In almartin82/projprep: Preps Fantasy Baseball Projection System Files

knitr::opts_chunk$set(echo = TRUE)

Get data

raw_steamer <- get_steamer(2017) 

Clean and score data

steamer_pp <- proj_prep(raw_steamer, catcher_fudge = 0.2)

Read in historical diaspora data

hpk_hist_url <- 'https://docs.google.com/spreadsheets/d/13MAtR58A9aktkgpP5NdlI1u355HqyyCsbe1YVpzbQhM/'

hpk_hist <- hpk_hist_url %>%
  googlesheets::gs_url() %>%
  googlesheets::gs_read(ws = 'auction_results') %>%
  janitor::clean_names()

head(hpk_hist)

What is the cost curve for our league?

hpk_hist <- hpk_hist %>%
  dplyr::group_by(year) %>%
  dplyr::mutate(
    rank = rank(-price, ties.method = 'first')
  )

library(ggplot2)
library(ggthemes)

modern_hpk <- hpk_hist[hpk_hist$year >= 2014, ]

ggplot(
  data = modern_hpk,
  aes(
    x = rank,
    y = price,
    group = factor(year),
    color = factor(year)
  )
) +
geom_line(size = 2) +
geom_point() +
scale_x_log10() +
theme_fivethirtyeight()

Determine average

avg_price <- modern_hpk %>%
  dplyr::group_by(rank) %>%
  dplyr::summarize(
    avg_price = mean(price)
  ) %>%
  dplyr::rename(
    adp_rank = rank
  )

avg_price

combine

combined_price <- dplyr::bind_rows(
  steamer_pp$h_final %>% left_join(raw_steamer$h[, c('mlbid', 'adp')]),
  steamer_pp$p_final %>% left_join(raw_steamer$p[, c('mlbid', 'adp')])
) %>%
dplyr::arrange(value) %>%
dplyr::mutate(
  adp = as.numeric(adp),
  adp_rank = rank(adp, ties.method = 'first') 
)

combined_price <- combined_price %>% 
  left_join(avg_price, by = 'adp_rank') %>%
  dplyr::arrange(avg_price)

put to google sheets

target_players <- combined_price %>%
  dplyr::filter(adp_rank < 400)
strat <- gs_title("auction strategy")

strat %>%
  gs_edit_cells(ws = "strat", input = target_players, trim = FALSE)

Write

library(readr)

readr::write_csv(steamer_pp$h_final, path = 'steamer_h.csv')
readr::write_csv(steamer_pp$p_final, path = 'steamer_p.csv')

almartin82/projprep documentation built on May 10, 2019, 9:57 a.m.