scripts/create_data_objects.R
In andykrause/FBB30x30: 30x30 Fantasy Baseball Description and Analysis
#*****************************************************************************************
#
# Create Data objeccts for 30x30Baseball
#
#*****************************************************************************************
library(readr)
library(tidyverse)
library(FBB30x30)
### Data Sources -------------------------------------------------------------------------
baseballref_df <- data.frame(data = c('Batting - Standard', 'Batting - Ratio',
'Batting - Value', 'Batting - Pitches',
'Batting - Win Probability', 'Batting - Advanced',
'Batting - Situational', 'Batting - Baserunning',
'Fielding - Appearances', 'Fielding - Standard',
'Pitching - Standard', 'Pitching - Starter',
'Pitching - Reliever', 'Pitching - Ratio',
'Pitching - Value', 'Pitching - Pitches',
'Pitching - Win Probability'),
code = c('standard-batting', 'ratio-batting',
'value-batting', 'pitches-batting',
'win_probability-batting',
'advanced-batting', 'situational-batting',
'baserunning-batting',
'appearances-fielding',
'standard-fielding', 'standard-pitching',
'starter-pitching', 'reliever-pitching',
'ratio-pitching', 'value-pitching',
'pitches-pitching',
'win_probability-pitching'),
stringsAsFactors = FALSE) %>%
tibble::as.tibble()
usethis::use_data(baseballref_df, overwrite=TRUE)
### People and Teams ---------------------------------------------------------------------
# Players
players_df <- readr::read_delim(file = file.path(getwd(), 'data', 'raw', 'players', 'people.csv'),
delim = ',')
players_df <- players_df %>%
dplyr::filter(as.Date(as.character(finalGame)) >= as.Date('2014-01-01')) %>%
dplyr::select(player_id = playerID, retro_id = retroID, bbref_id = bbrefID,
first = nameFirst, last = nameLast, given = nameGiven,
last_game = finalGame) %>%
dplyr::mutate(full = paste(first, last),
last_game = as.Date(as.character(last_game)),
player_id = as.character(player_id),
retro_id = as.character(retro_id),
bbref_id = as.character(bbref_id),
first = as.character(first),
last = as.character(last),
given = as.character(given))
class(players_df) <- c('players', class(players_df))
usethis::use_data(players_df, overwrite=TRUE)
# Teams
teams_df <- readr::read_delim(file.path(getwd(), 'data', 'raw', 'players', 'teams.csv'),
delim = ',')
teams_df <- teams_df %>%
dplyr::filter(yearID == 2017) %>%
dplyr::select(team_id = teamID, br_id = teamIDBR, lahman_id = teamIDlahman45,
retro_id = teamIDretro) %>%
dplyr::mutate(espn_id = c('ARI', 'ATL', 'BAL', 'BOS', 'CWS', 'CHC', 'CIN', 'CLE',
'COL', 'DET', 'HOU', 'KC', 'LAA', 'LAD', 'MIA', 'MIL',
'MIN', 'NYY', 'NYM', 'OAK', 'PHI', 'PIT', 'SD', 'SEA',
'SF', 'STL', 'TB', 'TEX', 'TOR', 'WAS'),
retrosheet_id = c('ARI N', 'ATL N', 'BAL A', 'BOS A', 'CHI A', 'CHI N',
'CIN N', 'CLE A', 'COL N', 'DET A', 'HOU A', 'KC A',
'ANA A', 'LA N', 'MIA N', 'MIL N', 'MIN A', 'NY A',
'NY N', 'OAK A', 'PHI N', 'PIT N', 'SD N', 'SEA A',
'SF N', 'STL N', 'TB A', 'TEX A', 'TOR A', 'WAS N'))
class(teams_df) <- c('teams', class(teams_df))
usethis::use_data(teams_df, overwrite=TRUE)
### Statistics ---------------------------------------------------------------------------
# Read in Fielding data
fielding_df <- purrr::map(.x = 2013:2019,
.f = getBRStats,
type ='fielding') %>%
dplyr::bind_rows()
usethis::use_data(fielding_df, overwrite=TRUE)
# Load Data
batting_df <- purrr::map(.x = 2013:2019,
.f = getBRStats,
type ='batting') %>%
dplyr::bind_rows()
usethis::use_data(batting_df, overwrite=TRUE)
## Pitching stats
pitching_df <- purrr::map(.x = 2013:2019,
.f = getBRStats,
type ='pitching') %>%
dplyr::bind_rows()
usethis::use_data(pitching_df, overwrite=TRUE)
### Projections --------------------------------------------------------------------------
## Marcel
marcel2019_proj <- scrapeMarcelProjections(year = 2019)
usethis::use_data(marcel2019_proj, overwrite = TRUE)
## Load Projection Data
# Hitters
hp_2014 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2014.csv'))
hp_2015 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2015.csv'))
hp_2016 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2016.csv'))
hp_2017 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2017.csv'))
hp_2018 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2018.csv')) %>%
dplyr::filter(Team != '158')
hp_2019 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2019.csv'))
hp_2020 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2020.csv'))
# Hitters
pp_2014 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2014.csv'))
pp_2015 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2015.csv'))
pp_2016 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2016.csv'))
pp_2017 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2017.csv'))
pp_2018 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2018.csv'))
pp_2019 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2019.csv'))
pp_2020 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2020.csv'))
## Standardize Projection Data
# 2014
names(hp_2014) <- tolower(names(hp_2014))
hp_2014 <- hp_2014 %>%
dplyr::rename(pos = position,
avg = ave,
h = hits,
hr = hrs,
player = name,
r = runs) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2014,
player = as.character(player),
team = as.character(team)) %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest(cols = c(pos_list)) %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list = c(pos_list)) %>%
dplyr::select(-c(cs, type)) %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2014, proj_year = 2014)
e_df <- projectErrors(fielding_df, hp_2014, proj_year = 2014)
hp_2014 <- hp_2014 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2015
names(hp_2015) <- tolower(names(hp_2015))
hp_2015 <- hp_2015 %>%
dplyr::mutate(player = paste0(substr(name, str_locate(name, ',')+2, 100),
" ", substr(name, 1, str_locate(name, ',') - 1)),
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2015,
player = as.character(player),
team = as.character(team)) %>%
dplyr::rename(so = k,
ops = obps) %>%
dplyr::select(-c(cs, g, al.nl.., mixed.., name))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2015, proj_year = 2015)
e_df <- projectErrors(fielding_df, hp_2015, proj_year = 2015)
hp_2015 <- hp_2015 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2016
names(hp_2016) <- tolower(names(hp_2016))
hp_2016 <- hp_2016 %>%
dplyr::rename(avg = ave,
h = hits,
hr = hrs,
player = name,
r = runs) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2016,
player = as.character(player),
team = as.character(team)) %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2016, proj_year = 2016)
e_df <- projectErrors(fielding_df, hp_2016, proj_year = 2016)
hp_2016 <- hp_2016 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2017
names(hp_2017) <- tolower(names(hp_2017))
hp_2017 <- hp_2017 %>%
fixFPName(.) %>%
dplyr::mutate(year = 2017,
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
player = as.character(player),
team = as.character(team)) %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2017, proj_year = 2017)
e_df <- projectErrors(fielding_df, hp_2017, proj_year = 2017)
hp_2017 <- hp_2017 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2018
names(hp_2018) <- tolower(names(hp_2018))
hp_2018 <- hp_2018 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(year = 2018,
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
player = as.character(player),
team = as.character(team))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2018, proj_year = 2018)
e_df <- projectErrors(fielding_df, hp_2018, proj_year = 2018)
hp_2018 <- hp_2018 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2019
names(hp_2019) <- tolower(names(hp_2019))
hp_2019 <- hp_2019 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(year = 2019,
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
player = as.character(player),
team = as.character(team)) %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2019, proj_year = 2019)
e_df <- projectErrors(fielding_df, hp_2019, proj_year = 2019)
hp_2019 <- hp_2019 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2020
names(hp_2020) <- tolower(names(hp_2020))
hp_2020 <- hp_2020 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(year = 2020,
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
player = as.character(player),
team = as.character(team)) %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2020, proj_year = 2020)
e_df <- projectErrors(fielding_df, hp_2020, proj_year = 2020)
hp_2020 <- hp_2020 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# Combine
batprojs_df <- rbind(hp_2014, hp_2015, hp_2016, hp_2017, hp_2018, hp_2019, hp_2020) %>%
tibble::as.tibble()%>%
dplyr::select(player_id, player, team, year, pos, pos_list,
tidyselect::everything())%>%
structure(., class = c('projections', 'batting', 'tbl_df', 'tbl', 'data.frame'))
## Pitchers
# 2014
names(pp_2014) <- tolower(names(pp_2014))
pp_2014 <- pp_2014 %>%
dplyr::rename(pos = position,
player = name,
bb = bbi) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2014,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hd, hr, type, bs))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2014, proj_year = 2014)
holds_df <- projectHolds(pitching_df, pp_2014, proj_year = 2014)
pp_2014 <- pp_2014 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2015
names(pp_2015) <- tolower(names(pp_2015))
pp_2015 <- pp_2015 %>%
dplyr::mutate(gsp = gs/g,
player = paste0(substr(name, str_locate(name, ',')+2, 100),
" ", substr(name, 1, str_locate(name, ',') - 1)),
pos = 'SP, RP',
pos = ifelse(gsp == 0, 'RP', pos),
pos = ifelse(gsp >= .85, 'SP', pos),
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2015,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(al.nl.., mixed.., sho, name, gsp))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2015, proj_year = 2015)
holds_df <- projectHolds(pitching_df, pp_2015, proj_year = 2015)
pp_2015 <- pp_2015 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2016
names(pp_2016) <- tolower(names(pp_2016))
pp_2016 <- pp_2016 %>%
dplyr::rename(player = name,
bb = bbi) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2016,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hr))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2016, proj_year = 2016)
holds_df <- projectHolds(pitching_df, pp_2016, proj_year = 2016)
pp_2016 <- pp_2016 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2017
names(pp_2017) <- tolower(names(pp_2017))
pp_2017 <- pp_2017 %>%
fixFPName() %>%
dplyr::mutate(year = 2017,
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hr))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2017, proj_year = 2017)
holds_df <- projectHolds(pitching_df, pp_2017, proj_year = 2017)
pp_2017 <- pp_2017 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2018
names(pp_2018) <- tolower(names(pp_2018))
pp_2018 <- pp_2018 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2018,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hr))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2018, proj_year = 2018)
holds_df <- projectHolds(pitching_df, pp_2018, proj_year = 2018)
pp_2018 <- pp_2018 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2019
names(pp_2019) <- tolower(names(pp_2019))
pp_2019 <- pp_2019 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2019,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hr))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2019, proj_year = 2019)
holds_df <- projectHolds(pitching_df, pp_2019, proj_year = 2019)
pp_2019 <- pp_2019 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2020
names(pp_2020) <- tolower(names(pp_2020))
pp_2020 <- pp_2020 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2020,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hr))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2020, proj_year = 2020)
holds_df <- projectHolds(pitching_df, pp_2020, proj_year = 2020)
pp_2020 <- pp_2020 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# Combine Al
pitchprojs_df <- rbind(pp_2014, pp_2015, pp_2016, pp_2017, pp_2018, pp_2019, pp_2020) %>%
tibble::as.tibble() %>%
dplyr::select(player_id, player, team, year, pos, pos_list,
tidyselect::everything()) %>%
structure(., class = c('projections', 'pitching', 'tbl_df', 'tbl', 'data.frame'))
## Save
usethis::use_data(batprojs_df, overwrite=TRUE)
usethis::use_data(pitchprojs_df, overwrite=TRUE)
### Rankings -----------------------------------------------------------------------------
## Read in Data
rank_2016 <- read.csv(file.path(getwd(), 'data', 'raw', 'rankings',
'fp_draftrankings_2016.csv'))
rank_2017 <- read.csv(file.path(getwd(), 'data', 'raw', 'rankings',
'fp_draftrankings_2017.csv'))
rank_2018 <- read.csv(file.path(getwd(), 'data', 'raw', 'rankings',
'fp_draftrankings_2018.csv'))
rank_2019 <- read.csv(file.path(getwd(), 'data', 'raw', 'rankings',
'fp_draftrankings_2019.csv'))
rank_2020 <- read.csv(file.path(getwd(), 'data', 'raw', 'rankings',
'fp_draftrankings_2020.csv'))
# 2016
names(rank_2016) <- tolower(names(rank_2016))
rank_2016 <- rank_2016 %>%
dplyr::rename(avg = ave,
st_dev = stdev,
ranking = ecr,
player = name) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2016,
x = NULL,
adp = as.numeric(as.character(adp)),
player = as.character(player),
team = as.character(team)) %>%
tibble::as.tibble()
rank_2016$adp[is.na(rank_2016$adp)] <- ceiling(rank_2016$avg[is.na(rank_2016$adp)])
# 2017
names(rank_2017) <- tolower(names(rank_2017))
x1 <- str_locate(rank_2017$name, '[(]')[,1]
name <- substr(rank_2017$name, 1, x1-2)
team <- substr(rank_2017$name, x1+1, x1+3)
team <- gsub(',', '', team)
team <- gsub(')', '', team)
pos <- substr(rank_2017$name, x1+5, 100)
x2 <- str_locate(pos, '[)]')[,1]
pos <- substr(pos, 1, x2-1)
pos <- gsub(',', ' | ', pos)
rank_2017$year <- 2017
rank_2017$name <- NULL
rank_2017$player <- name
rank_2017$team <- team
rank_2017$pos <- substr(pos, 2, 100)
rank_2017 <- rank_2017 %>%
dplyr::rename(st_dev = stdev,
avg = average) %>%
tibble::as.tibble() %>%
dplyr::select(player, team, pos, tidyselect::everything())
rank_2017$adp[is.na(rank_2017$adp)] <- ceiling(rank_2017$avg[is.na(rank_2017$adp)])
# 2018
names(rank_2018) <- tolower(names(rank_2018))
rank_2018 <- rank_2018 %>%
dplyr::rename(pos = positions,
ranking = rank,
st_dev = std.dev) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2018,
vs..adp = NULL,
team = as.character(team),
player = as.character(player)) %>%
tibble::as.tibble()
rank_2018$adp[is.na(rank_2018$adp)] <- ceiling(rank_2018$avg[is.na(rank_2018$adp)])
# 2019
names(rank_2019) <- tolower(names(rank_2019))
rank_2019 <- rank_2019 %>%
dplyr::rename(pos = positions,
ranking = rank,
st_dev = std.dev) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2019,
team = as.character(team),
player = as.character(player),
adp = as.numeric(adp),
avg = as.numeric(avg)) %>%
tibble::as.tibble() %>%
dplyr::select(-c(vs..adp))
# ## TEMP
# rank_2019$adp <- 0
rank_2019$adp[is.na(rank_2019$adp)] <- ceiling(rank_2019$avg[is.na(rank_2019$adp)])
# 2020
names(rank_2020) <- tolower(names(rank_2020))
rank_2020 <- rank_2020 %>%
dplyr::rename(pos = positions,
ranking = rank,
st_dev = std.dev) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2020,
team = as.character(team),
player = as.character(player),
adp = as.numeric(adp),
avg = as.numeric(avg)) %>%
tibble::as.tibble() %>%
dplyr::select(-c(vs..adp))
# ## TEMP
# rank_2020$adp <- 0
rank_2020$adp[is.na(rank_2020$adp)] <- ceiling(rank_2020$avg[is.na(rank_2020$adp)])
# Combine All
rankings_df <- rbind(rank_2016, rank_2017, rank_2018, rank_2019, ran_2020) %>%
tibble::as.tibble() %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
dplyr::select(player, team, pos, year, ranking, tidyselect::everything())
rankings_df <- rankings_df %>%
addPlayerID(., players_df) %>%
dplyr::select(player_id, player, year, team, pos, pos_list, ranking,
tidyselect::everything())
rankings_df <- structure(rankings_df,
class = c('draftRankings', 'tbl_df', 'tbl', 'data.frame'),
source = 'fantasy_pros',
team_value = NULL,
pos_value = NULL,
roster_value = NULL)
usethis::use_data(rankings_df, overwrite=TRUE)
### Seasons ------------------------------------------------------------------------------
## Read in Data
season2019_df <- readr::read_delim(file.path(getwd(), 'data', 'raw', 'seasons', 'season2019.csv'),
delim = ';')
season2018_df <- read.csv(file.path(getwd(), 'data', 'raw', 'seasons', 'season2018.csv'))
season2017_df <- read.csv(file.path(getwd(), 'data', 'raw', 'seasons', 'season2017.csv'))
season2016_df <- read.csv(file.path(getwd(), 'data', 'raw', 'seasons', 'season2016.csv'))
season2015_df <- read.csv(file.path(getwd(), 'data', 'raw', 'seasons', 'season2015.csv'))
# 2015
season2015_df <- season2015_df %>%
dplyr::mutate(Team = c(2, 3, 1, 5, 6, 4)) %>%
dplyr::select(team = Team, rank = Rank, r = R, hr = HR, rbi = RBI, so = K, hbp = HBP,
sbn = SBN, obp = OBP, slg = SLG, e = E, qs = QS, k = K.1, oba = OBA,
whip = WHIP, ptw = PTW, svhd = SVHD, moves = Moves)
# 2016
season2016_df <- season2016_df %>%
dplyr::mutate(Team = c(7, 1, 2, 4, 5, 8)) %>%
dplyr::select(team = Team, rank = Rank, r = R, hr = HR, rbi = RBI, so = K, sbn = SBN,
obp = OBP, slg = SLG, gidp = GIDP, ppa = PPA, fpct = FPCT, ip = IP,
qs = QS, k = K.1, hb = HB, sv = SV, hd = HD, whip = WHIP, moves = Moves)
# 2017
season2017_df <- season2017_df %>%
dplyr::mutate(Team = c(2, 9, 8, 3, 11, 1, 10)) %>%
dplyr::select(team = Team, rank = Rank, r = R, hr = HR, rbi = RBI, so = K, sbn = SBN,
obp = OBP, slg = SLG, gidp = GIDP, e = E, ip = IP, qs = QS, k = K.1,
sv = SV, hd = HD, whip = WHIP, moves = Moves, starts = Starts)
# 2018
season2018_df <- season2018_df %>%
dplyr::mutate(Team = c(8, 1, 12, 3, 4, 10, 2, 9)) %>%
dplyr::select(team = Team, rank = Rank, r = R, hr = HR, rbi = RBI, so = K, sb = SB,
obp = OBP, slg = SLG, gidp = GIDP, e = E, ip = IP, qs = QS, k = K.1,
sv = SV, hd = HD, whip = WHIP, moves = Moves, starts = Starts)
season2019_df <- season2019_df %>%
dplyr::select(team = Team, rank = Rank, r = R, hr = HR, rbi = RBI, so = K, sb = SB,
obp = OBP, slg = SLG, gidp = GIDP, e = E, ip = IP, qs = QS, k = K_1,
sv = SV, hd = HD, whip = WHIP, moves = Moves, starts = Starts)
seasons_ <- list(`2015` = structure(season2015_df,
class = c('season', class(season2015_df))),
`2016` = structure(season2016_df,
class = c('season', class(season2015_df))),
`2017` = structure(season2017_df,
class = c('season', class(season2015_df))),
`2018` = structure(season2018_df,
class = c('season', class(season2015_df))),
`2019` = structure(season2019_df,
class = c('season', class(season2015_df))))
usethis::use_data(seasons_, overwrite=TRUE)
andykrause/FBB30x30 documentation built on Dec. 29, 2021, 10:06 a.m.
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#*****************************************************************************************
#
# Create Data objeccts for 30x30Baseball
#
#*****************************************************************************************
library(readr)
library(tidyverse)
library(FBB30x30)
### Data Sources -------------------------------------------------------------------------
baseballref_df <- data.frame(data = c('Batting - Standard', 'Batting - Ratio',
'Batting - Value', 'Batting - Pitches',
'Batting - Win Probability', 'Batting - Advanced',
'Batting - Situational', 'Batting - Baserunning',
'Fielding - Appearances', 'Fielding - Standard',
'Pitching - Standard', 'Pitching - Starter',
'Pitching - Reliever', 'Pitching - Ratio',
'Pitching - Value', 'Pitching - Pitches',
'Pitching - Win Probability'),
code = c('standard-batting', 'ratio-batting',
'value-batting', 'pitches-batting',
'win_probability-batting',
'advanced-batting', 'situational-batting',
'baserunning-batting',
'appearances-fielding',
'standard-fielding', 'standard-pitching',
'starter-pitching', 'reliever-pitching',
'ratio-pitching', 'value-pitching',
'pitches-pitching',
'win_probability-pitching'),
stringsAsFactors = FALSE) %>%
tibble::as.tibble()
usethis::use_data(baseballref_df, overwrite=TRUE)
### People and Teams ---------------------------------------------------------------------
# Players
players_df <- readr::read_delim(file = file.path(getwd(), 'data', 'raw', 'players', 'people.csv'),
delim = ',')
players_df <- players_df %>%
dplyr::filter(as.Date(as.character(finalGame)) >= as.Date('2014-01-01')) %>%
dplyr::select(player_id = playerID, retro_id = retroID, bbref_id = bbrefID,
first = nameFirst, last = nameLast, given = nameGiven,
last_game = finalGame) %>%
dplyr::mutate(full = paste(first, last),
last_game = as.Date(as.character(last_game)),
player_id = as.character(player_id),
retro_id = as.character(retro_id),
bbref_id = as.character(bbref_id),
first = as.character(first),
last = as.character(last),
given = as.character(given))
class(players_df) <- c('players', class(players_df))
usethis::use_data(players_df, overwrite=TRUE)
# Teams
teams_df <- readr::read_delim(file.path(getwd(), 'data', 'raw', 'players', 'teams.csv'),
delim = ',')
teams_df <- teams_df %>%
dplyr::filter(yearID == 2017) %>%
dplyr::select(team_id = teamID, br_id = teamIDBR, lahman_id = teamIDlahman45,
retro_id = teamIDretro) %>%
dplyr::mutate(espn_id = c('ARI', 'ATL', 'BAL', 'BOS', 'CWS', 'CHC', 'CIN', 'CLE',
'COL', 'DET', 'HOU', 'KC', 'LAA', 'LAD', 'MIA', 'MIL',
'MIN', 'NYY', 'NYM', 'OAK', 'PHI', 'PIT', 'SD', 'SEA',
'SF', 'STL', 'TB', 'TEX', 'TOR', 'WAS'),
retrosheet_id = c('ARI N', 'ATL N', 'BAL A', 'BOS A', 'CHI A', 'CHI N',
'CIN N', 'CLE A', 'COL N', 'DET A', 'HOU A', 'KC A',
'ANA A', 'LA N', 'MIA N', 'MIL N', 'MIN A', 'NY A',
'NY N', 'OAK A', 'PHI N', 'PIT N', 'SD N', 'SEA A',
'SF N', 'STL N', 'TB A', 'TEX A', 'TOR A', 'WAS N'))
class(teams_df) <- c('teams', class(teams_df))
usethis::use_data(teams_df, overwrite=TRUE)
### Statistics ---------------------------------------------------------------------------
# Read in Fielding data
fielding_df <- purrr::map(.x = 2013:2019,
.f = getBRStats,
type ='fielding') %>%
dplyr::bind_rows()
usethis::use_data(fielding_df, overwrite=TRUE)
# Load Data
batting_df <- purrr::map(.x = 2013:2019,
.f = getBRStats,
type ='batting') %>%
dplyr::bind_rows()
usethis::use_data(batting_df, overwrite=TRUE)
## Pitching stats
pitching_df <- purrr::map(.x = 2013:2019,
.f = getBRStats,
type ='pitching') %>%
dplyr::bind_rows()
usethis::use_data(pitching_df, overwrite=TRUE)
### Projections --------------------------------------------------------------------------
## Marcel
marcel2019_proj <- scrapeMarcelProjections(year = 2019)
usethis::use_data(marcel2019_proj, overwrite = TRUE)
## Load Projection Data
# Hitters
hp_2014 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2014.csv'))
hp_2015 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2015.csv'))
hp_2016 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2016.csv'))
hp_2017 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2017.csv'))
hp_2018 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2018.csv')) %>%
dplyr::filter(Team != '158')
hp_2019 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2019.csv'))
hp_2020 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_hitters_2020.csv'))
# Hitters
pp_2014 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2014.csv'))
pp_2015 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2015.csv'))
pp_2016 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2016.csv'))
pp_2017 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2017.csv'))
pp_2018 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2018.csv'))
pp_2019 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2019.csv'))
pp_2020 <- read.csv(file.path(getwd(), 'data', 'raw', 'projections',
'fp_pitchers_2020.csv'))
## Standardize Projection Data
# 2014
names(hp_2014) <- tolower(names(hp_2014))
hp_2014 <- hp_2014 %>%
dplyr::rename(pos = position,
avg = ave,
h = hits,
hr = hrs,
player = name,
r = runs) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2014,
player = as.character(player),
team = as.character(team)) %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest(cols = c(pos_list)) %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list = c(pos_list)) %>%
dplyr::select(-c(cs, type)) %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2014, proj_year = 2014)
e_df <- projectErrors(fielding_df, hp_2014, proj_year = 2014)
hp_2014 <- hp_2014 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2015
names(hp_2015) <- tolower(names(hp_2015))
hp_2015 <- hp_2015 %>%
dplyr::mutate(player = paste0(substr(name, str_locate(name, ',')+2, 100),
" ", substr(name, 1, str_locate(name, ',') - 1)),
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2015,
player = as.character(player),
team = as.character(team)) %>%
dplyr::rename(so = k,
ops = obps) %>%
dplyr::select(-c(cs, g, al.nl.., mixed.., name))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2015, proj_year = 2015)
e_df <- projectErrors(fielding_df, hp_2015, proj_year = 2015)
hp_2015 <- hp_2015 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2016
names(hp_2016) <- tolower(names(hp_2016))
hp_2016 <- hp_2016 %>%
dplyr::rename(avg = ave,
h = hits,
hr = hrs,
player = name,
r = runs) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2016,
player = as.character(player),
team = as.character(team)) %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2016, proj_year = 2016)
e_df <- projectErrors(fielding_df, hp_2016, proj_year = 2016)
hp_2016 <- hp_2016 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2017
names(hp_2017) <- tolower(names(hp_2017))
hp_2017 <- hp_2017 %>%
fixFPName(.) %>%
dplyr::mutate(year = 2017,
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
player = as.character(player),
team = as.character(team)) %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2017, proj_year = 2017)
e_df <- projectErrors(fielding_df, hp_2017, proj_year = 2017)
hp_2017 <- hp_2017 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2018
names(hp_2018) <- tolower(names(hp_2018))
hp_2018 <- hp_2018 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(year = 2018,
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
player = as.character(player),
team = as.character(team))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2018, proj_year = 2018)
e_df <- projectErrors(fielding_df, hp_2018, proj_year = 2018)
hp_2018 <- hp_2018 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2019
names(hp_2019) <- tolower(names(hp_2019))
hp_2019 <- hp_2019 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(year = 2019,
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
player = as.character(player),
team = as.character(team)) %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2019, proj_year = 2019)
e_df <- projectErrors(fielding_df, hp_2019, proj_year = 2019)
hp_2019 <- hp_2019 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# 2020
names(hp_2020) <- tolower(names(hp_2020))
hp_2020 <- hp_2020 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(year = 2020,
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
player = as.character(player),
team = as.character(team)) %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
gidp_df <- projectGIDP(batting_df, hp_2020, proj_year = 2020)
e_df <- projectErrors(fielding_df, hp_2020, proj_year = 2020)
hp_2020 <- hp_2020 %>%
dplyr::left_join(gidp_df, by = 'player_id') %>%
dplyr::left_join(e_df, by = 'player_id')
# Combine
batprojs_df <- rbind(hp_2014, hp_2015, hp_2016, hp_2017, hp_2018, hp_2019, hp_2020) %>%
tibble::as.tibble()%>%
dplyr::select(player_id, player, team, year, pos, pos_list,
tidyselect::everything())%>%
structure(., class = c('projections', 'batting', 'tbl_df', 'tbl', 'data.frame'))
## Pitchers
# 2014
names(pp_2014) <- tolower(names(pp_2014))
pp_2014 <- pp_2014 %>%
dplyr::rename(pos = position,
player = name,
bb = bbi) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2014,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hd, hr, type, bs))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2014, proj_year = 2014)
holds_df <- projectHolds(pitching_df, pp_2014, proj_year = 2014)
pp_2014 <- pp_2014 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2015
names(pp_2015) <- tolower(names(pp_2015))
pp_2015 <- pp_2015 %>%
dplyr::mutate(gsp = gs/g,
player = paste0(substr(name, str_locate(name, ',')+2, 100),
" ", substr(name, 1, str_locate(name, ',') - 1)),
pos = 'SP, RP',
pos = ifelse(gsp == 0, 'RP', pos),
pos = ifelse(gsp >= .85, 'SP', pos),
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2015,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(al.nl.., mixed.., sho, name, gsp))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2015, proj_year = 2015)
holds_df <- projectHolds(pitching_df, pp_2015, proj_year = 2015)
pp_2015 <- pp_2015 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2016
names(pp_2016) <- tolower(names(pp_2016))
pp_2016 <- pp_2016 %>%
dplyr::rename(player = name,
bb = bbi) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2016,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hr))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2016, proj_year = 2016)
holds_df <- projectHolds(pitching_df, pp_2016, proj_year = 2016)
pp_2016 <- pp_2016 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2017
names(pp_2017) <- tolower(names(pp_2017))
pp_2017 <- pp_2017 %>%
fixFPName() %>%
dplyr::mutate(year = 2017,
pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hr))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2017, proj_year = 2017)
holds_df <- projectHolds(pitching_df, pp_2017, proj_year = 2017)
pp_2017 <- pp_2017 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2018
names(pp_2018) <- tolower(names(pp_2018))
pp_2018 <- pp_2018 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2018,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hr))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2018, proj_year = 2018)
holds_df <- projectHolds(pitching_df, pp_2018, proj_year = 2018)
pp_2018 <- pp_2018 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2019
names(pp_2019) <- tolower(names(pp_2019))
pp_2019 <- pp_2019 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2019,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hr))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2019, proj_year = 2019)
holds_df <- projectHolds(pitching_df, pp_2019, proj_year = 2019)
pp_2019 <- pp_2019 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# 2020
names(pp_2020) <- tolower(names(pp_2020))
pp_2020 <- pp_2020 %>%
dplyr::rename(pos = positions) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2020,
player = as.character(player),
team = as.character(team)) %>%
dplyr::select(-c(hr))%>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
addPlayerID(., players_df = players_df)
qs_df <- projectQS(pitching_df, pp_2020, proj_year = 2020)
holds_df <- projectHolds(pitching_df, pp_2020, proj_year = 2020)
pp_2020 <- pp_2020 %>%
dplyr::left_join(qs_df, by = 'player_id') %>%
dplyr::left_join(holds_df, by = 'player_id')
# Combine Al
pitchprojs_df <- rbind(pp_2014, pp_2015, pp_2016, pp_2017, pp_2018, pp_2019, pp_2020) %>%
tibble::as.tibble() %>%
dplyr::select(player_id, player, team, year, pos, pos_list,
tidyselect::everything()) %>%
structure(., class = c('projections', 'pitching', 'tbl_df', 'tbl', 'data.frame'))
## Save
usethis::use_data(batprojs_df, overwrite=TRUE)
usethis::use_data(pitchprojs_df, overwrite=TRUE)
### Rankings -----------------------------------------------------------------------------
## Read in Data
rank_2016 <- read.csv(file.path(getwd(), 'data', 'raw', 'rankings',
'fp_draftrankings_2016.csv'))
rank_2017 <- read.csv(file.path(getwd(), 'data', 'raw', 'rankings',
'fp_draftrankings_2017.csv'))
rank_2018 <- read.csv(file.path(getwd(), 'data', 'raw', 'rankings',
'fp_draftrankings_2018.csv'))
rank_2019 <- read.csv(file.path(getwd(), 'data', 'raw', 'rankings',
'fp_draftrankings_2019.csv'))
rank_2020 <- read.csv(file.path(getwd(), 'data', 'raw', 'rankings',
'fp_draftrankings_2020.csv'))
# 2016
names(rank_2016) <- tolower(names(rank_2016))
rank_2016 <- rank_2016 %>%
dplyr::rename(avg = ave,
st_dev = stdev,
ranking = ecr,
player = name) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2016,
x = NULL,
adp = as.numeric(as.character(adp)),
player = as.character(player),
team = as.character(team)) %>%
tibble::as.tibble()
rank_2016$adp[is.na(rank_2016$adp)] <- ceiling(rank_2016$avg[is.na(rank_2016$adp)])
# 2017
names(rank_2017) <- tolower(names(rank_2017))
x1 <- str_locate(rank_2017$name, '[(]')[,1]
name <- substr(rank_2017$name, 1, x1-2)
team <- substr(rank_2017$name, x1+1, x1+3)
team <- gsub(',', '', team)
team <- gsub(')', '', team)
pos <- substr(rank_2017$name, x1+5, 100)
x2 <- str_locate(pos, '[)]')[,1]
pos <- substr(pos, 1, x2-1)
pos <- gsub(',', ' | ', pos)
rank_2017$year <- 2017
rank_2017$name <- NULL
rank_2017$player <- name
rank_2017$team <- team
rank_2017$pos <- substr(pos, 2, 100)
rank_2017 <- rank_2017 %>%
dplyr::rename(st_dev = stdev,
avg = average) %>%
tibble::as.tibble() %>%
dplyr::select(player, team, pos, tidyselect::everything())
rank_2017$adp[is.na(rank_2017$adp)] <- ceiling(rank_2017$avg[is.na(rank_2017$adp)])
# 2018
names(rank_2018) <- tolower(names(rank_2018))
rank_2018 <- rank_2018 %>%
dplyr::rename(pos = positions,
ranking = rank,
st_dev = std.dev) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2018,
vs..adp = NULL,
team = as.character(team),
player = as.character(player)) %>%
tibble::as.tibble()
rank_2018$adp[is.na(rank_2018$adp)] <- ceiling(rank_2018$avg[is.na(rank_2018$adp)])
# 2019
names(rank_2019) <- tolower(names(rank_2019))
rank_2019 <- rank_2019 %>%
dplyr::rename(pos = positions,
ranking = rank,
st_dev = std.dev) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2019,
team = as.character(team),
player = as.character(player),
adp = as.numeric(adp),
avg = as.numeric(avg)) %>%
tibble::as.tibble() %>%
dplyr::select(-c(vs..adp))
# ## TEMP
# rank_2019$adp <- 0
rank_2019$adp[is.na(rank_2019$adp)] <- ceiling(rank_2019$avg[is.na(rank_2019$adp)])
# 2020
names(rank_2020) <- tolower(names(rank_2020))
rank_2020 <- rank_2020 %>%
dplyr::rename(pos = positions,
ranking = rank,
st_dev = std.dev) %>%
dplyr::mutate(pos = gsub(',', '/', pos),
pos = gsub('/', ' | ', pos),
year = 2020,
team = as.character(team),
player = as.character(player),
adp = as.numeric(adp),
avg = as.numeric(avg)) %>%
tibble::as.tibble() %>%
dplyr::select(-c(vs..adp))
# ## TEMP
# rank_2020$adp <- 0
rank_2020$adp[is.na(rank_2020$adp)] <- ceiling(rank_2020$avg[is.na(rank_2020$adp)])
# Combine All
rankings_df <- rbind(rank_2016, rank_2017, rank_2018, rank_2019, ran_2020) %>%
tibble::as.tibble() %>%
dplyr::mutate(pos_list = strsplit(pos, ' | ')) %>%
tidyr::unnest() %>%
dplyr::filter(pos_list != '|') %>%
tidyr::nest(pos_list, .key = 'pos_list') %>%
dplyr::select(player, team, pos, year, ranking, tidyselect::everything())
rankings_df <- rankings_df %>%
addPlayerID(., players_df) %>%
dplyr::select(player_id, player, year, team, pos, pos_list, ranking,
tidyselect::everything())
rankings_df <- structure(rankings_df,
class = c('draftRankings', 'tbl_df', 'tbl', 'data.frame'),
source = 'fantasy_pros',
team_value = NULL,
pos_value = NULL,
roster_value = NULL)
usethis::use_data(rankings_df, overwrite=TRUE)
### Seasons ------------------------------------------------------------------------------
## Read in Data
season2019_df <- readr::read_delim(file.path(getwd(), 'data', 'raw', 'seasons', 'season2019.csv'),
delim = ';')
season2018_df <- read.csv(file.path(getwd(), 'data', 'raw', 'seasons', 'season2018.csv'))
season2017_df <- read.csv(file.path(getwd(), 'data', 'raw', 'seasons', 'season2017.csv'))
season2016_df <- read.csv(file.path(getwd(), 'data', 'raw', 'seasons', 'season2016.csv'))
season2015_df <- read.csv(file.path(getwd(), 'data', 'raw', 'seasons', 'season2015.csv'))
# 2015
season2015_df <- season2015_df %>%
dplyr::mutate(Team = c(2, 3, 1, 5, 6, 4)) %>%
dplyr::select(team = Team, rank = Rank, r = R, hr = HR, rbi = RBI, so = K, hbp = HBP,
sbn = SBN, obp = OBP, slg = SLG, e = E, qs = QS, k = K.1, oba = OBA,
whip = WHIP, ptw = PTW, svhd = SVHD, moves = Moves)
# 2016
season2016_df <- season2016_df %>%
dplyr::mutate(Team = c(7, 1, 2, 4, 5, 8)) %>%
dplyr::select(team = Team, rank = Rank, r = R, hr = HR, rbi = RBI, so = K, sbn = SBN,
obp = OBP, slg = SLG, gidp = GIDP, ppa = PPA, fpct = FPCT, ip = IP,
qs = QS, k = K.1, hb = HB, sv = SV, hd = HD, whip = WHIP, moves = Moves)
# 2017
season2017_df <- season2017_df %>%
dplyr::mutate(Team = c(2, 9, 8, 3, 11, 1, 10)) %>%
dplyr::select(team = Team, rank = Rank, r = R, hr = HR, rbi = RBI, so = K, sbn = SBN,
obp = OBP, slg = SLG, gidp = GIDP, e = E, ip = IP, qs = QS, k = K.1,
sv = SV, hd = HD, whip = WHIP, moves = Moves, starts = Starts)
# 2018
season2018_df <- season2018_df %>%
dplyr::mutate(Team = c(8, 1, 12, 3, 4, 10, 2, 9)) %>%
dplyr::select(team = Team, rank = Rank, r = R, hr = HR, rbi = RBI, so = K, sb = SB,
obp = OBP, slg = SLG, gidp = GIDP, e = E, ip = IP, qs = QS, k = K.1,
sv = SV, hd = HD, whip = WHIP, moves = Moves, starts = Starts)
season2019_df <- season2019_df %>%
dplyr::select(team = Team, rank = Rank, r = R, hr = HR, rbi = RBI, so = K, sb = SB,
obp = OBP, slg = SLG, gidp = GIDP, e = E, ip = IP, qs = QS, k = K_1,
sv = SV, hd = HD, whip = WHIP, moves = Moves, starts = Starts)
seasons_ <- list(`2015` = structure(season2015_df,
class = c('season', class(season2015_df))),
`2016` = structure(season2016_df,
class = c('season', class(season2015_df))),
`2017` = structure(season2017_df,
class = c('season', class(season2015_df))),
`2018` = structure(season2018_df,
class = c('season', class(season2015_df))),
`2019` = structure(season2019_df,
class = c('season', class(season2015_df))))
usethis::use_data(seasons_, overwrite=TRUE)
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