Introduction
Often when I’m watching Major League Baseball games a player will come up to bat or pitch and I’ll comment “former Oakland Athletic” and the player’s name. It seems like there’s always one or two players on the roster of every team that used to be an Athletic.
Let’s find out. We’ll use the Retrosheet database again, this time using the roster lists from 1990 through 2014 and comparing it against the 40-man rosters of current teams. That data will have to be scraped off the web, since Retrosheet data doesn’t exist for the current season and rosters change frequently during the season.
Methods
As usual, I’ll use R for the analysis, and rmarkdown to produce this post.
library(plyr)
library(dplyr)
library(rvest)
options(stringsAsFactors=FALSE)
We’re using plyr and dplyr for most of the data manipulation and rvest to grab the 40-man rosters for each team from the MLB website. Setting stringsAsFactors to false prevents various base R packages from converting everything to factors. We're not doing any statistics with this data, so factors aren't necessary and make comparisons and joins between data frames more difficult.
Players by team for previous years
Load the roster data:
retrosheet_db <- src_postgres(host="localhost", port=5438,
dbname="retrosheet", user="cswingley")
rosters <- tbl(retrosheet_db, "rosters")
all_recent_players <-
rosters %>%
filter(year>1989) %>%
collect() %>%
mutate(player=paste(first_name, last_name),
team=team_id) %>%
select(player, team, year)
save(all_recent_players, file="all_recent_players.rdata", compress="gzip")
The Retrosheet database lives in PostgreSQL on my computer, but one of the advantages of using dplyr for retrieval is it would be easy to change the source statement to connect to another sort of database (SQLite, MySQL, etc.) and the rest of the commands would be the same.
We only grab data since 1990 and we combine the first and last names into a single field because that’s how player names are listed on the 40-man roster pages on the web.
Now we filter the list down to Oakland Athletic players, combine the rows for each Oakland player, summarizing the years they played for the A’s into a single column.
oakland_players <- all_recent_players %>%
filter(team=='OAK') %>%
group_by(player) %>%
summarise(years=paste(year, collapse=', '))
Here’s what that looks like:
kable(head(oakland_players))
| player | years |
|---|---|
| A.J. Griffin | 2012, 2013 |
| A.J. Hinch | 1998, 1999, 2000 |
| Aaron Cunningham | 2008, 2009 |
| Aaron Harang | 2002, 2003 |
| Aaron Small | 1996, 1997, 1998 |
| Adam Dunn | 2014 |
| ... | ... |
Current 40-man rosters
Major League Baseball has the 40-man rosters for each team on their site. In order to extract them, we create a list of the team identifiers (oak, sf, etc.), then loop over this list, grabbing the team name and all the player names. We also set up lists for the team names (“Athletics”, “Giants”, etc.) so we can replace the short identifiers with real names later.
teams=c("ana", "ari", "atl", "bal", "bos", "cws", "chc", "cin", "cle", "col",
"det", "mia", "hou", "kc", "la", "mil", "min", "nyy", "nym", "oak",
"phi", "pit", "sd", "sea", "sf", "stl", "tb", "tex", "tor", "was")
team_names = c("Angels", "Diamondbacks", "Braves", "Orioles", "Red Sox",
"White Sox", "Cubs", "Reds", "Indians", "Rockies", "Tigers",
"Marlins", "Astros", "Royals", "Dodgers", "Brewers", "Twins",
"Yankees", "Mets", "Athletics", "Phillies", "Pirates",
"Padres", "Mariners", "Giants", "Cardinals", "Rays", "Rangers",
"Blue Jays", "Nationals")
get_players <- function(team) {
# reads the 40-man roster data for a team, returns a data frame
roster_html <- html(paste("http://www.mlb.com/team/roster_40man.jsp?c_id=",
team,
sep=''))
players <- roster_html %>%
html_nodes("#roster_40_man a") %>%
html_text()
data.frame(team=team, player=players)
}
current_rosters <- ldply(teams, get_players)
save(current_rosters, file="current_rosters.rdata", compress="gzip")
Here’s what that data looks like:
kable(head(current_rosters))
| team | player |
|---|---|
| ana | Jose Alvarez |
| ana | Cam Bedrosian |
| ana | Andrew Heaney |
| ana | Jeremy McBryde |
| ana | Mike Morin |
| ana | Vinnie Pestano |
| ... | ... |
Combine the data
To find out how many players on each Major League team used to play for the A’s we combine the former A’s players with the current rosters using player name. This may not be perfect due to differences in spelling (accented characters being the most likely issue), but the results look pretty good.
roster_with_oakland_time <- current_rosters %>%
left_join(oakland_players, by="player") %>%
filter(!is.na(years))
kable(head(roster_with_oakland_time))
| team | player | years |
|---|---|---|
| ana | Huston Street | 2005, 2006, 2007, 2008 |
| ana | Grant Green | 2013 |
| ana | Collin Cowgill | 2012 |
| ari | Brad Ziegler | 2008, 2009, 2010, 2011 |
| ari | Cliff Pennington | 2008, 2009, 2010, 2011, 2012 |
| atl | Trevor Cahill | 2009, 2010, 2011 |
| ... | ... | ... |
You can see from this table (just the first six rows of the results) that the Angels have three players that were Athletics.
Let’s do the math and find out how many former A’s are on each team’s roster.
n_former_players_by_team <-
roster_with_oakland_time %>%
group_by(team) %>%
arrange(player) %>%
summarise(number_of_players=n(),
players=paste(player, collapse=", ")) %>%
arrange(desc(number_of_players)) %>%
inner_join(data.frame(team=teams, team_name=team_names),
by="team") %>%
select(team_name, number_of_players, players)
names(n_former_players_by_team) <- c('Team', 'Number',
'Former Oakland Athletics')
kable(n_former_players_by_team,
align=c('l', 'r', 'r'))
| Team | Number | Former Oakland Athletics |
|---|---|---|
| Athletics | 22 | A.J. Griffin, Andy Parrino, Billy Burns, Coco Crisp, Craig Gentry, Dan Otero, Drew Pomeranz, Eric O'Flaherty, Eric Sogard, Evan Scribner, Fernando Abad, Fernando Rodriguez, Jarrod Parker, Jesse Chavez, Josh Reddick, Nate Freiman, Ryan Cook, Sam Fuld, Scott Kazmir, Sean Doolittle, Sonny Gray, Stephen Vogt |
| Astros | 5 | Chris Carter, Dan Straily, Jed Lowrie, Luke Gregerson, Pat Neshek |
| Braves | 4 | Jim Johnson, Jonny Gomes, Josh Outman, Trevor Cahill |
| Rangers | 4 | Adam Rosales, Colby Lewis, Kyle Blanks, Michael Choice |
| Angels | 3 | Collin Cowgill, Grant Green, Huston Street |
| Cubs | 3 | Chris Denorfia, Jason Hammel, Jon Lester |
| Dodgers | 3 | Alberto Callaspo, Brandon McCarthy, Brett Anderson |
| Mets | 3 | Anthony Recker, Bartolo Colon, Jerry Blevins |
| Yankees | 3 | Chris Young, Gregorio Petit, Stephen Drew |
| Rays | 3 | David DeJesus, Erasmo Ramirez, John Jaso |
| Diamondbacks | 2 | Brad Ziegler, Cliff Pennington |
| Indians | 2 | Brandon Moss, Nick Swisher |
| White Sox | 2 | Geovany Soto, Jeff Samardzija |
| Tigers | 2 | Rajai Davis, Yoenis Cespedes |
| Royals | 2 | Chris Young, Joe Blanton |
| Marlins | 2 | Dan Haren, Vin Mazzaro |
| Padres | 2 | Derek Norris, Tyson Ross |
| Giants | 2 | Santiago Casilla, Tim Hudson |
| Nationals | 2 | Gio Gonzalez, Michael Taylor |
| Red Sox | 1 | Craig Breslow |
| Rockies | 1 | Carlos Gonzalez |
| Brewers | 1 | Shane Peterson |
| Twins | 1 | Kurt Suzuki |
| Phillies | 1 | Aaron Harang |
| Mariners | 1 | Seth Smith |
| Cardinals | 1 | Matt Holliday |
| Blue Jays | 1 | Josh Donaldson |
Pretty cool. I do notice one problem: there are actually two Chris Young’s playing in baseball today. Chris Young the outfielder played for the A’s in 2013 and now plays for the Yankees. There’s also a pitcher named Chris Young who shows up on our list as a former A’s player who now plays for the Royals. This Chris Young never actually played for the A’s. The Retrosheet roster data includes which hand (left and/or right) a player bats and throws with, and it’s possible this could be used with the MLB 40-man roster data to eliminate incorrect joins like this, but even with that enhancement, we still have the problem that we’re joining on things that aren’t guaranteed to uniquely identify a player. That’s the nature of attempting to combine data from different sources.
One other interesting thing. I kept the A’s in the list because the number of former A’s currently playing for the A’s is a measure of how much turnover there is within an organization. Of the 40 players on the current A’s roster, only 22 of them have ever played for the A’s. That means that 18 came from other teams or are promotions from the minors that haven’t played for any Major League teams yet.
All teams
Teams with players on other teams
Now that we’ve looked at how many A’s players have played for other teams, let’s see how the number of players playing for other teams is related to team. My gut feeling is that the A’s will be at the top of this list as a small market, low budget team who is forced to turn players over regularly in order to try and stay competitive.
We already have the data for this, but need to manipulate it in a different way to get the result.
teams <- c("ANA", "ARI", "ATL", "BAL", "BOS", "CAL", "CHA", "CHN", "CIN",
"CLE", "COL", "DET", "FLO", "HOU", "KCA", "LAN", "MIA", "MIL",
"MIN", "MON", "NYA", "NYN", "OAK", "PHI", "PIT", "SDN", "SEA",
"SFN", "SLN", "TBA", "TEX", "TOR", "WAS")
team_names <- c("Angels", "Diamondbacks", "Braves", "Orioles", "Red Sox",
"Angels", "White Sox", "Cubs", "Reds", "Indians", "Rockies",
"Tigers", "Marlins", "Astros", "Royals", "Dodgers", "Marlins",
"Brewers", "Twins", "Expos", "Yankees", "Mets", "Athletics",
"Phillies", "Pirates", "Padres", "Mariners", "Giants",
"Cardinals", "Rays", "Rangers", "Blue Jays", "Nationals")
players_on_other_teams <- all_recent_players %>%
group_by(player, team) %>%
summarise(years=paste(year, collapse=", ")) %>%
inner_join(current_rosters, by="player") %>%
mutate(current_team=team.y, former_team=team.x) %>%
select(player, current_team, former_team, years) %>%
inner_join(data.frame(former_team=teams, former_team_name=team_names),
by="former_team") %>%
group_by(former_team_name, current_team) %>%
summarise(n=n()) %>%
group_by(former_team_name) %>%
arrange(desc(n)) %>%
mutate(rank=row_number()) %>%
filter(rank!=1) %>%
summarise(n=sum(n)) %>%
arrange(desc(n))
This is a pretty complicated set of operations. The main trick (and possible flaw in the analysis) is to get a list similar to the one we got for the A’s earlier, and eliminate the first row (the number of players on a team who played for that same team in the past) before counting the total players who have played for other teams. It would probably be better to eliminate that case using team name, but the team codes vary between Retrosheet and the MLB roster data.
Here are the results:
names(players_on_other_teams) <- c('Former Team', 'Number of players')
kable(players_on_other_teams)
| Former Team | Number of players |
|---|---|
| Athletics | 57 |
| Padres | 57 |
| Marlins | 56 |
| Rangers | 55 |
| Diamondbacks | 51 |
| Braves | 50 |
| Yankees | 50 |
| Angels | 49 |
| Red Sox | 47 |
| Pirates | 46 |
| Royals | 44 |
| Dodgers | 43 |
| Mariners | 42 |
| Rockies | 42 |
| Cubs | 40 |
| Tigers | 40 |
| Astros | 38 |
| Blue Jays | 38 |
| Rays | 38 |
| White Sox | 38 |
| Indians | 35 |
| Mets | 35 |
| Twins | 33 |
| Cardinals | 31 |
| Nationals | 31 |
| Orioles | 31 |
| Reds | 28 |
| Brewers | 26 |
| Phillies | 25 |
| Giants | 24 |
| Expos | 4 |
The A’s are indeed on the top of the list, but surprisingly, the Padres are also at the top. I had no idea the Padres had so much turnover. At the bottom of the list are teams like the Giants and Phillies that have been on recent winning streaks and aren’t trading their players to other teams.
Current players on the same team
We can look at the reverse situation: how many players on the current roster played for that same team in past years. Instead of removing the current × former team combination with the highest number, we include only that combination, which is almost certainly the combination where the former and current team is the same.
players_on_same_team <- all_recent_players %>%
group_by(player, team) %>%
summarise(years=paste(year, collapse=", ")) %>%
inner_join(current_rosters, by="player") %>%
mutate(current_team=team.y, former_team=team.x) %>%
select(player, current_team, former_team, years) %>%
inner_join(data.frame(former_team=teams, former_team_name=team_names),
by="former_team") %>%
group_by(former_team_name, current_team) %>%
summarise(n=n()) %>%
group_by(former_team_name) %>%
arrange(desc(n)) %>%
mutate(rank=row_number()) %>%
filter(rank==1,
former_team_name!="Expos") %>%
summarise(n=sum(n)) %>%
arrange(desc(n))
names(players_on_same_team) <- c('Team', 'Number of players')
kable(players_on_same_team)
| Team | Number of players |
|---|---|
| Rangers | 31 |
| Rockies | 31 |
| Twins | 30 |
| Giants | 29 |
| Indians | 29 |
| Cardinals | 28 |
| Mets | 28 |
| Orioles | 28 |
| Tigers | 28 |
| Brewers | 27 |
| Diamondbacks | 27 |
| Mariners | 27 |
| Phillies | 27 |
| Reds | 26 |
| Royals | 26 |
| Angels | 25 |
| Astros | 25 |
| Cubs | 25 |
| Nationals | 25 |
| Pirates | 25 |
| Rays | 25 |
| Red Sox | 24 |
| Blue Jays | 23 |
| Athletics | 22 |
| Marlins | 22 |
| Padres | 22 |
| Yankees | 22 |
| Dodgers | 21 |
| White Sox | 20 |
| Braves | 13 |
The A’s are near the bottom of this list, along with other teams that have been retooling because of a lack of recent success such as the Yankees and Dodgers. You would think there would be an inverse relationship between this table and the previous one (if a lot of your former players are currently playing on other teams they’re not playing on your team), but this isn’t always the case. The White Sox, for example, only have 20 players on their roster that were Sox in the past, and there aren’t very many of them playing on other teams either. Their current roster must have been developed from their own farm system or international signings, rather than by exchanging players with other teams.
Memorial Stadium, 1971
photo by Tom Vivian
Yesterday, the Baltimore Orioles and Chicago White Sox played a game at Camden Yards in downtown Baltimore. The game was “closed to fans” due to the riots that broke out in the city after the funeral for a man who died in police custody. It’s the first time a Major League Baseball game has been played without any fans in the stands, but unfortunately it’s not the first time there have been riots in Baltimore.
After Martin Luther King, Jr. was murdered in April 1968, Baltimore rioted for six days, with local police, and more than eleven thousand National Guard, Army troops, and Marines brought in to restore order. According to wikipedia six people died, more than 700 were injured, 1,000 businesses were damaged and close to six thousand people were arrested.
At that time, the Orioles played in Memorial Stadium, about 4 miles north-northwest of where they play now. I don’t know much about that area of Baltimore, but I was curious to know whether the Orioles played any baseball games during those riots.
Retrosheet has one game, on April 10, 1968, with a reported attendance of 22,050. The Orioles defeated the Oakland Athletics by a score of 3–1. Thomas Phoebus got the win over future Hall of Famer Catfish Hunter. Other popular players in the game included Reggie Jackson, Sal Bando, Rick Mondy and Bert Campaneris for the A’s and Brooks Robinson, Frank Robinson, Davey Johnson, and Boog Powell for the Orioles.
The box score and play-by-play can be viewed here.