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shinySetup.R
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shinySetup.R
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# Shiny setup
library(rvest)
library(dplyr)
library(fastLink)
library(reshape2)
library(fplr)
library(parallel)
source('./GetFPLData.R')
source('./getOdds.R')
source('./Dreamteam/Dreamteam - recursive v2.R')
# Pick up previous data
load('.RData')
# Set upcoming gw
gw <- 35
# ----------------- FPL data ---------------
# Get player and team data
fpldat <- getFPLSummary() %>%
mutate_at(c(3,7,8), function(x) gsub("é","e",
gsub("á", "a",
gsub("õ", "o",
gsub("í","i",
gsub("ã","a",
gsub("Ö","O",
gsub("ß","ss",x)))))))) %>%
mutate('player_name' = paste(first_name, second_name))
# Get teams
teams <- as.character(unique(fpldat$team))
teams[teams == 'Spurs'] <- 'Tottenham'
# Get last week's dreamteam points
n <- length(dt.all)
if(!identical(dt.all[[n]]$element, dt.3$element)) {
dt.last <- dt.3 %>%
inner_join(select(fpldat, id, event_points), by = c('element'='id')) %>%
mutate(event_points = ifelse(captain == 1, event_points * 2, event_points))
# Make auto subs and add to list
dt.last.2 <- autosub(dt.last)
dt.all[[n+1]] <- dt.last.2
}
# Show total points
n <- length(dt.all)
sum(dt.all[[n]][1:11,'event_points'])
dt.last.2 <- dt.all[[n]]
# --------------------------------------- Fixtures -----------------------------
# Get next set of fixtures
fixtures = fixtures() %>%
filter(event == gw)
# Get upcoming fixture details by team
fix2 <- fixtures %>%
select(id, event_day, team_a, team_h) %>%
melt(id.vars=c('id','event_day')) %>%
group_by(value) %>%
mutate(order_fix = row_number(event_day)) %>%
inner_join(select(.,id, value), by = "id") %>%
filter(value.y != value.x) %>%
mutate(was_home = ifelse(variable=="team_a", FALSE, TRUE)) %>%
group_by(value.x) %>%
mutate(num_fix = n()) %>%
select(fixture=id, order_fix, num_fix, team_name=value.x, opponent_team=value.y, was_home)
# -------------------------------------- Goalscorer odds -----------------------
url <- 'https://sports.williamhill.com/bet/en-gb/betting/g/348/Anytime+Goalscorer.html'
# Get anytime goalscorer odds
result <- getOdds(url, teams)
# See the results
View(arrange(result, desc(probability)))
# -------------------------- Score a brace -------------------------------
url <- 'https://sports.williamhill.com/bet/en-gb/betting/g/135015/Player+To+Score+2+Or+More.html'
result2 <- getOdds(url, teams) %>%
rename(probBrace = probability)
# -------------------------- Score a hattrick -------------------------------
url <- 'https://sports.williamhill.com/bet/en-gb/betting/g/13428/Hat-trick.html'
result3 <- getOdds(url, teams) %>%
rename(probHt = probability)
# Merge with other goalscorer data
result <- result %>%
inner_join(result2, by = 'player') %>%
inner_join(result3, by = 'player')
# ----------------------- Data matching -----------------------
# Get player data
names.split <- do.call(rbind, strsplit(as.character(result$player), ' (?=[^ ]+$)', perl=TRUE)) %>%
as.data.frame(stringsAsFactors = F) %>%
rename("first_name" = V1, "second_name" = V2) %>%
cbind('player_name' = as.character(result$player),
'goalprob' = result$probability,
'probBrace' = result$probBrace,
'probHt' = result$probHt) %>%
mutate(web_name = second_name)
# Make names consistent
right <- names.split
# remove goalkeepers - won't be in goalscorer odds
left <- fpldat %>% filter(pos != "Goalkeeper")
# To do - sort out matching. Why doesn't partial match work? Check against the example.
# Link to FPL data
matches.out <- fastLink(
dfA = left,
dfB = right,
varnames = c("player_name","web_name", "first_name","second_name"),
stringdist.match = c("player_name", "web_name", "first_name","second_name"), # Specifies the variables you want to treat as strings for fuzzy matching
#partial.match = c("player_name","web_name", "first_name","second_name"), # Specifes variables where you want the algorithm to check for partial matches
verbose = T,
return.all = T
#threshold.match = .01 # Match probability threshold. The default is .85, and you can play around with different values
)
# Gives the match rate, estimated falst positive rate (FDR) and estimated false negative rate (FNR)
summary(matches.out)
# Extracts the matched data
a <- matches.out$matches$inds.a
b <- matches.out$matches$inds.b
# Compile matched data
left[a, 'matchindex'] <- b
namesmatched <- cbind(names.split[b,],"matchindex"=b, "match"=matches.out$posterior)
matched.data <- left_join(left,
namesmatched,
by="matchindex")
# Keep most likely match for each
dedup <- matched.data %>%
group_by(first_name.y, second_name.y) %>%
mutate(rank = ifelse(is.na(match),1, rank(match, ties.method='first'))) %>%
filter(rank == 1) %>%
rename(web_name = web_name.x, player_name = player_name.x) %>%
select(-web_name.y, -player_name.y)
# Get goalkeepers
keepers <- fpldat %>%
filter(pos == 'Goalkeeper') %>%
mutate(matchindex = as.numeric(NA),
first_name.y = first_name,
second_name.y = second_name,
goalprob = as.numeric(0),
probBrace = as.numeric(0),
probHt = as.numeric(0),
match = as.numeric(NA),
rank = 1) %>%
rename(first_name.x = first_name, second_name.x = second_name)
# Append goalkeepers
names(keepers) <- names(dedup)
fpl <- rbind(as.data.frame(dedup), as.data.frame(keepers))
# Show whose goalscorer odds are missing
fpl %>%
filter(pos != 'Goalkeeper', (is.na(goalprob))) %>%
select(web_name, team, goalprob, form) %>%
arrange(desc(form)) %>% View
# Get those that didn't match and sort out manually
nonmatched <- fpl %>%
filter(pos != 'Goalkeeper', (is.na(goalprob))) %>%
select(-goalprob,
-probBrace,
-probHt,
-match,
-rank) %>%
mutate(player=case_when(web_name=="Willian" ~ "Willian",
web_name=="Joao Mario" ~ "Joao Mario",
id==302 ~ "Ayoze",
web_name=="Bernardo Silva" ~ "Bernardo Silva")) %>%
inner_join(names.split, by=c("player"="player_name")) %>%
mutate(match = as.numeric(NA),
rank = 1) %>%
mutate(web_name = web_name.x) %>%
select(-first_name, -second_name, -web_name.y, -web_name.x, -player)
# Append manual matches to main file
fpl <- fpl %>%
filter(!id %in% nonmatched$id) %>%
union(nonmatched)
# Set goal probabilities to 0 if no fixture in the next gameweek
fpl <- fpl %>%
mutate(goalprob = ifelse(!team %in% fix2$team_name, 0, goalprob),
goalprob1 = ifelse(!team %in% fix2$team_name, 0, goalprob1),
probBrace = ifelse(!team %in% fix2$team_name, 0, probBrace),
probHt = ifelse(!team %in% fix2$team_name, 0, probHt)) %>%
mutate(goalprob1 = goalprob - probBrace - probHt)
# Show whose goalscorer odds are missing
fpl %>%
filter(pos != 'Goalkeeper', (is.na(goalprob))) %>%
select(web_name, team, status, goalprob, form) %>%
arrange(desc(form)) %>% View
# ------------------------ Clean sheet data ---------------------
url <- 'https://sports.williamhill.com/bet/en-gb/betting/g/158525/To+Keep+a+Clean+Sheet.html'
webpage <- read_html(url)
#Using CSS selectors to scrape the odds
teams_odds <- html_nodes(webpage,'.leftPad')
#Converting the pdds data to text
teams_data <- html_text(teams_odds)
# Filter out headers
teams_data <- teams_data[!grepl('To Keep a Clean Sheet', teams_data)]
teams_data <- grep(paste(teams,collapse="|"), teams_data, value=TRUE)
teams_data <- gsub('\n\t\t\t\t\t\n\t\t\t\t\t\t',
'',
teams_data)
teams_data <- gsub('\n\t\t\t\t\t\n\t\t\t\t',
'',
teams_data)
#Using CSS selectors to scrape the ods section
cs_html <- html_nodes(webpage,'.eventprice')
#Converting the odds data to text
cs <- html_text(cs_html)
cs <- gsub('\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t',
'',
cs)
cs <- gsub('\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t',
'',
cs)
# Replace 'evens' with 1/1, and missing events with 0
cs <- ifelse(cs == 'EVS', '1/1', cs)
cs <- ifelse(cs == '1/1000','0',cs)
# Divide odds by odds + 1
cs.split <- strsplit(cs, split = "/") %>%
lapply(as.numeric) %>%
lapply(function(x) x[1]/x[2]) %>%
lapply(function(x) round(1-(x/(x+1)), 4)) %>%
do.call(rbind, .)
# Bind players and odds
cs <- data.frame('team' = as.character(teams_data), 'cs' = cs.split, stringsAsFactors = F) %>%
group_by(team) %>%
mutate(order_fix=row_number())
# Keep one probability for each fixture in next gameweek
# cs <- cs[match(unique(cs$team), cs$team),]
cs$team <- ifelse(cs$team == "Tottenham", "Spurs", cs$team)
cs <- inner_join(fix2, cs, by=c("team_name"="team", "order_fix"))
# See the results
View(arrange(cs, desc(cs)))
# Match cs odds to fpl data
fpl <- fpl %>%
left_join(cs, by = c('team'='team_name'))
# Set cs probabilities to 0 if no fixture in the next gameweek
fpl <- fpl %>%
mutate(cs = ifelse(!team %in% fix2$team_name, 0, cs))
# ------------------------ Predict likelihood of playing 60 minutes ------------------------
# And get historic data for each player
source('./startprob.R')
# ------------------------ Deal with double gameweeks -------------------------------
# Some complex probability going on here
fpl.1.1 <- fpl.1 %>%
group_by(id) %>%
arrange(order_fix) %>%
mutate(prev_opp_strength = lag(opp_defence_strength)) %>%
mutate(goalprob=ifelse(order_fix==2, goalprob*prev_opp_strength/opp_defence_strength, goalprob),
goalprob1=ifelse(order_fix==2, goalprob1*prev_opp_strength/opp_defence_strength, goalprob1),
probBrace=ifelse(order_fix==2, probBrace*prev_opp_strength/opp_defence_strength, probBrace),
probHt=ifelse(order_fix==2, probHt*prev_opp_strength/opp_defence_strength, probHt)) %>%
mutate(goalprob_new = ifelse(order_fix==2,
1-(1-goalprob)*(1-lag(goalprob)),
goalprob),
goalprob1_new = ifelse(order_fix==2,
(1-lag(goalprob))*goalprob1 + lag(goalprob1)*(1-goalprob),
goalprob1),
probBrace_new = ifelse(order_fix==2,
(1-lag(goalprob))*probBrace +
lag(probBrace)*(1-goalprob) +
lag(goalprob1)*goalprob1,
probBrace),
probHt_new = ifelse(order_fix==2,
(1-lag(goalprob))*probHt +
lag(probHt)*(1-goalprob) +
lag(goalprob1)*probBrace +
lag(probBrace)*goalprob1,
probHt),
cs_new = ifelse(order_fix==2,
(1-lag(cs))*cs + lag(cs)*(1-cs),
cs),
cs2_new = ifelse(order_fix==2,
lag(cs)*cs,
0),
as_new = ifelse(order_fix==2,
(1-lag(probas))*probas + lag(probas)*(1-probas),
probas),
as2_new = ifelse(order_fix==2,
lag(probas)*probas,
0)
) %>%
mutate(goalprob=goalprob_new,
goalprob1=goalprob1_new,
probBrace=probBrace_new,
probHt=probHt_new,
cs=cs_new,
cs2=cs2_new,
probas=as_new,
probas2=as2_new) %>%
group_by(id) %>%
summarise_at(vars(goalprob:goalprob1, cs, cs2, prob60, probas, probas2), max)
fpl.2 <- fpldat %>%
inner_join(fpl.1.1, by = "id") %>%
mutate(xpas = 3 * probas + 6 * probas2)
# ------------------- FPL scout predicted lineups -----
result <- getLineups(fpl.2, "https://www.fantasyfootballscout.co.uk/team-news/")
# Adjust prob60 for blank gameweeks
fpl.2$prob60 = ifelse(fpl.2$team %in% fix2$team_name, fpl.2$prob60, 0)
# Bind to fpl file
fpl.2 <- fpl.2 %>%
left_join(result, by = "id") %>%
mutate(pred_lineup = ifelse(is.na(pred_lineup), 0, 1))
fpl.2$pred_lineup <- ifelse(fpl.2$team %in% fix2$team_name, fpl.2$pred_lineup, 0)
# Check discrepancies
fpl.2 %>% filter(pred_lineup == 1 & prob60 < .75) %>% select(web_name, team, prob60) %>% View
fpl.2 %>% filter(pred_lineup == 0 & prob60 > .75) %>% select(web_name, team, prob60) %>% View
# Manual overrides
fpl.2 <- fpl.2 %>%
mutate(prob60 = ifelse(web_name %in% c("Jesus",
"Cech",
"Bellerin",
"Ozil",
"Ramsey",
"Tarkowski",
"Lacazette",
"Salah",
"Robertson",
"De Bruyne",
"Walker",
"Jordan Ayew"), 0.9, prob60)) %>%
mutate(prob60 = ifelse(web_name %in% c("Aubameyang"), 0.5 * prob60, prob60))
# ------------------- Expected points -----------------
# Check how many have odds available
print(c(paste0(100*round(sum(!is.na(fpl.2$goalprob))/nrow(fpl.2[fpl.2$pos != 'Goalkeeper',]),3), '% of players have goalscorer odds'),
paste0(100*round(sum(fpl.2$probas>0, na.rm = T)/nrow(fpl.2[fpl.2$pos != 'Goalkeeper',]),3), '% of players have assist predictions'),
paste0(100*round(sum(!is.na(fpl.2$cs))/nrow(fpl.2),3), '% of players have clean sheet odds'),
paste0(100*round(sum(!is.na(fpl.2$prob60))/nrow(fpl.2),3), '% of players have a playing time prediction')))
# Show whose goalscorer odds are missing
fpl.2 %>%
filter(pos != 'Goalkeeper',
(is.na(goalprob) | is.na(prob60)),
team %in% fix2$team_name) %>%
select(web_name, team, goalprob, prob60) %>%
arrange(desc(prob60)) %>% View
# Match on points lookup
points <- data.frame('pos' = sort(unique(fpl$pos)),
'goal' = c(6,6,5,4),
'cleansheet' = c(4,4,1,0))
# Calculate expected points
fpl.3 <- fpl.2 %>%
ungroup %>%
inner_join(points, by='pos') %>%
mutate(points_per_game = as.numeric(points_per_game)) %>%
mutate(games = total_points/points_per_game,
prob60 = prob60) %>%
mutate(prob0 = .2 * (1-prob60),
probless60 = .8 * (1-prob60)) %>%
mutate(prob60 = ifelse(prob60 < 0.15, 0, prob60)) %>%
mutate(prob60 = ifelse(as.numeric(ep_next) <= 0, 0, prob60)) %>% # Set probability of playing to 0 if no fixture
mutate(xpas = prob60*(3*probas + 6*probas2),
xgp1 = prob60*goalprob1 * goal,
xgp2 = prob60*probBrace * goal * 2,
xgp3 = prob60*probHt * goal * 3,
xm = prob60 * 90,
xgp = xgp1 + xgp2 + xgp3) %>%
mutate(xm = ifelse(is.na(xm), 0, xm)) %>%
mutate(xpap = ifelse(xm >= 60, 2, ifelse(xm > 0, 1, 0)),
xpcs = (prob60 * cs * cleansheet) + (prob60 * cs2 * cleansheet)) %>%
mutate(xp = ifelse(is.na(xgp),0,xgp) + ifelse(is.na(xpap),0,xpap) + xpcs + ifelse(is.na(xpas),0,xpas)) %>%
mutate(xp = ifelse(prob60 == 0, 0, xp)) #%>% # Set to 0 if not predicted to play
#mutate(xp = ifelse(is.na(goalprob), as.numeric(ep_next), xp)) # Set to modelled value if goal odds not present.
# Show ep for those whose goalscorer odds are missing
# fpl.3 %>%
# filter(pos != 'Goalkeeper', (is.na(goalprob) | is.na(prob60))) %>%
# select(web_name, team, goalprob, prob60, xp) %>%
# arrange(desc(prob60)) %>% View
# Get dreamteam
dt.3 <- dreamteam(fpl.3)
dt.3
sum(dt.3[1:11,'xp'])
sum(dt.3['now_cost'])
# Get all fpl pairs
fpl.3$dum <- 1
fplsquad <- fpl.3 %>%
select(dum, id, first_name, second_name, pos, team, now_cost, xp) %>%
inner_join(select(fpl.3, id, dum, first_name, second_name, pos, team, now_cost, xp), by = 'dum') %>%
filter(!(first_name.x == first_name.y & second_name.x == second_name.y)) %>%
mutate(pos = paste(pos.x, pos.y, sep="-"),
price = now_cost.x + now_cost.y,
xp = xp.x + xp.y) %>%
filter(xp > 2) %>%
select(id.x,
id.y,
second_name.x,
second_name.y,
pos, price, xp)
# Remove duplicates
fplsquad <- fplsquad[!duplicated(data.frame(t(apply(fplsquad[,c(1,2)], 1, sort)), fplsquad$price)),]
# Remove unnecessary objects
rm(list = c('cs',
'cs_html',
'cs.split',
'dedup',
'dt.1',
'dt.2',
'dt.2.1',
'dt.2.2',
'dt.2.3',
'first11',
'fpl.1',
'fpl.2',
'fpldat',
'keepers',
'left',
'matched.data',
'matches.out',
'model',
'modeldata',
'modeldata2',
'modeldata3',
'modelresults',
'names.split',
'namesmatched',
'odds_html',
'odds.split',
'players',
'result',
'result2',
'result3',
'right',
'subs',
't',
't.1',
't.i',
'teams_odds',
'train',
'webpage',
'a',
'b',
'bank',
'fitted.results',
'i',
'index11',
'iter_max',
'misClasificError',
'n',
'odds',
'players_data',
'teams_data',
'url',
'bestTeam',
'test',
'data',
'myteam2',
'mysquad',
'squad',
'cl',
'double_transfers',
'dt.f11',
't.1.1',
'tmp',
'autosubs',
'dt.squad',
'nonmatched',
'as_modeldata',
'as_modeldata.2',
'as_modelresults',
'clubs',
'dt',
'dt_other',
'dt.details',
'dt.last',
'fb',
'fix2',
'fixtures',
'fpl.1.1',
'kap',
'model2',
'modelresults',
'modelresults.2',
'p_as',
'players.2',
'probs',
'sim',
't2',
'teamdetails',
'teams.2',
'teamsim.1',
'teamsim.2',
'x',
'xt'))
# Clean up remaining objects
fpl <- fpl %>%
select(id, web_name, first_name.x, second_name.x, pos, team, now_cost, total_points, cs, goalprob)
# Save everything for shinyApp
save.image()