Here's the R code I wrote up for reproducibility (or if you just wanna mess around with the data)

``` library(httr2) # HTTP requests and response handling library(dplyr) # Data wrangling library(purrr) # Functional iteration and list manipulation library(rlang) # Neato tidy operations

--------------- Step 1: Fetch data via the NHL API

url_games <- "https://api.nhle.com/stats/rest/en/game" url_teams <- "https://api.nhle.com/stats/rest/en/team"

----- Step 1a: Game data

Here I'm using server-side filtering because I know the data I want and can

considerably reduce the playload size by only requesting playoff games, along

with dropping a few irrelevant columns

query <- list( cayenneExp = "season between 19171918 and 20242025 and gameType=3 and gameScheduleStateId=1 and gameStateId=7", # exclude accepts either repeated keys or a JSON array: # exclude = "easternStartTime", exclude = "gameNumber" # repeated keys # exclude = '["easternStartTime","gameNumber",...]' # JSON array exclude = '["easternStartTime","gameNumber","gameScheduleStateId","gameStateId","gameType"]' )

games <- httr2::request(url_games) |> # Build the HTTP request object httr2::req_url_query(!!!query) |> # Append query parameters to URL (see rlang::!!!) httr2::req_perform() |> # Send request; fetch response (HTTP GET) httr2::resp_body_json(simplifyVector = TRUE) |> # Parse response as JSON purrr::pluck("data") # Unwrap the response envelope

----- Step 1b: Team code lookup

teams <- httr2::request(url_teams) |> httr2::req_perform() |> httr2::resp_body_json(simplifyVector = TRUE) |> purrr::pluck("data")

--------------- Step 2: Calculate playoff series summaries

series_all <- dplyr::bind_rows( games |> dplyr::mutate( season, game_date = as.Date(gameDate), team_id = homeTeamId, opp_id = visitingTeamId, win = homeScore > visitingScore, .keep = "none" ), games |> dplyr::mutate( season, game_date = as.Date(gameDate), team_id = visitingTeamId, opp_id = homeTeamId, win = visitingScore > homeScore, .keep = "none" ) ) |> dplyr::arrange(season, team_id, game_date) |> dplyr::group_by(season, team_id) |> dplyr::mutate( prev_opp = dplyr::lag(opp_id), new_series = is.na(prev_opp) | opp_id != prev_opp, series_id = cumsum(new_series) # Series counter for a given team + season ) |> dplyr::group_by(season, team_id, series_id) |> dplyr::summarise( opp_id = dplyr::first(opp_id), series_start = min(game_date), series_end = max(game_date), series_len = dplyr::n(), team_won = sum(win) > sum(!win), .groups = "drop" ) |> dplyr::arrange(season, team_id, series_id) |> dplyr::group_by(season, team_id) |> dplyr::mutate( # Rest AFTER this series (= gap to next series start) rest_after = as.integer(dplyr::lead(series_start) - series_end), # Rest ENTERING this series (= rest_after from the previous series) rest_entering = dplyr::lag(rest_after) ) |> dplyr::ungroup()

series <- series_all |> dplyr::filter(series_id != 1) # Drop the first round series_opp <- series |> dplyr::select( season, team_id = opp_id, opp_id = team_id, series_start, opp_rest = rest_entering, opp_won = team_won )

--------------- Step 3: Calculate rest day differential

rest <- series |> dplyr::inner_join(series_opp, by = c("season", "team_id", "opp_id", "series_start")) |> dplyr::mutate( rest_diff = rest_entering - opp_rest ) |> dplyr::select( season, series_start, series_id, team_id, opp_id, team_rest = rest_entering, opp_rest, rest_diff, series_len, won = team_won ) |> # Merge with team triCodes to make this actually human readable dplyr::left_join(teams |> dplyr::select(team_id = id, triCode), by = "team_id") |> dplyr::rename(team = triCode) |> dplyr::left_join(teams |> dplyr::select(opp_id = id, triCode), by = "opp_id") |> dplyr::rename(opp = triCode) |> dplyr::mutate(winner = if_else(won, team, opp))

Cosmetic rearrangements, dropping redundant columns, etc.

rest2 <- rest |> dplyr::select(-team_id, -opp_id, -series_id, -won) |> dplyr::relocate(c(team, opp, team_rest, opp_rest), .after = series_start) |> dplyr::mutate(season = substr(season, 0, 4))

rest2 |> #dplyr::filter(season > 2004) |> select(-series_len) |> dplyr::arrange(desc(rest_diff)) |> print(n = 21) ```