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https://github.com/papagorgio23/marchmadness
Predicting 2018 March Madness Basketball games
https://github.com/papagorgio23/marchmadness
basketball college-basketball kaggle-march-madness march-madness monte-carlo monte-carlo-simulation probability
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Predicting 2018 March Madness Basketball games
- Host: GitHub
- URL: https://github.com/papagorgio23/marchmadness
- Owner: papagorgio23
- License: mit
- Created: 2018-05-19T23:40:28.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-03-21T15:43:47.000Z (almost 3 years ago)
- Last Synced: 2023-07-25T04:29:44.441Z (over 1 year ago)
- Topics: basketball, college-basketball, kaggle-march-madness, march-madness, monte-carlo, monte-carlo-simulation, probability
- Language: R
- Size: 235 KB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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
```r
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.
```r
# 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.
```r
## 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$chance, decreasing=TRUE), ]
df
}
```
### Set Up
```r
set.seed(1234)
simulation.results <- c()
# Set number of simulations at 5,000
num_sims = 5000
i = 1
```
## Simulate Tourney
Here's where the magic happens. (5,000 Sims can take up to 90 mins... very slow but I didn't have time to fix this)
```r
tictoc::tic()
while (i <= num_sims) {
tourney.seeds <- seeds
play.teams <- seeds %>% filter(seed_playin == "a" | seed_playin == "b")
play.seeds <- unique(play.teams$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
```r
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
```r
# 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
```r
# 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% |
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