expected")
alex2_8_num <- c(teams[match(alex2_8_nam, teams$team),]$f2[1], 320*sum(alex2_8_nam %in%teams$team), 320*sum(teams[match(alex2_8_nam, teams$team),]$f2, na.rm = TRUE))

jerry1_8_nam  <- c("Duke", "Max", "Expected")
jerry1_8_num <- c(teams[match(jerry1_8_nam, teams$team),]$f2[1], 320*sum(jerry1_8_nam %in%teams$team), 320*sum(teams[match(jerry1_8_nam, teams$team),]$f2, na.rm = TRUE))

jerry2_8_nam  <- c("Kansas", "Max", "Expected")
jerry2_8_num <- c(teams[match(jerry2_8_nam, teams$team),]$f2[1], 320*sum(jerry2_8_nam %in%teams$team), 320*sum(teams[match(jerry2_8_nam, teams$team),]$f2, na.rm = TRUE))

jerrod1_8_nam <- c("Kansas", "Max", "Expected")
jerrod1_8_num <- c(teams[match(jerrod1_8_nam, teams$team),]$f2[1], 320*sum(jerrod1_8_nam %in%teams$team), 320*sum(teams[match(jerrod1_8_nam, teams$team),]$f2, na.rm = TRUE))

jerrod2_8_nam <- c("Duke", "Max", "Expected")
jerrod2_8_num <- c(teams[match(jerrod2_8_nam, teams$team),]$f2[1], 320*sum(jerrod2_8_nam %in%teams$team), 320*sum(teams[match(jerrod2_8_nam, teams$team),]$f2, na.rm = TRUE))

nikki1_8_nam  <- c("Virginia", "Max", "Expected")
nikki1_8_num <- c(teams[match(nikki1_8_nam, teams$team),]$f2[1], 320*sum(nikki1_8_nam %in%teams$team), 320*sum(teams[match(nikki1_8_nam, teams$team),]$f2, na.rm = TRUE))

nikki2_8_nam  <- c("Kansas", "Max", "Expected")
nikki2_8_num <- c(teams[match(nikki2_8_nam, teams$team),]$f2[1], 320*sum(nikki2_8_nam %in%teams$team), 320*sum(teams[match(nikki2_8_nam, teams$team),]$f2, na.rm = TRUE))



data.table(`Name` = alex1_8_nam,   `Prob` = alex1_8_num, 
           `Name` = alex2_8_nam,   `Prob` = alex2_8_num,
           `Name` = jerry1_8_nam,  `Prob` = jerry1_8_num,
           `Name` = jerry2_8_nam,  `Prob` = jerry2_8_num,
           `Name` = jerrod1_8_nam, `Prob` = jerrod1_8_num,
           `Name` = jerrod2_8_nam, `Prob` = jerrod2_8_num,
           `Name` = nikki1_8_nam,  `Prob` = nikki1_8_num,
           `Name` = nikki2_8_nam,  `Prob` = nikki2_8_num) %>%
  kable() %>%
  add_header_above(c("Alex 1" = 2,   "Alex 2" = 2,
                     "Jerry 1" = 2,  "Jerry 2" = 2,
                     "Jerrod 1" = 2, "Jerrod 2" = 2,
                     "Nikki 1" = 2,  "Nikki 2" = 2), bold = T, color = "white", background = "#5e78d6")%>%
  kable_styling("striped", full_width = F, position = "center", font_size = 11) %>%
  column_spec(c(1,3,5,7,9,11,13,15), bold=T) %>%
  row_spec(0, bold = T, color = "white", background = "#5e78d6")  %>%
  row_spec(c(2, 3), bold = T, background = "#E5E5E5")

Alex 1		Alex 2		Jerry 1		Jerry 2		Jerrod 1		Jerrod 2		Nikki 1		Nikki 2
Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob	Name	Prob
Duke	0.2	Gonzaga	0.14	Duke	0.2	Kansas	NA	Kansas	NA	Duke	0.2	Virginia	0.18	Kansas	NA
Max	320.0	Max	320.00	Max	320.0	Max	0	Max	0	Max	320.0	Max	320.00	Max	0
Expected	64.0	Expected	44.80	Expected	64.0	Expected	0	Expected	0	Expected	64.0	Expected	57.60	Expected	0

Bracket Challenge 2019

Alex Kaechele

March 25, 2019