Trade Deadline Decline or Second Half Surge?

Every year I start looking for ways to take advantage of trade rumors as a fantasy manager. But, is there actually any reason to? Maybe if a slugger goes from a pitcher-friendly home park to a hitter-friendly home park. Perhaps if a base stealer, who has had his restrictor plates put on while playing for a team that doesn’t steal bases, gets traded to a run happy team. These are the situations that seem easy to identify and are most likely smaller transactions that savvy managers can take advantage of. But what about the big dogs?

Let me define big dogs:

  1. The player must have had at least 400 plate appearances by the deadline (July 31st)

In this analysis, I’ve used our leaderboards to find players with at least 400 PA’s between the start of the season and July 31st. This limits the analysis to contributing players and excludes all the prospects that get swapped during the deadline.

      2. The player does not need to have any plate appearances after the deadline

This allows for players who may have gone from an everyday role to a bench role to populate the analysis.

      3. The player had to have been traded on or around the deadline (July 31st). 

Let’s take a look at what these parameters gave between 2016 and 2019. In the table below, the _1 variables represent the start of the season through July 31st and the _2 variables represent August 1st through the end of the season:

Pre and Post Trade Deadline Player Performance
Name SLG_1 wRC+_1 WAR_1 SLG_2 wRC+_2 WAR_2 SLG_diff wRC+_diff WAR_diff
2019
Yasiel Puig 0.475 95 0.6 0.423 113 0.7 -0.052 18 0.1
Edwin Encarnacion 0.519 125 2.0 0.650 170 0.5 0.131 45 -1.5
Nick Castellanos 0.462 104 0.8 0.646 154 2.0 0.184 50 1.2
2018
Manny Machado 0.563 153 4.5 0.491 116 1.8 -0.072 -37 -2.7
Eduardo Escobar 0.511 125 2.6 0.446 103 1.0 -0.065 -22 -1.6
Asdrubal Cabrera 0.475 117 2.0 0.414 93 0.6 -0.061 -24 -1.4
Mike Moustakas 0.458 103 1.6 0.461 110 0.8 0.003 7 -0.8
Jon Jay 0.364 96 1.2 0.293 53 -0.4 -0.071 -43 -1.6
Brian Dozier 0.405 93 1.1 0.350 83 -0.2 -0.055 -10 -1.3
Justin Bour 0.398 108 0.4 0.432 101 0.1 0.034 -7 -0.3
2017
Justin Upton 0.507 133 3.2 0.600 145 2.0 0.093 12 -1.2
Jay Bruce 0.523 124 2.1 0.474 106 0.5 -0.049 -18 -1.6
Melky Cabrera 0.431 105 0.5 0.406 86 -0.6 -0.025 -19 -1.1
2016
Eduardo Nunez 0.437 102 1.8 0.422 97 1.1 -0.015 -5 -0.7
Jay Bruce 0.559 124 0.6 0.391 83 -0.3 -0.168 -41 -0.9
Matt Kemp 0.489 101 0.4 0.519 120 0.4 0.030 19 0.0
The player must have played for at least two teams.
Start of Season – 7-31: 400 PAs
7-31 – End of Season: 0 PAs

 

Out of the 16 players who met the constraints, only two players (Nick Castellanos and Matt Kemp) increased their slugging, wRC+, and WAR. Four players (Yasiel Puig, Edwin Encarnacion, Mike Moustakas, and Justin Upton) increased in two of these metrics, while one player (Justin Bour) increased in one metric. That leaves us with break down like this:

Improvement (or stayed the same) in all three categories: 2 out of 16 or 13%

Improvement (or stayed the same) in two categories: 4 out of 16 or 25%

Improvement (or stayed the same) in one category: 1 out of 16 or 6%

Degradation (negative diff) in all three categories: 9 out of 16 or 56%

We can certainly claim small sample sizes and question the parameters of this query. That’s the beauty of research. For example, many of these players were not traded exactly at the deadline, while the statistical midway point is July 31st for all. I don’t think that affects the analysis too much. What you’re looking for is an improvement around the time of the trade. I’ve used the MLB Trade Rumors’ Transaction Tracker Tool to look at the exact date of transactions and thin out players who shouldn’t be included. For example, Andrew McCutchen was traded on August 31st and was excluded from this analysis due to very few statistics on his new team after the trade.

One thing that is clear from this small analysis is that a huge increase in performance from pre-deadline to post-deadline is rare. This makes sense. It seems like it should be rare for a player to suddenly take off once they are traded to a new team and it would be nearly impossible to tell if that was a product of the trade itself or just a great second half. On the other hand, players can improve significantly after the trade. MLB teams have many analysts who are trying to find trade targets that will improve the team. Those people are smart and sometimes they get it right. If you’re a fantasy manager looking to take advantage of the real-world trade market, tread lightly and don’t get wrapped up in the recency bias that seeing a name in a headline can create.





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HappyFunBall
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More to the point, degradation is common. Which I don’t think should be too surprising. High performing players are more attractive trade targets, but may be riding a luck dragon over their true talent level.