My “real” job for the past 20 years has been a researcher. It’s a well-known saying that good research raises more questions than it answers. My previous blog post on singles hitters versus sluggers raised a few questions and comments. One comment came from through Twitter from Geoff M.:

@BullpenByComm @ootpbaseball The flaw is only using those 4 teams. You need to infuse those teams into a neutral league to see who's better

— Geoff M. (@gmtt17) January 21, 2015

@gmtt17 @ootpbaseball Thanks for the feedback. Why is it flawed? And what would other teams in a "neutral league" consist of?

— Bullpen By Committee (@BullpenByComm) January 21, 2015

@BullpenByComm @ootpbaseball Only Slap v Slug doesn't have RL applications. 1 Slap 1 Slug 28 blended squads should prove who's superior

— Geoff M. (@gmtt17) January 21, 2015

Another well-known fact of research is that a single study will always have inherent limitations (or flaws, if you like). Using just a league of Slappy’s and Sluggers has the shortcoming of potentially amplifying any differences between the two. Just because it shows up in a league made completely out of those types of players doesn’t mean it would have any kind of noticeable impact in a league more representative of MLB.

So I went ahead with Geoff’s suggestion.

The original Slappy vs. Slugger sim gave each player an arbitrary OPS of .800. For my initial sim, I gave each player the league average after a 2014 MLB sim, which came out to .732. Turning off injuries, player development, and not allowing the AI to make any roster changes, I simmed 10 singular seasons with 1 team of Slappy’s, 1 team of Slugger’s, and 28 MLB teams. Both the Slappy and Slugger teams had Average Pitchers who were created with expected stats to be the league average.

The first set of 10 seasons was a bit eye-opening:

In only 2 seasons did the Slapsticks win more games than the Sluggers, and as you can see, both teams made up of league average players were just utterly awful, losing on average more than 100 games a season.

This brought up the question of whether the OPS value used affected the outcome. So I did two additional sims: one replicated the original 4-team Slappy/Slugger league with everyone having a .732 OPS and the other replicated the Slappy/Slugger in MLB with each Slappy and Slugger having an .800 OPS.

First, the original 4-team league. Turns out changing the OPS to .732 made no difference, with season after season having the two teams of Slappy’s well ahead of the Sluggers (I also ran several more seasons of the original experiment just to be sure). The Slappy’s consistently won 90+ games with the Sluggers winning 60+. So the second level of OPS made no difference in that sim.

The MLB sim with both Slappy’s and Sluggers having .800 OPS was different. Here is the average performance of each team over ten seasons comparing both sets of sims:

In this, the Slappy’s greatly improved their win total and beat out the Sluggers in every category (though OPS was very close). The Slappy’s even had two winning seasons. I wish I had a compelling answer for why the Sluggers outplayed the Slapsticks when each had a low OPS in an MLB environment but the Slapsticks won out in an MLB environment with a higher OPS while the sims with just the 4-team league always showed a consistent Slappy advantage.

At least with the four different sims we ran, the Slappy’s outperformed the Sluggers in three of them, though in a real-life environment it may depend on the value of OPS and not be a very straightforward answer.

If you have any hypotheses feel free to comment below or send us a tweet at @BullpenByComm.