Skip to main content

Data can be equity: Merging of Major League Baseball and Negro League Baseball data.

I know it is January 2025, but I want to write about something that happened during the Spring of 2024.

I think it is a story about how it is never too late to do the right thing, making it great thing to think about here at the New Year. Data can't undo the past, but the way we manage it moving forward can provide the opportunity for some measure of equity.

Back in May, professional baseball decided to include Negro League (NL), which existed from 2910 to 1948, baseball stats as part of Major League Baseball (MLB) stats. This is was done to allow for proper recognition of talented ML players. This changed some storied records for the league:

https://www.mlb.com/news/stats-leaderboard-changes-negro-leagues-mlb

This was a lot more than merging a couple of spreadsheets. As such, this story also serves as a lesson in data management and making desperate datasets the same. One that is a lot more moving than your typical story of data-cleaning. The following screenshots are from:  https://www.mlb.com/news/mlb-negro-league-stats-added-after-statistical-review-committee-announces-findings

First, they had to find the data. Since the Negro League scores weren't authenticated at the time, data detectives have participated in "box-score archeology". 



Then they needed to decide how to make different leagues, with different numbers of games, the same. As such, data expressed as a proportion, not a count, are where you see NL players shine.



Thanks COVID? It appears that part of the journey of merging the data occurred during COVID, with the league coped with how to integrate statistics collected during the irregular 2020 season.







For more in-depth reading, see:

Popular posts from this blog

Ways to use funny meme scales in your stats classes

Have you ever heard of the theory that there are multiple people worldwide thinking about the same novel thing at the same time? It is the multiple discovery hypothesis of invention . Like, multiple great minds around the world were working on calculus at the same time. Well, I think a bunch of super-duper psychology professors were all thinking about scale memes and pedagogy at the same time. Clearly, this is just as impressive as calculus. Who were some of these great minds? 1) Dr.  Molly Metz maintains a curated list of hilarious "How you doing?" scales.  2) Dr. Esther Lindenström posted about using these scales as student check-ins. 3) I was working on a blog post about using such scales to teach the basics of variables.  So, I decided to create a post about three ways to use these scales in your stats classes:  1) Teaching the basics of variables. 2) Nominal vs. ordinal scales.  3) Daily check-in with your students.  1. Teach your students the basics...

Leo DiCaprio Romantic Age Gap Data: UPDATE

Does anyone else teach correlation and regression together at the end of the semester? Here is a treat for you: Updated data on Leonardo DiCaprio, his age, and his romantic partner's age when they started dating. A few years ago, there was a dust-up when a clever Redditor r/TrustLittleBrother realized that DiCaprio had never dated anyone over 25. I blogged about this when it happened. But the old data was from 2022. Inspired by this sleuthing,  I created a wee data set, including up-to-date information on his current relationship with Vittoria Ceretti, so your students can suss out the patterns that exist in this data.

Tyler Vigen's Spurious Correlations

Tyler Vigen has has created  a long list of easy-to-paste-into-a-powerpoint graphs that illustrate that correlation does not equal causation. For instance, while per capita consumption of cheese and number of people who die by become tangled in their bed sheets may have a strong relationship (r = 0.947091), no one is saying that cheese consumption leads to bed sheet-related death. Although, you could pose The Third Variable question to your students for some of these relationships). Property of Tyler Vigens, http://i.imgur.com/OfQYQW8.png Vigen has also provided a menu of frequently used variables (deaths by tripping, sunlight by state) to help you look for specific examples. This portion is interactive, as you and your students can generate your own graphs. Below, I generated a graph of marriage rates in Pennsylvania and consumption of high fructose corn syrup. Generated at http://www.tylervigen.com/