What’s In A Trade?

At trade deadlines, baseball media analysts and journalists always talk about how transactions will help the teams. Acquiring Yu Darvish will put the Dodgers over the top; getting Robertson and Kahnle gives the Yankees an edge in any playoff series; JD Martinez will make the Diamondbacks one of the most feared offenses in baseball.

All these questions are important; teams, not players, win championships. But what impact does a trade have on an individual player?

In an early podcast, Brett mentioned that players always seem to play better after they’ve been traded mid-season. There may be some logic to that. A player who struggled for the first half of the season gets to move into a new setting and get a fresh start. They’re motivated to prove themselves in front of their new teammates. Maybe a new hitting or pitching coach can help them fix those mechanical glitches that have been plaguing them all season.

Chicago White Sox v Boston Red Sox
Eduardo Nunez enjoyed his new digs in Boston after getting traded from the Giants in 2017.

But there’s also the potential flipside. A player that’s been comfortable in his environment is suddenly thrust into a new team. They need to adjust to a different spot in the lineup, the different quirks of his teammates–both on the diamond and in the locker room. Depending on the player, there can also be the hassle of moving the wife and kids to your new city.

Qualitative arguments exist on both sides. But what do the quantitative arguments say?

I went back and looked at all the players traded in the midst of the 2017 season to see if there was any clear signs of either improvement or regression among the sample size. I included only those players who played a considerable role for both their previous team and their new team (position players min. 20 games; pitchers min. 10 appearances).

I focused primarily on rate statistics (slash line, K-rate, walk-rate) given the potential disparity in games played between the previous team and the former team. I also included WAR, though I did not rely heavily on it for this study, again due to the difference in games played. More games played means more chances for a player to add (or subtract) value. You can see the spreadsheets on Google Docs here.

After compiling the data, I analyzed each player’s before and after stats and assigned them a rating on a 0-10 scale. A 5 signified that the player performed at the same skill level with both teams. Numbers above 5 demonstrate improvement; numbers below 5 demonstrate regression.

Now, I can hear the saberheads screaming right now. How can I arbitrarily assign “ratings” to a player? Won’t that make the data subjective?

Yes, it will, within reason. An arbitrary ratings system such as the one I’ve used will be far from perfect, but it also won’t be entirely random. There will be a margin of error, but not an egregious one. It’s pretty clear to tell whether a player has improved (7-9 rating) or regressed (1-3 rating) significantly, or whether they’ve performed at about the same level (4-6 rating). Another analyst could come in and assign their own ratings using this system, and the results would be in the same ballpark.

Besides, for the purposes of this analysis, I’m only looking for a ballpark figure. If there was a clear trend towards improvement or regression after a trade, it should be outside a margin of error from the ratings system. In other words, even with some fudging the data should conclusively demonstrate that players either improve or regress upon being traded.

Upon rating the change in player performance, I then averaged the cumulative ratings for position players and pitchers (throughout the process, I kept the two groups separate). The results? Position players averaged a 4.35 rating; pitchers averaged a 5.3. Do the calculation again without separating the two groups, and the average comes to 4.93.

In other words, the data shows that players who have been traded play at almost exactly the same level before and after the trade.

There’s of course a margin for error on these calculations, particularly with the “ratings” system I used. But again, even with the rather haphazard rating system a trend one way or the other should have appeared, if such a trend exists.

The data are also evenly balanced. Take the cases of Jonathan Lucroy and Pat Neshek. Lucroy improved after his midseason trade to the Rockies–probably due to the friendly confines of Coors Field. Neshek, a reliever, struggled a bit more after leaving Philadelphia for the homer-happy Rockies ballpark. How much of the changes to Lucroy and Neshek are a result of the change in ballpark? It’s hard to say definitively, but it’s also pretty clear that Coors Field at least played a role.

Nolan Arenado Cycle
When someone tells you your improved offense is only because of Coors Field.

While some players may take kindly to their new digs, the league-wide trends show that midseason trades do not have a noticeable impact on player performance. For every Tim Beckham and Blake Treinen that improved, there’s a Lucas Duda and a Justin Wilson that failed to perform for their new team. Trades can have a major impact on a team, but don’t expect them to be a silver bullet to improve player performance.

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