SVM with out of bound RSquared

library(e1071)
set.seed(100)

df <- data.frame(x = cumsum(rnorm(100)),
                 y = cumsum(rnorm(100)))
#df <- df[sample(1:NROW(df), size=NROW(df)), ] # Shuffler
train_df <- df[1:80,]
test_df <- df[81:100,]
train <- svm(y ~ x, data=train_df)
r2 <- function(data, model, response) {
   preds <- predict(model, data)
   y <- data[, response]
   null_mse <- mean( (y - mean(y))^2)
   model_mse <- mean( (y - preds))
   return((null_mse - model_mse) / null_mse)
}
r2(train_df, train, "y") # Unshuffled: 1.005112, Shuffled: 1.007262
r2(test_df, train, "y") # Unshuffled: -0.09819974, Shuffled: 1.123228