Your problem is that each time df.to_excel is called, you are overwriting the file, so only the last df will be left. What you need to do is use a writer and specify a sheet name for each separate df e.g:
url = 'https://www.vegasinsider.com/college-football/matchups/'
writer = pd.ExcelWriter('VegasInsiderCFB.xlsx', engine='xlsxwriter')
dfs = pd.read_html(url)
counter = 0
for df in dfs:
if len(df.columns) > 4:
counter += 1
df.to_excel(writer, sheet_name = f"sheet_{counter}", index = False)
writer.save()
You might need pip install xlsxwriter xlwt to make it work.
Exporting to a csv will never work, since a csv is a single data table (like a single sheet in excel), so in that case you would need to use a new csv for each df.
As pointed out in the comments, it would be possible to write the data onto a single sheet without changing the dfs, but it is likely much better to merge them:
import pandas as pd
import numpy as np
url = 'https://www.vegasinsider.com/college-football/matchups/'
dfs = pd.read_html(url)
dfs = [df for df in dfs if len(df.columns) > 4]
columns = ["gameid","game time", "team"] + list(dfs[0].iloc[1])[1:]
N = len(dfs)
values = np.empty((2*N,len(columns)),dtype=np.object)
for i,df in enumerate(dfs):
time = df.iloc[0,0].replace(" Game Time","")
values[2*i:2*i+2,2:] = df.iloc[2:,:]
values[2*i:2*i+2,:2] = np.array([[i,time],[i,time]])
newdf = pd.DataFrame(values,columns = columns)
newdf.to_excel("output.xlsx",index = False)
I used a numpy.array of object type to be able to copy a submatrix from the original dataframes easily into their intended place. I also needed to create a gameid, that connects the games across rows. It should be now trivial to rewrite this so you loop through a list of urls and write these to separate sheets.