Problem Set 5 - BABIP

Transcription

1 Problem Set 5 - BABIP Assignment 1. Use a join command to link the Batting and Master data frames in the Lahman package. Make a new column called name in which you combine the first and last name of each player, producing output such as Tim Lincecum. batting <- left_join(batting,master) ## Joining by: "playerid" batting$name <- paste(batting$namefirst,batting$namelast,sep=" ") glimpse(batting) ## Observations: 99,846 ## Variables: 48 ## $ playerid (chr) "abercda01", "addybo01", "allisar01", "allisdo01"... ## $ yearid (int) 1871, 1871, 1871, 1871, 1871, 1871, 1871, 1871, 1... ## $ stint (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1... ## $ teamid (fctr) TRO, RC1, CL1, WS3, RC1, FW1, RC1, BS1, FW1, BS1... ## $ lgid (fctr) NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,... ## $ G (int) 1, 25, 29, 27, 25, 12, 1, 31, 1, 18, 22, 1, 10, 3... ## $ AB (int) 4, 118, 137, 133, 120, 49, 4, 157, 5, 86, 89, 3,... ## $ R (int) 0, 30, 28, 28, 29, 9, 0, 66, 1, 13, 18, 0, 6, 7,... ## $ H (int) 0, 32, 40, 44, 39, 11, 1, 63, 1, 13, 27, 0, 7, 6,... ## $ X2B (int) 0, 6, 4, 10, 11, 2, 0, 10, 1, 2, 1, 0, 0, 0, 9, 3... ## $ X3B (int) 0, 0, 5, 2, 3, 1, 0, 9, 0, 1, 10, 0, 0, 0, 1, 3,... ## $ HR (int) 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0... ## $ RBI (int) 0, 13, 19, 27, 16, 5, 2, 34, 1, 11, 18, 0, 1, 5,... ## $ SB (int) 0, 8, 3, 1, 6, 0, 0, 11, 0, 1, 0, 0, 2, 2, 4, 4,... ## $ CS (int) 0, 1, 1, 1, 2, 1, 0, 6, 0, 0, 1, 0, 0, 0, 0, 4, 0... ## $ BB (int) 0, 4, 2, 0, 2, 0, 1, 13, 0, 0, 3, 1, 2, 0, 2, 9,... ## $ SO (int) 0, 0, 5, 2, 1, 1, 0, 1, 0, 0, 4, 0, 0, 0, 2, 2, 3... ## $ IBB (int) NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N... ## $ HBP (int) NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N... ## $ SH (int) NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N... ## $ SF (int) NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N... ## $ GIDP (int) NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N... ## $ birthyear (int) 1850, 1842, 1849, 1846, 1852, 1850, 1839, 1850, N... ## $ birthmonth (int) 1, 2, 1, 7, 4, NA, 1, 5, NA, 10, NA, 11, 8, NA, 9... ## $ birthday (int) 2, NA, 29, 12, 17, NA, 18, 8, NA, 22, NA, 11, NA,... ## $ birthcountry (chr) "USA", "CAN", "USA", "USA", "USA", "USA", "USA",... ## $ birthstate (chr) "OK", "ON", "PA", "PA", "IA", "MD", "IN", "NY", "... ## $ birthcity (chr) "Fort Towson", "Port Hope", "Philadelphia", "Phil... ## $ deathyear (int) 1939, 1910, 1916, 1916, 1922, NA, 1912, 1915, NA,... ## $ deathmonth (int) 11, 4, 2, 12, 4, NA, 9, 2, NA, 2, 9, 12, 10, NA,... ## $ deathday (int) 11, 9, 25, 19, 14, NA, 15, 5, NA, 6, 25, 10, 2, N... ## $ deathcountry (chr) "USA", "USA", "USA", "USA", "USA", NA, "USA", "US... ## $ deathstate (chr) "PA", "ID", "DC", "DC", "IL", NA, "IL", "IL", NA,... ## $ deathcity (chr) "Philadelphia", "Pocatello", "Washington", "Washi... 1

2 ## $ namefirst (chr) "Frank", "Bob", "Art", "Doug", "Cap", "Robert", "... ## $ namelast (chr) "Abercrombie", "Addy", "Allison", "Allison", "Ans... ## $ namegiven (chr) "Francis Patterson", "Robert Edward", "Arthur Alg... ## $ weight (int) NA, 160, 150, 160, 227, 160, NA, 145, NA, NA, ## $ height (int) NA, 68, 68, 70, 72, 74, NA, 68, NA, NA, 66, 70, 6... ## $ bats (fctr) NA, L, NA, R, R, NA, NA, R, NA, NA, NA, R, R, NA... ## $ throws (fctr) NA, L, NA, R, R, NA, NA, R, NA, NA, NA, R, NA, N... ## $ debut (chr) " ", " ", " ", " ## $ finalgame (chr) " ", " ", " ", " ## $ retroid (chr) "aberd101", "addyb101", "allia101", "allid101", "... ## $ bbrefid (chr) "abercda01", "addybo01", "allisar01", "allisdo01"... ## $ deathdate (date) , , , ,... ## $ birthdate (date) , NA, , , ## $ name (chr) "Frank Abercrombie", "Bob Addy", "Art Allison", " Filter your data frame to include only years from 1970 to For each player, find the sum of H, AB, HR, SO, and SF for his career. Calculate career BABIP for each player. The formula is BABIP = (H - HR)/(AB - HR - SO + SF). batting1 <- batting %>% filter(yearid >= 1970, yearid <=2014) %>% group_by(name) %>% summarize(tot.h = sum(h),tot.hr = sum(hr),tot.ab = sum(ab), tot.so = sum(so),tot.sf = sum(sf), careerbabip = round((tot.h - tot.hr)/(tot.ab - tot.hr - tot.so + tot.sf),3)) 3. Filter your data frame to include only players with at least 2000 AB in their career. Find the four players with the highest career BABIP and the four players with the lowest career BABIP. batting1 %>% filter(tot.ab >=2000) %>% arrange(desc(careerbabip)) ## Source: local data frame [1,136 x 7] ## ## name tot.h tot.hr tot.ab tot.so tot.sf careerbabip ## (chr) (int) (int) (int) (int) (int) (dbl) ## 1 Rod Carew ## 2 Chris Johnson ## 3 Joey Votto ## 4 Austin Jackson ## 5 Matt Kemp ## 6 Derek Jeter ## 7 Reggie Jefferson ## 8 Dexter Fowler ## 9 Joe Mauer ## 10 Ron LeFlore ## batting1 %>% filter(tot.ab >=2000) %>% arrange(careerbabip) ## Source: local data frame [1,136 x 7] ## 2

3 ## name tot.h tot.hr tot.ab tot.so tot.sf careerbabip ## (chr) (int) (int) (int) (int) (int) (dbl) ## 1 Charlie O'Brien ## 2 Enzo Hernandez ## 3 Wayne Gross ## 4 Barry Foote ## 5 Jeff Newman ## 6 Buck Martinez ## 7 Dave Duncan ## 8 Dick McAuliffe ## 9 Graig Nettles ## 10 Harmon Killebrew ## Take your original joined data frame and now calculate BABIP for each season for each player. batting2 <- batting %>% group_by(yearid,name) %>% mutate(babip=round((h-hr)/(ab-hr-so+sf),3)) 5. Make three graphs using one of the facet commands in ggplot. Each graph should have year on the x-axis and BABIP for that year on the y-axis. Connect the points with the geom_line() function. One of your faceted graphs should have the four players with the highest career BABIP, one should have the four players with the lowest career BABIP and the third should have the following players: Carlos Beltran, Adrian Beltre, Albert Pujols and Alex Rodriguez. batting2 %>% filter(name == "Rod Carew" name=="chris Johnson" name=="joey Votto" name=="austin Jackson") %>% ggplot(., aes(yearid,babip)) + geom_point() + geom_line() + facet_wrap(~name) 3

4 Austin Jackson Chris Johnson 0.3 BABIP Joey Votto Rod Carew yearid batting2 %>% filter(name == "Charlie O'Brien" name=="enzo Hernandez" name=="barry Foote" name=="wayne Gross") %>% ggplot(., aes(yearid,babip)) + geom_point() + geom_line() + facet_wrap(~name) 4

5 Barry Foote Charlie O'Brien 0.6 BABIP Enzo Hernandez Wayne Gross yearid batting2 %>% filter(name == "Alex Rodriguez" name=="adrian Beltre" name=="carlos Beltran" name=="albert Pujols") %>% ggplot(., aes(yearid,babip)) + geom_point() + geom_line() + facet_wrap(~name) 5

6 Adrian Beltre Albert Pujols BABIP Alex Rodriguez Carlos Beltran yearid 6. Do the four players with the highest career BABIPs consistently show a BABIP over each year? Can you suggest a possible explanation? These four players all showed consistent BABIPs above the league average of Rod Carew was a line-drive hitter. Line drives have a better chance of falling for a hit than flyballs. He also had pretty good speed so he could leg out some infield hits. Votto is the consummate line-drive hitter. He rarely pops up. Austin Jackson has very good speed and is also a pretty good line-drive hitter. Chris Johnson is a good line-drive hitter who hits lots of doubles. Do the four players with the lowest career BABIPs consistently show a BABIP below each year? Can you suggest a possible explanation? All four had BABIPs consistently below the league average. Barry Foote and Charlie O Brien were both slow-footed catchers. They likely were thrown out at first far more than typical batters. Charlie O Brien had one SB in his whole career. Enzo Hernandez was a light-hitting shortstop. I suspect most of his batted balls failed to go very far. Wayne Gross is harder to figure. I presume he was a pretty slow runner or perhaps hit a lot of flyballs. How would you characterize the patterns of variation of yearly BABIP for Beltran, Beltre, Pujols and ARod? All four of these players have BABIPs that bounce around the league average. Even though all four have Hall of Fame accomplishments, they are pretty typical players as far as BABIP is concerned. 6