The Baseball Strike

After what looked like a settlement late Monday night, apparently the two sides weren’t close. The issues are much the same as the 1994 strike that cancelled the World Series. Players’ share of revenues has fallen over the last almost 30 years.

Players deserve an equal share of the loot each season generates from their talent being the reason everyone spends that $13B/year to see them. How to divvy up the pie is extremely complicated financial mathematics that is way above my pay grade.

After building this data model over many baseball seasons it has become apparent that very good non free agent players are massively underpaid compared to their older teammates; many if not most of whom have declining productivity.

That said this data model measures productivity so it would be interesting exercise to see how much more productive pre-free agents are compared to post-free agent players.

My hypothesis before generating this database query is that non free agents are far more productive than the free agent set.

The rules for this study are as follows:

  • Data from 1998-2021 will be used. MLB went to 30 teams in 1998 and this season is well after the 1994 labor dispute and 23 years should be enough.
  • We will count first 7 years after being a September call up. Free Agent rules are complicated and I don’t want to get bogged down in details that wouldn’t affect the final result very much if there are a few errors.
  • Salary numbers won’t be tabulated: Yearly salary for player contracts is extremely complicated and doubt any site has accurate salary numbers. If a good salary source becomes available in the future that can be added.
  • Result data will break down by team, by year, and overall total.
  • Both WAR and WAA data models will be represented. We always like to compare our data model with WAR.

That’s it. Now I have to switch from finding and fixing all the bugs in baseball-handbook and code this.