### PowRful ###
# Last edit: 2016-12-28
# Manny

# Dependencies
require(dplyr)
require(ggplot2)
require(glmnet)
require(doMC)
require(Kmisc)

# Load data
load(url("http://fenwicka.com/shiny/powrful.RData"))

# Functions
do_shooter <- function(shooter, data) {
  
  vect <- 1*(data$Shooter == shooter)
  
  vect[which(is.na(vect) == TRUE)] <- 0
  
  return(vect)
  
}
do_goalie <- function(goalie, data) {
  
  vect <- 1*(data$Goalie == goalie)
  
  vect[which(is.na(vect) == TRUE)] <- 0
  
  return(vect)
  
}
do_strength <- function(strength, data) {
  
  vect <- 1*(data$Strength.State == strength)
  
  vect[which(is.na(vect) == TRUE)] <- 0
  
  return(vect)
  
}
do_score <- function(score, data) {
  
  vect <- 1*(data$Score.Cat == score)
  
  vect[which(is.na(vect) == TRUE)] <- 0
  
  return(vect)
  
}

### TEAM CORSI ###

# Subset 5v5
pbp %>%
  filter(Strength.State == "5v5") ->
  pbp_5

# Compile shots
bind_rows(pbp_5 %>%
            group_by(Home.Team) %>%
            rename(Team = Home.Team) %>%
            summarise(Venue = "Home",
                      GP = length(unique(Game.ID)),
                      TOI = sum(na.omit(Event.Length))/60,
                      CF = sum(Event %in% c("GOAL", "SHOT", "MISS", "BLOCK") & ev.team == Team),
                      CA = sum(Event %in% c("GOAL", "SHOT", "MISS", "BLOCK") & ev.team == Away.Team),
                      GF = sum(Event == "GOAL" & ev.team == Team),
                      GA = sum(Event == "GOAL" & ev.team == Away.Team)
                      ),
          
          pbp_5 %>%
            group_by(Away.Team) %>%
            rename(Team = Away.Team) %>%
            summarise(Venue = "Away",
                      GP = length(unique(Game.ID)),
                      TOI = sum(na.omit(Event.Length))/60,
                      CF = sum(Event %in% c("GOAL", "SHOT", "MISS", "BLOCK") & ev.team == Team),
                      CA = sum(Event %in% c("GOAL", "SHOT", "MISS", "BLOCK") & ev.team == Home.Team),
                      GF = sum(Event == "GOAL" & ev.team == Team),
                      GA = sum(Event == "GOAL" & ev.team == Home.Team)
                      )
          ) %>%
  data.frame() %>%
  group_by(Team) %>%
  summarise(GP = sum(GP),
            TOI = sum(TOI),
            CF = sum(CF),
            CA = sum(CA),
            GF = sum(GF),
            GA = sum(GA)
            ) %>%
  mutate(Corsi = CF/(CF + CA),
         Goals = GF/(GF + GA)
         ) %>%
  data.frame() ->
  team_corsi

# Top 5 teams
team_corsi %>%
  arrange(desc(Corsi)) %>%
  mutate(Corsi = scales::percent(Corsi)) %>%
  select(Team, GP, CF, CA, Corsi) %>%
  slice(1:5)

# Bottom 5 teams
team_corsi %>%
  arrange(Corsi) %>%
  mutate(Corsi = scales::percent(Corsi)) %>%
  select(Team, GP, CF, CA, Corsi) %>%
  slice(1:5)

# Scatter plot
team_corsi %>%
  mutate(CF60 = CF/TOI*60,
         CA60 = CA/TOI*60,
         GF60 = GF/TOI*60,
         GA60 = GA/TOI*60
         ) ->
  newdata

ggplot(newdata, 
       aes(x = CF60, y = CA60)
       ) +
  geom_point(size = 3,
             alpha = 0.8,
             colour = "darkblue"
             ) +
  geom_text(aes(label = Team),
            vjust = -1,
            size = 3,
            fontface = "bold"
            ) +
  theme_bw() +
  scale_y_reverse()

## Correlation
# Plot data
plot(newdata$GF60, newdata$CF60)

# Linear regression
fit1 <- lm(data = newdata,
           formula = GF60 ~ CF60
           )

summary(fit1)

##################
##################



### PLAYER RATINGS ###

# Prepare data
bind_rows(pbp %>%
            filter(Event %in% c("GOAL", "SHOT", "MISS"),
                   Strength.State %in% c("3v4", "3v5", "4v5", "5v5", "4v4", "3v3", "5v3", "5v4", "4v3"),
                   ev.team == Away.Team
                   ) %>%
            mutate(Goalie = Home.Goalie,
                   Venue = "Away",
                   Score.Cat = -Score.Cat,
                   Strength.State = str_rev(Strength.State)
                   ),
          
          pbp %>%
            filter(Event %in% c("GOAL", "SHOT", "MISS"),
                   Strength.State %in% c("3v4", "3v5", "4v5", "5v5", "4v4", "3v3", "5v3", "5v4", "4v3"),
                   ev.team == Home.Team
                   ) %>%
            mutate(Goalie = Away.Goalie,
                   Venue = "Home"
                   )
          ) %>%
  data.frame() %>%
  rename(Shooter = p1) %>%
  select(Event,
         Shooter,
         Goalie,
         xG,
         Strength.State,
         Venue,
         Score.Cat
         ) %>%
  mutate(is.Goal = 1*(Event == "GOAL")) %>%
  data.frame() ->
  fulldata

ymat <- as.matrix(fulldata$is.Goal)

unique(fulldata$Shooter) %>%
  lapply(do_shooter,
         data = fulldata
         ) %>%
  unlist() %>%
  matrix(ncol = length(unique(fulldata$Shooter)),
         byrow = FALSE
         ) ->
  shootermat

unique(fulldata$Goalie) %>%
  lapply(do_goalie,
         data = fulldata
         ) %>%
  unlist() %>%
  matrix(ncol = length(unique(fulldata$Goalie)),
         byrow = FALSE
         ) ->
  goaliemat

unique(fulldata$Strength.State) %>%
  lapply(do_strength,
         data = fulldata
         ) %>%
  unlist() %>%
  matrix(ncol = length(unique(fulldata$Strength.State)),
         byrow = FALSE
         ) ->
  strengthmat

unique(fulldata$Score.Cat) %>%
  lapply(do_score,
         data = fulldata
         ) %>%
  unlist() %>%
  matrix(ncol = length(unique(fulldata$Score.Cat)),
         byrow = FALSE
         ) ->
  scoremat

xmat <- cbind(shootermat,
              goaliemat,
              strengthmat,
              scoremat,
              fulldata$xG,
              as.numeric(as.factor(fulldata$Venue))
              )

# Regression
registerDoMC(cores = 4)
cv <- cv.glmnet(xmat,
                ymat,
                family = "binomial",
                nfolds = 4,
                nlambda = 100,
                alpha = 0,
                parallel = TRUE
                )

ratings <- as.numeric(exp(coef(cv, s = "lambda.min")))

variables <- c("Intercept",
               unique(fulldata$Shooter),
               unique(fulldata$Goalie),
               unique(fulldata$Strength.State),
               unique(fulldata$Score.Cat),
               "xG",
               "Venue"
               )

table <- data.frame(cbind(variables, ratings))

shooters <- filter(table, variables %in% fulldata$Shooter)
goalies <- filter(table, variables %in% fulldata$Goalie)

######################
######################