Plotting baseball team progress

I noticed today that the Cubs and Yankees have the same record. I knew that the Cubs have had some extreme ups and downs this year, but I wasn’t sure if the Yankees had, so I decided to plot their progress over the course of the season so far.

A plot of how far above or below.500 they were seemed like the best way to compare their seasons, so I got their records from baseball-reference.com ( Cubs results, Yankees results ) and cleaned up the data through a combination of Numbers and BBEdit. The result was a pair of CSV files that looked like this:

Date,Home,Opponent,Win Mar 26 2026,TRUE,WSN,-1 Mar 28 2026,TRUE,WSN,1 Mar 29 2026,TRUE,WSN,-1 Mar 30 2026,TRUE,LAA,1 Mar 31 2026,TRUE,LAA,-1 Apr 1 2026,TRUE,LAA,1 Apr 3 2026,FALSE,CLE,-1 Apr 5 2026,FALSE,CLE,1 Apr 5 2026,FALSE,CLE,-1 Apr 6 2026,FALSE,TBR,-1 [etc.]

These are the first ten games for the Cubs. The Win field contains a +1 for wins and a -1 for losses. The plot of their progress looks like this:

The teams were running in parallel from mid-April through early May, before the Cubs’ disastrous slide. The Yankees had their own smaller slide starting in mid-June but seem to have recovered. History has taught Cub fans not to trust the team’s recent success; 60 more games is plenty of time for another slide (or two).

The plot was made with Python, Pandas, NumPy, and Matplotlib. Here’s the code:

python: 1: #!/usr/bin/env python3 2: 3: import pandas as pd 4: import numpy as np 5: from datetime import datetime 6: import matplotlib.pyplot as plt 7: from matplotlib.ticker import MultipleLocator, AutoMinorLocator 8: from matplotlib.dates import DateFormatter, YearLocator, MonthLocator 9: 10: # Read the files into dataframes 11: dfCubs = pd.read_csv('cubs-2026.csv', parse_dates=[0]) 12: dfYankees = pd.read_csv('yankees-2026.csv', parse_dates=[0]) 13: 14: # Calculate the games above.500 15: dfCubs['Above'] = np.cumsum(dfCubs.Win) 16: dfYankees['Above'] = np.cumsum(dfYankees.Win) 17: 18: # Create the plot with a given size in inches 19: fig, ax = plt.subplots(figsize=(6, 4)) 20: 21: # Add lines for each team. Team colors from teamcolorcodes.com. 22: ax.plot(dfCubs.Date, dfCubs.Above, '-', color='#0E3386', lw=1.5, label='Cubs') 23: ax.plot(dfYankees.Date, dfYankees.Above, '.', color='#0C2340',ms=5, label='Yankees') 24: 25: # Set the limits 26: plt.ylim(ymin=-5, ymax=20) 27: 28: # Set the ticks and add a grid 29: ax.xaxis.set_major_locator(MonthLocator()) 30: ax.xaxis.set_major_formatter(DateFormatter('%-m/%-d/%y')) 31: ax.yaxis.set_major_locator(MultipleLocator(5)) 32: ax.grid(linewidth=.5, axis='x', which='major', color='#dddddd', linestyle='-') 33: ax.grid(linewidth=.5, axis='y', which='major', color='#dddddd', linestyle='-') 34: 35: # Title and axis labels 36: plt.title('Cubs and Yankees 2026') 37: plt.ylabel('Games above.500') 38: 39: # Make the border and tick marks 0.5 points wide 40: [ i.set_linewidth(0.5) for i in ax.spines.values() ] 41: ax.tick_params(which='both', width=.5) 42: 43: # Add the legend 44: ax.legend(loc='lower right') 45: 46: # Save as PDF 47: plt.savefig('20260723-Cubs and Yankees 2026.png', format='png', dpi=200)

I wanted to use lines for both teams and let the team colors distinguish them, but the team colors are too close, so I used markers for the Yankees and added a legend. I did still use the team colors, which I got from the Team Color Codes website. I fiddled with the marker size and line width until the two looked to be of roughly equal importance.

It may seem like this is a lot of code to write for a simple plot, but I use a Typinator abbreviation to insert a code template and just tweak the template to get things looking the way I want. The Pandas and NumPy part of the code, Lines 10–16, was trivial.