Add a trend line to see with how many points the Tampa Bay Lightnings could finish the season

tampa_totalPoints.R

library(rvest)
library(tidyverse)
library(lubridate)

url <- "https://www.hockey-reference.com/teams/TBL/2019_games.html"

webpage <- read_html(url)

results <- webpage %>% 
  html_table(fill = TRUE) %>% 
  .[[1]] %>% 
  repair_names() %>% # Some column names are empty
  dplyr::select(GP, Date, Time, Opponent,
                GF, GA, V3) %>% 
  as.tibble() %>% 
  rename(ExtraTime = V3) %>%
  filter(GP != "GP") %>% # Remove embedded repeat headers
  mutate(GP = as.numeric(GP),
         GF = as.numeric(GF),
         GA = as.numeric(GA),
         Points = case_when(
           is.na(GF) ~ NA_integer_,
           GF > GA ~ 2L,
           ExtraTime != "" ~ 1L,
           TRUE ~ 0L
         ),
         TotalPoints = cumsum(Points),
         Date = ymd_hm(paste(Date, Time)))

results %>% 
  ggplot(aes(Date, TotalPoints)) + 
  geom_line() +
  theme_minimal() +
  geom_smooth(fullrange = TRUE, method = "lm",
              formula = y ~ splines::bs(x, df = 3)) +
  ggtitle("Tampa Bay Lightning-Total Points over time",
          subtitle = "Trend line: B-splines with 3 dfs")

This is the resulting graph:

What looks like a loosing streak at the end of January is actually the all-star break. Looking at the total points as a function of the number games played gives a higher estimate.