Matt Spence https://www.mattspence.com/posts/ This is Matt Spence's personal landing page and occasional blog quarto-1.8.26 Sat, 04 Mar 2023 00:00:00 GMT Revisiting Opening Day Win-Loss Statistics Matthew Spence https://www.mattspence.com/posts/2023/03/opening-day-revisited/ I wanted to revisit my earlier post about opening day win-loss statistics to update the numbers with eight additional years of data and also to fix the a few issues that I didn’t have the skills to address back in 2015.

First, my old script had separate entries for the two teams that renamed themselves during the period — the Los Angeles (California) Angels and the Miami (Florida) Marlins. I wanted those combined, no question. I also wondered about what to do with a franchise that renamed itself and moved. Should the Expos and the Nationals really get combined into a single franchise record?

My first attempts to fix the former and keep the Nats/Expos apart were unsuccessful. I combined the renamed teams by creating the win-loss records by team_id (as before), but then merging those with the historical franchise ID (hist_franchise_id, as before) and bringing over the current_team_id as well.

Code 
import os, requests, zipfile, io
from IPython.display import HTML, Markdown
#from itables import show
import pandas as pd

DATADIR = os.path.join(os.getenv("MY_PROJECT_DIR"), "data", "gl")
YEARSTART, YEAREND = 1980, 2022 # will add +1 to YEAREND for range()

if not os.path.exists(DATADIR):
    os.makedirs(DATADIR)

if not os.path.exists(os.path.join(DATADIR,"CurrentNames.csv")):
    r = requests.get("https://www.retrosheet.org/CurrentNames.csv")
    with open(os.path.join(DATADIR,"CurrentNames.csv"),"w") as f:
        f.write("current_team_id,hist_franchise_id,league,division,location,nickname,alt_nickname,first_used,last_used,city,state\n")
        f.write(r.text)

for year in range(YEARSTART,YEAREND+1): 
    if not os.path.exists(os.path.join(DATADIR,f"gl{year}.txt")):
        zip_file_url = f"https://www.retrosheet.org/gamelogs/gl{year}.zip"
        r = requests.get(zip_file_url)
        z = zipfile.ZipFile(io.BytesIO(r.content))
        z.extractall(path=DATADIR)

fields = ['date','visit_team_id','visit_team_league','visit_game_number','home_team_id','home_team_league','home_game_number','visit_final_score','home_final_score']
cols = [0,3,4,5,6,7,8,9,10]

def assign_outcome(row):
    if row.final_score>row.opponent_final_score:
        return 'W'
    elif row.final_score<row.opponent_final_score:
        return 'L'
    else:
        return 'T' # 2000's opening of Reds vs Brewers ended in an official tie

def read_in_data(year):
    dat = pd.read_csv(f"{DATADIR}/gl{year}.txt", header=None,names=fields, usecols=cols, parse_dates=['date'])
    v = dat.loc[dat.visit_game_number==1,].rename(columns=lambda x: x.replace('visit_','')).rename(columns=lambda x: x.replace('home_','opponent_'))
    h = dat.loc[dat.home_game_number==1,].rename(columns=lambda x: x.replace('home_','')).rename(columns=lambda x: x.replace('visit_','opponent_'))
    games = pd.concat([v,h], ignore_index=True)
    games['outcome'] = games.apply(assign_outcome,axis=1)
    games['season'] = year
    return games

teamnames = pd.read_csv(os.path.join(DATADIR,'CurrentNames.csv'), parse_dates=['first_used','last_used']) 

dflist = []
for year in range(YEARSTART,YEAREND+1):
    g = read_in_data(year)
    dflist.append(g)
    # df = pd.concat([df,g], ignore_index=True)

df = pd.concat(dflist, ignore_index=True, sort=False)

# def f(group):
#     return pd.DataFrame({'count': group.value_counts(),
#                          'total': group.size})

winpct = (df.groupby('team_id').apply(lambda x: x.outcome.value_counts()/x.outcome.size, include_groups=False).round(3)*100)[:,"W"].sort_values(ascending=False)

wlcnt = df.groupby('team_id').apply(lambda x: x.outcome.value_counts(), include_groups=False).unstack(-1)

# drop any team name that was last_used before the start of our analysis. 
# "Drop duplicates" by current_team_id and hist_franchise_id will get rid of divisional changes (e.g. Pirates moving from NL E to NL C in 1994) and minor name changes (TBA Devil Rays --> Rays; Anaheim Angles --> LA Angels)
pctteams = teamnames[(teamnames['last_used'].dt.year>YEARSTART)|(teamnames['last_used'].isna())].drop_duplicates(subset=['current_team_id','hist_franchise_id'], keep="last") 

wlcntteam = pd.merge(wlcnt, pctteams[['current_team_id', 'hist_franchise_id']], left_index=True, right_on='hist_franchise_id', how="left").groupby(['current_team_id'])[['W','L','T']].sum()

finaltable = pd.merge(wlcntteam, pctteams, left_index=True, right_on='hist_franchise_id', how="left").assign(Team = lambda x: x['location'] + " " + x['nickname'], WnPct = lambda x: 100*round(x['W']/(x['W']+x['L']+x['T']), 3)).rename(columns={'W':'Wins','L':'Losses','T':'Ties', 'WnPct':'Win (%)'})[['Team','Wins','Losses', 'Ties', 'Win (%)']].sort_values("Win (%)", ascending=False)

HTML(finaltable.to_html(index=False))
Table 1: Opening Day Records, 1980 – 2022
Team Wins Losses Ties Win (%)
New York Mets 31.0 12.0 0.0 72.1
Baltimore Orioles 28.0 15.0 0.0 65.1
Seattle Mariners 26.0 17.0 0.0 60.5
Chicago White Sox 25.0 18.0 0.0 58.1
Los Angeles Dodgers 25.0 18.0 0.0 58.1
Detroit Tigers 25.0 18.0 0.0 58.1
Cincinnati Reds 24.0 18.0 1.0 55.8
St. Louis Cardinals 23.0 20.0 0.0 53.5
New York Yankees 23.0 20.0 0.0 53.5
Toronto Blue Jays 23.0 20.0 0.0 53.5
San Francisco Giants 23.0 20.0 0.0 53.5
Houston Astros 23.0 20.0 0.0 53.5
Colorado Rockies 16.0 14.0 0.0 53.3
Tampa Bay Rays 13.0 12.0 0.0 52.0
Arizona Diamondbacks 13.0 12.0 0.0 52.0
Atlanta Braves 22.0 21.0 0.0 51.2
Milwaukee Brewers 22.0 20.0 1.0 51.2
Chicago Cubs 21.0 22.0 0.0 48.8
Pittsburgh Pirates 20.0 23.0 0.0 46.5
Los Angeles Angels 20.0 23.0 0.0 46.5
Boston Red Sox 20.0 23.0 0.0 46.5
Washington Nationals 20.0 23.0 0.0 46.5
Texas Rangers 19.0 24.0 0.0 44.2
Miami Marlins 12.0 18.0 0.0 40.0
Philadelphia Phillies 17.0 26.0 0.0 39.5
Oakland Athletics 17.0 26.0 0.0 39.5
San Diego Padres 17.0 26.0 0.0 39.5
Minnesota Twins 17.0 26.0 0.0 39.5
Cleveland Indians 16.0 27.0 0.0 37.2
Kansas City Royals 15.0 28.0 0.0 34.9
Code 
wlcntteam_bycity = pd.merge(wlcnt, pctteams[['current_team_id', 'hist_franchise_id', 'city', 'state']], left_index=True, right_on='hist_franchise_id', how="left").groupby(['current_team_id','city'])[['W','L','T']].sum().reset_index()

wlcntteam_bycity.loc[wlcntteam_bycity['city'] == "Montreal", "current_team_id"] = "MON"

finaltable_exposfix = pd.merge(wlcntteam_bycity, pctteams, left_on=['current_team_id','city'], right_on=['hist_franchise_id','city'], how="left").assign(Team = lambda x: x['location'] + " " + x['nickname'], WnPct = lambda x: 100*round(x['W']/(x['W']+x['L']+x['T']), 3)).rename(columns={'W':'Wins','L':'Losses','T':'Ties', 'WnPct':'Win (%)'})[['Team','Wins','Losses','Ties', 'Win (%)']].sort_values("Win (%)", ascending=False)

HTML(finaltable_exposfix.to_html(index=False))
Table 2: Opening Day Records, 1980 – 2022, Expos and Nationals separated
Team Wins Losses Ties Win (%)
New York Mets 31.0 12.0 0.0 72.1
Baltimore Orioles 28.0 15.0 0.0 65.1
Seattle Mariners 26.0 17.0 0.0 60.5
Chicago White Sox 25.0 18.0 0.0 58.1
Los Angeles Dodgers 25.0 18.0 0.0 58.1
Detroit Tigers 25.0 18.0 0.0 58.1
Cincinnati Reds 24.0 18.0 1.0 55.8
Toronto Blue Jays 23.0 20.0 0.0 53.5
Houston Astros 23.0 20.0 0.0 53.5
St. Louis Cardinals 23.0 20.0 0.0 53.5
San Francisco Giants 23.0 20.0 0.0 53.5
New York Yankees 23.0 20.0 0.0 53.5
Colorado Rockies 16.0 14.0 0.0 53.3
Tampa Bay Rays 13.0 12.0 0.0 52.0
Arizona Diamondbacks 13.0 12.0 0.0 52.0
Atlanta Braves 22.0 21.0 0.0 51.2
Milwaukee Brewers 22.0 20.0 1.0 51.2
Chicago Cubs 21.0 22.0 0.0 48.8
Montreal Expos 12.0 13.0 0.0 48.0
Pittsburgh Pirates 20.0 23.0 0.0 46.5
Los Angeles Angels 20.0 23.0 0.0 46.5
Boston Red Sox 20.0 23.0 0.0 46.5
Washington Nationals 8.0 10.0 0.0 44.4
Texas Rangers 19.0 24.0 0.0 44.2
Miami Marlins 12.0 18.0 0.0 40.0
Philadelphia Phillies 17.0 26.0 0.0 39.5
San Diego Padres 17.0 26.0 0.0 39.5
Oakland Athletics 17.0 26.0 0.0 39.5
Minnesota Twins 17.0 26.0 0.0 39.5
Cleveland Indians 16.0 27.0 0.0 37.2
Kansas City Royals 15.0 28.0 0.0 34.9
]]> baseball code https://www.mattspence.com/posts/2023/03/opening-day-revisited/ Sat, 04 Mar 2023 00:00:00 GMT Opening Day Matthew Spence https://www.mattspence.com/posts/2015/04/opening-day/ In honor of Opening Day (and the Nats’ loss), I wanted to see which teams have had the most success on opening day.

It’s…the Mets (?)

Code 
import os, requests, zipfile, io
from IPython.display import HTML#, Markdown
#from itables import show
import pandas as pd

DATADIR = os.path.join(os.getenv("MY_PROJECT_DIR"), "data", "gl") # This is not how I coded 

if not os.path.exists(DATADIR):
    os.makedirs(DATADIR)

DOWNLOADFROMRS = False

if DOWNLOADFROMRS:
    r = requests.get("https://www.retrosheet.org/CurrentNames.csv")
    with open(os.path.join(DATADIR,"CurrentNames.csv"),"w") as f:
        f.write("current_team_id,hist_franchise_id,league,division,location,nickname,alt_nickname,first_used,last_used,city,state\n")
        f.write(r.text)

"""
From https://www.retrosheet.org/Nickname.htm:
The fields are:

Current franchise ID
Franchise ID (may the same as current one) for earlier data
League
Division (blank before divisional play)
Location name (e.g. Brooklyn, Texas)
Nickname
Alternate nicknames (blank if none for the period)
Date of the first game with this combination
Date of the last game with combination (blank if current one)
City
State
"""

if DOWNLOADFROMRS:
    r = requests.get("https://www.retrosheet.org/gamelogs/glfields.txt")
    with open(os.path.join(DATADIR, "glfields.txt"),"w") as f:
        f.write(r.text)

if DOWNLOADFROMRS:
    for y in range(1980,2015):
        zip_file_url = f"https://www.retrosheet.org/gamelogs/gl{y}.zip"
        r = requests.get(zip_file_url)
        z = zipfile.ZipFile(io.BytesIO(r.content))
        z.extractall(path=DATADIR)

fields = ['date','visit_team_id','visit_team_league','visit_game_number','home_team_id','home_team_league','home_game_number','visit_final_score','home_final_score']
cols = [0,3,4,5,6,7,8,9,10]

namefields = ['current_team_id','hist_franchise_id','league','division','location','nickname','alt_nickname','first_used','last_used','city','state']

def assign_outcome(row):
    if row.final_score>row.opponent_final_score:
        return 'W'
    elif row.final_score<row.opponent_final_score:
        return 'L'

def read_in_data(year):
    dat = pd.read_csv(f"{DATADIR}/GL{year}.txt", header=None,names=fields, usecols=cols, parse_dates=['date'])
    v = dat.loc[dat.visit_game_number==1,].rename(columns=lambda x: x.replace('visit_','')).rename(columns=lambda x: x.replace('home_','opponent_'))
    h = dat.loc[dat.home_game_number==1,].rename(columns=lambda x: x.replace('home_','')).rename(columns=lambda x: x.replace('visit_','opponent_'))
    games = pd.concat([v,h], ignore_index=True)
    games['outcome'] = games.apply(assign_outcome,axis=1)
    games['season'] = year
    return games

teamnames = pd.read_csv(os.path.join(DATADIR,'CurrentNames.csv'), parse_dates=['first_used','last_used']) 

df = pd.DataFrame()
for year in range(1980,2015):
    g = read_in_data(year)
    df = pd.concat([df,g], ignore_index=True)

teamsgrouped = df.groupby('team_id')

pct = teamsgrouped.apply(lambda x: x.outcome.value_counts()/x.outcome.size).round(3)*100
pct = pct.unstack(1)
pct = pct.sort_values('W', ascending=False) ## decent answer here, but let's merge in team names

pctteamids = list(pct.index.values)
pctteams = teamnames[teamnames.hist_franchise_id.isin(pctteamids)].sort_values('last_used').drop_duplicates(subset='hist_franchise_id',keep="last")

finaltable = pct.merge(pctteams, left_index=True, right_on='hist_franchise_id', how="left").reset_index().assign(Team = lambda x: x['location'] + " " + x['nickname']).rename(columns={'W':'Wins','L':'Losses'})[['Team','Wins','Losses']]

HTML(finaltable.to_html(index=False))  # alternative to using the itables package
# with pd.option_context("display.float_format", "{:,.1f}%".format):
#     show(finaltable, dom="lrtp", lengthMenu = [5,10,len(finaltable)]) #paging=False to show full table
Table 1: Opening Day Records, 1980 – 2014
Team Wins Losses
New York Mets 71.4 28.6
Baltimore Orioles 65.7 34.3
Chicago White Sox 65.7 34.3
Los Angeles Angels 61.1 38.9
Seattle Mariners 60.0 40.0
Arizona Diamondbacks 58.8 41.2
Atlanta Braves 57.1 42.9
Los Angeles Dodgers 54.3 45.7
San Francisco Giants 54.3 45.7
New York Yankees 54.3 45.7
Cincinnati Reds 54.3 42.9
Milwaukee Brewers 54.3 42.9
Detroit Tigers 54.3 45.7
Tampa Bay Rays 52.9 47.1
Florida Marlins 52.6 47.4
Toronto Blue Jays 51.4 48.6
St. Louis Cardinals 51.4 48.6
Colorado Rockies 50.0 50.0
Texas Rangers 48.6 51.4
Pittsburgh Pirates 48.6 51.4
Montreal Expos 48.0 52.0
California Angels 47.1 52.9
Chicago Cubs 45.7 54.3
Boston Red Sox 45.7 54.3
Houston Astros 42.9 57.1
Washington Nationals 40.0 60.0
San Diego Padres 40.0 60.0
Minnesota Twins 40.0 60.0
Cleveland Indians 40.0 60.0
Philadelphia Phillies 37.1 62.9
Oakland Athletics 37.1 62.9
Miami Marlins 33.3 66.7
Kansas City Royals 28.6 71.4

Here’s how I did it:

I downloaded1 game logs from 1980 – 2014 from RetroSheet and wrote the following script to analyze them. The key was changing the “dyadic” structure of the raw game logs (one row contains information about the two teams playing the game) into one that had one row for each team. I did this in the read_in_data function:

def read_in_data(year):
    dat = pd.read_csv(f"{DATADIR}/GL{year}.txt", header=None,names=fields, usecols=cols, parse_dates=['date'])
    v = dat.loc[dat.visit_game_number==1,].rename(columns=lambda x: x.replace('visit_','')).rename(columns=lambda x: x.replace('home_','opponent_'))
    h = dat.loc[dat.home_game_number==1,].rename(columns=lambda x: x.replace('home_','')).rename(columns=lambda x: x.replace('visit_','opponent_'))
    games = pd.concat([v,h], ignore_index=True)
    games['outcome'] = games.apply(assign_outcome,axis=1)
    games['season'] = year
    return games

Here’s an example of one year’s worth of gamelog data once it’s been run through read_in_data. 2014 was a bit of an odd season, since the Dodgers and the Diamondbacks started their seasons on March 22 by playing three exhibition games in Syndey, Australia. Each team went on to be another team’s opening day opponent – the Dodgers faced the Padres and the Diamondbacks went up against the Giants.

date team_id game_number opponent_team_id opponent_game_number final_score opponent_final_score outcome
2014-03-22 LAN 1 ARI 1 3 1 W
2014-03-22 ARI 1 LAN 1 1 3 L
2014-03-30 SDN 1 LAN 3 3 1 W
2014-03-31 MIA 1 COL 1 10 1 W
2014-03-31 CIN 1 SLN 1 0 1 L
2014-03-31 TEX 1 PHI 1 10 14 L
2014-03-31 TBA 1 TOR 1 9 2 W
2014-03-31 OAK 1 CLE 1 0 2 L
2014-03-31 DET 1 KCA 1 4 3 W
2014-03-31 CHA 1 MIN 1 5 3 W
2014-03-31 BAL 1 BOS 1 2 1 W
2014-03-31 ANA 1 SEA 1 3 10 L
2014-03-31 PIT 1 CHN 1 1 0 W
2014-03-31 CHN 1 PIT 1 0 1 L
2014-03-31 WAS 1 NYN 1 9 7 W
2014-03-31 ATL 1 MIL 1 0 2 L
2014-03-31 COL 1 MIA 1 1 10 L
2014-03-31 SLN 1 CIN 1 1 0 W
2014-03-31 SFN 1 ARI 3 9 8 W
2014-03-31 PHI 1 TEX 1 14 10 W
2014-03-31 TOR 1 TBA 1 2 9 L
2014-03-31 CLE 1 OAK 1 2 0 W
2014-03-31 KCA 1 DET 1 3 4 L
2014-03-31 MIN 1 CHA 1 3 5 L
2014-03-31 BOS 1 BAL 1 1 2 L
2014-03-31 SEA 1 ANA 1 10 3 W
2014-03-31 MIL 1 ATL 1 2 0 W
2014-03-31 NYN 1 WAS 1 7 9 L
2014-04-01 NYA 1 HOU 1 2 6 L
2014-04-01 HOU 1 NYA 1 6 2 W

The results table includes two teams that have renamed themselves (the Angels and the Marlins), and one that moved and changed names (Expos). I’d like to update my script to deal with these, but should I really lump the Expos in with the Nats?

Also, I was relieved to see the Dodgers and the Giants were tied, at least.

Footnotes

  1. I did this manually back in 2015, but as I was porting this to the new site, I wrote a script to automatically download and unzip the files.↩︎

]]> baseball code https://www.mattspence.com/posts/2015/04/opening-day/ Thu, 09 Apr 2015 00:00:00 GMT