March Madness

Predicting 2022 March Madness Basketball games

Monte Carlo Simulation

We are going to determine what percent chance each team has on winning the tournment

Load Libraries

library(knitr)
library(data.table)
library(tidyverse)
library(magrittr)

Load Data

I have already created a predicted probability for each team in the tournament for a kaggle competition.

We will load those results plus a few of the data files provided in the competition.

# gets seeding info
seeds <- read_csv('Data/MNCAATourneySeeds.csv') %>% # data from https://www.kaggle.com/c/mens-march-mania-2022/data
  filter(Season == 2022) %>% 
  select(TeamID, Season, Seed) %>%
  mutate(
    seed_n = str_sub(Seed, 2, -1),
    seed_playin = str_sub(Seed, 4),
    seed_n = as.numeric(str_replace_all(seed_n, "[a-z]", "")), 
    seed_region = str_sub(Seed, 1, 1),
    Seed = str_sub(Seed, 1, 3)
  )

# gets team info
teams <- read_csv('Data/MTeams.csv') %>% # data from https://www.kaggle.com/c/mens-march-mania-2022/data
  select(TeamID, TeamName) %>%
  inner_join(seeds, by = "TeamID") %>% 
  select(TeamID, TeamName, Seed)

# gets the predicted results for previous project
MarchMadness <- read_csv('Data/MarchMadness2022.csv') # data from A.I. Sports and https://www.kaggle.com/c/mens-march-mania-2022/data

Functions

There are several functions we have to write in order to perform the simulation.

## simulate play in game
simulate.playin.game <- function(team1, team2){
  if(team1 > team2){
    tmp <- team1
    team1 <- team2
    team2 <- tmp
  }
  
  # Extract Probabilities for each team in the matchup
  p.1.2 <- MarchMadness %>% 
    filter(Team1 == team1,
           Team2 == team2) %>% 
    pull(pred)
  p.2.1 <- 1 - p.1.2
  
  # simulate Game
  game.result <- sample(c(team1, team2), size = 1, prob = c(p.1.2, p.2.1), replace=TRUE)
  
  if (game.result == team1){ 
    loser <- team2
  } else {
    loser <- team1
  }
  # return the winner
  loser
}

## simulate regular game
simulate.game <- function(team1seed, team2seed){
  
  team1 <- tourney.seeds %>% 
    filter(Seed == team1seed) %>% 
    pull(TeamID)
  team2 <- tourney.seeds %>% 
    filter(Seed == team2seed) %>% 
    pull(TeamID)
  
  if(team1 > team2){
    tmp   <- team1
    team1 <- team2
    team2 <- tmp
  }
  
  # Extract Probabilities for each team in the matchup
  p.1.2 <- MarchMadness %>% 
    filter(Team1 == team1,
           Team2 == team2) %>% 
    pull(pred)
  p.2.1 <- 1 - p.1.2
  
  
  # simulate Game
  game.result <- sample(c(team1, team2), size = 1, prob = c(p.1.2, p.2.1), replace=TRUE)
  
  if (game.result == team1){ 
    winner <- tourney.seeds %>% 
      filter(TeamID == team1) %>% 
      pull("Seed")
  } else {
    winner <- tourney.seeds %>% 
      filter(TeamID == team2) %>% 
      pull("Seed")
  }
  # return the winner
  winner
}


## chance.df
chance.df <- function(series){
  tbl <- table(sim.results.df[ , series])
  df <- data.frame(team = names(tbl), chance = as.numeric(tbl)/sum(tbl))
  df <- df[order(df$chance, decreasing=TRUE), ]
  df
}

Set Up

set.seed(1234)
simulation.results <- c()

# Set number of simulations at 5,000
num_sims = 5000
i = 1

Simulate Tourney

Here's where the magic happens. (5,000 Sims can take up to 90 mins... very slow but I didn't have time to fix this)

tictoc::tic()
while (i <= num_sims) {
  tourney.seeds <- seeds
  play.teams <- seeds %>% filter(seed_playin == "a" | seed_playin == "b")
  play.seeds <- unique(play.teams$Seed)
  
  # play in games
  for(seeding in play.seeds) {
    play.1.2 <- play.teams %>% filter(Seed == seeding) %>% select(TeamID)
    team1 <- play.1.2$TeamID[1]
    team2 <- play.1.2$TeamID[2]
    loser <- simulate.playin.game(team1, team2)
    tourney.seeds <- tourney.seeds %>% 
      filter(TeamID != loser)
  }
  
  
  ##### West games Round 1
  # Top Round of 64
  R32.1.w <- simulate.game("W01", "W16")
  R32.2.w <- simulate.game("W08", "W09")
  R32.3.w <- simulate.game("W05", "W12")
  R32.4.w <- simulate.game("W04", "W13")
  
  # Bottom Round of 64
  R32.5.w <- simulate.game("W06", "W11")
  R32.6.w <- simulate.game("W03", "W14")
  R32.7.w <- simulate.game("W07", "W10")
  R32.8.w <- simulate.game("W02", "W15")
  
  # Round of 32
  S16.1.w <- simulate.game(R32.1.w, R32.2.w)
  S16.2.w <- simulate.game(R32.3.w, R32.4.w)
  S16.3.w <- simulate.game(R32.5.w, R32.6.w)
  S16.4.w <- simulate.game(R32.7.w, R32.8.w)
  
  # Sweet 16
  E8.1.w <- simulate.game(S16.1.w, S16.2.w)
  E8.2.w <- simulate.game(S16.3.w, S16.4.w)
  
  # Elite 8
  F4.w <- simulate.game(E8.1.w, E8.2.w)
  
  ##### X games Round 1
  # Top Round of 64
  R32.1.x <- simulate.game("X01", "X16")
  R32.2.x <- simulate.game("X08", "X09")
  R32.3.x <- simulate.game("X05", "X12")
  R32.4.x <- simulate.game("X04", "X13")
  
  # Bottom Round of 64
  R32.5.x <- simulate.game("X06", "X11")
  R32.6.x <- simulate.game("X03", "X14")
  R32.7.x <- simulate.game("X07", "X10")
  R32.8.x <- simulate.game("X02", "X15")
  
  # Round of 32
  S16.1.x <- simulate.game(R32.1.x, R32.2.x)
  S16.2.x <- simulate.game(R32.3.x, R32.4.x)
  S16.3.x <- simulate.game(R32.5.x, R32.6.x)
  S16.4.x <- simulate.game(R32.7.x, R32.8.x)
  
  # Sxeet 16
  E8.1.x <- simulate.game(S16.1.x, S16.2.x)
  E8.2.x <- simulate.game(S16.3.x, S16.4.x)
  
  # Elite 8
  F4.x <- simulate.game(E8.1.x, E8.2.x)
  
  ##### Y games Round 1
  # Top Round of 64
  R32.1.y <- simulate.game("Y01", "Y16")
  R32.2.y <- simulate.game("Y08", "Y09")
  R32.3.y <- simulate.game("Y05", "Y12")
  R32.4.y <- simulate.game("Y04", "Y13")
  
  # Bottom Round of 64
  R32.5.y <- simulate.game("Y06", "Y11")
  R32.6.y <- simulate.game("Y03", "Y14")
  R32.7.y <- simulate.game("Y07", "Y10")
  R32.8.y <- simulate.game("Y02", "Y15")
  
  # Round of 32
  S16.1.y <- simulate.game(R32.1.y, R32.2.y)
  S16.2.y <- simulate.game(R32.3.y, R32.4.y)
  S16.3.y <- simulate.game(R32.5.y, R32.6.y)
  S16.4.y <- simulate.game(R32.7.y, R32.8.y)
  
  # Syeet 16
  E8.1.y <- simulate.game(S16.1.y, S16.2.y)
  E8.2.y <- simulate.game(S16.3.y, S16.4.y)
  
  # Elite 8
  F4.y <- simulate.game(E8.1.y, E8.2.y)
  
  ##### Z games Round 1
  # Top Round of 64
  R32.1.z <- simulate.game("Z01", "Z16")
  R32.2.z <- simulate.game("Z08", "Z09")
  R32.3.z <- simulate.game("Z05", "Z12")
  R32.4.z <- simulate.game("Z04", "Z13")
  
  # Bottom Round of 64
  R32.5.z <- simulate.game("Z06", "Z11")
  R32.6.z <- simulate.game("Z03", "Z14")
  R32.7.z <- simulate.game("Z07", "Z10")
  R32.8.z <- simulate.game("Z02", "Z15")
  
  # Round of 32
  S16.1.z <- simulate.game(R32.1.z, R32.2.z)
  S16.2.z <- simulate.game(R32.3.z, R32.4.z)
  S16.3.z <- simulate.game(R32.5.z, R32.6.z)
  S16.4.z <- simulate.game(R32.7.z, R32.8.z)
  
  # Szeet 16
  E8.1.z <- simulate.game(S16.1.z, S16.2.z)
  E8.2.z <- simulate.game(S16.3.z, S16.4.z)
  
  # Elite 8
  F4.z <- simulate.game(E8.1.z, E8.2.z)
  
  ## Final Four!!! Game Time Baby!!!
  # Semi Finals
  F4.1 <- simulate.game(F4.w, F4.x)
  F4.2 <- simulate.game(F4.y, F4.z)
  
  # Championship Game
  Champ.1 <- simulate.game(F4.1, F4.2)
  
  #print(paste0("This is the winner ",Champ.1))
  
  results.all <- c( 
    i,  R32.1.w, 
    R32.2.w,
    R32.3.w,
    R32.4.w,
    R32.5.w,
    R32.6.w,
    R32.7.w,
    R32.8.w,
    R32.1.x,
    R32.2.x,
    R32.3.x,
    R32.4.x,
    R32.5.x,
    R32.6.x,
    R32.7.x,
    R32.8.x,
    R32.1.y, 
    R32.2.y,
    R32.3.y,
    R32.4.y,
    R32.5.y,
    R32.6.y,
    R32.7.y,
    R32.8.y,
    R32.1.z, 
    R32.2.z,
    R32.3.z,
    R32.4.z,
    R32.5.z,
    R32.6.z,
    R32.7.z,
    R32.8.z,
    S16.1.w,
    S16.2.w,
    S16.3.w,
    S16.4.w,
    S16.1.x,
    S16.2.x,
    S16.3.x,
    S16.4.x,
    S16.1.y,
    S16.2.y,
    S16.3.y,
    S16.4.y,
    S16.1.z,
    S16.2.z,
    S16.3.z,
    S16.4.z,
    E8.1.w,
    E8.2.w,
    E8.1.x,
    E8.2.x,
    E8.1.y,
    E8.2.y,
    E8.1.z,
    E8.2.z,
    F4.w, F4.x, F4.y, F4.z,
    F4.1, F4.2, Champ.1
  )
  
  simulation.results <- c(simulation.results, results.all)
  
  i <- i + 1 
}
tictoc::toc() # 5796.496 sec elapsed for 5,000 sims

Process Results

sim.results.mat <- matrix(simulation.results, ncol=64, byrow=TRUE)
sim.results.df <- as.data.frame(sim.results.mat)
names(sim.results.df) <- c( 
  "sim", "R32.1.w", 
  "R32.2.w",
  "R32.3.w",
  "R32.4.w",
  "R32.5.w",
  "R32.6.w",
  "R32.7.w",
  "R32.8.w",
  "R32.1.x",
  "R32.2.x",
  "R32.3.x",
  "R32.4.x",
  "R32.5.x",
  "R32.6.x",
  "R32.7.x",
  "R32.8.x",
  "R32.1.y", 
  "R32.2.y",
  "R32.3.y",
  "R32.4.y",
  "R32.5.y",
  "R32.6.y",
  "R32.7.y",
  "R32.8.y",
  "R32.1.z", 
  "R32.2.z",
  "R32.3.z",
  "R32.4.z",
  "R32.5.z",
  "R32.6.z",
  "R32.7.z",
  "R32.8.z",
  "S16.1.w",
  "S16.2.w",
  "S16.3.w",
  "S16.4.w",
  "S16.1.x",
  "S16.2.x",
  "S16.3.x",
  "S16.4.x",
  "S16.1.y",
  "S16.2.y",
  "S16.3.y",
  "S16.4.y",
  "S16.1.z",
  "S16.2.z",
  "S16.3.z",
  "S16.4.z",
  "E8.1.w",
  "E8.2.w",
  "E8.1.x",
  "E8.2.x",
  "E8.1.y",
  "E8.2.y",
  "E8.1.z",
  "E8.2.z",
  "F4.w", "F4.x", "F4.y", "F4.z",
  "F4.1", "F4.2", "Champ.1"
)

Table Probabilities

# NCAA Champions
champs.df <- chance.df("Champ.1")

# Semi Finals Champions
SF1.df <- chance.df("F4.1")
SF2.df <- chance.df("F4.2")
finals <- rbind(SF1.df, SF2.df)

# Final 4
w.1.df <- chance.df("F4.w")
x.1.df <- chance.df("F4.x")
y.1.df <- chance.df("F4.y")
z.1.df <- chance.df("F4.z")
Final4 <- rbind(w.1.df, x.1.df, y.1.df, z.1.df)

# Elite 8
E8w.1.df <- chance.df("E8.1.w")
E8w.2.df <- chance.df("E8.2.w")
E8x.1.df <- chance.df("E8.1.x")
E8x.2.df <- chance.df("E8.2.x")
E8y.1.df <- chance.df("E8.1.y")
E8y.2.df <- chance.df("E8.2.y")
E8z.1.df <- chance.df("E8.1.z")
E8z.2.df <- chance.df("E8.2.z")
Elite8 <- rbind(E8w.1.df, E8w.2.df, E8x.1.df, E8x.2.df, E8y.1.df, E8y.2.df, E8z.1.df, E8z.2.df)


# Sweet 16
S16w.1.df <- chance.df("S16.1.w")
S16w.2.df <- chance.df("S16.2.w")
S16w.3.df <- chance.df("S16.3.w")
S16w.4.df <- chance.df("S16.4.w")
S16x.1.df <- chance.df("S16.1.x")
S16x.2.df <- chance.df("S16.2.x")
S16x.3.df <- chance.df("S16.3.x")
S16x.4.df <- chance.df("S16.4.x")
S16y.1.df <- chance.df("S16.1.y")
S16y.2.df <- chance.df("S16.2.y")
S16y.3.df <- chance.df("S16.3.y")
S16y.4.df <- chance.df("S16.4.y")
S16z.1.df <- chance.df("S16.1.z")
S16z.2.df <- chance.df("S16.2.z")
S16z.3.df <- chance.df("S16.3.z")
S16z.4.df <- chance.df("S16.4.z")
Sweet16 <- rbind(S16w.1.df, S16w.2.df, S16w.3.df, S16w.4.df, S16x.1.df, S16x.2.df, S16x.3.df, S16x.4.df, S16y.1.df, S16y.2.df, S16y.3.df, S16y.4.df, S16z.1.df, S16z.2.df, S16z.3.df, S16z.4.df)

# Round of 32
R32w.1.df <- chance.df("R32.1.w")
R32w.2.df <- chance.df("R32.2.w")
R32w.3.df <- chance.df("R32.3.w")
R32w.4.df <- chance.df("R32.4.w")
R32w.5.df <- chance.df("R32.5.w")
R32w.6.df <- chance.df("R32.6.w")
R32w.7.df <- chance.df("R32.7.w")
R32w.8.df <- chance.df("R32.8.w")
R32x.1.df <- chance.df("R32.1.x")
R32x.2.df <- chance.df("R32.2.x")
R32x.3.df <- chance.df("R32.3.x")
R32x.4.df <- chance.df("R32.4.x")
R32x.5.df <- chance.df("R32.5.x")
R32x.6.df <- chance.df("R32.6.x")
R32x.7.df <- chance.df("R32.7.x")
R32x.8.df <- chance.df("R32.8.x")
R32y.1.df <- chance.df("R32.1.y")
R32y.2.df <- chance.df("R32.2.y")
R32y.3.df <- chance.df("R32.3.y")
R32y.4.df <- chance.df("R32.4.y")
R32y.5.df <- chance.df("R32.5.y")
R32y.6.df <- chance.df("R32.6.y")
R32y.7.df <- chance.df("R32.7.y")
R32y.8.df <- chance.df("R32.8.y")
R32z.1.df <- chance.df("R32.1.z")
R32z.2.df <- chance.df("R32.2.z")
R32z.3.df <- chance.df("R32.3.z")
R32z.4.df <- chance.df("R32.4.z")
R32z.5.df <- chance.df("R32.5.z")
R32z.6.df <- chance.df("R32.6.z")
R32z.7.df <- chance.df("R32.7.z")
R32z.8.df <- chance.df("R32.8.z")
Round32 <- rbind(R32w.1.df, R32w.2.df, R32w.3.df, R32w.4.df, R32w.5.df, R32w.6.df, R32w.7.df, R32w.8.df,
                 R32x.1.df, R32x.2.df, R32x.3.df, R32x.4.df, R32x.5.df, R32x.6.df, R32x.7.df, R32x.8.df, 
                 R32y.1.df, R32y.2.df, R32y.3.df, R32y.4.df, R32y.5.df, R32y.6.df, R32y.7.df, R32y.8.df, 
                 R32z.1.df, R32z.2.df, R32z.3.df, R32z.4.df, R32z.5.df, R32z.6.df, R32z.7.df, R32z.8.df)






# Merge all probabilities
all.chances.df <- merge(Round32, Sweet16, by="team")
names(all.chances.df) <- c("team", "Round32", "Sweet16")

all.chances.df %<>% left_join(Elite8, by = "team") %>%
  rename(Elite8 = chance) %>%
  left_join(Final4, by = "team") %>%
  rename(Final4 = chance) %>%
  left_join(finals, by = "team") %>%
  rename(Finals = chance) %>%
  left_join(champs.df, by = "team") %>%
  rename(Champs = chance) %>%
  arrange(desc(Champs), desc(Finals), desc(Final4), desc(Elite8), desc(Sweet16), desc(Round32))

# Fix percentages
all.chances.df$Sweet16 <- ifelse(is.na(all.chances.df$Sweet16), 0, all.chances.df$Sweet16)
all.chances.df$Elite8 <- ifelse(is.na(all.chances.df$Elite8), 0, all.chances.df$Elite8)
all.chances.df$Final4 <- ifelse(is.na(all.chances.df$Final4), 0, all.chances.df$Final4)
all.chances.df$Finals <- ifelse(is.na(all.chances.df$Finals), 0, all.chances.df$Finals)
all.chances.df$Champs <- ifelse(is.na(all.chances.df$Champs), 0, all.chances.df$Champs)

all.chances.df[,2:7] <- sapply(all.chances.df[,2:7], convert_pct)

# get team names
all.chances.df %<>% 
  left_join(teams, by = c("team" = "Seed")) %>%
  select(TeamName, everything(), -team, -TeamID)

View Results

# View results
kable(all.chances.df)

TeamName	Round32	Sweet16	Elite8	Final4	Finals	Champs
Gonzaga	97.558%	88.098%	71.948%	55.697%	41.658%	30.188%
Villanova	99.924%	76.780%	53.815%	30.748%	20.066%	9.995%
Arizona	98.550%	91.556%	61.801%	31.434%	18.515%	9.054%
Kentucky	99.898%	75.432%	40.590%	25.305%	10.809%	5.671%
Texas Tech	86.521%	66.582%	47.024%	17.726%	9.893%	4.985%
Iowa	91.302%	68.616%	40.895%	24.695%	11.877%	4.934%
Baylor	99.898%	70.829%	45.270%	22.660%	9.664%	4.654%
Wisconsin	87.513%	59.817%	39.598%	22.279%	10.580%	4.095%
Purdue	91.506%	52.645%	29.196%	17.904%	7.325%	3.942%
Kansas	95.270%	67.319%	37.614%	20.524%	8.978%	3.230%
Tennessee	86.851%	62.182%	27.060%	13.479%	7.375%	3.103%
Texas	67.981%	35.987%	19.379%	11.597%	4.476%	2.340%
Illinois	88.606%	51.958%	19.023%	8.037%	4.527%	2.085%
Auburn	99.924%	80.341%	37.462%	15.972%	5.519%	2.009%
Houston	84.664%	42.192%	15.437%	7.986%	4.680%	1.653%
Connecticut	86.902%	60.936%	17.904%	9.003%	3.967%	1.602%
Duke	84.308%	51.399%	23.678%	8.087%	3.332%	1.246%
Michigan	72.864%	27.035%	8.087%	4.145%	2.442%	1.043%
UCLA	79.552%	45.982%	21.745%	8.545%	2.136%	0.839%
St Mary's CA	65.463%	34.435%	16.684%	5.722%	2.111%	0.738%
LSU	68.769%	28.688%	14.446%	6.511%	1.984%	0.585%
Ohio St	63.911%	16.175%	6.511%	2.238%	0.865%	0.254%
Alabama	50.839%	16.938%	8.952%	2.009%	0.687%	0.254%
Providence	62.208%	20.677%	8.291%	3.739%	0.890%	0.203%
San Francisco	43.388%	11.470%	3.255%	1.272%	0.407%	0.178%
Seton Hall	55.773%	5.036%	2.213%	0.636%	0.305%	0.178%
Michigan St	62.208%	28.891%	8.444%	1.907%	0.610%	0.127%
Arkansas	52.798%	19.049%	3.332%	1.119%	0.280%	0.127%
San Diego St	63.784%	23.093%	8.316%	2.976%	0.610%	0.102%
Virginia Tech	32.019%	10.097%	3.128%	1.068%	0.381%	0.102%
Vermont	47.202%	16.455%	2.340%	0.712%	0.229%	0.102%
Marquette	49.746%	16.556%	6.612%	1.704%	0.356%	0.051%
Indiana	34.537%	14.954%	5.163%	1.577%	0.305%	0.051%
Wyoming	34.537%	14.954%	5.163%	1.577%	0.305%	0.051%
Davidson	37.792%	16.785%	4.908%	0.916%	0.229%	0.051%
Iowa St	31.231%	8.800%	4.171%	1.322%	0.203%	0.051%
Notre Dame	49.161%	12.564%	5.544%	1.348%	0.432%	0.025%
Rutgers	49.161%	12.564%	5.544%	1.348%	0.432%	0.025%
Murray St	56.612%	13.072%	4.273%	1.679%	0.305%	0.025%
Boise St	50.356%	6.943%	2.518%	0.839%	0.203%	0.025%
Colorado St	27.136%	8.698%	2.263%	0.509%	0.153%	0.025%
S Dakota St	37.792%	8.571%	2.696%	0.712%	0.127%	0.025%
Loyola-Chicago	36.089%	7.045%	1.984%	0.356%	0.076%	0.025%
Memphis	49.644%	4.603%	1.704%	0.407%	0.025%	0.025%
North Carolina	50.254%	12.564%	3.789%	0.763%	0.153%	0.000%
Creighton	36.216%	8.647%	1.958%	0.458%	0.076%	0.000%
Longwood	13.149%	2.085%	0.280%	0.153%	0.076%	0.000%
Miami FL	62.157%	14.471%	3.204%	0.636%	0.051%	0.000%
TCU	44.227%	3.255%	0.839%	0.229%	0.025%	0.000%
Montana St	13.479%	3.917%	1.068%	0.127%	0.025%	0.000%
Akron	20.448%	4.629%	0.738%	0.153%	0.000%	0.000%
Colgate	12.487%	2.696%	0.432%	0.102%	0.000%	0.000%
USC	37.843%	5.188%	0.687%	0.051%	0.000%	0.000%
CS Fullerton	15.692%	2.925%	0.381%	0.051%	0.000%	0.000%
New Mexico St	13.098%	3.561%	0.229%	0.051%	0.000%	0.000%
Yale	8.494%	1.272%	0.178%	0.051%	0.000%	0.000%
UAB	15.336%	3.306%	0.407%	0.025%	0.000%	0.000%
Chattanooga	11.394%	2.543%	0.280%	0.025%	0.000%	0.000%
Richmond	8.698%	2.136%	0.178%	0.025%	0.000%	0.000%
TAM C. Christi	4.730%	0.941%	0.051%	0.000%	0.000%	0.000%
TX Southern	4.730%	0.941%	0.051%	0.000%	0.000%	0.000%
Georgia St	2.442%	0.356%	0.025%	0.000%	0.000%	0.000%
Bryant	1.450%	0.153%	0.000%	0.000%	0.000%	0.000%
Wright St	1.450%	0.153%	0.000%	0.000%	0.000%	0.000%
Norfolk St	0.102%	0.051%	0.000%	0.000%	0.000%	0.000%
St Peter's	0.102%	0.025%	0.000%	0.000%	0.000%	0.000%

=======

Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
Data		Data
.DS_Store		.DS_Store
BRACKET.R		BRACKET.R
LICENSE		LICENSE
Monte Carlo Sim R32.R		Monte Carlo Sim R32.R
Monte Carlo Sim.R		Monte Carlo Sim.R
Monte Carlo Sim.Rmd		Monte Carlo Sim.Rmd
README.md		README.md
prep_data.R		prep_data.R

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

March Madness

Monte Carlo Simulation

We are going to determine what percent chance each team has on winning the tournment

Load Libraries

Load Data

Functions

Set Up

Simulate Tourney

Process Results

Table Probabilities

View Results

About

Releases

Packages

Languages

License

papagorgio23/MarchMadness

Folders and files

Latest commit

History

Repository files navigation

March Madness

Monte Carlo Simulation

We are going to determine what percent chance each team has on winning the tournment

Load Libraries

Load Data

Functions

Set Up

Simulate Tourney

Process Results

Table Probabilities

View Results

About

Topics

Resources

License

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages