Calculate homecourt advantage for each NBA team

nba_hca_by_team.R

library(sportsTools)
library(dplyr)
library(ggplot2)

start.year <- 2001
end.year <- 2015

all.games <- data.frame()

for (year in start.year:end.year) {
  
  schedule <- GetSchedule(year, 'regular')
  
  # Create model matrix where home teams have a 1, away teams have a -1
  home.model <- model.matrix(~ home.name + 0, data = schedule)
  away.model <- model.matrix(~ away.name + 0, data = schedule)
  model <- data.frame(home.model - away.model)
  colnames(model) <- gsub('home.name(.*)', paste0('\\1.', year), colnames(model))
  
  # Add more columns to represent home court location with a 1
  model <- cbind(model, data.frame(home.model))
  colnames(model) <- gsub('home.name(.*)', paste0('\\1.Home'), colnames(model))
  
  # Add outcome column
  model$Outcome <- schedule$home.margin
  
  all.games <- bind_rows(all.games, model)
}

# Replace all NA with 0
all.games[is.na(all.games)] <- 0

# Remove NOP / OKC home column
bad.col <- which(colnames(all.games) == 'New.Orleans.Oklahoma.City.Hornets.Home')
all.games <- all.games[, -bad.col]

# Combine Charlotte Bobcats and Charlotte Hornets
old.col <- which(colnames(all.games) == 'Charlotte.Bobcats.Home')
new.col <- which(colnames(all.games) == 'Charlotte.Hornets.Home')
all.games[, new.col] <- all.games[, old.col] + all.games[, new.col]
all.games <- all.games[, -old.col]

# Combine New Orleans Hornets and New Orleans Pelicans
old.col <- which(colnames(all.games) == 'New.Orleans.Hornets.Home')
new.col <- which(colnames(all.games) == 'New.Orleans.Pelicans.Home')
all.games[, new.col] <- all.games[, old.col] + all.games[, new.col]
all.games <- all.games[, -old.col]

# Compute regression and save coefficients
fit <- lm(Outcome ~ ., data = all.games)
results <- as.data.frame(summary(fit)$coefficients)
intercept <- results[1, ]
results <- results[grep('Home', row.names(results)), ]

# Incorporate intercept
results$Estimate <- results$Estimate + intercept[1, 1]
#results$`Std. Error` <- sqrt(results$`Std. Error`^2 + intercept[1, 2]^2)

# Calculate quantity for each home
results$Sample <- sapply(row.names(results), function(x) sum(all.games[, x]))

# Calculate 90% confidence interval for each home
results$crit.T <- qt(.95, results$Sample - 1)
results <- results[, c(1, 2, 6)]
results$Upper <- results[, 1] + results[, 2] * results[, 3]
results$Lower <- results[, 1] - results[, 2] * results[, 3]

# Clean Up Results
results$Team <- row.names(results)
row.names(results) <- NULL
results <- results[, c('Team', 'Estimate')]
results$Team <- gsub('\\.', ' ', results$Team)
results$Team <- gsub(' Home', '', results$Team)
results <- results[order(results$Estimate, decreasing = TRUE), ]

write.csv(results, 'hca_results.csv', row.names = FALSE)