Category: Sports

With the World Baseball Classic coming up, I pulled every baseball player in the Lahman Database whose name perfectly matches a country. Here are some simple statistics among the country representatives with a batting record.

Here’s a fun bit of trivia about these Jordan folks: Until 1999, every player matching Jordan had it as their last name. Since 1999, all but two have the first name Jordan.

Additionally, the first Jordan’s first name was Jordan Zimmerman.¹No, not that one. You’re thinking of Jordan Zimmermann.

• 1
  No, not that one. You’re thinking of Jordan Zimmermann.

I first joined the Society for American Baseball Research (SABR) as a student member around 2016. Back then, I only knew about SABR by the semi-eponymous term “Sabermetrics” that roughly refers to the growing list of statistics and measures used to evaluate baseball. I was studying math and dabbling in computer science with vague hopes and dreams that I could bring those interests to bear on the game I loved. I knew little else about the organization, so when I met some excellent members of the Halsey Hall chapter in Minnesota at TwinsFest that winter, I was a bit shocked to find them all rather old. Not that they couldn’t have been career statisticians or have other relevant skills to analyzing baseball, but I was an introverted college student and didn’t appreciate the interaction.

Not until rejoining SABR four years ago did I realize the scope of their work and how many options I had for volunteering my time and talents to progress their mission. There are a few dozen research committees with varying scopes and interests. The two I’ve been most involved with are wide-ranging and ambitious: the Games Project provides accounts of significant games and their historical context.¹This is the only project for which I’ve written an article. The Biography Project writes comprehensive biographical articles of people in and around baseball. These volunteer-led committees are amazing. Some of their processes are charmingly stuck in the past, like their heavy reliance on email server lists. Still, they don’t let anything get in the way of producing well-written and thoroughly researched materials.

And though I’m not directly familiar with the other committees, a quick glance at the list shows the breadth of topics SABR volunteers have been inspired to cover. But don’t stop there: a recent edition of the Baseball Research Journal ²SABR publishes two editions each year. shows a tremendous range of topics that have sufficiently interested other baseball sickos. Two fun articles in that Spring 2025 edition are placed in sequence: The Impact of LASIK on MLB Batting Performance, and It’s Supposed to Be Hard: Why It’s Harder to Get an Orchestra Job than to Play Professional Baseball. There are also historical articles, baseball performance models, and an absolutely spitfire Editor’s Note.

SABR is a wonderful organization. It speaks to how well baseball captures the imagination of fans (and nerds) around the world, its historical importance, and the unique aspects of its design as a game that lend it to statistical revelations before any advanced camera-tracking technology was available. While I’m sure my involvement will fluctuate, I’m comfortable saying that my relatively inexpensive membership will be renewed for years to come.

• 1
  This is the only project for which I’ve written an article.
• 2
  SABR publishes two editions each year.

It comes down to extra innings and squandered opportunities, a year of firsts and not quite enough. Series were earned and given away, and the emotional pendulums of games were like rocket-propelled swing sets.

Mark Armour has worked on his Satchel Paige Project for a few years. It’s an amazing feat of historical research about one of the most enigmatic characters and players in baseball history. It’s worth looking through regardless of your overall interest in baseball.

If you’d like to hear a good conversation about the project, I suggest listening to episode 2352 of Effectively Wild, which is how I first learned about this work.

I’m fairly confident all Major League Baseball players have gotten bigger over time, but I specifically decided to use the newest version of the Lahman Baseball Database to look at the average weight of catchers by the decade in which they debuted. Their listed weights are static so we can’t be certain what their debut weights were, but we’re looking at large trends. I also required any catcher in the list to have caught at least 200 career games.

We can also look only at the average weights per decade to get a clearer sense of the overall trend.

There’s a pronounced increase in the 1940s and again in the 1990s through 2000s, the latter of which being when players started eating balanced breakfasts.

Technical Details

I first ran this query in the Lahman Database loaded on my computer.

WITH
	"catchers" AS (
		SELECT
			People.playerID,
			People.nameFirst,
			People.nameLast,
			MAX(People.weight) AS "weight",
			SUM(Appearances.G_c) AS "gamesCaught",
			SUBSTRING(People.debut, 1, 4) AS "debutYear"
		FROM
			People
			LEFT JOIN Appearances ON Appearances.playerID = People.playerID
		WHERE
			Appearances.G_c >= 10
			AND People.weight > 0
		GROUP BY
			People.playerID,
			People.nameFirst,
			People.nameLast
		ORDER BY
			weight
	)
SELECT
	"playerID",
	"nameFirst",
	"nameLast",
	"weight",
	"gamesCaught",
	"debutYear"
FROM
	"catchers"
WHERE
	"gamesCaught" >= 200
ORDER BY
	"debutYear"

I exported the resulting data as catchers.csv and used a Jupyter Notebook for the rest.

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from os import path


DATA_DIR = '/Users/markrichard/Downloads'

df = pd.read_csv(path.join(DATA_DIR, 'catchers.csv'))

# Remove 2020 partial decade
df = df[df['Decade'] != 2020]

### MAKE DISTRIBUTIONS ###

# Create base plot
g = sns.displot(df, 
                x='weight',
                kind='kde',
                col='Decade',
                col_wrap=3,
                fill=True,
                common_norm=False,
                aspect = 1.75)

# Calculate and add average lines
decade_stats = df.groupby('Decade')['weight'].agg(['mean', 'count']).round(1)

for decade, ax in zip(g.col_names, g.axes.flat):
    if decade in decade_stats.index:
        mean_weight = decade_stats.loc[decade, 'mean']
        player_count = decade_stats.loc[decade, 'count']
        
        # Add vertical line at mean
        ax.axvline(mean_weight, color='red', linestyle='--', linewidth=2, alpha=0.8)
        
        # Add text annotation
        ax.text(0.02, 0.98, f'Avg: {mean_weight} lbs\nN: {player_count}', 
                transform=ax.transAxes, 
                verticalalignment='top',
                bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8))

plt.tight_layout()
plt.show()

### MAKE AVERAGES SCATTER PLOT ###

plt.figure(figsize=(10, 6))

# Simple scatter with consistent sizing
plt.scatter(decade_stats.index, decade_stats['mean'], 
           s=100, 
           alpha=0.8, 
           color='steelblue',
           edgecolors='white',
           linewidth=2)

# Connect points with a line
plt.plot(decade_stats.index, decade_stats['mean'], 
         color='steelblue', 
         alpha=0.6,
         linewidth=2)

# Clean styling
plt.xlabel('Decade')
plt.ylabel('Average Weight (lbs)')
plt.title('Average MLB Catcher Weight by Debut Decade')
plt.grid(True, alpha=0.2)


plt.tight_layout()
plt.show()