sat, 30-may-2015, 09:21


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.


As usual, I’ll use R for the analysis, and rmarkdown to produce this post.


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:

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("",

    players <- roster_html %>%
        html_nodes("#roster_40_man a") %>%

    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:

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") %>%

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) %>%
                  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')
      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)) %>%

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')

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()) %>%
           former_team_name!="Expos") %>%
    summarise(n=sum(n)) %>%

names(players_on_same_team) <- c('Team', 'Number of players')

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.

sun, 03-may-2015, 09:22

Yesterday I saw something I’ve never seen in a baseball game before: a runner getting hit by a batted ball, which according to Rule 7.08(f) means the runner is out and the ball is dead. It turns out that this isn’t as unusual an event as I’d thought (see below), but what was unusal is that this ended the game between the Angels and Giants. Even stranger, this is also how the game between the Diamondbacks and Dodgers ended.

Let’s use Retrosheet data to see how often this happens. Retrosheet data is organized into game data, roster data and event data. Event files contain a record of every event in a game and include the code BR for when a runner is hit by a batted ball. Here’s a SQL query to find all the matching events, who got hit and whether it was the last out in the game.

SELECT sub.game_id, teams, date, inn_ct, outs_ct, bat_team, event_tx,
   first_name || ' ' || last_name AS runner,
   CASE WHEN event_id = max_event_id THEN 'last out' ELSE '' END AS last_out
   SELECT year, game_id, away_team_id || ' @ ' || home_team_id AS teams,
      date, inn_ct,
      CASE WHEN bat_home_id = 1
            THEN home_team_id
            ELSE away_team_id END AS bat_team, outs_ct, event_tx,
      CASE regexp_replace(event_tx, '.*([1-3])X[1-3H].*', E'\\1')
            WHEN '1' THEN base1_run_id
            WHEN '2' THEN base2_run_id
            WHEN '3' THEN base3_run_id END AS runner_id,
   FROM events
   WHERE event_tx ~ 'BR'
) AS sub
   INNER JOIN rosters
      ON sub.year=rosters.year
            AND runner_id=player_id
            AND rosters.team_id = bat_team
      SELECT game_id, max(event_id) AS max_event_id
      FROM events
      GROUP BY game_id
   ) AS max_events
   ON sub.game_id = max_events.game_id
ORDER BY date;

Here's what the query does. The first sub-query sub finds all the events with the BR code, determines which team was batting and finds the id for the player who was running. This is joined with the roster table so we can assign a name to the runner. Finally, it’s joined with a subquery, max_events, which finds the last event in each game. Once we’ve got all that, the SELECT statement at the very top retrieves the columns of interest, and records whether the event was the last out of the game.

Retrosheet has event data going back to 1922, but the event files don’t include every game played in a season until the mid-50s. Starting in 1955 a runner being hit by a batted ball has a game twelve times, most recently in 2010. On average, runners get hit (and are called out) about fourteen times a season.

Here are the twelve times a runner got hit to end the game, since 1955. Until yesterday, when it happened twice in one day:

Runners hit by a batted ball to end a game, since 1955
Date Teams Batting Event Runner
1956-09-30 NY1 @ PHI PHI S4/BR.1X2(4)# Granny Hamner
1961-09-16 PHI @ CIN PHI S/BR/G4.1X2(4) Clarence Coleman
1971-08-07 SDN @ HOU SDN S/BR.1X2(3) Ed Spiezio
1979-04-07 CAL @ SEA SEA S/BR.1X2(4) Larry Milbourne
1979-08-15 TOR @ OAK TOR S/BR.3-3;2X3(4)# Alfredo Griffin
1980-09-22 CLE @ NYA CLE S/BR.3XH(5) Toby Harrah
1984-04-06 MIL @ SEA MIL S/BR.1X2(4) Robin Yount
1987-06-25 ATL @ LAN ATL S/L3/BR.1X2(3) Glenn Hubbard
1994-06-13 HOU @ SFN HOU S/BR.1X2(4) James Mouton
2001-08-04 NYN @ ARI ARI S/BR.2X3(6) David Dellucci
2003-04-09 KCA @ DET DET S/BR.1X2(4) Bobby Higginson
2010-06-27 PIT @ OAK PIT S/BR/G.1X2(3) Pedro Alvarez

And all runners hit last season:

Runners hit by a batted ball in the 2014 MLB season
Date Teams Batting Event Runner
2014-05-07 SEA @ OAK OAK S/BR/G.1X2(3) Derek Norris
2014-05-11 MIN @ DET DET S/BR/G.3-3;2X3(6);1-2 Austin Jackson
2014-05-23 CLE @ BAL BAL S/BR/G.2X3(6);1-2 Chris Davis
2014-05-27 NYA @ SLN SLN S/BR/G.1X2(3) Matt Holliday
2014-06-14 CHN @ PHI CHN S/BR/G.1X2(4) Justin Ruggiano
2014-07-13 OAK @ SEA SEA S/BR/G.1X2(4) Kyle Seager
2014-07-18 PHI @ ATL PHI S/BR/G.1X2(4) Grady Sizemore
2014-07-25 BAL @ SEA SEA S/BR/G.1X2(4) Brad Miller
2014-08-05 NYN @ WAS WAS S/BR/G.2X3(6);3-3 Asdrubal Cabrera
2014-09-04 SLN @ MIL SLN S/BR/G.2X3(6);1-2 Matt Carpenter
2014-09-09 SDN @ LAN LAN S/BR/G.2X3(6) Matt Kemp
2014-09-18 BOS @ PIT BOS S/BR/G.3XH(5);1-2;B-1 Jemile Weeks
thu, 30-apr-2015, 18:14
Memorial Stadium, 1971

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.

fri, 12-apr-2013, 19:00

Next month I’ll be attending a game at Wrigley Field and my brother and I had some discussion about the best strategy for us to meet up somewhere in Chicago after the game. Knowing how long a game could be, and how many people are likely to be crowding the train platforms is useful information that can be inferred from the game data that collects and distributes.

It’s also a good excuse to fiddle around the the IPython Notebook, pandas and the rest of the Python scientific computing stack.

I downloaded the game log data from using this bash one-liner:

for i in `seq 1871 2012`; \
do  wget${i}.zip ; \
    unzip gl${i}.zip; \
    rm gl${i}.zip; \

Game Length

Game length in minutes is in column 19. Let’s read it and analyze it with Pandas.

import pandas as pd
import matplotlib.pyplot as plt
import datetime

def fix_df(input_df, cols):
    """ Pulls out the columns from cols, labels them and returns
        a new DataFrame """
    df = pd.DataFrame(index=range(len(input_df)))
    for k, v in cols.items():
        df[v] = input_df[k]

    return df

cols = {0:'dt_str', 2:'day_of_week', 3:'visiting_team',
        6:'home_team', 9:'visitor_score', 10:'home_score',
       11:'game_outs', 12:'day_night', 13:'complete',
       16:'park_id', 17:'attendance', 18:'game_time_min'}
raw_gamelogs = []
for i in range(1871, 2013):
    fn = "GL{0}.TXT".format(i)
    raw_gamelogs.append(pd.read_csv(fn, header=None, index_col=None))
raw_gamelog = pd.concat(raw_gamelogs, ignore_index=True)
gamelog = fix_df(raw_gamelog, cols)
gamelog['dte'] = gamelog.apply(
    lambda x: datetime.datetime.strptime(str(x['dt_str']), '%Y%m%d').date(),
gamelog.ix[0:5, 1:] # .head() but without the dt_str col
  dow vis hme vis hme outs day park att time dte
0 Thu CL1 FW1 0 2 54 D FOR01 200 120 1871-05-04
1 Fri BS1 WS3 20 18 54 D WAS01 5000 145 1871-05-05
2 Sat CL1 RC1 12 4 54 D RCK01 1000 140 1871-05-06
3 Mon CL1 CH1 12 14 54 D CHI01 5000 150 1871-05-08
4 Tue BS1 TRO 9 5 54 D TRO01 3250 145 1871-05-09
5 Thu CH1 CL1 18 10 48 D CLE01 2500 120 1871-05-11

(Note that I’ve abbreviated the column names so they fit)

The data looks reasonable, although I don’t know quite what to make of the team names from back in 1871. Now I’ll take a look at the game time data for the whole data set:

times = gamelog['game_time_min']
count    162259.000000
mean        153.886145
std          74.850459
min          21.000000
25%         131.000000
50%         152.000000
75%         173.000000
max       16000.000000

The statistics look reasonable, except that it’s unlikley that there was a completed game in 21 minutes, or that a game took 11 days, so we have some outliers in the data. Let’s see if something else in the data might help us filter out these records.

First the games longer than 24 hours:

print(gamelog[gamelog.game_time_min > 60 * 24][['visiting_team',
                    'home_team', 'game_outs', 'game_time_min']])
print("Removing all NaN game_outs: {0}".format(
print("Max date removed: {0}".format(
      visiting_team home_team  game_outs  game_time_min
26664           PHA       WS1        NaN          12963
26679           PHA       WS1        NaN           4137
26685           WS1       PHA        NaN          16000
26707           NYA       PHA        NaN          15115
26716           CLE       CHA        NaN           3478
26717           DET       SLA        NaN           1800
26787           PHA       NYA        NaN           3000
26801           PHA       NYA        NaN           6000
26880           CHA       WS1        NaN           2528
26914           SLA       WS1        NaN           2245
26921           SLA       WS1        NaN           1845
26929           CLE       WS1        NaN           3215
Removing all NaN game_outs: 37890
Max date removed: 1915-10-03

There’s no value for game_outs, so there isn’t data for how long the game actually was. We remove 37,890 records by eliminating this data, but these are all games from prior to the 1916 season, so it seems reasonable:

gamelog = gamelog[pd.notnull(gamelog.game_outs)]
count    156639.000000
mean        154.808975
std          32.534916
min          21.000000
25%         133.000000
50%         154.000000
75%         174.000000
max        1150.000000

What about the really short games?

gamelog[gamelog.game_time_min < 60][[
        'dte', 'game_time_min', 'game_outs']].tail()
               dte  game_time_min  game_outs
79976   1948-07-02             24         54
80138   1948-07-22             59         30
80982   1949-05-29             21         42
113455  1971-07-30             48         27
123502  1976-09-10             57         30

Many of these aren’t nine inning games because there are less than 51 outs (8 innings for a home team, 9 for the visitor in a home team win ✕ 3 innings = 51). At the moment, I’m interested in looking at how long a game is likely to be, rather than the pattern of game length over time, so we can leave these records in the data.

Now we filter the data to just the games played since 2000.

twenty_first = gamelog[gamelog['dte'] >, 12, 31)]
times_21 = twenty_first['game_time_min']
count    31580.000000
mean       175.084421
std         26.621124
min         79.000000
25%        157.000000
50%        172.000000
75%        189.000000
max        413.000000

The average game length between 2000 and 2012 is 175 minutes, or just under three hours. And three quarters of all the games played are under three hours and ten minutes.

Here’s the code to look at the distribution of these times. The top plot shows a histogram (the count of the games in each bin) and a density estimation of the same data.

The second plot is a cumulative density plot, which makes it easier to see what percentage of games are played within a certain time.

from scipy import stats
# Calculate a kernel density function
density = stats.kde.gaussian_kde(times_21)
x = range(90, 300)
rc('grid', linestyle='-', alpha=0.25)
fig, axes = plt.subplots(ncols=1, nrows=2, figsize=(8, 6))

# First plot (histogram / density)
ax = axes[0]
ax2 = ax.twinx()
h = ax.hist(times_21, bins=50, facecolor="lightgrey", histtype="stepfilled")
d = ax2.plot(x, density(x))
plt.title("Distribution of MLB game times, 2000-2012")
ax.set_xlim(90, 300)
ax.set_xticks(range(90, 301, 15))

# Second plot (cumulative histogram)
ax1 = axes[1]
n, bins, patches = ax1.hist(times_21, bins=50, normed=True, cumulative=True,
        facecolor="lightgrey", histtype="stepfilled")
y = n / float(n[-1]) # Convert counts to percentage of total
y = np.concatenate(([0], y))
bins = bins - ((bins[1] - bins[0]) / 2.0) # Center curve on bars
ax1.plot(bins, y, color="blue")
ax1.set_ylabel("Cumulative percentage")
ax1.set_xlabel("Game time (minutes)")
ax1.set_xlim(90, 300)
ax1.set_xticks(range(90, 301, 15))
ax1.set_yticks(np.array(range(0, 101, 10)) / 100.0)
plt.savefig('game_time.png', dpi=87.5)

The two plots show what the descriptive statistics did: 70% of games are under three hours but it’s not uncommon for games to last three hours and fifteen minutes. Beyond that, it’s pretty uncommon.

Change in game times in the last 100 years

Now let’s look at how game times have changed over the years. First we eliminate all the games that weren’t complete in 51 or 54 innings to standardize the “game” were’re evaluating.

nine_innings = gamelog[gamelog.game_outs.isin([51, 54])]
nine_innings['year'] = nine_innings.apply(lambda x: x['dte'].year, axis=1)
nine_groupby_year = nine_innings.groupby('year')
mean_time_by_year = nine_groupby_year['game_time_min'].aggregate(np.mean)
fig = plt.figure()
p = mean_time_by_year.plot(figsize=(8, 4))
p.set_xlim(1915, 2015)
p.set_ylabel('Game Time (minutes)')
p.set_title('Average MLB nine-inning game time since 1915')
p.set_xticks(range(1915, 2016, 10))
plt.savefig('game_time_by_year.png', dpi=87.5)

That shows a pretty clear upward trend interrupted by a slight decline between 1960 and 1975 (when offense was down across baseball). Since the mid-90s, game times have hovered around 2:50, so maybe MLB’s efforts at increasing the pace of the game have at least stopped what had been an almost continual rise in game times.


I’ll be seeing a game at Wrigley Field, so let’s examine attendance at Wrigley.

Attendance is field 17, “Park ID” is field 16, but we can probably use Home team (field 6) = “CHN” for seasons after 1914 when it opened.

twenty_first[twenty_first['home_team'] == 'CHN']['attendance'].describe()
count     1053.000000
mean     37326.561254
std       5544.121773
min          0.000000
25%      36797.000000
50%      38938.000000
75%      40163.000000
max      55000.000000

We see the minimum is zero, which may indicate bad or missing data. Let’s look at all the games at Wrigley with less than 10,000 fans:

twenty_first[(twenty_first.home_team == 'CHN') &
             (twenty_first.attendance < 10000)][[
            'dte', 'visiting_team', 'home_team', 'visitor_score',
            'home_score', 'game_outs', 'day_night', 'attendance',
  dte vis hme vis hme outs day att game_time
172776 2000-06-01 ATL CHN 3 5 51 D 5267 160
173897 2000-08-25 LAN CHN 5 3 54 D 0 189
174645 2001-04-18 PHI CHN 3 4 51 D 0 159
176290 2001-08-20 MIL CHN 4 7 51 D 0 180
177218 2002-04-28 LAN CHN 5 4 54 D 0 196
177521 2002-05-21 PIT CHN 12 1 54 N 0 158
178910 2002-09-02 MIL CHN 4 2 54 D 0 193
181695 2003-09-27 PIT CHN 2 4 51 D 0 169
183806 2004-09-10 FLO CHN 7 0 54 D 0 146
184265 2005-04-13 SDN CHN 8 3 54 D 0 148
188186 2006-08-03 ARI CHN 10 2 54 D 0 197

Looks like the zeros are just missing data because these games have relevant data for the other fields and it’s impossible to believe that not a single person was in the stands. We’ll get rid of them for the attendance analysis.

Now we’ll filter the games so we’re only looking at day games played at Wrigley where the attendance value is above zero, and group the data by day of the week.

groupby_dow = twenty_first[(twenty_first['home_team'] == 'CHN') &
           (twenty_first['day_night'] == 'D') &
           (twenty_first['attendance'] > 0)].groupby('day_of_week')
Tue            34492.428571
Wed            35312.265957
Thu            35684.737864
Mon            37938.757576
Fri            38066.060976
Sun            38583.833333
Sat            39737.428571
Name: attendance

And plot it:

filtered = twenty_first[(twenty_first['home_team'] == 'CHN') &
                           (twenty_first['day_night'] == 'D') &
                           (twenty_first['attendance'] > 0)]
dows = {'Sun':0, 'Mon':1, 'Tue':2, 'Wed':3, 'Thu':4, 'Fri':5, 'Sat':6}
filtered['dow'] = filtered.apply(lambda x: dows[x['day_of_week']], axis=1)
filtered['attendance'] = filtered['attendance'] / 1000.0
fig = plt.figure()
d = filtered.boxplot(column='attendance', by='dow', figsize=(8, 4))
d.set_xlabel('Day of the week')
d.set_ylabel('Attendance (thousands)')
d.set_title('Wrigley Field attendance by day of the week, 2000-2012')
labels = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']
l = d.set_xticklabels(labels)
d.set_ylim(15, 45)
plt.savefig('attendance.png', dpi=87.5)
plt.savefig('attendance.svg') # lines don't show up?

There’s a pretty clear pattern here, with increasing attendance from Tuesday through Monday, and larger variances in attendance during the week.

It’s a bit surprising that Monday games are as popular as Saturday games, especially since we’re only looking at day games. On any given Monday when the Cubs are at home, there’s a good chance that there will be fourty thousand people not showing up to work!

tags: baseball  python 
tue, 02-apr-2013, 19:08
A’s v Mariners

Yesterday I attempted to watch the A’s Opening Day game against the Mariners in the Oakland Coliseum. Unfortunately, it turns out the entire state of Alaska is within the blackout region for all Seattle Mariners games, regardless of where the Mariners are playing.

The A’s lost the game, so maybe I’m not too disappointed I didn’t see it, but for curiousity, let’s extend Major League Baseball’s Alaska blackout “logic” to the rest of the country and see what that looks like.

First, load in the locations of all Major League stadiums into a PostGIS database. There are a variety of sources for this on the Internet, but many are missing the more recent stadiums. I downloaded one and updated those that weren’t correct using information on Wikipedia.

CREATE TABLE stadiums (
   division text, opened integer, longitude numeric,
   latitude numeric, class text, team text,
   stadium text, league text, capacity integer
COPY stadiums FROM 'stadium_data.csv' CSV HEADER;

Turn the latitude and longitudes into geographic objects:

SELECT addgeometrycolumn('stadiums', 'geom_wgs84', 4326, 'POINT', 2);
CREATE SEQUENCE stadium_id_seq;
ALTER TABLE stadiums
   ADD COLUMN id integer default (nextval('stadium_id_seq'));
UPDATE stadiums
   SET geom_wgs84 = ST_SetSRID(
      ST_MakePoint(longitude, latitude), 4326

Now load in a states polygon layer (which came from:

$ shp2pgsql -s 4269 states.shp | psql -d stadiums

Calculate how far the farthest location in Alaska is to Safeco Field:

SELECT ST_MaxDistance(safeco, alaska) / 1609.344 AS miles
   SELECT ST_Transform(geom_wgs84, 3338) AS safeco
   FROM stadiums WHERE stadium ~* 'safeco'
) AS foo, (
   SELECT ST_Transform(geom, 3338) AS alaska
   FROM states
   WHERE state_name ~* 'alaska'
) AS bar;


Yep. Folks in Alaska are supposed to travel 2,468 miles (3,971,338 meters, to be exact) in order to attend a game at Safeco Field (or farther if they’re playing elsewhere because the blackout rules aren’t just for home games anymore).

Now we add a 2,468 mile buffer around each of the 30 Major League stadiums to see what the blackout rules would look like if the rule for Alaska was applied everywhere:

SELECT addgeometrycolumn(
   'stadiums', 'ak_rules_buffer', 3338, 'POLYGON', 2);
UPDATE stadiums
SET ak_rules_buffer =
   ST_Buffer(ST_Transform(geom_wgs84, 3338), 3971338);

Here's a map showing what these buffers look like:

Or, shown another way, here’s the number of teams that would be blacked out for everyone in each state. Keep in mind that there are 30 teams in Major League Baseball so the states on the top of the list shouldn’t be able to watch any team if Alaska rules were applied evenly to the country:

Number of blacked out teams using Alaska blackout rules
Blacked out teams States
30 Alabama, Arkansas, Colorado, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Michigan, Minnesota, Mississippi, Missouri, Nebraska, New Mexico, North Dakota, Ohio, Oklahoma, South Dakota, Tennessee, Texas, Utah, Wisconsin, Wyoming
29 Idaho, Montana, Arizona
28 District of Columbia, West Virginia
27 Georgia, South Carolina
26 Nevada
25 Pennsylvania, Florida, Vermont
24 Rhode Island, New Hampshire, Connecticut, Delaware, New Jersey, New York, North Carolina, Virginia, Maryland, Massachusetts
23 Maine
22 Oregon, Washington, California
1 Alaska
0 Hawaii

Based on this, Hawaii would be the place to live as they can actually watch all 30 teams! Except the blackout rules for Hawaii are even stupider than Alaska: they can’t watch any of the teams on the West Coast. Ouch!

This is stupid-crazy. It seems to me that a more appropriate rule might be buffers that represent a two hour drive, or whatever distance is considered “reasonable” for traveling to attend a game. When I lived in Davis California we drove to the Bay Area several times a season to watch the A’s or Giants play, and that’s about the farthest I’d expect someone to go in order to see a three hour baseball game.

This makes a much more reasonable map:

I’m sure there are other issues going on here, such as cable television deals and other sources of revenue that MLB is trying to protect. But for the fans, those who ultimately pay for this game, the current situation is idiotic. I don’t have access to cable television (and even if I did, I won’t buy it until it’s possible to pick and choose which stations I want to pay for), and I’m more than 2,000 miles from the nearest ballpark. My only avenue for enjoying the game is to pay MLB $130 for a MLB.TV subscription. Which I happily do. Unfortunately, this year I can’t watch my teams whenever they happen to play the Mariners.

tags: baseball  SQL  GIS 

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