bdilday

from bs4 import BeautifulSoup
from pybaseball.league_batting_stats import get_table, session

START_DT = "2021-05-02"

def fetch_date(dt):
    url = (
            "http://www.baseball-reference.com/leagues/daily.cgi?"
            "user_team=&bust_cache=&type=b&lastndays=7&dates=fromandto&"
            f"fromandto={START_DT}.{dt}&level=mlb&franch="

bdilday

library(dplyr)
library(ggplot2)
library(ggrepel)
library(rvest)
library(stringr)


fg_current_standings <- function() {
  url = "https://www.fangraphs.com/depthcharts.aspx?position=Standings"

bdilday

bdilday

"playerID","yearID","WAA","WAR","WAR_def","WAR_off","POS"
"aaronha01",1954,-0.61,1.27,-0.71,1.98,"OF"
"aaronha01",1955,3.77,6.25,-0.01,6.26,"OF"
"aaronha01",1956,4.63,7.18,0.65,6.53,"OF"
"aaronha01",1957,5.5,7.97,-0.13,8.1,"OF"
"aaronha01",1958,4.84,7.32,0.21,7.11,"OF"
"aaronha01",1959,6.13,8.64,-1.08,9.72,"OF"
"aaronha01",1960,5.47,7.98,0.77,7.21,"OF"
"aaronha01",1961,6.86,9.4,2.02,7.38,"OF"
"aaronha01",1962,6.07,8.6,0.3,8.3,"OF"

bdilday

"game_id","year_id","bat_home_id","off_score","def_pitcher","def_catcher","off_team","def_team","park_id","park"
"WS2196109210",1961,0,3,"danib102_1961","retzk101_1961","MIN_1961","WS2_1961","WS2_1961","WS2"
"WS2196109190",1961,0,2,"mcclj104_1961","retzk101_1961","MIN_1961","WS2_1961","WS2_1961","WS2"
"WS2196109150",1961,0,1,"danib102_1961","retzk101_1961","KC1_1961","WS2_1961","WS2_1961","WS2"
"WS2196109130",1961,0,1,"hobae101_1961","retzk101_1961","BOS_1961","WS2_1961","WS2_1961","WS2"
"WS2196109120",1961,0,0,"burnp102_1961","retzk101_1961","BOS_1961","WS2_1961","WS2_1961","WS2"
"WS2196109032",1961,0,2,"gablg102_1961","dalep101_1961","CHA_1961","WS2_1961","WS2_1961","WS2"
"WS2196109170",1961,0,2,"maesh101_1961","retzk101_1961","KC1_1961","WS2_1961","WS2_1961","WS2"
"WS2196109160",1961,0,0,"burnp102_1961","retzk101_1961","KC1_1961","WS2_1961","WS2_1961","WS2"
"WS2196109031",1961,0,2,"kutym101_1961","dalep101_1961","CHA_1961","WS2_1961","WS2_1961","WS2"

bdilday

bdilday

library(baseballr)
library(dplyr)

obtain_data = function(type="postgres", 
                       start_date="2017-03-29", 
                       end_date="2017-10-03", infile=NULL) {
  
  if (type=="postgres") {
    # postgres db connection here

bdilday

library(dplyr)
library(ggplot2)
library(rvest)
library(jsonlite)

scrape_statcast_expected_stats = function(year=2018, min_pa=25) {
  url = sprintf("https://baseballsavant.mlb.com/expected_statistics?type=batter&year=%s&position=&team=&min=%d", year, min_pa)
  h = xml2::read_html(url) 
  s = html_nodes(h, "script")[[10]]

bdilday

library(readr)
library(ggplot2)
library(dplyr)
library(Lahman)
br_war = read_csv("https://www.baseball-reference.com/data/war_daily_bat.txt")
m = Lahman::Teams
ndf = br_war %>% merge(m %>% select(teamIDBR, yearID, name, franchID), by.x=c("team_ID", "year_ID"), by.y=c("teamIDBR", "yearID")) %>% group_by(franchID, name, player_ID) %>% summarise(w=sum(as.numeric(WAR), na.rm=TRUE))
xx = ndf %>% arrange(franchID, -w) %>% mutate(i=row_number()) %>% group_by(franchID, name) %>% summarise(m=max(i)) %>% arrange(franchID,-m) %>% mutate(ir=row_number()) %>% group_by(franchID) %>% mutate(m2=max(m), m=sum(m)) %>% filter(ir==1) %>% select(franchID, m=m2)

p = ndf %>% merge(xx, by="franchID") %>% group_by(franchID) %>% arrange(-w) %>% mutate(i=row_number()) %>% filter(i<=10) %>% filter(m>=110) %>% ggplot(aes(x=i, y=w)) + geom_bar(stat='identity') + facet_wrap(~franchID) + theme_minimal() + labs(x='franchise rank', y='career WAR')

bdilday

trajectory_pars <- list(
  # constants
  mass = 5.125, # oz,
  circumference = 9.125, # in
  beta = 1.217e-4, # 1 / meter
  cd0 = 0.3008,
  cdspin = 0.0292,
  cl0 = 0.583,
  cl1 = 2.333,