Setup

To begin let’s load some packages. Swimmer will be our workhorse for getting hold of the results. Then we’ll use the various tidyverse packages dplyr, stringr and purrr to facilitate our analysis and flextable for nice tables of the results.

library(SwimmeR)
library(dplyr)
library(stringr)
library(purrr)
library(flextable)

Please note the following analysis was updated November 22nd 2020 to reflect changes beginning with SwimmeR v0.6.0 released via CRAN on November 22nd 2020. Please make sure your version of SwimmeR is up-to-date.

Raw Results

New York State has two seasons in a given school year. A girls’ season in the fall, followed by a boys season in the winter. They also have an excellent repository of results. We’re just interested in the most recent state meets, Girls 2019 and Boys 2020, so let’s put links to those results into a list.

NY_Boys <- "http://www.nyhsswim.com/Results/Boys/2020/NYS/Single.htm"
NY_Girls <- "http://nyhsswim.com/Results/Girls/2019/NYS/Single.htm"

NY_Links <- c(NY_Boys, NY_Girls)

The goal is to read in and clean the raw results, which we’ll do with SwimmeR::read_results and SwimmeR::swim_parse respectively. Before doing so however it’s useful to look the raw results over for potential issues.

We can see in the New York raw results that federation and state records for the two meets are recorded as “Federation”, “NYS Fed”, “NYSPHSAA” and “NYS Meet Rec”. Those are strings we’ll want to tell SwimmeR::swim_parse to avoid.

NY_Boys_Header

NY_Girls_Header

NY_Avoid <- c("Federation", "NYS Fed", "NYSPHSAA", "NYS Meet Rec")

Pennsylvania is a similar story, with a nice results repository. Unlike New York however Pennsylvania has two different divisions for their state championships, somewhat confusingly called 2A and 3A. The 3A championships were held in 2020 (boys and girls) but the 2A where canceled due to COVID-19. Also diving wasn’t included in the Girls 3A 2020 results so as State-Off meet director I’ll be subsisting 2019 results for 3A diving and all of 2A. There will be five total links.

PA_Boys_3A <- "http://www.paswimming.com/19_20/results/state/PIAA_3_A_boys_states_Results.htm"
PA_Girls_3A <- "http://www.paswimming.com/19_20/results/state/PIAA_3_A_girls_states_Results.htm"
PA_Girls_3A_Diving <- "http://www.piaa.org/assets/web/documents/2019_3a_girls_f_dive_results.htm"
PA_Boys_2A <- "http://www.paswimming.com/18_19/results/states/Results/2_A_Boys_Results_2019.htm"
PA_Girls_2A <- "http://www.paswimming.com/18_19/results/states/Results/2_A_Girls_Results_2019.htm"

PA_Links <- c(PA_Boys_3A, PA_Girls_3A, PA_Girls_3A_Diving, PA_Boys_2A, PA_Girls_2A)

Inspecting the Pennsylvania raw results gives us a few more strings to avoid, namely “PIAA” (PA record), plus “NFHS” and “NF Hon. Roll”.

PA_Avoid <- c("PIAA", "NFHS", "NF Hon. Rol")

Reading in Results with the SwimmeR Package

Getting our results is now a simple matter of mapping read_results and swim_parse over our list of links with our avoid lists passed to the avoid argument of swim_parse.

We’ll then add columns State and Gender since those are the parameters of our meet - each state is a team, with boys and girls meets, plus a combined total.

Results <- map(c(NY_Links, PA_Links), Read_Results, node = "pre") %>% 
  map(swim_parse, avoid = c(NY_Avoid, PA_Avoid)) %>% 
  set_names(c("NY_Boys", "NY_Girls", "PA_Boys", "PA_Girls", "PA_Girls", "PA_Boys", "PA_Girls")) %>% 
  bind_rows(.id = "Source")

Results <- Results %>%
  mutate(
    State = str_split_fixed(Source, "_", n = 2)[, 1],
    Gender = str_split_fixed(Source, "_", n = 2)[, 2]
  ) %>%
  select(-Source) %>% 
  filter(str_detect(Event, "Swim-off") == FALSE) # remove swim-offs

Point_Values <- c(20, 17, 16, 15, 14, 13, 12, 11, 9, 7, 6, 5, 4, 3, 2, 1, 0)
names(Point_Values) <- 1:17

General Workflow

1. Break up Results into relays, diving, and individual swimming using filter.
2. Take only the first instance of an athlete/team in an event using group_by and slice.
3. For relays and individual swims convert times in minutes:seconds.hundreths to seconds with SwimmeR:sec_format.
4. Reorder and record finishes on basis of time (or score) across the new NY vs. PA meet using arrange and mutate.
5. Award points, accounting for ties using a nifty little combo of rank, summarize and inner_join

Relay_Results <- Results %>%
  filter(str_detect(Event, "Relay") == TRUE) %>% # only want relays
  group_by(Event, Team) %>%
  slice(1) %>% # select first occurrence of team in each event
  ungroup() %>%
  mutate(Finals_Time_sec = sec_format(Finals_Time)) %>% # convert time to seconds
  group_by(Event) %>%
  mutate(Place = rank(Finals_Time_sec, ties.method = "min")) %>% # places, low number wins
  filter(Place <= 16) %>% # only top 16 score
  select(-Points)

Relay_Results <- Relay_Results %>% # deal with ties
  mutate(New_Place = rank(Place, ties.method = "first"),
         Points = Point_Values[New_Place]) %>% 
  group_by(Place, Event) %>% 
  summarize(Points = mean(Points)) %>% 
  inner_join(Relay_Results) %>% 
  mutate(Points = Points * 2) # double point values for relays

Diving_Results <- Results %>%
  filter(str_detect(Event, "Diving") == TRUE) %>% # only want diving events
  mutate(Finals_Time = as.numeric(Finals_Time)) %>% 
  group_by(Event, Name) %>% 
  slice(1) %>% # first instance of every diver
  ungroup() %>% 
  group_by(Event) %>% 
  mutate(Place = rank(desc(Finals_Time), ties.method = "min"), # again, highest score gets rank 1
         Finals_Time = as.character(Finals_Time)) %>%
  filter(Place <= 16) %>% #only top 16 score
  select(-Points)

Diving_Results <- Diving_Results %>% # deal with ties
  mutate(New_Place = rank(Place, ties.method = "first"),
         Points = Point_Values[New_Place]) %>% 
  group_by(Place, Event) %>% 
  summarize(Points = mean(Points)) %>% 
  inner_join(Diving_Results)

Ind_Swimming_Results <- Results %>%
  filter(str_detect(Event, "Diving") == FALSE,
         str_detect(Event, "Relay") == FALSE) %>% 
  group_by(Event, Name) %>% 
  slice(1) %>% # first instance of every swimmer
  ungroup() %>% 
  group_by(Event) %>% 
  mutate(Finals_Time_sec = sec_format(Finals_Time)) %>% # time as seconds
  mutate(Place = rank(Finals_Time_sec, ties.method = "min")) %>% # places, low number wins
  filter(Place <= 16) %>% #only top 16 score
  select(-Points)

Ind_Swimming_Results <- Ind_Swimming_Results %>% # deal with ties
  mutate(New_Place = rank(Place, ties.method = "first"),
         Points = Point_Values[New_Place]) %>% 
  group_by(Place, Event) %>% 
  summarize(Points = mean(Points)) %>% 
  inner_join(Ind_Swimming_Results)

The Final Results

Let’s bind together the results from our three cases (relays, diving and individual swims) and do a but of cleaning up. Pennsylvania for example has all their results in block capitals. That can be fixed with str_to_title.

Results_Final <-
  bind_rows(Relay_Results, Diving_Results, Ind_Swimming_Results) %>%
  mutate(Name = str_to_title(Name),
         Team = str_to_title(Team)) %>%
  mutate(Team = str_remove_all(Team, "[:punct:]"),
         Team = str_remove_all(Team, "[0-9]"))

Scores

Now we summarise and see who won!

Scores <- Results_Final %>%
  group_by(State, Gender) %>%
  summarise(Score = sum(Points))

Scores %>%
  arrange(Gender, desc(Score)) %>% 
  flextable() %>%
  bold(part = "header") %>%
  bg(bg = "#D3D3D3", part = "header")

Scores %>%
  group_by(State) %>%
  summarise(Score = sum(Score)) %>%
  arrange(desc(Score)) %>% 
  flextable() %>%
  bold(part = "header") %>%
  bg(bg = "#D3D3D3", part = "header")

Pennsylvania wins both meets and the combined in an upset, by quite a wide margin!

It’s interesting to think for a moment about why this might be. The State-Off is seeded by population. New York has about 19 million people, but about 8 million of them live in New York City. New York City doesn’t have very many swimmers. Swimmers from the new York City Public High School Athletic League have a -P designation after their school name in the raw results. The cleaning we did on Final_Results reduced this to a trailing P, which we can search for with str_detect.

Results_Final %>%
  ungroup() %>%
  filter(str_detect(Team, "P$")) %>%
  summarise(Count = n())

## # A tibble: 1 x 1
##   Count
##   <int>
## 1     3

Results_Final %>%
  ungroup() %>%
  filter(State == "NY") %>%
  summarise(Count = n())

## # A tibble: 1 x 1
##   Count
##   <int>
## 1   134

Only three swims out of New York’s total of 134 swims are from New York City. Pools take up a lot space so they’re difficult to install in cities generally. New York City is also very dense, which makes building pools that much harder. Pennsylvania on the other hand has a total population of 12 million. Philadelphia (1.5 million) and Pittsburgh (300k) are much smaller than New York City, so it’s possible that much more of the Pennsylvania population lives in areas conducive to swimming. There’s also a racial component. New York City has a higher proportion of African American residents than New York State as a whole, and African Americans have been subjected to segregation and systematic discrimination including specifically with respect to swimming pools to the extent that even today black children drown at a rate 3x that of white children. New York’s larger than expected non-swimming population may be reflected in its lower than expected State-Off score.

Swimmers of the Meet

To determine the swimmers of the meet there will be two qualifications: 1. An athlete must have competed in two events - sorry divers. Winner will be the athlete with the lowest average place (winning two events gives an average place of 1). This is an individual award so relays don’t count. 2. As a tiebreaker from 1. above, the athlete whose times are fastest across their two events relative to the All-American cuts will be Swimmer of the Meet.

Now if only someone had the All-American cuts readily accessible. Oh wait someone does and that someone is me. Let’s grab those cuts and join them to Ind_Swimming_Results. Then we can do some math to calculate each athlete’s average difference from the All-American cut.

Cuts_Link <- "https://raw.githubusercontent.com/gpilgrim2670/Pilgrim_Data/master/State_Cuts.csv"
Cuts <- read.csv(url(Cuts_Link))

'%!in%' <- function(x,y)!('%in%'(x,y)) # "not in" function

Cuts <- Cuts %>% # clean up Cuts
  filter(Stroke %!in% c("MR", "FR", "11 Dives")) %>% 
  rename(Gender = Sex) %>% 
  mutate(
    Event = case_when((Distance == 200 & #match events
                         Stroke == 'Free') ~ "200 Yard Freestyle",
                      (Distance == 200 &
                         Stroke == 'IM') ~ "200 Yard IM",
                      (Distance == 50 &
                         Stroke == 'Free') ~ "50 Yard Freestyle",
                      (Distance == 100 &
                         Stroke == 'Fly') ~ "100 Yard Butterfly",
                      (Distance == 100 &
                         Stroke == 'Free') ~ "100 Yard Freestyle",
                      (Distance == 500 &
                         Stroke == 'Free') ~ "500 Yard Freestyle",
                      (Distance == 100 &
                         Stroke == 'Back') ~ "100 Yard Backstroke",
                      (Distance == 100 &
                         Stroke == 'Breast') ~ "100 Yard Breaststroke",
                      TRUE ~ paste(Distance, "Yard", Stroke, sep = " ")), 
    
    Event = case_when(Gender == "M" ~ paste("Boys", Event, sep = " "),
                      Gender == "F" ~ paste("Girls", Event, sep = " ")))

Ind_Swimming_Results <- Ind_Swimming_Results %>%
  left_join(Cuts %>% filter((Gender == "M" &
                               Year == 2020) |
                              (Gender == "F" &
                                 Year == 2019)) %>%
                     select(AAC_Cut, AA_Cut, Event),
            by = 'Event')

Swimmer_Of_Meet <- Ind_Swimming_Results %>% 
  mutate(AA_Diff = (Finals_Time_sec - sec_format(AA_Cut))/sec_format(AA_Cut),
         Name = str_to_title(Name)) %>% 
  group_by(Name) %>% 
  filter(n() == 2) %>% # get swimmers that competed in two events
  summarise(Avg_Place = sum(Place)/2,
         AA_Diff_Avg = round(mean(AA_Diff, na.rm = TRUE), 2),
         Gender = unique(Gender),
         State = unique(State)) %>% 
  arrange(Avg_Place, AA_Diff_Avg) %>% 
  group_split(Gender) # split out a dataframe for boys (1) and girls (2)

Swimmer_Of_Meet[[1]] %>% 
  slice_head(n = 5) %>% 
  select(-Gender) %>% 
  flextable::flextable() %>%
  bold(part = "header") %>%
  bg(bg = "#D3D3D3", part = "header")

Results_Final %>%
  filter(Name == "Brownstead, Matt") %>%
  select(Place, Name, Team, Finals_Time, Event) %>%
  arrange(desc(Event)) %>% 
  flextable::flextable() %>%
  bold(part = "header") %>%
  bg(bg = "#D3D3D3", part = "header")

Swimmer_Of_Meet[[2]] %>%
  slice_head(n = 5) %>%
  select(-Gender) %>%
  flextable::flextable() %>%
  bold(part = "header") %>%
  bg(bg = "#D3D3D3", part = "header")

Results_Final %>%
  filter(Name == "Chloe Stepanek") %>%
  select(Place, Name, Team, Finals_Time, Event) %>%
  arrange(desc(Event)) %>% 
  flextable::flextable() %>%
  bold(part = "header") %>%
  bg(bg = "#D3D3D3", part = "header")

In Closing

That wraps up this match up. Join us next time here at Swimming + Data Science for another Round 1 match up - number 1 seed California vs. number 8 seed Georgia. We’ll see you then!