Setup

install.packages("SwimmeR")

We’ll use a few tidyverse packages plus flextable.

library(SwimmeR)
library(dplyr)
library(stringr)
library(tidyr)
library(flextable)

flextable_style <- function(x) {
  x %>%
    flextable() %>%
    bold(part = "header") %>% # bolds header
    bg(bg = "#D3D3D3", part = "header")  # puts gray background behind the header row
}

Collecting the meet results

Collecting meet results with SwimmeR is easy. Simply find a link to the results and pipe them into read_results followed by swim_parse. here we’ll do that with both the 2006 and 2021 Section VI Class C meet results.

C_2006 <- "http://www.section4swim.com/Results/GirlsHS/2006/Sec4/C/Single.htm" %>% 
  read_results() %>% 
  swim_parse() %>% 
  mutate(Year = 2006) %>% 
  mutate(Event = str_remove(Event, "Girls "))

C_2006 <- C_2006 %>% 
    mutate(Event = factor(Event, levels = unique(C_2006$Event)))
  
C_2021 <- "http://www.section4swim.com/Results/GirlsHS/2021/Sec4/C/Single.htm" %>% 
  read_results() %>% 
  swim_parse() %>% 
  mutate(Year = 2021) %>% 
  mutate(Event = str_remove(Event, "Girls "))

C_2021 <- C_2021 %>% 
    mutate(Event = factor(Event, levels = unique(C_2021$Event)))

Assertion #1 – Faster and Deeper

C_Combined <- C_2021 %>% 
  bind_rows(C_2006) %>% 
    filter(Exhibition < 1,
         DQ < 1) %>% 
  group_by(Event) %>% 
  select(Place, Name, Team, Event, Finals_Time, Points, Year) %>% 
  filter(Place <= 16) %>% 
  filter(is.na(Points) == FALSE)

C_Combined %>% 
  group_by(Event, Year) %>% 
  summarise(W_Avg = mmss_format(weighted.mean(sec_format(Finals_Time), Points, na.rm = TRUE))) %>% 
  mutate(W_Avg = case_when(str_detect(Event, "Diving") ~ as.character(sec_format(W_Avg)),
                           TRUE ~ W_Avg)) %>% 
  pivot_wider(names_from = "Year", values_from = "W_Avg") %>% 
  flextable_style()

C_Combined %>% 
  ungroup() %>% 
  filter(str_detect(Event, "Diving")) %>% 
  select(Place, "Score" = Finals_Time, Year) %>% 
  pivot_wider(names_from = "Year", values_from = "Score") %>% 
  select(Place, `2006`, `2021`) %>% 
  flextable_style()

C_Combined %>% 
  group_by(Event, Year) %>% 
  mutate(Finals_Time = sec_format(Finals_Time)) %>% 
  summarise(Range = max(Finals_Time, na.rm = TRUE) - min(Finals_Time, na.rm = TRUE)) %>% 
    mutate(Range = case_when(str_detect(Event, "Diving", negate = TRUE) ~ mmss_format(Range),
                           TRUE ~ as.character(Range))) %>% 
  pivot_wider(names_from = "Year", values_from = "Range") %>% 
  flextable_style()

Assertion #2 – An old team returns

The team in question here is the 2006 Dryden Lady Lions, headed by eventual DIII All-American Sheila Rhoades.

To start we’ll need to remove all of the Dryden results from C_2021 – it wouldn’t be fair for Dryden to have two teams. Then we’ll collect only the Dryden results from 2006 and join the two sets of results together. The 2006 Dryden team will then be “swimming” in the 2021 meet.

C_2021_No_Dryden <- C_2021 %>% 
  filter(Team != "Dryden")

C_2006_Only_Dryden <- C_2006 %>% 
  filter(Team == "Dryden")

C_Comp <- C_2021_No_Dryden %>%
  bind_rows(C_2006_Only_Dryden) %>% 
  mutate(Team = str_replace(Team, "Susquehanna Vall$", "Susquehanna Valley"),
         Team = str_replace(Team, "Notre Dame-E$", "Notre Dame-Elmira"),
         Team = str_replace(Team, "Watkins Glen-OM", "Watkins Glen-Odessa Montour"))

It’s more complicated than that though. We need to reconstruct prelims to sort athletes into A Finals and B Finals before the finals themselves can be scored. Fortunately we have access to everyone’s Prelims_Time, although because of how SwimmeR works some of them are in the Finals_Time column. In SwimmeR the Finals_Time represents the final swim for each swimmer, which may or may not actually be in the finals. It’s confusing I know and I apologize, but it’s important to the underlying functioning of swim_parse so that’s the way it will stay. Anyway, swimmers who have Finals_Times but no Prelims_Time in a prelims-finals meet actually have their prelims performance listed in Finals_Time. Let’s fix our results.

We’ll also need to handle diving separately, because in diving the highest value (score) wins, whereas in swimming the lowest value (time) wins.

C_Comp <- C_Comp %>%
  mutate(Prelims_NA = case_when(is.na(Prelims_Time) ~ 1,
                                TRUE ~ 0)) %>%
  mutate(Prelims_Time = case_when(is.na(Prelims_Time) ~ Finals_Time,
                                  TRUE ~ Prelims_Time)) %>%
  mutate(Finals_Time = case_when(Prelims_NA > 0 ~ "NA",
                                 TRUE ~ Finals_Time)) %>%
  na_if("NA") %>% 
  select(-Prelims_NA)

C_Comp_Diving <- C_Comp %>% 
  filter(str_detect(Event, "Diving") == TRUE) %>% 
  mutate(Prelims_Time = as.numeric(Prelims_Time)) %>%
    mutate(
      Place = rank(desc(Prelims_Time), ties.method = "min"),
      # highest score gets rank 1
      Prelims_Time = as.character(Prelims_Time)
    ) 

C_Comp_Swimming <- C_Comp %>%
  filter(str_detect(Event, "Diving") == FALSE) %>%
  filter(DQ < 1) %>% 
  mutate(Prelims_Time_sec = SwimmeR::sec_format(Prelims_Time)) %>% # time as seconds
  group_by(Event) %>% 
  mutate(Place = rank(Prelims_Time_sec, ties.method = "min")) %>% # places, low number wins
  arrange(Place)

C_Comp_Prelims <- C_Comp_Diving %>% 
  bind_rows(C_Comp_Swimming)

Great, now we have our meet, with athletes sorted into A and B finals by place, with 8th place as the cutoff for the A finals. We can use results_score to rescore the meet on the basis of Finals_Time.

C_Finals_Score <- C_Comp_Prelims %>% 
  results_score(
    events = unique(C_2021$Event),
    meet_type = "prelims_finals",
    lanes = 8,
    point_values = c(20, 17, 16, 15, 14, 13, 12, 11, 9, 7, 6, 5, 4, 3, 2, 1),
    scoring_heats = 2
  )

C_Finals_Score %>% 
  group_by(Team) %>% 
  summarise(Score = sum(Points, na.rm = TRUE)) %>% 
  mutate(Place = round(rank(desc(Score), ties.method = "min"), 1)) %>% 
  arrange(desc(Score)) %>% 
  select(Place, everything()) %>% 
  flextable_style()

My friends are vindicated again. Dryden 2006 would have indeed won the 2021 meet – although only by 12 points. The real question is how did they know?

In Conclusion

We’ve come up with a quantitative framework for determining the speed and depth of a swim meet, which I’m sure will come in handy somewhere. We’ve also made use of some SwimmeR functions and lots of tidyverse functions and hopefully learned a little bit on the way. Join us next time back here at Swimming + Data Science where we’ll be doing something else fun.