| Title: | Sean 'Lahman' Baseball Database |
|---|---|
| Description: | Provides the tables from the 'Sean Lahman Baseball Database' as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2023, as recorded in the 2024 version of the database. Documentation examples show how many baseball questions can be investigated. |
| Authors: | Michael Friendly [aut], Chris Dalzell [cre, aut], Martin Monkman [aut], Dennis Murphy [aut], Vanessa Foot [ctb], Justeena Zaki-Azat [ctb], Sean Lahman [cph] |
| Maintainer: | Chris Dalzell <[email protected]> |
| License: | GPL |
| Version: | 12.0-0 |
| Built: | 2024-11-21 16:27:56 UTC |
| Source: | https://github.com/cdalzell/Lahman |
This database contains pitching, hitting, and fielding statistics for Major League Baseball from 1871 through 2023. It includes data from the two current leagues (American and National), the four other "major" leagues (American Association, Union Association, Players League, and Federal League), and the National Association of 1871-1875.
This database was created by Sean Lahman, who pioneered the effort to make baseball statistics freely available to the general public. What started as a one man effort in 1994 has grown tremendously, and now a team of researchers have collected their efforts to make this the largest and most accurate source for baseball statistics available anywhere.
This database, in the form of an R package offers a variety of interesting challenges and opportunities for data processing and visualization in R.
In the current version, the examples make extensive use of the dplyr
package for data manipulation (tabulation, queries, summaries, merging, etc.),
reflecting the original relational database design
and ggplot2 for graphics.
| Package: | Lahman |
| Type: | Package |
| Version: | 12.0-0 |
| Date: | 2024-08-24 |
| License: | GPL version 2 or newer |
| LazyLoad: | yes |
| LazyData: | yes |
The main form of this database is a relational database in Microsoft Access format.
The design follows these general principles: Each player is assigned a
unique code (playerID). All of the information in different tables relating to that player
is tagged with his playerID. The playerIDs are linked to names and
birthdates in the People table. Similar links exist among other tables
via analogous *ID variables.
The database is composed of the following main tables:
PeoplePlayer names, dates of birth, death and other biographical info
Battingbatting statistics
Pitchingpitching statistics
Fieldingfielding statistics
A collection of other tables is also provided:
Teams:
Teams |
yearly stats and standings |
TeamsHalf |
split season data for teams |
TeamsFranchises |
franchise information |
Post-season play:
BattingPost |
post-season batting statistics |
PitchingPost |
post-season pitching statistics |
FieldingPost
|
post-season fielding data |
SeriesPost |
post-season series information |
Awards:
AwardsManagers |
awards won by managers |
AwardsPlayers |
awards won by players |
AwardsShareManagers |
award voting for manager awards |
AwardsSharePlayers |
award voting for player awards |
Hall of Fame: links to People via hofID
HallOfFame |
Hall of Fame voting data |
Other tables:
AllstarFull - All-Star games appearances;
Managers - managerial statistics;
FieldingOF - outfield position data;
ManagersHalf - split season data for managers;
Salaries - player salary data;
Appearances - data on player appearances;
Schools - Information on schools players attended;
CollegePlaying - Information on schools players attended, by player and year;
Variable label tables are provided for some of the tables:
battingLabels,
pitchingLabels,
fieldingLabels
Michael Friendly, Dennis Murphy, Chris Dalzell, Martin Monkman
Maintainer: Chris Dalzell <[email protected]>
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, Main page, http://www.seanlahman.com/
All Star appearances by players
data(AllstarFull)data(AllstarFull)
A data frame with 5673 observations on the following 8 variables.
playerIDPlayer ID code
yearIDYear
gameNumGame number (for years in which more than one game was played)
gameIDGame ID code
teamIDTeam; a factor
lgIDLeague; a factor with levels AL NL
GPGame played (zero if player did not appear in game)
startingPosIf the player started, what position he played
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
data(AllstarFull) # find number of appearances by players in the All Star games player_appearances <- with(AllstarFull, rev(sort(table(playerID)))) # How many All-Star players, in total? length(player_appearances) # density plot of the whole distribution plot(density(player_appearances), main="Player appearances in All Star Games") rug(jitter(player_appearances)) # who has played in more than 10 ASGs? player_appearances[player_appearances > 10] hist(player_appearances[player_appearances > 10]) # Hank Aaron's All-Star record: subset(AllstarFull, playerID == "aaronha01") # Years that Stan Musial played in the ASG: with(AllstarFull, yearID[playerID == "musiast01"]) # Starting positions he played (NA means did not start) with(AllstarFull, startingPos[playerID == "musiast01"]) # All-Star rosters from the 1966 ASG subset(AllstarFull, gameID == "NLS196607120") # All-Stars from the Washington Nationals subset(AllstarFull, teamID == "WAS") # Teams with the fewest All-Stars rare <- names(which(table(AllstarFull$teamID) < 10)) # Records associated with the 'rare' teams: # (There are a few teamID typos: can you spot them?) subset(AllstarFull, teamID %in% rare)data(AllstarFull) # find number of appearances by players in the All Star games player_appearances <- with(AllstarFull, rev(sort(table(playerID)))) # How many All-Star players, in total? length(player_appearances) # density plot of the whole distribution plot(density(player_appearances), main="Player appearances in All Star Games") rug(jitter(player_appearances)) # who has played in more than 10 ASGs? player_appearances[player_appearances > 10] hist(player_appearances[player_appearances > 10]) # Hank Aaron's All-Star record: subset(AllstarFull, playerID == "aaronha01") # Years that Stan Musial played in the ASG: with(AllstarFull, yearID[playerID == "musiast01"]) # Starting positions he played (NA means did not start) with(AllstarFull, startingPos[playerID == "musiast01"]) # All-Star rosters from the 1966 ASG subset(AllstarFull, gameID == "NLS196607120") # All-Stars from the Washington Nationals subset(AllstarFull, teamID == "WAS") # Teams with the fewest All-Stars rare <- names(which(table(AllstarFull$teamID) < 10)) # Records associated with the 'rare' teams: # (There are a few teamID typos: can you spot them?) subset(AllstarFull, teamID %in% rare)
Data on player appearances
data(Appearances)data(Appearances)
A data frame with 113720 observations on the following 21 variables.
yearIDYear
teamIDTeam; a factor
lgIDLeague; a factor with levels AA AL FL NL PL UA
playerIDPlayer ID code
G_allTotal games played
GSGames started
G_battingGames in which player batted
G_defenseGames in which player appeared on defense
G_pGames as pitcher
G_cGames as catcher
G_1bGames as firstbaseman
G_2bGames as secondbaseman
G_3bGames as thirdbaseman
G_ssGames as shortstop
G_lfGames as leftfielder
G_cfGames as centerfielder
G_rfGames as right fielder
G_ofGames as outfielder
G_dhGames as designated hitter
G_phGames as pinch hitter
G_prGames as pinch runner
The Appearances table in the original version has some incorrect variable names.
In particular, the 5th column is career_year.
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
data(Appearances) library("dplyr") library("tidyr") # Henry Aaron's last two years as a DH in Milwaukee Appearances %>% filter(playerID == "aaronha01" & teamID == "ML4") %>% select(yearID:G_batting, G_of:G_ph) # subset variables # Herb Washington, strictly a pinch runner for Oakland in 1974-5 Appearances %>% filter(playerID == "washihe01") # A true utility player - Jerry Hairston, Jr. Appearances %>% filter(playerID == "hairsje02") # Appearances for the 1984 Cleveland Indians Appearances %>% filter(teamID == "CLE" & yearID == 1984) # Pete Rose's primary position each year of his career Appearances %>% filter(playerID == "rosepe01") %>% group_by(yearID, teamID) %>% gather(pos, G, G_1b:G_rf) %>% filter(G == max(G)) %>% select(yearID:G_all, pos, G) %>% mutate(pos = substring(as.character(pos), 3, 4)) %>% arrange(yearID, teamID) # Most pitcher appearances each year since 1950 Appearances %>% filter(yearID >= 1950) %>% group_by(yearID) %>% summarise(maxPitcher = playerID[which.max(G_p)], maxAppear = max(G_p)) # Individuals who have played all 162 games since 1961 all162 <- Appearances %>% filter(yearID > 1960 & G_all == 162) %>% arrange(yearID, playerID) %>% select(yearID:G_all) # Number of all-gamers by year (returns a vector) table(all162$yearID) # Players with most pinch hitting appearances in a year Appearances %>% arrange(desc(G_ph)) %>% select(playerID, yearID, teamID, lgID, G_all, G_ph) %>% head(., 10) # Players with most pinch hitting appearances, career Appearances %>% group_by(playerID) %>% select(playerID, G_all, G_ph) %>% summarise(G = sum(G_all), PH = sum(G_ph)) %>% arrange(desc(PH)) %>% head(., 10) # Players with most career appearances at each position Appearances %>% select(playerID, G_c:G_rf) %>% rename(C = G_c, `1B` = G_1b, `2B` = G_2b, SS = G_ss, `3B` = G_3b, LF = G_lf, CF = G_cf, RF = G_rf) %>% gather(pos, G, C:RF) %>% group_by(pos, playerID) %>% summarise(G = sum(G)) %>% arrange(desc(G)) %>% do(head(., 1))data(Appearances) library("dplyr") library("tidyr") # Henry Aaron's last two years as a DH in Milwaukee Appearances %>% filter(playerID == "aaronha01" & teamID == "ML4") %>% select(yearID:G_batting, G_of:G_ph) # subset variables # Herb Washington, strictly a pinch runner for Oakland in 1974-5 Appearances %>% filter(playerID == "washihe01") # A true utility player - Jerry Hairston, Jr. Appearances %>% filter(playerID == "hairsje02") # Appearances for the 1984 Cleveland Indians Appearances %>% filter(teamID == "CLE" & yearID == 1984) # Pete Rose's primary position each year of his career Appearances %>% filter(playerID == "rosepe01") %>% group_by(yearID, teamID) %>% gather(pos, G, G_1b:G_rf) %>% filter(G == max(G)) %>% select(yearID:G_all, pos, G) %>% mutate(pos = substring(as.character(pos), 3, 4)) %>% arrange(yearID, teamID) # Most pitcher appearances each year since 1950 Appearances %>% filter(yearID >= 1950) %>% group_by(yearID) %>% summarise(maxPitcher = playerID[which.max(G_p)], maxAppear = max(G_p)) # Individuals who have played all 162 games since 1961 all162 <- Appearances %>% filter(yearID > 1960 & G_all == 162) %>% arrange(yearID, playerID) %>% select(yearID:G_all) # Number of all-gamers by year (returns a vector) table(all162$yearID) # Players with most pinch hitting appearances in a year Appearances %>% arrange(desc(G_ph)) %>% select(playerID, yearID, teamID, lgID, G_all, G_ph) %>% head(., 10) # Players with most pinch hitting appearances, career Appearances %>% group_by(playerID) %>% select(playerID, G_all, G_ph) %>% summarise(G = sum(G_all), PH = sum(G_ph)) %>% arrange(desc(PH)) %>% head(., 10) # Players with most career appearances at each position Appearances %>% select(playerID, G_c:G_rf) %>% rename(C = G_c, `1B` = G_1b, `2B` = G_2b, SS = G_ss, `3B` = G_3b, LF = G_lf, CF = G_cf, RF = G_rf) %>% gather(pos, G, C:RF) %>% group_by(pos, playerID) %>% summarise(G = sum(G)) %>% arrange(desc(G)) %>% do(head(., 1))
Award information for managers awards
data(AwardsManagers)data(AwardsManagers)
A data frame with 193 observations on the following 6 variables.
playerIDManager (player) ID code
awardIDName of award won
yearIDYear
lgIDLeague; a factor with levels AL NL
tieAward was a tie (Y or N)
notesNotes about the award
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
# Post-season managerial awards # Number of recipients of each award by year with(AwardsManagers, table(yearID, awardID)) # 1996 award winners subset(AwardsManagers, yearID == 1996) # AL winners of the BBWAA managerial award subset(AwardsManagers, awardID == "BBWAA Manager of the year" & lgID == "AL") # Tony LaRussa's manager of the year awards subset(AwardsManagers, playerID == "larusto01")# Post-season managerial awards # Number of recipients of each award by year with(AwardsManagers, table(yearID, awardID)) # 1996 award winners subset(AwardsManagers, yearID == 1996) # AL winners of the BBWAA managerial award subset(AwardsManagers, awardID == "BBWAA Manager of the year" & lgID == "AL") # Tony LaRussa's manager of the year awards subset(AwardsManagers, playerID == "larusto01")
Award information for players awards
data(AwardsPlayers)data(AwardsPlayers)
A data frame with 6797 observations on the following 6 variables.
playerIDPlayer ID code
awardIDName of award won
yearIDYear
lgIDLeague; a factor with levels AA AL ML NL
tieAward was a tie (Y or N)
notesNotes about the award
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
data(AwardsPlayers) # Which awards have been given and how many? with(AwardsPlayers, table(awardID)) awardtab <- with(AwardsPlayers, table(awardID)) # Plot the awardtab table as a Cleveland dot plot library("lattice") dotplot(awardtab) # Restrict to MVP awards mvp <- subset(AwardsPlayers, awardID == "Most Valuable Player") # Who won in 1994? mvp[mvp$yearID == 1994L, ] goldglove <- subset(AwardsPlayers, awardID == "Gold Glove") # which players won most often? GGcount <- table(goldglove$playerID) GGcount[GGcount>10] # Triple Crown winners subset(AwardsPlayers, awardID == "Triple Crown") # Simultaneous Triple Crown and MVP winners # (compare merged file to TC) TC <- subset(AwardsPlayers, awardID == "Triple Crown") MVP <- subset(AwardsPlayers, awardID == "Most Valuable Player") keepvars <- c("playerID", "yearID", "lgID.x") merge(TC, MVP, by = c("playerID", "yearID"))[ ,keepvars]data(AwardsPlayers) # Which awards have been given and how many? with(AwardsPlayers, table(awardID)) awardtab <- with(AwardsPlayers, table(awardID)) # Plot the awardtab table as a Cleveland dot plot library("lattice") dotplot(awardtab) # Restrict to MVP awards mvp <- subset(AwardsPlayers, awardID == "Most Valuable Player") # Who won in 1994? mvp[mvp$yearID == 1994L, ] goldglove <- subset(AwardsPlayers, awardID == "Gold Glove") # which players won most often? GGcount <- table(goldglove$playerID) GGcount[GGcount>10] # Triple Crown winners subset(AwardsPlayers, awardID == "Triple Crown") # Simultaneous Triple Crown and MVP winners # (compare merged file to TC) TC <- subset(AwardsPlayers, awardID == "Triple Crown") MVP <- subset(AwardsPlayers, awardID == "Most Valuable Player") keepvars <- c("playerID", "yearID", "lgID.x") merge(TC, MVP, by = c("playerID", "yearID"))[ ,keepvars]
Batting table - batting statistics
data(Batting)data(Batting)
A data frame with 113799 observations on the following 22 variables.
playerIDPlayer ID code
yearIDYear
stintplayer's stint (order of appearances within a season)
teamIDTeam; a factor
lgIDLeague; a factor with levels AA AL FL NL PL UA
GGames: number of games in which a player played
ABAt Bats
RRuns
HHits: times reached base because of a batted, fair ball without error by the defense
X2BDoubles: hits on which the batter reached second base safely
X3BTriples: hits on which the batter reached third base safely
HRHomeruns
RBIRuns Batted In
SBStolen Bases
CSCaught Stealing
BBBase on Balls
SOStrikeouts
IBBIntentional walks
HBPHit by pitch
SHSacrifice hits
SFSacrifice flies
GIDPGrounded into double plays
Variables X2B and X3B are named 2B and 3B in the original database
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
battingStats for calculating batting average (BA) and other derived statistics
baseball for a similar dataset, but a subset of players who played 15 or more seasons.
Baseball for data on batting in the 1987 season.
data(Batting) head(Batting) require("dplyr") ## Prelude: Extract information from Salaries and People ## to be merged with the batting data. # Subset of Salaries data salaries <- Salaries %>% select(playerID, yearID, teamID, salary) # Subset of People table (player metadata) peopleInfo <- People %>% select(playerID, birthYear, birthMonth, nameLast, nameFirst, bats) # Left join salaries and peopleInfo to batting data, # create an age variable and sort by playerID, yearID and stint # Returns an ignorable warning. batting <- battingStats() %>% left_join(salaries, by =c("playerID", "yearID", "teamID")) %>% left_join(peopleInfo, by = "playerID") %>% mutate(age = yearID - birthYear - 1L *(birthMonth >= 10)) %>% arrange(playerID, yearID, stint) ## Generate a ggplot similar to the NYT graph in the story about Ted ## Williams and the last .400 MLB season # http://www.nytimes.com/interactive/2011/09/18/sports/baseball/WILLIAMS-GRAPHIC.html # Restrict the pool of eligible players to the years after 1899 and # players with a minimum of 450 plate appearances (this covers the # strike year of 1994 when Tony Gwynn hit .394 before play was suspended # for the season - in a normal year, the minimum number of plate appearances is 502) eligibleHitters <- batting %>% filter(yearID >= 1900 & PA > 450) # Find the hitters with the highest BA in MLB each year (there are a # few ties). Include all players with BA > .400, whether they # won a batting title or not, and add an indicator variable for # .400 average in a season. topHitters <- eligibleHitters %>% group_by(yearID) %>% filter(BA == max(BA)| BA >= .400) %>% mutate(ba400 = BA >= 0.400) %>% select(playerID, yearID, nameLast, nameFirst, BA, ba400) # Sub-data frame for the .400 hitters plus the outliers after 1950 # (averages above .380) - used to produce labels in the plot below bignames <- topHitters %>% filter(ba400 | (yearID > 1950 & BA > 0.380)) %>% arrange(desc(BA)) # Variable to provide a vertical offset to certain # labels in the ggplot below bignames$yoffset <- c(0, 0, 0, 0, 0.002, 0, 0, 0, 0.001, -0.001, 0, -0.002, 0, 0, 0.002, 0, 0) # Produce the plot require("ggplot2") ggplot(topHitters, aes(x = yearID, y = BA)) + geom_point(aes(colour = ba400), size = 2.5) + geom_hline(yintercept = 0.400, size = 1, colour = "gray70") + geom_text(data = bignames, aes(y = BA + yoffset, label = nameLast), size = 3, hjust = 1.2) + scale_colour_manual(values = c("FALSE" = "black", "TRUE" = "red")) + xlim(1899, 2015) + xlab("Year") + scale_y_continuous("Batting average", limits = c(0.330, 0.430), breaks = seq(0.34, 0.42, by = 0.02), labels = c(".340", ".360", ".380", ".400", ".420")) + geom_smooth() + theme(legend.position = "none") ########################################################## # after Chris Green, # http://sabr.org/research/baseball-s-first-power-surge-home-runs-late-19th-century-major-leagues # Total home runs by year totalHR <- Batting %>% group_by(yearID) %>% summarise(HomeRuns = sum(as.numeric(HR), na.rm=TRUE), Games = sum(as.numeric(G), na.rm=TRUE)) # Plot HR by year, pre-1919 (dead ball era) totalHR %>% filter(yearID <= 1918) %>% ggplot(., aes(x = yearID, y = HomeRuns)) + geom_line() + geom_point() + labs(x = "Year", y = "Home runs hit") # Take games into account totalHR %>% filter(yearID <= 1918) %>% ggplot(., aes(x = yearID, y = HomeRuns/Games)) + geom_line() + geom_point() + labs(x = "Year", y = "Home runs per game played") # Widen perspective to all years from 1871 ggplot(totalHR, aes(x = yearID, y = HomeRuns)) + geom_point() + geom_path() + geom_smooth() + labs(x = "Year", y = "Home runs hit") # Similar plot for HR per game played by year - # shows several eras with spikes in HR hit ggplot(totalHR, aes(x = yearID, y = HomeRuns/Games)) + geom_point() + geom_path() + geom_smooth(se = FALSE) + labs(x = "Year", y = "Home runs per game played")data(Batting) head(Batting) require("dplyr") ## Prelude: Extract information from Salaries and People ## to be merged with the batting data. # Subset of Salaries data salaries <- Salaries %>% select(playerID, yearID, teamID, salary) # Subset of People table (player metadata) peopleInfo <- People %>% select(playerID, birthYear, birthMonth, nameLast, nameFirst, bats) # Left join salaries and peopleInfo to batting data, # create an age variable and sort by playerID, yearID and stint # Returns an ignorable warning. batting <- battingStats() %>% left_join(salaries, by =c("playerID", "yearID", "teamID")) %>% left_join(peopleInfo, by = "playerID") %>% mutate(age = yearID - birthYear - 1L *(birthMonth >= 10)) %>% arrange(playerID, yearID, stint) ## Generate a ggplot similar to the NYT graph in the story about Ted ## Williams and the last .400 MLB season # http://www.nytimes.com/interactive/2011/09/18/sports/baseball/WILLIAMS-GRAPHIC.html # Restrict the pool of eligible players to the years after 1899 and # players with a minimum of 450 plate appearances (this covers the # strike year of 1994 when Tony Gwynn hit .394 before play was suspended # for the season - in a normal year, the minimum number of plate appearances is 502) eligibleHitters <- batting %>% filter(yearID >= 1900 & PA > 450) # Find the hitters with the highest BA in MLB each year (there are a # few ties). Include all players with BA > .400, whether they # won a batting title or not, and add an indicator variable for # .400 average in a season. topHitters <- eligibleHitters %>% group_by(yearID) %>% filter(BA == max(BA)| BA >= .400) %>% mutate(ba400 = BA >= 0.400) %>% select(playerID, yearID, nameLast, nameFirst, BA, ba400) # Sub-data frame for the .400 hitters plus the outliers after 1950 # (averages above .380) - used to produce labels in the plot below bignames <- topHitters %>% filter(ba400 | (yearID > 1950 & BA > 0.380)) %>% arrange(desc(BA)) # Variable to provide a vertical offset to certain # labels in the ggplot below bignames$yoffset <- c(0, 0, 0, 0, 0.002, 0, 0, 0, 0.001, -0.001, 0, -0.002, 0, 0, 0.002, 0, 0) # Produce the plot require("ggplot2") ggplot(topHitters, aes(x = yearID, y = BA)) + geom_point(aes(colour = ba400), size = 2.5) + geom_hline(yintercept = 0.400, size = 1, colour = "gray70") + geom_text(data = bignames, aes(y = BA + yoffset, label = nameLast), size = 3, hjust = 1.2) + scale_colour_manual(values = c("FALSE" = "black", "TRUE" = "red")) + xlim(1899, 2015) + xlab("Year") + scale_y_continuous("Batting average", limits = c(0.330, 0.430), breaks = seq(0.34, 0.42, by = 0.02), labels = c(".340", ".360", ".380", ".400", ".420")) + geom_smooth() + theme(legend.position = "none") ########################################################## # after Chris Green, # http://sabr.org/research/baseball-s-first-power-surge-home-runs-late-19th-century-major-leagues # Total home runs by year totalHR <- Batting %>% group_by(yearID) %>% summarise(HomeRuns = sum(as.numeric(HR), na.rm=TRUE), Games = sum(as.numeric(G), na.rm=TRUE)) # Plot HR by year, pre-1919 (dead ball era) totalHR %>% filter(yearID <= 1918) %>% ggplot(., aes(x = yearID, y = HomeRuns)) + geom_line() + geom_point() + labs(x = "Year", y = "Home runs hit") # Take games into account totalHR %>% filter(yearID <= 1918) %>% ggplot(., aes(x = yearID, y = HomeRuns/Games)) + geom_line() + geom_point() + labs(x = "Year", y = "Home runs per game played") # Widen perspective to all years from 1871 ggplot(totalHR, aes(x = yearID, y = HomeRuns)) + geom_point() + geom_path() + geom_smooth() + labs(x = "Year", y = "Home runs hit") # Similar plot for HR per game played by year - # shows several eras with spikes in HR hit ggplot(totalHR, aes(x = yearID, y = HomeRuns/Games)) + geom_point() + geom_path() + geom_smooth(se = FALSE) + labs(x = "Year", y = "Home runs per game played")
These data frames provide descriptive labels for the variables in the
Batting,
Pitching and
Fielding files (and related *Post files).
They are useful for plots and other output using Label.
data(battingLabels) data(fieldingLabels) data(pitchingLabels)data(battingLabels) data(fieldingLabels) data(pitchingLabels)
Each is data frame with observations on the following 2 variables.
variablevariable name
labelvariable label
data(battingLabels) str(battingLabels) require("dplyr") # find and plot maximum number of homers per year batHR <- Batting %>% filter(!is.na(HR)) %>% group_by(yearID) %>% summarise(max=max(HR)) with(batHR, { plot(yearID, max, xlab=Label("yearID"), ylab=paste("Maximum", Label("HR")), cex=0.8) lines(lowess(yearID, max), col="blue", lwd=2) abline(lm(max ~ yearID), col="red", lwd=2) })data(battingLabels) str(battingLabels) require("dplyr") # find and plot maximum number of homers per year batHR <- Batting %>% filter(!is.na(HR)) %>% group_by(yearID) %>% summarise(max=max(HR)) with(batHR, { plot(yearID, max, xlab=Label("yearID"), ylab=paste("Maximum", Label("HR")), cex=0.8) lines(lowess(yearID, max), col="blue", lwd=2) abline(lm(max ~ yearID), col="red", lwd=2) })
Post season batting statistics
data(BattingPost)data(BattingPost)
A data frame with 16857 observations on the following 22 variables.
yearIDYear
roundLevel of playoffs
playerIDPlayer ID code
teamIDTeam
lgIDLeague; a factor with levels AA AL NL
GGames
ABAt Bats
RRuns
HHits
X2BDoubles
X3BTriples
HRHomeruns
RBIRuns Batted In
SBStolen Bases
CSCaught stealing
BBBase on Balls
SOStrikeouts
IBBIntentional walks
HBPHit by pitch
SHSacrifices
SFSacrifice flies
GIDPGrounded into double plays
Variables X2B and X3B are named 2B and 3B in the original database
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
# Post-season batting data # Requires care since intra-league playoffs have evolved since 1969 # Simplest case: World Series require("dplyr") # Create a sub-data frame for modern World Series play ws <- BattingPost %>% filter(round == "WS" & yearID >= 1903) %>% mutate(BA = 0 + (AB > 0) * round(H/AB, 3), TB = H + X2B + 2 * X3B + 3 * HR, SA = 0 + (AB > 0) * round(TB/AB, 3), PA = AB + BB + IBB + HBP + SH + SF, OB = H + BB + IBB + HBP, OBP = 0 + (AB > 0) * round(OB/PA, 3) ) # Players with most appearances in the WS: ws %>% group_by(playerID) %>% summarise(appearances = n()) %>% arrange(desc(appearances)) %>% head(., 10) # Non-Yankees with most WS appearances ws %>% filter(teamID != "NYA") %>% group_by(playerID) %>% summarise(appearances = n()) %>% arrange(desc(appearances)) %>% head(., 10) # Top ten single WS batting averages ( >= 10 AB ) ws %>% filter(AB > 10) %>% arrange(desc(BA)) %>% head(., 10) # Top ten slugging averages in a single WS ws %>% filter(AB > 10) %>% arrange(desc(SA)) %>% head(., 10) # Hitting stats for the 1946 St. Louis Cardinals, ordered by BA ws %>% filter(teamID == "SLN" & yearID == 1946) %>% arrange(desc(BA)) # Babe Ruth's WS profile ws %>% filter(playerID == "ruthba01") %>% arrange(yearID)# Post-season batting data # Requires care since intra-league playoffs have evolved since 1969 # Simplest case: World Series require("dplyr") # Create a sub-data frame for modern World Series play ws <- BattingPost %>% filter(round == "WS" & yearID >= 1903) %>% mutate(BA = 0 + (AB > 0) * round(H/AB, 3), TB = H + X2B + 2 * X3B + 3 * HR, SA = 0 + (AB > 0) * round(TB/AB, 3), PA = AB + BB + IBB + HBP + SH + SF, OB = H + BB + IBB + HBP, OBP = 0 + (AB > 0) * round(OB/PA, 3) ) # Players with most appearances in the WS: ws %>% group_by(playerID) %>% summarise(appearances = n()) %>% arrange(desc(appearances)) %>% head(., 10) # Non-Yankees with most WS appearances ws %>% filter(teamID != "NYA") %>% group_by(playerID) %>% summarise(appearances = n()) %>% arrange(desc(appearances)) %>% head(., 10) # Top ten single WS batting averages ( >= 10 AB ) ws %>% filter(AB > 10) %>% arrange(desc(BA)) %>% head(., 10) # Top ten slugging averages in a single WS ws %>% filter(AB > 10) %>% arrange(desc(SA)) %>% head(., 10) # Hitting stats for the 1946 St. Louis Cardinals, ordered by BA ws %>% filter(teamID == "SLN" & yearID == 1946) %>% arrange(desc(BA)) # Babe Ruth's WS profile ws %>% filter(playerID == "ruthba01") %>% arrange(yearID)
The Batting does not contain batting statistics derived from those
present in the data.frame. This function calculates
batting average (BA),
plate appearances (PA),
total bases (TB),
slugging percentage (SlugPct),
on-base percentage (OBP),
on-base percentage + slugging (OPS), and
batting average on balls in play (BABIP)
for each record in a Batting-like data.frame.
battingStats(data = Lahman::Batting, idvars = c("playerID", "yearID", "stint", "teamID", "lgID"), cbind = TRUE)battingStats(data = Lahman::Batting, idvars = c("playerID", "yearID", "stint", "teamID", "lgID"), cbind = TRUE)
data |
input data, typically |
idvars |
ID variables to include in the output data.frame |
cbind |
If |
Standard calculations, e.g., BA <- H/AB are problematic because of the
presence of NAs and zeros. This function tries to deal with those
problems.
A data.frame with all the observations in data.
If cbind==FALSE, only the idvars and the calculated variables are returned.
Michael Friendly, Dennis Murphy
bstats <- battingStats() str(bstats) bstats <- battingStats(cbind=FALSE) str(bstats)bstats <- battingStats() str(bstats) bstats <- battingStats(cbind=FALSE) str(bstats)
Information on schools players attended, by player
data(CollegePlaying)data(CollegePlaying)
A data frame with 17350 observations on the following 3 variables.
playerIDPlayer ID code
schoolIDschool ID code
yearIDYear player attended school
This data set reflects a change in the Lahman schema for the 2015 version.
The old SchoolsPlayers table was replaced with
this new table called CollegePlaying.
According to the documentation, this change reflects advances in the compilation of this data, largely led by Ted Turocy. The old table reported college attendance for major league players by listing a start date and end date. The new version has a separate record for each year that a player attended. This allows us to better account for players who attended multiple colleges or skipped a season, as well as to identify teammates.
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
data(CollegePlaying) head(CollegePlaying) ## Q: What are the top universities for producing MLB players? SPcount <- table(CollegePlaying$schoolID) SPcount[SPcount>50] library("lattice") dotplot(SPcount[SPcount>50]) dotplot(sort(SPcount[SPcount>50])) ## Q: How many schools are represented in this dataset? length(table(CollegePlaying$schoolID)) # Histogram of the number of players from each school who played in MLB: with(CollegePlaying, hist(table(schoolID), xlab = "Number of players", main = ""))data(CollegePlaying) head(CollegePlaying) ## Q: What are the top universities for producing MLB players? SPcount <- table(CollegePlaying$schoolID) SPcount[SPcount>50] library("lattice") dotplot(SPcount[SPcount>50]) dotplot(sort(SPcount[SPcount>50])) ## Q: How many schools are represented in this dataset? length(table(CollegePlaying$schoolID)) # Histogram of the number of players from each school who played in MLB: with(CollegePlaying, hist(table(schoolID), xlab = "Number of players", main = ""))
Fielding table
data(Fielding)data(Fielding)
A data frame with 151507 observations on the following 18 variables.
playerIDPlayer ID code
yearIDYear
stintplayer's stint (order of appearances within a season)
teamIDTeam; a factor
lgIDLeague; a factor with levels AA AL FL NL PL UA
POSPosition
GGames
GSGames Started
InnOutsTime played in the field expressed as outs
POPutouts
AAssists
EErrors
DPDouble Plays
PBPassed Balls (by catchers)
WPWild Pitches (by catchers)
SBOpponent Stolen Bases (by catchers)
CSOpponents Caught Stealing (by catchers)
ZRZone Rating
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
data(Fielding) # Basic fielding data require("dplyr") # Roberto Clemente's fielding profile # pitching and catching related data removed # subset(Fielding, playerID == "clemero01")[, 1:13] Fielding %>% filter(playerID == "clemero01") %>% select(1:13) # Yadier Molina's fielding profile # PB, WP, SP and CS apply to catchers Fielding %>% subset(playerID == "molinya01") %>% select(-WP, -ZR) # Pedro Martinez's fielding profile Fielding %>% subset(playerID == "martipe02") # Table of games played by Pete Rose at different positions with(subset(Fielding, playerID == "rosepe01"), xtabs(G ~ POS)) # Career total G/PO/A/E/DP for Luis Aparicio Fielding %>% filter(playerID == "aparilu01") %>% select(G, PO, A, E, DP) %>% summarise_each(funs(sum)) # Top ten 2B/SS in turning DPs Fielding %>% subset(POS %in% c("2B", "SS")) %>% group_by(playerID) %>% summarise(TDP = sum(DP, na.rm = TRUE)) %>% arrange(desc(TDP)) %>% head(., 10) # League average fielding statistics, 1961-present Fielding %>% filter(yearID >= 1961 & POS != "DH") %>% select(yearID, lgID, POS, InnOuts, PO, A, E) %>% group_by(yearID, lgID) %>% summarise_at(vars(InnOuts, PO, A, E), funs(sum), na.rm = TRUE) %>% mutate(fpct = round( (PO + A)/(PO + A + E), 3), OPE = round(InnOuts/E, 3))data(Fielding) # Basic fielding data require("dplyr") # Roberto Clemente's fielding profile # pitching and catching related data removed # subset(Fielding, playerID == "clemero01")[, 1:13] Fielding %>% filter(playerID == "clemero01") %>% select(1:13) # Yadier Molina's fielding profile # PB, WP, SP and CS apply to catchers Fielding %>% subset(playerID == "molinya01") %>% select(-WP, -ZR) # Pedro Martinez's fielding profile Fielding %>% subset(playerID == "martipe02") # Table of games played by Pete Rose at different positions with(subset(Fielding, playerID == "rosepe01"), xtabs(G ~ POS)) # Career total G/PO/A/E/DP for Luis Aparicio Fielding %>% filter(playerID == "aparilu01") %>% select(G, PO, A, E, DP) %>% summarise_each(funs(sum)) # Top ten 2B/SS in turning DPs Fielding %>% subset(POS %in% c("2B", "SS")) %>% group_by(playerID) %>% summarise(TDP = sum(DP, na.rm = TRUE)) %>% arrange(desc(TDP)) %>% head(., 10) # League average fielding statistics, 1961-present Fielding %>% filter(yearID >= 1961 & POS != "DH") %>% select(yearID, lgID, POS, InnOuts, PO, A, E) %>% group_by(yearID, lgID) %>% summarise_at(vars(InnOuts, PO, A, E), funs(sum), na.rm = TRUE) %>% mutate(fpct = round( (PO + A)/(PO + A + E), 3), OPE = round(InnOuts/E, 3))
Outfield position data: information about positions played in the outfield
data(FieldingOF)data(FieldingOF)
A data frame with 12380 observations on the following 6 variables.
playerIDPlayer ID code
yearIDYear
stintplayer's stint (order of appearances within a season)
GlfGames played in left field
GcfGames played in center field
GrfGames played in right field
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
require("dplyr") require("tidyr") ## Data set only goes through 1955 ## Can get a more complete record from the Fielding data frame ## or from the Appearances data (see below) ## Output directly from the FieldingOF data ## Barry Bonds (no records: post-1955 player) FieldingOF %>% filter(playerID == "bondsba01") ## Willie Mays (first few years) FieldingOF %>% filter(playerID == "mayswi01") ## Ty Cobb (complete) FieldingOF %>% filter(playerID == "cobbty01") ## One way to get OF game information from the Fielding data ## Note: OF games != sum(LF, CF, RF) because players can switch ## OF positions within a game. Players can also switch from ## other positions to outfield during a game. OF represents ## the number of games a player started in the outfield. Fielding %>% select(playerID, yearID, stint, POS, G) %>% filter(POS %in% c("LF", "CF", "RF", "OF")) %>% tidyr::spread(POS, G, fill = 0) %>% filter(playerID == "trumbma01") ## Another way is through the Appearances data (no stint). ## Provides a somewhat nicer table than the above. ## Mark Trumbo (active player) Appearances %>% select(playerID, yearID, G_lf, G_cf, G_rf, G_of) %>% filter(playerID == "trumbma01") ## A slightly better format, perhaps Appearances %>% select(playerID, yearID, G_lf, G_cf, G_rf, G_of) %>% rename(LF = G_lf, CF = G_cf, RF = G_rf, OF = G_of) %>% filter(playerID == "trumbma01") ## Willie Mays (1951-1973) Appearances %>% select(playerID, yearID, G_lf, G_cf, G_rf, G_of) %>% filter(playerID == "mayswi01") ## Joe DiMaggio (1936-1951) Appearances %>% select(playerID, yearID, G_lf, G_cf, G_rf, G_of) %>% filter(playerID == "dimagjo01")require("dplyr") require("tidyr") ## Data set only goes through 1955 ## Can get a more complete record from the Fielding data frame ## or from the Appearances data (see below) ## Output directly from the FieldingOF data ## Barry Bonds (no records: post-1955 player) FieldingOF %>% filter(playerID == "bondsba01") ## Willie Mays (first few years) FieldingOF %>% filter(playerID == "mayswi01") ## Ty Cobb (complete) FieldingOF %>% filter(playerID == "cobbty01") ## One way to get OF game information from the Fielding data ## Note: OF games != sum(LF, CF, RF) because players can switch ## OF positions within a game. Players can also switch from ## other positions to outfield during a game. OF represents ## the number of games a player started in the outfield. Fielding %>% select(playerID, yearID, stint, POS, G) %>% filter(POS %in% c("LF", "CF", "RF", "OF")) %>% tidyr::spread(POS, G, fill = 0) %>% filter(playerID == "trumbma01") ## Another way is through the Appearances data (no stint). ## Provides a somewhat nicer table than the above. ## Mark Trumbo (active player) Appearances %>% select(playerID, yearID, G_lf, G_cf, G_rf, G_of) %>% filter(playerID == "trumbma01") ## A slightly better format, perhaps Appearances %>% select(playerID, yearID, G_lf, G_cf, G_rf, G_of) %>% rename(LF = G_lf, CF = G_cf, RF = G_rf, OF = G_of) %>% filter(playerID == "trumbma01") ## Willie Mays (1951-1973) Appearances %>% select(playerID, yearID, G_lf, G_cf, G_rf, G_of) %>% filter(playerID == "mayswi01") ## Joe DiMaggio (1936-1951) Appearances %>% select(playerID, yearID, G_lf, G_cf, G_rf, G_of) %>% filter(playerID == "dimagjo01")
Outfield position data: information about positions played in the outfield
data(FieldingOFsplit)data(FieldingOFsplit)
A data frame with 35995 observations on the following 18 variables.
playerIDPlayer ID code
yearIDYear
stintplayer's stint (order of appearances within a season)
teamIDTeam; a factor
lgIDLeague; a factor with levels AA AL FL NL PL UA
POSPosition
GGames
GSGames Started
InnOutsTime played in the field expressed as outs
POPutouts
AAssists
EErrors
DPDouble Plays
PBPassed Balls (by catchers)
WPWild Pitches (by catchers)
SBOpponent Stolen Bases (by catchers)
CSOpponents Caught Stealing (by catchers)
ZRZone Rating
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
require("dplyr") require("tidyr") ## Data set starts in 1954 ## Can get a more complete record from the Fielding data frame ## or from the Appearances data (see below) ## Output directly from the FieldingOFsplit data ## Joe DiMaggio (no records: pre-1954 player) FieldingOFsplit %>% filter(playerID == "dimagjo01") ## Willie Mays (all but his first few years) FieldingOF %>% filter(playerID == "mayswi01") ## Mike Trout (complete) FieldingOF %>% filter(playerID == "troutmi01")require("dplyr") require("tidyr") ## Data set starts in 1954 ## Can get a more complete record from the Fielding data frame ## or from the Appearances data (see below) ## Output directly from the FieldingOFsplit data ## Joe DiMaggio (no records: pre-1954 player) FieldingOFsplit %>% filter(playerID == "dimagjo01") ## Willie Mays (all but his first few years) FieldingOF %>% filter(playerID == "mayswi01") ## Mike Trout (complete) FieldingOF %>% filter(playerID == "troutmi01")
Post season fielding data
data(FieldingPost)data(FieldingPost)
A data frame with 16606 observations on the following 17 variables.
playerIDPlayer ID code
yearIDYear
teamIDTeam; a factor
lgIDLeague; a factor with levels AL NL
roundLevel of playoffs
POSPosition
GGames
GSGames Started
InnOutsTime played in the field expressed as outs
POPutouts
AAssists
EErrors
DPDouble Plays
TPTriple Plays
PBPassed Balls
SBStolen Bases allowed (by catcher)
CSCaught Stealing (by catcher)
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
require("dplyr") ## World Series fielding record for Yogi Berra FieldingPost %>% filter(playerID == "berrayo01" & round == "WS") ## Yogi's career efficiency in throwing out base stealers ## in his WS appearances and CS as a percentage of his ## overall assists FieldingPost %>% filter(playerID == "berrayo01" & round == "WS" & POS == "C") %>% summarise(cs_pct = round(100 * sum(CS)/sum(SB + CS), 2), cs_assists = round(100 * sum(CS)/sum(A), 2)) ## Innings per error for several selected shortstops in the WS FieldingPost %>% filter(playerID %in% c("belanma01", "jeterde01", "campabe01", "conceda01", "bowala01"), round == "WS") %>% group_by(playerID) %>% summarise(G = sum(G), InnOuts = sum(InnOuts), Eper9 = round(27 * sum(E)/sum(InnOuts), 3)) ## Top 10 center fielders in innings played in the WS FieldingPost %>% filter(POS == "CF" & round == "WS") %>% group_by(playerID) %>% summarise(inn_total = sum(InnOuts)) %>% arrange(desc(inn_total)) %>% head(., 10) ## Most total chances by position FieldingPost %>% filter(round == "WS" & !(POS %in% c("DH", "OF", "P"))) %>% group_by(POS, playerID) %>% summarise(TC = sum(PO + A + E)) %>% arrange(desc(TC)) %>% do(head(., 1)) # provides top player by positionrequire("dplyr") ## World Series fielding record for Yogi Berra FieldingPost %>% filter(playerID == "berrayo01" & round == "WS") ## Yogi's career efficiency in throwing out base stealers ## in his WS appearances and CS as a percentage of his ## overall assists FieldingPost %>% filter(playerID == "berrayo01" & round == "WS" & POS == "C") %>% summarise(cs_pct = round(100 * sum(CS)/sum(SB + CS), 2), cs_assists = round(100 * sum(CS)/sum(A), 2)) ## Innings per error for several selected shortstops in the WS FieldingPost %>% filter(playerID %in% c("belanma01", "jeterde01", "campabe01", "conceda01", "bowala01"), round == "WS") %>% group_by(playerID) %>% summarise(G = sum(G), InnOuts = sum(InnOuts), Eper9 = round(27 * sum(E)/sum(InnOuts), 3)) ## Top 10 center fielders in innings played in the WS FieldingPost %>% filter(POS == "CF" & round == "WS") %>% group_by(playerID) %>% summarise(inn_total = sum(InnOuts)) %>% arrange(desc(inn_total)) %>% head(., 10) ## Most total chances by position FieldingPost %>% filter(round == "WS" & !(POS %in% c("DH", "OF", "P"))) %>% group_by(POS, playerID) %>% summarise(TC = sum(PO + A + E)) %>% arrange(desc(TC)) %>% do(head(., 1)) # provides top player by position
Hall of Fame table. This is composed of the voting results for all candidates nominated for the Baseball Hall of Fame.
data(HallOfFame)data(HallOfFame)
A data frame with 6382 observations on the following 9 variables.
playerIDPlayer ID code
yearIDYear of ballot
votedByMethod by which player was voted upon. See Details
ballotsTotal ballots cast in that year
neededNumber of votes needed for selection in that year
votesTotal votes received
inductedWhether player was inducted by that vote or not (Y or N)
categoryCategory of candidate; a factor with levels Manager Pioneer/Executive Player Umpire
needed_noteExplanation of qualifiers for special elections
This table links to the People table via the playerID.
votedBy: Most Hall of Fame inductees have been elected by the
Baseball Writers Association of America (BBWAA). Rules for election are
described in https://en.wikipedia.org/wiki/National_Baseball_Hall_of_Fame_and_Museum#Selection_process.
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
## Some examples for Hall of Fame induction data require("dplyr") require("ggplot2") ############################################################ ## Some simple queries # What are the different types of HOF voters? table(HallOfFame$votedBy) # What was the first year of Hall of Fame elections? sort(unique(HallOfFame$yearID))[1] # Who comprised the original class? subset(HallOfFame, yearID == 1936 & inducted == "Y") # Result of a player's last year on the BBWAA ballot # Restrict to players voted by BBWAA: HOFplayers <- subset(HallOfFame, votedBy == "BBWAA" & category == "Player") # Number of years as HOF candidate, last pct vote, etc. # for a given player playerOutcomes <- HallOfFame %>% filter(votedBy == "BBWAA" & category == "Player") %>% group_by(playerID) %>% mutate(nyears = length(ballots)) %>% arrange(yearID) %>% do(tail(., 1)) %>% mutate(lastPct = 100 * round(votes/ballots, 3)) %>% select(playerID, nyears, inducted, lastPct, yearID) %>% rename(lastYear = yearID) ############################################################ # How many voting years until election? inducted <- subset(playerOutcomes, inducted == "Y") table(inducted$nyears) # Bar chart of years to induction for inductees barplot(table(inducted$nyears), main="Number of voting years until election", ylab="Number of players", xlab="Years") box() # What is the form of this distribution? require("vcd") goodfit(inducted$nyears) plot(goodfit(inducted$nyears), xlab="Number of years", main="Poissonness plot of number of years voting until election") Ord_plot(table(inducted$nyears), xlab="Number of years") # First ballot inductees sorted by vote percentage: playerOutcomes %>% filter(nyears == 1L & inducted == "Y") %>% arrange(desc(lastPct)) # Who took at least ten years on the ballot before induction? playerOutcomes %>% filter(nyears >= 10L & inducted == "Y") ############################################################ ## Plots of voting percentages over time for the borderline ## HOF candidates, according to the BBWAA: # Identify players on the BBWAA ballot for at least 10 years # Returns a character vector of playerIDs longTimers <- as.character(unlist(subset(playerOutcomes, nyears >= 10, select = "playerID"))) # Extract their information from the HallOfFame data HOFlt <- HallOfFame %>% filter(playerID %in% longTimers & votedBy == "BBWAA") %>% group_by(playerID) %>% mutate(elected = ifelse(any(inducted == "Y"), "Elected", "Not elected"), pct = 100 * round(votes/ballots, 3)) # Plot the voting profiles: ggplot(HOFlt, aes(x = yearID, y = pct, group = playerID)) + ggtitle("Profiles of BBWAA voting percentage, long-time HOF candidates") + geom_line() + geom_hline(yintercept = 75, colour = 'red') + labs(x = "Year", y = "Percentage of votes") + facet_wrap(~ elected, ncol = 1) ## Eventual inductees tend to have increasing support over time. ## Fit simple linear regression models to each player's voting ## percentage profile and extract the slopes. Then compare the ## distributions of the slopes in each group. # data frame for playerID and induction status among # long term candidates HOFstatus <- HOFlt %>% group_by(playerID) %>% select(playerID, elected, inducted) %>% do(tail(., 1)) # data frame of regression slopes, which represent average # increase in percentage support by BBWAA members over a # player's candidacy. HOFslope <- HOFlt %>% group_by(playerID) %>% do(mod = lm(pct ~ yearID, data = .)) %>% do(data.frame(slope = coef(.$mod)[2])) ## Boxplots of regression slopes by induction group ggplot(data.frame(HOFstatus, HOFslope), aes(x = elected, y = slope)) + geom_boxplot(width = 0.5) + geom_point(position = position_jitter(width = 0.2)) # Note 1: Only two players whose maximum voting percentage # was over 60% were not eventually inducted # into the HOF: Gil Hodges and Jack Morris. # Red Ruffing was elected in a 1967 runoff election while # the others have been voted in by the Veterans Committee. # Note 2: Of the players whose slope was >= 2.5 among # non-inductees, only Jack Morris has not (yet) been # subsequently inducted into the HOF; however, his last year of # eligibility was 2014 so he could be inducted by a future # Veterans Committee.## Some examples for Hall of Fame induction data require("dplyr") require("ggplot2") ############################################################ ## Some simple queries # What are the different types of HOF voters? table(HallOfFame$votedBy) # What was the first year of Hall of Fame elections? sort(unique(HallOfFame$yearID))[1] # Who comprised the original class? subset(HallOfFame, yearID == 1936 & inducted == "Y") # Result of a player's last year on the BBWAA ballot # Restrict to players voted by BBWAA: HOFplayers <- subset(HallOfFame, votedBy == "BBWAA" & category == "Player") # Number of years as HOF candidate, last pct vote, etc. # for a given player playerOutcomes <- HallOfFame %>% filter(votedBy == "BBWAA" & category == "Player") %>% group_by(playerID) %>% mutate(nyears = length(ballots)) %>% arrange(yearID) %>% do(tail(., 1)) %>% mutate(lastPct = 100 * round(votes/ballots, 3)) %>% select(playerID, nyears, inducted, lastPct, yearID) %>% rename(lastYear = yearID) ############################################################ # How many voting years until election? inducted <- subset(playerOutcomes, inducted == "Y") table(inducted$nyears) # Bar chart of years to induction for inductees barplot(table(inducted$nyears), main="Number of voting years until election", ylab="Number of players", xlab="Years") box() # What is the form of this distribution? require("vcd") goodfit(inducted$nyears) plot(goodfit(inducted$nyears), xlab="Number of years", main="Poissonness plot of number of years voting until election") Ord_plot(table(inducted$nyears), xlab="Number of years") # First ballot inductees sorted by vote percentage: playerOutcomes %>% filter(nyears == 1L & inducted == "Y") %>% arrange(desc(lastPct)) # Who took at least ten years on the ballot before induction? playerOutcomes %>% filter(nyears >= 10L & inducted == "Y") ############################################################ ## Plots of voting percentages over time for the borderline ## HOF candidates, according to the BBWAA: # Identify players on the BBWAA ballot for at least 10 years # Returns a character vector of playerIDs longTimers <- as.character(unlist(subset(playerOutcomes, nyears >= 10, select = "playerID"))) # Extract their information from the HallOfFame data HOFlt <- HallOfFame %>% filter(playerID %in% longTimers & votedBy == "BBWAA") %>% group_by(playerID) %>% mutate(elected = ifelse(any(inducted == "Y"), "Elected", "Not elected"), pct = 100 * round(votes/ballots, 3)) # Plot the voting profiles: ggplot(HOFlt, aes(x = yearID, y = pct, group = playerID)) + ggtitle("Profiles of BBWAA voting percentage, long-time HOF candidates") + geom_line() + geom_hline(yintercept = 75, colour = 'red') + labs(x = "Year", y = "Percentage of votes") + facet_wrap(~ elected, ncol = 1) ## Eventual inductees tend to have increasing support over time. ## Fit simple linear regression models to each player's voting ## percentage profile and extract the slopes. Then compare the ## distributions of the slopes in each group. # data frame for playerID and induction status among # long term candidates HOFstatus <- HOFlt %>% group_by(playerID) %>% select(playerID, elected, inducted) %>% do(tail(., 1)) # data frame of regression slopes, which represent average # increase in percentage support by BBWAA members over a # player's candidacy. HOFslope <- HOFlt %>% group_by(playerID) %>% do(mod = lm(pct ~ yearID, data = .)) %>% do(data.frame(slope = coef(.$mod)[2])) ## Boxplots of regression slopes by induction group ggplot(data.frame(HOFstatus, HOFslope), aes(x = elected, y = slope)) + geom_boxplot(width = 0.5) + geom_point(position = position_jitter(width = 0.2)) # Note 1: Only two players whose maximum voting percentage # was over 60% were not eventually inducted # into the HOF: Gil Hodges and Jack Morris. # Red Ruffing was elected in a 1967 runoff election while # the others have been voted in by the Veterans Committee. # Note 2: Of the players whose slope was >= 2.5 among # non-inductees, only Jack Morris has not (yet) been # subsequently inducted into the HOF; however, his last year of # eligibility was 2014 so he could be inducted by a future # Veterans Committee.
Data mapping teams to the stadiums they played regular season games in as the home team.
data(HomeGames)data(HomeGames)
A data frame with 3233 observations on the following 9 variables.
year.keyYear
league.keyLeague; a factor with levels AA AL FL NL PL UA
team.keyTeam; a factor
park.keyUnique identifier for each ballpark
span.firstFirst date the park began acting as home field for the team
span.lastLast date the park began acting as home field for the team
gamesTotal games in this time span
openingsTotal opening in this time span
attendanceTotal attendance in this time span
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
data(HomeGames) library(dplyr) # How many parks has every team played in as the home team for even a single game? HomeGames %>% count(team.key) %>% arrange(team.key) # What parks have the Toronto Blue Jays played in as the home team? HomeGames %>% filter(team.key == "TOR") %>% arrange(span.last) # What parks have the Boston Red Sox played in as the home team? HomeGames %>% filter(team.key == "BOS") %>% arrange(span.last) # What is the Toronto Blue Jays annual total home attendance by year? HomeGames %>% filter(team.key == "TOR") %>% group_by(year.key) %>% summarize(total.attendance = sum(attendance)) %>% arrange(year.key)data(HomeGames) library(dplyr) # How many parks has every team played in as the home team for even a single game? HomeGames %>% count(team.key) %>% arrange(team.key) # What parks have the Toronto Blue Jays played in as the home team? HomeGames %>% filter(team.key == "TOR") %>% arrange(span.last) # What parks have the Boston Red Sox played in as the home team? HomeGames %>% filter(team.key == "BOS") %>% arrange(span.last) # What is the Toronto Blue Jays annual total home attendance by year? HomeGames %>% filter(team.key == "TOR") %>% group_by(year.key) %>% summarize(total.attendance = sum(attendance)) %>% arrange(year.key)
Extracts the label for a variable from one or more of the *Labels
files. This is useful for plots and other displays because the variable
names are often cryptically short.
Label(var, labels = rbind(Lahman::battingLabels, Lahman::pitchingLabels, Lahman::fieldingLabels))Label(var, labels = rbind(Lahman::battingLabels, Lahman::pitchingLabels, Lahman::fieldingLabels))
var |
name of a variable |
labels |
label table(s) to search, a 2-column dataframe containing variable names and labels. |
Returns the variable label, or var if no label is found
Michael Friendly
battingLabels,
pitchingLabels,
fieldingLabels
require("dplyr") # find and plot maximum number of homers per year batHR <- Batting %>% filter(!is.na(HR)) %>% group_by(yearID) %>% summarise(max = max(HR)) with(batHR, { plot(yearID, max, xlab=Label("yearID"), ylab=paste("Maximum", Label("HR")), cex=0.8) lines(lowess(yearID, max), col="blue", lwd=2) abline(lm(max ~ yearID), col="red", lwd=2) })require("dplyr") # find and plot maximum number of homers per year batHR <- Batting %>% filter(!is.na(HR)) %>% group_by(yearID) %>% summarise(max = max(HR)) with(batHR, { plot(yearID, max, xlab=Label("yearID"), ylab=paste("Maximum", Label("HR")), cex=0.8) lines(lowess(yearID, max), col="blue", lwd=2) abline(lm(max ~ yearID), col="red", lwd=2) })
This dataset gives a concise description of the data files in the Lahman package. It may be useful for computing on the various files.
data(LahmanData)data(LahmanData)
A data frame with 24 observations on the following 5 variables.
filename of dataset
classclass of dataset
nobsnumber of observations
nvarnumber of variables
titledataset title
This dataset is generated using vcdExtra::datasets(package="Lahman")
with some post-processing.
data(LahmanData) # find ID variables in the datasets IDvars <- lapply(LahmanData[,"file"], function(x) grep('.*ID$', colnames(get(x)), value=TRUE)) names(IDvars) <- LahmanData[,"file"] str(IDvars) # vector of unique ID variables unique(unlist(IDvars)) # which datasets have playerID? names(which(sapply(IDvars, function(x) "playerID" %in% x))) ################################################ # Visualize relations among datasets via an MDS ################################################ # jaccard distance between two sets; assure positivity jaccard <- function(A, B) { max(1 - length(intersect(A,B)) / length(union(A,B)), .00001) } distmat <- function(vars, FUN=jaccard) { nv <- length(vars) d <- matrix(0, nv, nv, dimnames=list(names(vars), names(vars))) for(i in 1:nv) { for (j in 1:nv) { if (i != j) d[i,j] <- FUN(vars[[i]], vars[[j]]) } } d[is.nan(d)] = 0 d } # do an MDS on distances distID <- distmat(IDvars) config <- cmdscale(distID) pos=rep(1:4, length=nrow(config)) plot(config[,1], config[,2], xlab = "", ylab = "", asp = 1, axes=FALSE, main="MDS of ID variable distances of Lahman tables") abline(h=0, v=0, col="gray80") text(config[,1], config[,2], rownames(config), cex = 0.75, pos=pos, xpd=NA)data(LahmanData) # find ID variables in the datasets IDvars <- lapply(LahmanData[,"file"], function(x) grep('.*ID$', colnames(get(x)), value=TRUE)) names(IDvars) <- LahmanData[,"file"] str(IDvars) # vector of unique ID variables unique(unlist(IDvars)) # which datasets have playerID? names(which(sapply(IDvars, function(x) "playerID" %in% x))) ################################################ # Visualize relations among datasets via an MDS ################################################ # jaccard distance between two sets; assure positivity jaccard <- function(A, B) { max(1 - length(intersect(A,B)) / length(union(A,B)), .00001) } distmat <- function(vars, FUN=jaccard) { nv <- length(vars) d <- matrix(0, nv, nv, dimnames=list(names(vars), names(vars))) for(i in 1:nv) { for (j in 1:nv) { if (i != j) d[i,j] <- FUN(vars[[i]], vars[[j]]) } } d[is.nan(d)] = 0 d } # do an MDS on distances distID <- distmat(IDvars) config <- cmdscale(distID) pos=rep(1:4, length=nrow(config)) plot(config[,1], config[,2], xlab = "", ylab = "", asp = 1, axes=FALSE, main="MDS of ID variable distances of Lahman tables") abline(h=0, v=0, col="gray80") text(config[,1], config[,2], rownames(config), cex = 0.75, pos=pos, xpd=NA)
Managers table: information about individual team managers, teams they managed and some basic statistics for those teams in each year.
data(Managers)data(Managers)
A data frame with 3749 observations on the following 10 variables.
playerIDManager (player) ID code
yearIDYear
teamIDTeam; a factor
lgIDLeague; a factor with levels AA AL FL NL PL UA
inseasonManagerial order. Zero if the individual managed the team the entire year. Otherwise denotes where the manager appeared in the managerial order (1 for first manager, 2 for second, etc.)
GGames managed
WWins
LLosses
rankTeam's final position in standings that year
plyrMgrPlayer Manager (denoted by 'Y'); a factor with levels N Y
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
#################################### # Basic career summaries by manager #################################### library("dplyr") mgrSumm <- Managers %>% group_by(playerID) %>% summarise(nyear = length(unique(yearID)), yearBegin = min(yearID), yearEnd = max(yearID), nTeams = length(unique(teamID)), nfirst = sum(rank == 1L), W = sum(W), L = sum(L), WinPct = round(W/(W + L), 3)) MgrInfo <- People %>% filter(!is.na(playerID)) %>% select(playerID, nameLast, nameFirst) # Merge names into the table mgrTotals <- right_join(MgrInfo, mgrSumm, by = "playerID") # add total games managed mgrTotals <- mgrTotals %>% mutate(games = W + L) ########################## # Some basic queries ########################## # Top 20 managers in terms of years of service: mgrTotals %>% arrange(desc(nyear)) %>% head(., 20) # Top 20 winningest managers (500 games minimum) mgrTotals %>% filter((W + L) >= 500) %>% arrange(desc(WinPct)) %>% head(., 20) # Most of these are 19th century managers. # How about the modern era? mgrTotals %>% filter(yearBegin >= 1901 & (W + L) >= 500) %>% arrange(desc(WinPct)) %>% head(., 20) # Top 10 managers in terms of percentage of titles # (league or divisional) - should bias toward managers # post-1970 since more first place finishes are available mgrTotals %>% filter(yearBegin >= 1901 & (W + L) >= 500) %>% arrange(desc(round(nfirst/nyear, 3))) %>% head(., 10) # How about pre-1969? mgrTotals %>% filter(yearBegin >= 1901 & yearEnd <= 1969 & (W + L) >= 500) %>% arrange(desc(round(nfirst/nyear, 3))) %>% head(., 10) ## Tony LaRussa's managerial record by team Managers %>% filter(playerID == "larusto01") %>% group_by(teamID) %>% summarise(nyear = length(unique(yearID)), yearBegin = min(yearID), yearEnd = max(yearID), games = sum(G), nfirst = sum(rank == 1L), W = sum(W), L = sum(L), WinPct = round(W/(W + L), 3)) ############################################## # Density plot of the number of games managed: ############################################## library("ggplot2") ggplot(mgrTotals, aes(x = games)) + geom_density(fill = "red", alpha = 0.3) + labs(x = "Number of games managed") # Who managed more than 4000 games? mgrTotals %>% filter(W + L >= 4000) %>% arrange(desc(W + L)) # Connie Mack's advantage: he owned the Philadelphia A's :) # Table of Tony LaRussa's team finishes (rank order): Managers %>% filter(playerID == "larusto01") %>% count(rank) ############################################## # Scatterplot of winning percentage vs. number # of games managed (min 100) ############################################## ggplot(subset(mgrTotals, yearBegin >= 1900 & games >= 100), aes(x = games, y = WinPct)) + geom_point() + geom_smooth() + labs(x = "Number of games managed") ############################################ # Division titles ############################################ # Plot of number of first place finishes by managers who # started in the divisional era (>= 1969) with # at least 8 years of experience mgrTotals %>% filter(yearBegin >= 1969 & nyear >= 8) %>% ggplot(., aes(x = nyear, y = nfirst)) + geom_point(position = position_jitter(width = 0.2)) + labs(x = "Number of years", y = "Number of divisional titles") + geom_smooth() # Change response to proportion of titles relative # to years managed mgrTotals %>% filter(yearBegin >= 1969 & nyear >= 8) %>% ggplot(., aes(x = nyear, y = round(nfirst/nyear, 3))) + geom_point(position = position_jitter(width = 0.2)) + labs(x = "Number of years", y = "Proportion of divisional titles") + geom_smooth()#################################### # Basic career summaries by manager #################################### library("dplyr") mgrSumm <- Managers %>% group_by(playerID) %>% summarise(nyear = length(unique(yearID)), yearBegin = min(yearID), yearEnd = max(yearID), nTeams = length(unique(teamID)), nfirst = sum(rank == 1L), W = sum(W), L = sum(L), WinPct = round(W/(W + L), 3)) MgrInfo <- People %>% filter(!is.na(playerID)) %>% select(playerID, nameLast, nameFirst) # Merge names into the table mgrTotals <- right_join(MgrInfo, mgrSumm, by = "playerID") # add total games managed mgrTotals <- mgrTotals %>% mutate(games = W + L) ########################## # Some basic queries ########################## # Top 20 managers in terms of years of service: mgrTotals %>% arrange(desc(nyear)) %>% head(., 20) # Top 20 winningest managers (500 games minimum) mgrTotals %>% filter((W + L) >= 500) %>% arrange(desc(WinPct)) %>% head(., 20) # Most of these are 19th century managers. # How about the modern era? mgrTotals %>% filter(yearBegin >= 1901 & (W + L) >= 500) %>% arrange(desc(WinPct)) %>% head(., 20) # Top 10 managers in terms of percentage of titles # (league or divisional) - should bias toward managers # post-1970 since more first place finishes are available mgrTotals %>% filter(yearBegin >= 1901 & (W + L) >= 500) %>% arrange(desc(round(nfirst/nyear, 3))) %>% head(., 10) # How about pre-1969? mgrTotals %>% filter(yearBegin >= 1901 & yearEnd <= 1969 & (W + L) >= 500) %>% arrange(desc(round(nfirst/nyear, 3))) %>% head(., 10) ## Tony LaRussa's managerial record by team Managers %>% filter(playerID == "larusto01") %>% group_by(teamID) %>% summarise(nyear = length(unique(yearID)), yearBegin = min(yearID), yearEnd = max(yearID), games = sum(G), nfirst = sum(rank == 1L), W = sum(W), L = sum(L), WinPct = round(W/(W + L), 3)) ############################################## # Density plot of the number of games managed: ############################################## library("ggplot2") ggplot(mgrTotals, aes(x = games)) + geom_density(fill = "red", alpha = 0.3) + labs(x = "Number of games managed") # Who managed more than 4000 games? mgrTotals %>% filter(W + L >= 4000) %>% arrange(desc(W + L)) # Connie Mack's advantage: he owned the Philadelphia A's :) # Table of Tony LaRussa's team finishes (rank order): Managers %>% filter(playerID == "larusto01") %>% count(rank) ############################################## # Scatterplot of winning percentage vs. number # of games managed (min 100) ############################################## ggplot(subset(mgrTotals, yearBegin >= 1900 & games >= 100), aes(x = games, y = WinPct)) + geom_point() + geom_smooth() + labs(x = "Number of games managed") ############################################ # Division titles ############################################ # Plot of number of first place finishes by managers who # started in the divisional era (>= 1969) with # at least 8 years of experience mgrTotals %>% filter(yearBegin >= 1969 & nyear >= 8) %>% ggplot(., aes(x = nyear, y = nfirst)) + geom_point(position = position_jitter(width = 0.2)) + labs(x = "Number of years", y = "Number of divisional titles") + geom_smooth() # Change response to proportion of titles relative # to years managed mgrTotals %>% filter(yearBegin >= 1969 & nyear >= 8) %>% ggplot(., aes(x = nyear, y = round(nfirst/nyear, 3))) + geom_point(position = position_jitter(width = 0.2)) + labs(x = "Number of years", y = "Proportion of divisional titles") + geom_smooth()
Split season data for managers
data(ManagersHalf)data(ManagersHalf)
A data frame with 93 observations on the following 10 variables.
playerIDManager (player) ID code
yearIDYear
teamIDTeam; a factor
lgIDLeague; a factor with levels AL NL
inseasonManagerial order. One if the individual managed the team
the entire year. Otherwise denotes where the manager appeared
in the managerial order (1 for first manager, 2 for second, etc.).
A factor with levels 1 2 3 4 5
halfFirst or second half of season
GGames managed
WWins
LLosses
rankTeam's position in standings for the half
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
library("dplyr") library("reshape2") # Only have data for 1892 and 1981 # League rank by half for 1981 teams with the same # manager in both halves who were hired in-season ManagersHalf %>% filter(yearID >= 1901) %>% group_by(teamID, yearID) %>% filter(all(playerID == playerID[1])) %>% # same manager in both halves mutate(winPct = round(W/G, 3)) %>% reshape2::dcast(playerID + yearID + teamID + lgID ~ half, value.var = "rank") %>% rename(rank1 = `1`, rank2 = `2`)library("dplyr") library("reshape2") # Only have data for 1892 and 1981 # League rank by half for 1981 teams with the same # manager in both halves who were hired in-season ManagersHalf %>% filter(yearID >= 1901) %>% group_by(teamID, yearID) %>% filter(all(playerID == playerID[1])) %>% # same manager in both halves mutate(winPct = round(W/G, 3)) %>% reshape2::dcast(playerID + yearID + teamID + lgID ~ half, value.var = "rank") %>% rename(rank1 = `1`, rank2 = `2`)
Name and location data for baseball stadiums.
data(Parks)data(Parks)
A data frame with 260 observations on the following 6 variables.
park.keyunique identifier for each ballpark
park.namethe name of the ballpark
park.aliasa semicolon delimited list of other names for the ballpark if they exist
citycity where the ballpark is located
statestate where the ballpark is located
countrycountry where the ballpark is located
This dataset apparently includes all ballparks that were ever used in baseball. There is no indication of the years they were used, nor the teams that played there.
The ballparks can be associated with teams through the park variable in the
Teams table.
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2023 version, http://www.seanlahman.com/
data(Parks) library(dplyr) # how many parks in each country? table(Parks$country) # how many parks in each US state? Parks %>% filter(country=="US") %>% count(state, sort=TRUE) # ballparks in NYC Parks %>% filter(state=="NY") %>% filter(city %in% c("New York", "Brooklyn", "Queens")) # ballparks in Canada Parks %>% filter(country=="CA") %>% count(state, sort=TRUE) # what are the Canadian parks? Parks %>% dplyr::filter(country=="CA")data(Parks) library(dplyr) # how many parks in each country? table(Parks$country) # how many parks in each US state? Parks %>% filter(country=="US") %>% count(state, sort=TRUE) # ballparks in NYC Parks %>% filter(state=="NY") %>% filter(city %in% c("New York", "Brooklyn", "Queens")) # ballparks in Canada Parks %>% filter(country=="CA") %>% count(state, sort=TRUE) # what are the Canadian parks? Parks %>% dplyr::filter(country=="CA")
People table - Player names, DOB, and biographical info. This file is to be used to get details
about players listed in the Batting, Pitching, and other files
where players are identified only by playerID.
data(People)data(People)
A data frame with 21010 observations on the following 26 variables.
playerIDA unique code assigned to each player. The playerID links
the data in this file with records on players in the other files.
birthYearYear player was born
birthMonthMonth player was born
birthDayDay player was born
birthCountryCountry where player was born
birthStateState where player was born
birthCityCity where player was born
deathYearYear player died
deathMonthMonth player died
deathDayDay player died
deathCountryCountry where player died
deathStateState where player died
deathCityCity where player died
nameFirstPlayer's first name
nameLastPlayer's last name
nameGivenPlayer's given name (typically first and middle)
weightPlayer's weight in pounds
heightPlayer's height in inches
batsa factor: Player's batting hand (left (L), right (R), or both (B))
throwsa factor: Player's throwing hand (left(L) or right(R))
debutDate that player made first major league appearance
finalGameDate that player made first major league appearance (blank if still active)
retroIDID used by retrosheet, https://www.retrosheet.org/
bbrefIDID used by Baseball Reference website, https://www.baseball-reference.com/
birthDatePlayer's birthdate, in as.Date format
deathDatePlayer's deathdate, in as.Date format
debut, finalGame were converted from character strings with as.Date.
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
data(People); data(Batting) ## add player's name to Batting data People$name <- paste(People$nameFirst, People$nameLast, sep=" ") batting <- merge(Batting, People[,c("playerID","name")], by="playerID", all.x=TRUE) ## batting and throwing # right-handed batters are much less ambidexterous in throwing than left-handed batters # (should only include batters) BT <- with(People, table(bats, throws)) require(vcd) structable(BT) mosaic(BT, shade=TRUE) ## Who is Shoeless Joe Jackson? subset(People, nameLast=="Jackson" & nameFirst=="Joe") subset(People, nameLast=="Jackson" & nameFirst=="Shoeless Joe") joeID <-c(subset(People, nameLast=="Jackson" & nameFirst=="Shoeless Joe")["playerID"]) subset(Batting, playerID==joeID) subset(Fielding, playerID==joeID)data(People); data(Batting) ## add player's name to Batting data People$name <- paste(People$nameFirst, People$nameLast, sep=" ") batting <- merge(Batting, People[,c("playerID","name")], by="playerID", all.x=TRUE) ## batting and throwing # right-handed batters are much less ambidexterous in throwing than left-handed batters # (should only include batters) BT <- with(People, table(bats, throws)) require(vcd) structable(BT) mosaic(BT, shade=TRUE) ## Who is Shoeless Joe Jackson? subset(People, nameLast=="Jackson" & nameFirst=="Joe") subset(People, nameLast=="Jackson" & nameFirst=="Shoeless Joe") joeID <-c(subset(People, nameLast=="Jackson" & nameFirst=="Shoeless Joe")["playerID"]) subset(Batting, playerID==joeID) subset(Fielding, playerID==joeID)
Pitching table
data(Pitching)data(Pitching)
A data frame with 50402 observations on the following 30 variables.
playerIDPlayer ID code
yearIDYear
stintplayer's stint (order of appearances within a season)
teamIDTeam; a factor
lgIDLeague; a factor with levels AA AL FL NL PL UA
WWins
LLosses
GGames
GSGames Started
CGComplete Games
SHOShutouts
SVSaves
IPoutsOuts Pitched (innings pitched x 3)
HHits
EREarned Runs
HRHomeruns
BBWalks
SOStrikeouts
BAOppOpponent's Batting Average
ERAEarned Run Average
IBBIntentional Walks
WPWild Pitches
HBPBatters Hit By Pitch
BKBalks
BFPBatters faced by Pitcher
GFGames Finished
RRuns Allowed
SHSacrifices by opposing batters
SFSacrifice flies by opposing batters
GIDPGrounded into double plays by opposing batter
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com
# Pitching data require("dplyr") ################################### # cleanup, and add some other stats ################################### # Restrict to AL and NL data, 1901+ # All data re SH, SF and GIDP are missing, so remove # Intentional walks (IBB) not recorded until 1955 pitching <- Pitching %>% filter(yearID >= 1901 & lgID %in% c("AL", "NL")) %>% select(-(28:30)) %>% # remove SH, SF, GIDP mutate(BAOpp = round(H/(H + IPouts), 3), # loose def'n WHIP = round((H + BB) * 3/IPouts, 2), KperBB = round(ifelse(yearID >= 1955, SO/(BB - IBB), SO/BB), 2)) ##################### # some simple queries ##################### # Team pitching statistics, Toronto Blue Jays, 1993 tor93 <- pitching %>% filter(yearID == 1993 & teamID == "TOR") %>% arrange(ERA) # Career pitching statistics, Greg Maddux subset(pitching, playerID == "maddugr01") # Best ERAs for starting pitchers post WWII pitching %>% filter(yearID >= 1946 & IPouts >= 600) %>% group_by(lgID) %>% arrange(ERA) %>% do(head(., 5)) # Best K/BB ratios post-1955 among starters (excludes intentional walks) pitching %>% filter(yearID >= 1955 & IPouts >= 600) %>% mutate(KperBB = SO/(BB - IBB)) %>% arrange(desc(KperBB)) %>% head(., 10) # Best K/BB ratios among relievers post-1950 (min. 20 saves) pitching %>% filter(yearID >= 1950 & SV >= 20) %>% arrange(desc(KperBB)) %>% head(., 10) ############################################### # Winningest pitchers in each league each year: ############################################### # Add name & throws information: peopleInfo <- People %>% select(playerID, nameLast, nameFirst, throws) # Merge peopleInfo into the pitching data pitching1 <- right_join(peopleInfo, pitching, by = "playerID") # Extract the pitcher with the maximum number of wins # each year, by league winp <- pitching1 %>% group_by(yearID, lgID) %>% filter(W == max(W)) %>% select(nameLast, nameFirst, teamID, W, throws) # A simple ANCOVA model of wins vs. year, league and hand (L/R) anova(lm(formula = W ~ yearID + I(yearID^2) + lgID + throws, data = winp)) # Nature of managing pitching staffs has altered importance of # wins over time ## Not run: require("ggplot2") # compare loess smooth with quadratic fit ggplot(winp, aes(x = yearID, y = W)) + geom_point(aes(colour = throws, shape=lgID), size = 2) + geom_smooth(method="loess", size=1.5, color="blue") + geom_smooth(method = "lm", se=FALSE, color="black", formula = y ~ poly(x,2)) + ylab("League maximum Wins") + xlab("Year") + ggtitle("Maximum pitcher wins by year") ## To reinforce this, plot the mean IPouts by year and league, ## which gives some idea of pitcher usage. Restrict pitcher ## pool to those who pitched at least 100 innings in a year. pitching %>% filter(IPouts >= 300) %>% # >= 100 IP ggplot(., aes(x = yearID, y = IPouts, color = lgID)) + geom_smooth(method="loess") + labs(x = "Year", y = "IPouts") ## Another indicator: total number of complete games pitched ## (Mirrors the trend from the preceding plot.) pitching %>% group_by(yearID, lgID) %>% summarise(totalCG = sum(CG, na.rm = TRUE)) %>% ggplot(., aes(x = yearID, y = totalCG, color = lgID)) + geom_point() + geom_path() + labs(x = "Year", y = "Number of complete games") ## End(Not run)# Pitching data require("dplyr") ################################### # cleanup, and add some other stats ################################### # Restrict to AL and NL data, 1901+ # All data re SH, SF and GIDP are missing, so remove # Intentional walks (IBB) not recorded until 1955 pitching <- Pitching %>% filter(yearID >= 1901 & lgID %in% c("AL", "NL")) %>% select(-(28:30)) %>% # remove SH, SF, GIDP mutate(BAOpp = round(H/(H + IPouts), 3), # loose def'n WHIP = round((H + BB) * 3/IPouts, 2), KperBB = round(ifelse(yearID >= 1955, SO/(BB - IBB), SO/BB), 2)) ##################### # some simple queries ##################### # Team pitching statistics, Toronto Blue Jays, 1993 tor93 <- pitching %>% filter(yearID == 1993 & teamID == "TOR") %>% arrange(ERA) # Career pitching statistics, Greg Maddux subset(pitching, playerID == "maddugr01") # Best ERAs for starting pitchers post WWII pitching %>% filter(yearID >= 1946 & IPouts >= 600) %>% group_by(lgID) %>% arrange(ERA) %>% do(head(., 5)) # Best K/BB ratios post-1955 among starters (excludes intentional walks) pitching %>% filter(yearID >= 1955 & IPouts >= 600) %>% mutate(KperBB = SO/(BB - IBB)) %>% arrange(desc(KperBB)) %>% head(., 10) # Best K/BB ratios among relievers post-1950 (min. 20 saves) pitching %>% filter(yearID >= 1950 & SV >= 20) %>% arrange(desc(KperBB)) %>% head(., 10) ############################################### # Winningest pitchers in each league each year: ############################################### # Add name & throws information: peopleInfo <- People %>% select(playerID, nameLast, nameFirst, throws) # Merge peopleInfo into the pitching data pitching1 <- right_join(peopleInfo, pitching, by = "playerID") # Extract the pitcher with the maximum number of wins # each year, by league winp <- pitching1 %>% group_by(yearID, lgID) %>% filter(W == max(W)) %>% select(nameLast, nameFirst, teamID, W, throws) # A simple ANCOVA model of wins vs. year, league and hand (L/R) anova(lm(formula = W ~ yearID + I(yearID^2) + lgID + throws, data = winp)) # Nature of managing pitching staffs has altered importance of # wins over time ## Not run: require("ggplot2") # compare loess smooth with quadratic fit ggplot(winp, aes(x = yearID, y = W)) + geom_point(aes(colour = throws, shape=lgID), size = 2) + geom_smooth(method="loess", size=1.5, color="blue") + geom_smooth(method = "lm", se=FALSE, color="black", formula = y ~ poly(x,2)) + ylab("League maximum Wins") + xlab("Year") + ggtitle("Maximum pitcher wins by year") ## To reinforce this, plot the mean IPouts by year and league, ## which gives some idea of pitcher usage. Restrict pitcher ## pool to those who pitched at least 100 innings in a year. pitching %>% filter(IPouts >= 300) %>% # >= 100 IP ggplot(., aes(x = yearID, y = IPouts, color = lgID)) + geom_smooth(method="loess") + labs(x = "Year", y = "IPouts") ## Another indicator: total number of complete games pitched ## (Mirrors the trend from the preceding plot.) pitching %>% group_by(yearID, lgID) %>% summarise(totalCG = sum(CG, na.rm = TRUE)) %>% ggplot(., aes(x = yearID, y = totalCG, color = lgID)) + geom_point() + geom_path() + labs(x = "Year", y = "Number of complete games") ## End(Not run)
Post season pitching statistics
data(PitchingPost)data(PitchingPost)
A data frame with 6757 observations on the following 30 variables.
playerIDPlayer ID code
yearIDYear
roundLevel of playoffs
teamIDTeam; a factor
lgIDLeague; a factor with levels AA AL NL
WWins
LLosses
GGames
GSGames Started
CGComplete Games
SHOShutouts
SVSaves
IPoutsOuts Pitched (innings pitched x 3)
HHits
EREarned Runs
HRHomeruns
BBWalks
SOStrikeouts
BAOppOpponents' batting average
ERAEarned Run Average
IBBIntentional Walks
WPWild Pitches
HBPBatters Hit By Pitch
BKBalks
BFPBatters faced by Pitcher
GFGames Finished
RRuns Allowed
SHSacrifice Hits allowed
SFSacrifice Flies allowed
GIDPGrounded into Double Plays
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
library("dplyr") library(ggplot2) # Restrict data to World Series in modern era ws <- PitchingPost %>% filter(yearID >= 1903 & round == "WS") # Pitchers with ERA 0.00 in WS play (> 10 IP) ws %>% filter(IPouts > 30 & ERA == 0.00) %>% arrange(desc(IPouts)) %>% select(playerID, yearID, teamID, lgID, IPouts, W, L, G, CG, SHO, H, R, SO, BFP) # Pitchers with the most IP in a series # 1903 Series went eight games - for details, see # https://en.wikipedia.org/wiki/1903_World_Series ws %>% arrange(desc(IPouts)) %>% select(playerID, yearID, teamID, lgID, IPouts, W, L, G, CG, SHO, H, SO, BFP, ERA) %>% head(., 10) # Pitchers with highest strikeout rate in WS # (minimum 20 IP) ws %>% filter(IPouts >= 60) %>% mutate(K_rate = 27 * SO/IPouts) %>% arrange(desc(K_rate)) %>% select(playerID, yearID, teamID, lgID, IPouts, H, SO, K_rate) %>% head(., 10) # Pitchers with the most IP in WS history ws %>% group_by(playerID) %>% summarise_at(vars(IPouts, H, ER, CG, BB, SO, W, L), sum, na.rm = TRUE) %>% mutate(ERA = round(27 * ER/IPouts, 2), Kper9 = round(27 * SO/IPouts, 3), WHIP = round(3 * (H + BB)/IPouts, 3)) %>% arrange(desc(IPouts)) %>% select(-H, -ER) %>% head(., 10) # Plot of K/9 by year ws %>% group_by(yearID) %>% summarise(Kper9 = 27 * sum(SO)/sum(IPouts)) %>% ggplot(., aes(x = yearID, y = Kper9)) + geom_point() + geom_smooth() + labs(x = "Year", y = "K per 9 innings")library("dplyr") library(ggplot2) # Restrict data to World Series in modern era ws <- PitchingPost %>% filter(yearID >= 1903 & round == "WS") # Pitchers with ERA 0.00 in WS play (> 10 IP) ws %>% filter(IPouts > 30 & ERA == 0.00) %>% arrange(desc(IPouts)) %>% select(playerID, yearID, teamID, lgID, IPouts, W, L, G, CG, SHO, H, R, SO, BFP) # Pitchers with the most IP in a series # 1903 Series went eight games - for details, see # https://en.wikipedia.org/wiki/1903_World_Series ws %>% arrange(desc(IPouts)) %>% select(playerID, yearID, teamID, lgID, IPouts, W, L, G, CG, SHO, H, SO, BFP, ERA) %>% head(., 10) # Pitchers with highest strikeout rate in WS # (minimum 20 IP) ws %>% filter(IPouts >= 60) %>% mutate(K_rate = 27 * SO/IPouts) %>% arrange(desc(K_rate)) %>% select(playerID, yearID, teamID, lgID, IPouts, H, SO, K_rate) %>% head(., 10) # Pitchers with the most IP in WS history ws %>% group_by(playerID) %>% summarise_at(vars(IPouts, H, ER, CG, BB, SO, W, L), sum, na.rm = TRUE) %>% mutate(ERA = round(27 * ER/IPouts, 2), Kper9 = round(27 * SO/IPouts, 3), WHIP = round(3 * (H + BB)/IPouts, 3)) %>% arrange(desc(IPouts)) %>% select(-H, -ER) %>% head(., 10) # Plot of K/9 by year ws %>% group_by(yearID) %>% summarise(Kper9 = 27 * sum(SO)/sum(IPouts)) %>% ggplot(., aes(x = yearID, y = Kper9)) + geom_point() + geom_smooth() + labs(x = "Year", y = "K per 9 innings")
These functions use grep to lookup information about players
(from the People file)
and teams (from the Teams file).
playerInfo(playerID, nameFirst, nameLast, data = Lahman::People, extra = NULL, ...) teamInfo(teamID, name, data = Lahman::Teams, extra = NULL, ...)playerInfo(playerID, nameFirst, nameLast, data = Lahman::People, extra = NULL, ...) teamInfo(teamID, name, data = Lahman::Teams, extra = NULL, ...)
playerID |
pattern for |
nameFirst |
pattern for first name |
nameLast |
pattern for last name |
data |
The name of the dataset to search |
extra |
A character vector of other fields to include in the result |
... |
other arguments passed to |
teamID |
pattern for |
name |
pattern for team name |
Returns a data frame for unique matching rows from data
Michael Friendly
grep, ~~~
playerInfo("aaron") teamInfo("CH", extra="park")playerInfo("aaron") teamInfo("CH", extra="park")
Player salary data.
data(Salaries)data(Salaries)
A data frame with 26428 observations on the following 5 variables.
yearIDYear
teamIDTeam; a factor
lgIDLeague; a factor
playerIDPlayer ID code
salarySalary
There is no real coverage of player's salaries until 1985.
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
# what years are included? summary(Salaries$yearID) # how many players included each year? table(Salaries$yearID) # Team salary data require("dplyr") require("ggplot2") # Total team salaries by league, team and year teamSalaries <- Salaries %>% group_by(lgID, teamID, yearID) %>% summarise(Salary = sum(as.numeric(salary))) %>% group_by(yearID, lgID) %>% arrange(desc(Salary)) ####################################### # Highest paid players each year: maxSal <- Salaries %>% group_by(yearID) %>% filter(salary == max(salary)) maxPlayers <- bind_rows(lapply(maxSal$playerID, playerInfo)) %>% select(-playerID) maxSal <- bind_cols(maxPlayers, maxSal) # Plot maximum MLB salary by year (1985-present) ggplot(maxSal, aes(x = yearID, y = salary/1e6)) + geom_point() + geom_smooth(se = FALSE) + labs(x = "Year", y = "Salary (millions)") # Plot salary distributions by year for all players ggplot(Salaries, aes(x = factor(yearID), y = salary/1e5)) + geom_boxplot(fill = "lightblue", outlier.size = 1) + labs(x = "Year", y = "Salary ($100,000)") + coord_flip() # Plot median MLB salary per year Salaries %>% group_by(yearID) %>% summarise(medsal = median(salary)) %>% ggplot(., aes(x = yearID, y = medsal/1e6)) + geom_point() + geom_smooth() + labs(x = "Year", y = "Median MLB salary (millions)") # add salary to Batting data batting <- Batting %>% filter(yearID >= 1985) %>% left_join(select(Salaries, playerID, yearID, teamID, salary), by=c("playerID", "yearID", "teamID")) str(batting) ####################################### # Average salaries by teams, over years ####################################### # Some franchises are multiply named, so add a new variable # 'franchise' to the Salaries data as a lookup table franchise <- c(`ANA` = "LAA", `ARI` = "ARI", `ATL` = "ATL", `BAL` = "BAL", `BOS` = "BOS", `CAL` = "LAA", `CHA` = "CHA", `CHN` = "CHN", `CIN` = "CIN", `CLE` = "CLE", `COL` = "COL", `DET` = "DET", `FLO` = "MIA", `HOU` = "HOU", `KCA` = "KCA", `LAA` = "LAA", `LAN` = "LAN", `MIA` = "MIA", `MIL` = "MIL", `MIN` = "MIN", `ML4` = "MIL", `MON` = "WAS", `NYA` = "NYA", `NYM` = "NYN", `NYN` = "NYN", `OAK` = "OAK", `PHI` = "PHI", `PIT` = "PIT", `SDN` = "SDN", `SEA` = "SEA", `SFG` = "SFN", `SFN` = "SFN", `SLN` = "SLN", `TBA` = "TBA", `TEX` = "TEX", `TOR` = "TOR", `WAS` = "WAS") Salaries$franchise <- unname(franchise[Salaries$teamID]) # Average salaries annual salaries by team, in millions USD avg_team_salaries <- Salaries %>% group_by(yearID, franchise, lgID) %>% summarise(salary= mean(salary)/1e6) %>% filter(!(franchise == "CLE" & lgID == "NL")) # Spaghetti plot of team salary over time by team # Yankees have largest average team salary since 2003 ggplot(avg_team_salaries, aes(x = yearID, y = salary, group = factor(franchise))) + geom_path() + labs(x = "Year", y = "Average team salary (millions USD)")# what years are included? summary(Salaries$yearID) # how many players included each year? table(Salaries$yearID) # Team salary data require("dplyr") require("ggplot2") # Total team salaries by league, team and year teamSalaries <- Salaries %>% group_by(lgID, teamID, yearID) %>% summarise(Salary = sum(as.numeric(salary))) %>% group_by(yearID, lgID) %>% arrange(desc(Salary)) ####################################### # Highest paid players each year: maxSal <- Salaries %>% group_by(yearID) %>% filter(salary == max(salary)) maxPlayers <- bind_rows(lapply(maxSal$playerID, playerInfo)) %>% select(-playerID) maxSal <- bind_cols(maxPlayers, maxSal) # Plot maximum MLB salary by year (1985-present) ggplot(maxSal, aes(x = yearID, y = salary/1e6)) + geom_point() + geom_smooth(se = FALSE) + labs(x = "Year", y = "Salary (millions)") # Plot salary distributions by year for all players ggplot(Salaries, aes(x = factor(yearID), y = salary/1e5)) + geom_boxplot(fill = "lightblue", outlier.size = 1) + labs(x = "Year", y = "Salary ($100,000)") + coord_flip() # Plot median MLB salary per year Salaries %>% group_by(yearID) %>% summarise(medsal = median(salary)) %>% ggplot(., aes(x = yearID, y = medsal/1e6)) + geom_point() + geom_smooth() + labs(x = "Year", y = "Median MLB salary (millions)") # add salary to Batting data batting <- Batting %>% filter(yearID >= 1985) %>% left_join(select(Salaries, playerID, yearID, teamID, salary), by=c("playerID", "yearID", "teamID")) str(batting) ####################################### # Average salaries by teams, over years ####################################### # Some franchises are multiply named, so add a new variable # 'franchise' to the Salaries data as a lookup table franchise <- c(`ANA` = "LAA", `ARI` = "ARI", `ATL` = "ATL", `BAL` = "BAL", `BOS` = "BOS", `CAL` = "LAA", `CHA` = "CHA", `CHN` = "CHN", `CIN` = "CIN", `CLE` = "CLE", `COL` = "COL", `DET` = "DET", `FLO` = "MIA", `HOU` = "HOU", `KCA` = "KCA", `LAA` = "LAA", `LAN` = "LAN", `MIA` = "MIA", `MIL` = "MIL", `MIN` = "MIN", `ML4` = "MIL", `MON` = "WAS", `NYA` = "NYA", `NYM` = "NYN", `NYN` = "NYN", `OAK` = "OAK", `PHI` = "PHI", `PIT` = "PIT", `SDN` = "SDN", `SEA` = "SEA", `SFG` = "SFN", `SFN` = "SFN", `SLN` = "SLN", `TBA` = "TBA", `TEX` = "TEX", `TOR` = "TOR", `WAS` = "WAS") Salaries$franchise <- unname(franchise[Salaries$teamID]) # Average salaries annual salaries by team, in millions USD avg_team_salaries <- Salaries %>% group_by(yearID, franchise, lgID) %>% summarise(salary= mean(salary)/1e6) %>% filter(!(franchise == "CLE" & lgID == "NL")) # Spaghetti plot of team salary over time by team # Yankees have largest average team salary since 2003 ggplot(avg_team_salaries, aes(x = yearID, y = salary, group = factor(franchise))) + geom_path() + labs(x = "Year", y = "Average team salary (millions USD)")
Information on schools players attended, by school
data(Schools)data(Schools)
A data frame with 1241 observations on the following 5 variables.
schoolIDschool ID code
name_fullschool name
citycity where school is located
statestate where school's city is located
countrycountry where school is located
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
require("dplyr") # How many different schools are listed in each state? table(Schools$state) # How many different schools are listed in each country? table(Schools$country) # Top 20 schools schoolInfo <- Schools %>% select(-country) schoolCount <- CollegePlaying %>% group_by(schoolID) %>% summarise(players = length(schoolID)) %>% left_join(schoolInfo, by = "schoolID") %>% arrange(desc(players)) head(schoolCount, 20) # sum counts by state schoolStates <- schoolCount %>% group_by(state) %>% summarise(players = sum(players), schools = length(state)) str(schoolStates) summary(schoolStates)require("dplyr") # How many different schools are listed in each state? table(Schools$state) # How many different schools are listed in each country? table(Schools$country) # Top 20 schools schoolInfo <- Schools %>% select(-country) schoolCount <- CollegePlaying %>% group_by(schoolID) %>% summarise(players = length(schoolID)) %>% left_join(schoolInfo, by = "schoolID") %>% arrange(desc(players)) head(schoolCount, 20) # sum counts by state schoolStates <- schoolCount %>% group_by(state) %>% summarise(players = sum(players), schools = length(state)) str(schoolStates) summary(schoolStates)
Post season series information
data(SeriesPost)data(SeriesPost)
A data frame with 389 observations on the following 9 variables.
yearIDYear
roundLevel of playoffs
teamIDwinnerTeam ID of the team that won the series; a factor
lgIDwinnerLeague ID of the team that won the series; a factor with levels AL NL
teamIDloserTeam ID of the team that lost the series; a factor
lgIDloserLeague ID of the team that lost the series; a factor with levels AL NL
winsWins by team that won the series
lossesLosses by team that won the series
tiesTie games
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
data(SeriesPost) # How many times has each team won the World Series? # Notes: # - the SeriesPost table includes an identifier for the # team (teamID), but not the franchise (e.g. the Brooklyn Dodgers # [BRO] and Los Angeles Dodgers [LAN] are counted separately) # # - the World Series was first played in 1903, but the # Lahman data tables have the final round of the earlier # playoffs labelled "WS", so it is necessary to # filter the SeriesPost table to exclude years prior to 1903. # using the dplyr data manipulation package library("dplyr") library("tidyr") library("ggplot2") ## WS winners, arranged in descending order of titles won ws_winner_table <- SeriesPost %>% filter(yearID > "1902", round == "WS") %>% group_by(teamIDwinner) %>% summarise(wincount = n()) %>% arrange(desc(wincount)) ws_winner_table ## Expanded form of World Series team data in modern era ws <- SeriesPost %>% filter(yearID >= 1903 & round == "WS") %>% select(-ties, -round) %>% mutate(lgIDloser = droplevels(lgIDloser), lgIDwinner = droplevels(lgIDwinner)) # Bar chart of length of series (# games played) # 1903, 1919 and 1921 had eight games ggplot(ws, aes(x = wins + losses)) + geom_bar(fill = "dodgerblue") + labs(x = "Number of games", y = "Frequency") # Last year the Cubs appeared in the WS ws %>% filter(teamIDwinner == "CHN" | teamIDloser == "CHN") %>% summarise(max(yearID)) # Dot chart of number of WS appearances by teamID ws %>% gather(wl, team, teamIDwinner, teamIDloser) %>% count(team) %>% arrange(desc(n)) %>% ggplot(., aes(x = reorder(team, n), y = n)) + theme_bw() + geom_point(size = 3, color = "dodgerblue") + geom_segment(aes(xend = reorder(team, n), yend = 0), linetype = "dotted", color = "dodgerblue", size = 1) + labs(x = NULL, y = "Number of WS appearances") + scale_y_continuous(expand = c(0, 0), limits = c(0, 42)) + coord_flip() + theme(axis.text.y = element_text(size = rel(0.8)), axis.ticks.y = element_blank()) # Initial year of each round of championship series in modern era SeriesPost %>% filter(yearID >= 1903) %>% # modern WS started in 1903 group_by(round) %>% summarise(first_year = min(yearID)) %>% arrange(first_year) # Ditto, but with more information about each series played SeriesPost %>% filter(yearID >= 1903) %>% group_by(round) %>% arrange(yearID) %>% do(head(., 1)) %>% select(-lgIDwinner, -lgIDloser) %>% arrange(yearID, round)data(SeriesPost) # How many times has each team won the World Series? # Notes: # - the SeriesPost table includes an identifier for the # team (teamID), but not the franchise (e.g. the Brooklyn Dodgers # [BRO] and Los Angeles Dodgers [LAN] are counted separately) # # - the World Series was first played in 1903, but the # Lahman data tables have the final round of the earlier # playoffs labelled "WS", so it is necessary to # filter the SeriesPost table to exclude years prior to 1903. # using the dplyr data manipulation package library("dplyr") library("tidyr") library("ggplot2") ## WS winners, arranged in descending order of titles won ws_winner_table <- SeriesPost %>% filter(yearID > "1902", round == "WS") %>% group_by(teamIDwinner) %>% summarise(wincount = n()) %>% arrange(desc(wincount)) ws_winner_table ## Expanded form of World Series team data in modern era ws <- SeriesPost %>% filter(yearID >= 1903 & round == "WS") %>% select(-ties, -round) %>% mutate(lgIDloser = droplevels(lgIDloser), lgIDwinner = droplevels(lgIDwinner)) # Bar chart of length of series (# games played) # 1903, 1919 and 1921 had eight games ggplot(ws, aes(x = wins + losses)) + geom_bar(fill = "dodgerblue") + labs(x = "Number of games", y = "Frequency") # Last year the Cubs appeared in the WS ws %>% filter(teamIDwinner == "CHN" | teamIDloser == "CHN") %>% summarise(max(yearID)) # Dot chart of number of WS appearances by teamID ws %>% gather(wl, team, teamIDwinner, teamIDloser) %>% count(team) %>% arrange(desc(n)) %>% ggplot(., aes(x = reorder(team, n), y = n)) + theme_bw() + geom_point(size = 3, color = "dodgerblue") + geom_segment(aes(xend = reorder(team, n), yend = 0), linetype = "dotted", color = "dodgerblue", size = 1) + labs(x = NULL, y = "Number of WS appearances") + scale_y_continuous(expand = c(0, 0), limits = c(0, 42)) + coord_flip() + theme(axis.text.y = element_text(size = rel(0.8)), axis.ticks.y = element_blank()) # Initial year of each round of championship series in modern era SeriesPost %>% filter(yearID >= 1903) %>% # modern WS started in 1903 group_by(round) %>% summarise(first_year = min(yearID)) %>% arrange(first_year) # Ditto, but with more information about each series played SeriesPost %>% filter(yearID >= 1903) %>% group_by(round) %>% arrange(yearID) %>% do(head(., 1)) %>% select(-lgIDwinner, -lgIDloser) %>% arrange(yearID, round)
Yearly statistics and standings for teams
data(Teams)data(Teams)
A data frame with 3045 observations on the following 48 variables.
yearIDYear
lgIDLeague; a factor with levels AA AL FL NL PL UA
teamIDTeam; a factor
franchIDFranchise (links to TeamsFranchises table)
divIDTeam's division; a factor with levels C E W
RankPosition in final standings
GGames played
GhomeGames played at home
WWins
LLosses
DivWinDivision Winner (Y or N)
WCWinWild Card Winner (Y or N)
LgWinLeague Champion(Y or N)
WSWinWorld Series Winner (Y or N)
RRuns scored
ABAt bats
HHits by batters
X2BDoubles
X3BTriples
HRHomeruns by batters
BBWalks by batters
SOStrikeouts by batters
SBStolen bases
CSCaught stealing
HBPBatters hit by pitch
SFSacrifice flies
RAOpponents runs scored
EREarned runs allowed
ERAEarned run average
CGComplete games
SHOShutouts
SVSaves
IPoutsOuts Pitched (innings pitched x 3)
HAHits allowed
HRAHomeruns allowed
BBAWalks allowed
SOAStrikeouts by pitchers
EErrors
DPDouble Plays
FPFielding percentage
nameTeam's full name
parkName of team's home ballpark
attendanceHome attendance total
BPFThree-year park factor for batters
PPFThree-year park factor for pitchers
teamIDBRTeam ID used by Baseball Reference website
teamIDlahman45Team ID used in Lahman database version 4.5
teamIDretroTeam ID used by Retrosheet
Variables X2B and X3B are named 2B and 3B in the original database
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
data(Teams) library("dplyr") library("tidyr") # Add some selected measures to the Teams data frame # Restrict to AL and NL in modern era teams <- Teams %>% filter(yearID >= 1901 & lgID %in% c("AL", "NL")) %>% group_by(yearID, teamID) %>% mutate(TB = H + X2B + 2 * X3B + 3 * HR, WinPct = W/G, rpg = R/G, hrpg = HR/G, tbpg = TB/G, kpg = SO/G, k2bb = SO/BB, whip = 3 * (H + BB)/IPouts) # Function to create a ggplot by year for selected team stats # Both arguments are character strings yrPlot <- function(yvar, label) { require("ggplot2") ggplot(teams, aes_string(x = "yearID", y = yvar)) + geom_point(size = 0.5) + geom_smooth(method="loess") + labs(x = "Year", y = paste(label, "per game")) } ## Run scoring in the modern era by year yrPlot("rpg", "Runs") ## Home runs per game by year yrPlot("hrpg", "Home runs") ## Total bases per game by year yrPlot("tbpg", "Total bases") ## Strikeouts per game by year yrPlot("kpg", "Strikeouts") ## Plot win percentage vs. run differential (R - RA) ggplot(teams, aes(x = R - RA, y = WinPct)) + geom_point(size = 0.5) + geom_smooth(method="loess") + geom_hline(yintercept = 0.5, color = "orange") + geom_vline(xintercept = 0, color = "orange") + labs(x = "Run differential", y = "Win percentage") ## Plot attendance vs. win percentage by league, post-1980 teams %>% filter(yearID >= 1980) %>% ggplot(., aes(x = WinPct, y = attendance/1000)) + geom_point(size = 0.5) + geom_smooth(method="loess", se = FALSE) + facet_wrap(~ lgID) + labs(x = "Win percentage", y = "Attendance (1000s)") ## Teams with over 4 million attendance in a season teams %>% filter(attendance >= 4e6) %>% select(yearID, lgID, teamID, Rank, attendance) %>% arrange(desc(attendance)) ## Average season HRs by park, post-1980 teams %>% filter(yearID >= 1980) %>% group_by(park) %>% summarise(meanHRpg = mean((HR + HRA)/Ghome), nyears = n()) %>% filter(nyears >= 10) %>% arrange(desc(meanHRpg)) %>% head(., 10) ## Home runs per game at Fenway Park and Wrigley Field, ## the two oldest MLB parks, by year. Fenway opened in 1912. teams %>% filter(yearID >= 1912 & teamID %in% c("BOS", "CHN")) %>% mutate(hrpg = (HR + HRA)/Ghome) %>% ggplot(., aes(x = yearID, y = hrpg, color = teamID)) + geom_line(size = 1) + geom_point() + labs(x = "Year", y = "Home runs per game", color = "Team") + scale_color_manual(values = c("red", "blue")) ## Ditto for total strikeouts per game teams %>% filter(yearID >= 1912 & teamID %in% c("BOS", "CHN")) %>% mutate(kpg = (SO + SOA)/Ghome) %>% ggplot(., aes(x = yearID, y = kpg, color = teamID)) + geom_line(size = 1) + geom_point() + labs(x = "Year", y = "Strikeouts per game", color = "Team") + scale_color_manual(values = c("red", "blue")) ## Not run: if(require(googleVis)) { motion1 <- gvisMotionChart(as.data.frame(teams), idvar="teamID", timevar="yearID", chartid="gvisTeams", options=list(width=700, height=600)) plot(motion1) #print(motion1, file="gvisTeams.html") # Merge with avg salary for years where salary is available teamsal <- Salaries %>% group_by(yearID, teamID) %>% summarise(Salary = sum(salary, na.rm = TRUE)) %>% select(yearID, teamID, Salary) teamsSal <- teams %>% filter(yearID >= 1985) %>% left_join(teamsal, by = c("yearID", "teamID")) %>% select(yearID, teamID, attendance, Salary, WinPct) %>% as.data.frame(.) motion2 <- gvisMotionChart(teamsSal, idvar="teamID", timevar="yearID", xvar="attendance", yvar="salary", sizevar="WinPct", chartid="gvisTeamsSal", options=list(width=700, height=600)) plot(motion2) #print(motion2, file="gvisTeamsSal.html") } ## End(Not run)data(Teams) library("dplyr") library("tidyr") # Add some selected measures to the Teams data frame # Restrict to AL and NL in modern era teams <- Teams %>% filter(yearID >= 1901 & lgID %in% c("AL", "NL")) %>% group_by(yearID, teamID) %>% mutate(TB = H + X2B + 2 * X3B + 3 * HR, WinPct = W/G, rpg = R/G, hrpg = HR/G, tbpg = TB/G, kpg = SO/G, k2bb = SO/BB, whip = 3 * (H + BB)/IPouts) # Function to create a ggplot by year for selected team stats # Both arguments are character strings yrPlot <- function(yvar, label) { require("ggplot2") ggplot(teams, aes_string(x = "yearID", y = yvar)) + geom_point(size = 0.5) + geom_smooth(method="loess") + labs(x = "Year", y = paste(label, "per game")) } ## Run scoring in the modern era by year yrPlot("rpg", "Runs") ## Home runs per game by year yrPlot("hrpg", "Home runs") ## Total bases per game by year yrPlot("tbpg", "Total bases") ## Strikeouts per game by year yrPlot("kpg", "Strikeouts") ## Plot win percentage vs. run differential (R - RA) ggplot(teams, aes(x = R - RA, y = WinPct)) + geom_point(size = 0.5) + geom_smooth(method="loess") + geom_hline(yintercept = 0.5, color = "orange") + geom_vline(xintercept = 0, color = "orange") + labs(x = "Run differential", y = "Win percentage") ## Plot attendance vs. win percentage by league, post-1980 teams %>% filter(yearID >= 1980) %>% ggplot(., aes(x = WinPct, y = attendance/1000)) + geom_point(size = 0.5) + geom_smooth(method="loess", se = FALSE) + facet_wrap(~ lgID) + labs(x = "Win percentage", y = "Attendance (1000s)") ## Teams with over 4 million attendance in a season teams %>% filter(attendance >= 4e6) %>% select(yearID, lgID, teamID, Rank, attendance) %>% arrange(desc(attendance)) ## Average season HRs by park, post-1980 teams %>% filter(yearID >= 1980) %>% group_by(park) %>% summarise(meanHRpg = mean((HR + HRA)/Ghome), nyears = n()) %>% filter(nyears >= 10) %>% arrange(desc(meanHRpg)) %>% head(., 10) ## Home runs per game at Fenway Park and Wrigley Field, ## the two oldest MLB parks, by year. Fenway opened in 1912. teams %>% filter(yearID >= 1912 & teamID %in% c("BOS", "CHN")) %>% mutate(hrpg = (HR + HRA)/Ghome) %>% ggplot(., aes(x = yearID, y = hrpg, color = teamID)) + geom_line(size = 1) + geom_point() + labs(x = "Year", y = "Home runs per game", color = "Team") + scale_color_manual(values = c("red", "blue")) ## Ditto for total strikeouts per game teams %>% filter(yearID >= 1912 & teamID %in% c("BOS", "CHN")) %>% mutate(kpg = (SO + SOA)/Ghome) %>% ggplot(., aes(x = yearID, y = kpg, color = teamID)) + geom_line(size = 1) + geom_point() + labs(x = "Year", y = "Strikeouts per game", color = "Team") + scale_color_manual(values = c("red", "blue")) ## Not run: if(require(googleVis)) { motion1 <- gvisMotionChart(as.data.frame(teams), idvar="teamID", timevar="yearID", chartid="gvisTeams", options=list(width=700, height=600)) plot(motion1) #print(motion1, file="gvisTeams.html") # Merge with avg salary for years where salary is available teamsal <- Salaries %>% group_by(yearID, teamID) %>% summarise(Salary = sum(salary, na.rm = TRUE)) %>% select(yearID, teamID, Salary) teamsSal <- teams %>% filter(yearID >= 1985) %>% left_join(teamsal, by = c("yearID", "teamID")) %>% select(yearID, teamID, attendance, Salary, WinPct) %>% as.data.frame(.) motion2 <- gvisMotionChart(teamsSal, idvar="teamID", timevar="yearID", xvar="attendance", yvar="salary", sizevar="WinPct", chartid="gvisTeamsSal", options=list(width=700, height=600)) plot(motion2) #print(motion2, file="gvisTeamsSal.html") } ## End(Not run)
Information about team franchises
data(TeamsFranchises)data(TeamsFranchises)
A data frame with 120 observations on the following 4 variables.
franchIDFranchise ID; a factor
franchNameFranchise name
activeWhether team is currently active (Y or N)
NAassocID of National Association team franchise played as
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
data(TeamsFranchises) # Which of the active Major League Baseball teams had a National Association predecessor? # Notes: # - the National Association was founded in 1871, and continued through the # 1875 season. In 1876, six clubs from the National Association and two other # independent clubs formed the National League, which exists to this day. # - the `active` field has "NA" for the National Association franchises # - where appropriate, the `NAassoc` field has the `franchID` of the successor National League team # using the dplyr data manipulation package library("dplyr") NatAssoc_active_table <- TeamsFranchises %>% filter(active == "Y") %>% filter(!is.na(NAassoc)) NatAssoc_active_table # Merge current team IDs with franchise IDs currentTeams <- Teams %>% filter(yearID == 2014) %>% select(teamID, franchID, lgID, park) # Merge TeamsFranchises with currentTeams TeamsFranchises %>% filter(active == "Y") %>% select(-active, -NAassoc) %>% left_join(currentTeams, by = "franchID")data(TeamsFranchises) # Which of the active Major League Baseball teams had a National Association predecessor? # Notes: # - the National Association was founded in 1871, and continued through the # 1875 season. In 1876, six clubs from the National Association and two other # independent clubs formed the National League, which exists to this day. # - the `active` field has "NA" for the National Association franchises # - where appropriate, the `NAassoc` field has the `franchID` of the successor National League team # using the dplyr data manipulation package library("dplyr") NatAssoc_active_table <- TeamsFranchises %>% filter(active == "Y") %>% filter(!is.na(NAassoc)) NatAssoc_active_table # Merge current team IDs with franchise IDs currentTeams <- Teams %>% filter(yearID == 2014) %>% select(teamID, franchID, lgID, park) # Merge TeamsFranchises with currentTeams TeamsFranchises %>% filter(active == "Y") %>% select(-active, -NAassoc) %>% left_join(currentTeams, by = "franchID")
Split season data for teams
data(TeamsHalf)data(TeamsHalf)
A data frame with 52 observations on the following 10 variables.
yearIDYear
lgIDLeague; a factor with levels AL NL
teamIDTeam; a factor
HalfFirst or second half of season
divIDDivision
DivWinWon Division (Y or N)
RankTeam's position in standings for the half
GGames played
WWins
LLosses
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
# 1981 season team data split into half seasons data(TeamsHalf) library("dplyr") # List standings with winning percentages by # season half, league and division TeamsHalf %>% group_by(Half, lgID, divID) %>% mutate(WinPct = round(W/G, 3)) %>% arrange(Half, lgID, divID, Rank) %>% select(Half, lgID, divID, Rank, teamID, WinPct)# 1981 season team data split into half seasons data(TeamsHalf) library("dplyr") # List standings with winning percentages by # season half, league and division TeamsHalf %>% group_by(Half, lgID, divID) %>% mutate(WinPct = round(W/G, 3)) %>% arrange(Half, lgID, divID, Rank) %>% select(Half, lgID, divID, Rank, teamID, WinPct)