Change of Scenery: Justin Upton

I’m excited to be writing the last installment of Change of Scenery today. It’s not that I haven’t had fun looking at the major offseason MLB moves from a DFS context; I have. But the merciful end of offseason analysis means it’s almost time to play ball in 2016! We’re not quite there yet though, so let’s look at one more big name who switched teams this past winter.

I’m sure Justin Upton loved batting .250 for a 74-win team last season, but DFSers have to be intrigued about his move to Detroit. Even though the Tigers caught several bad breaks last season, they were still a respectable offense, ranking 15th overall in runs scored (San Diego was 23rd). In addition to joining an offense that boasts more firepower, J-Up will also reap the benefits of playing home games at Comerica Park. When comparing right-handed bats at Comerica and PETCO Parks, there is around a half fantasy point per game difference, using DraftKings scoring:

jup1

 

That’s great news for Upton, who has seen an enormous split between home and away production over his career:

jup2

 

Those raw stats from FanGraphs translate to a 1.5-point swing in Plus/Minus. Over the past two seasons, Upton has scored 251.01 more raw fantasy points when playing at home. That number is even sillier when put in the proper context – both Turner Field and PETCO Park have been negative venues for right-handed bats overall (-0.2 and -0.1 respectively).

jip3

 

This is interesting because for Upton, it seems the most important thing to consider in terms of stadium splits is whether or not his team is playing at home. In general, we’re more concerned with the physical attributes of the stadium when weighing a batter’s matchup (short fence at Yankee Stadium, altitude at Coors Field). To further that point, take a look at the below screenshot. The sample size is obviously tiny, but this is still something you just don’t see every day:

jup4

 

In this next section, I’m going to show off some of FantasyLabs’ new advanced Trend filters we’ll be including in the 2016 version of our MLB product. In 2015, Upton’s BABIP was his lowest since his rookie season (2007). Compared to league average though, his 2015 BABIP of .304 was still above average. That means we’re looking at a somewhat unique batter, so let’s go under the hood.

Between 2014 and 2015, Upton saw a fairly significant three-percent downgrade in Line Drive Percent (LD%) and a four-percent increase in Flyball Percent (FB%). A high FB% can be good, but when the flyballs are weaker, that leads to popups rather than homeruns. It’s not good then that Upton also posted a career high 10 percent score in Infield Hit Percentage (IFH%) and saw a three-percent decrease in Hard Hit% (HH%) between 2014 and 2015.

Players with a higher FB% and lower LD% (like Upton in 2015) have generally not performed well against their implied point totals based on salary:

jup5

 

Now, here’s what’s cool about this. Take a look at two of the players with the most matches in the category:

jup6

 

Both Mookie Betts and Nolan Arenado went through extended slumps last season. When they were slumping, their FB% and LD% matched Upton’s. (Although Betts posts a +0.2 here, his overall Plus/Minus on the season was +0.9, meaning he was underperforming in these games relative to his season average). Both Betts and Arenado were able to bust out of their slumps and turn their seasons around and if you look at their year-end number, you’ll see that once they increased their LD%, the raw stats improved as well. Betts finished 2015 with a LD% of 19.5% and Arenado finished at 21.7%. The problem with Upton is that he was never really able to turn things around – 17.3% was his year-end number.

The reason for optimism is that if we plug in Upton’s 2014 FB% of 40.2% and LD% of 20.1%, we get this:

jup7

 

Nearly an increase of a full fantasy point per game.

We can talk about Upton’s switch from the NL to the AL, what hitting second in the Tigers lineup will mean for his value, etc., etc. To me, I’m going to be most interested in two things:

1.) We used Trends to show that when a batter has a high FB%, but is making weaker contact, that is bad for their value. Upton was guilty of this last season, but those numbers are not typical of his career. Can he regress towards his career averages in these categories?

2.) Regardless of whether was playing for the Braves or Padres, he was a much better hitter at home. Because of this, it’s essential that his profile translates well to Comerica Park, a place he has never played a regular season game prior to 2016. I’d recommend setting up a custom trend to track Upton’s 2016 performance in home games so that you will have this information at your fingertips as the games get underway.

I’m excited to be writing the last installment of Change of Scenery today. It’s not that I haven’t had fun looking at the major offseason MLB moves from a DFS context; I have. But the merciful end of offseason analysis means it’s almost time to play ball in 2016! We’re not quite there yet though, so let’s look at one more big name who switched teams this past winter.

I’m sure Justin Upton loved batting .250 for a 74-win team last season, but DFSers have to be intrigued about his move to Detroit. Even though the Tigers caught several bad breaks last season, they were still a respectable offense, ranking 15th overall in runs scored (San Diego was 23rd). In addition to joining an offense that boasts more firepower, J-Up will also reap the benefits of playing home games at Comerica Park. When comparing right-handed bats at Comerica and PETCO Parks, there is around a half fantasy point per game difference, using DraftKings scoring:

jup1

 

That’s great news for Upton, who has seen an enormous split between home and away production over his career:

jup2

 

Those raw stats from FanGraphs translate to a 1.5-point swing in Plus/Minus. Over the past two seasons, Upton has scored 251.01 more raw fantasy points when playing at home. That number is even sillier when put in the proper context – both Turner Field and PETCO Park have been negative venues for right-handed bats overall (-0.2 and -0.1 respectively).

jip3

 

This is interesting because for Upton, it seems the most important thing to consider in terms of stadium splits is whether or not his team is playing at home. In general, we’re more concerned with the physical attributes of the stadium when weighing a batter’s matchup (short fence at Yankee Stadium, altitude at Coors Field). To further that point, take a look at the below screenshot. The sample size is obviously tiny, but this is still something you just don’t see every day:

jup4

 

In this next section, I’m going to show off some of FantasyLabs’ new advanced Trend filters we’ll be including in the 2016 version of our MLB product. In 2015, Upton’s BABIP was his lowest since his rookie season (2007). Compared to league average though, his 2015 BABIP of .304 was still above average. That means we’re looking at a somewhat unique batter, so let’s go under the hood.

Between 2014 and 2015, Upton saw a fairly significant three-percent downgrade in Line Drive Percent (LD%) and a four-percent increase in Flyball Percent (FB%). A high FB% can be good, but when the flyballs are weaker, that leads to popups rather than homeruns. It’s not good then that Upton also posted a career high 10 percent score in Infield Hit Percentage (IFH%) and saw a three-percent decrease in Hard Hit% (HH%) between 2014 and 2015.

Players with a higher FB% and lower LD% (like Upton in 2015) have generally not performed well against their implied point totals based on salary:

jup5

 

Now, here’s what’s cool about this. Take a look at two of the players with the most matches in the category:

jup6

 

Both Mookie Betts and Nolan Arenado went through extended slumps last season. When they were slumping, their FB% and LD% matched Upton’s. (Although Betts posts a +0.2 here, his overall Plus/Minus on the season was +0.9, meaning he was underperforming in these games relative to his season average). Both Betts and Arenado were able to bust out of their slumps and turn their seasons around and if you look at their year-end number, you’ll see that once they increased their LD%, the raw stats improved as well. Betts finished 2015 with a LD% of 19.5% and Arenado finished at 21.7%. The problem with Upton is that he was never really able to turn things around – 17.3% was his year-end number.

The reason for optimism is that if we plug in Upton’s 2014 FB% of 40.2% and LD% of 20.1%, we get this:

jup7

 

Nearly an increase of a full fantasy point per game.

We can talk about Upton’s switch from the NL to the AL, what hitting second in the Tigers lineup will mean for his value, etc., etc. To me, I’m going to be most interested in two things:

1.) We used Trends to show that when a batter has a high FB%, but is making weaker contact, that is bad for their value. Upton was guilty of this last season, but those numbers are not typical of his career. Can he regress towards his career averages in these categories?

2.) Regardless of whether was playing for the Braves or Padres, he was a much better hitter at home. Because of this, it’s essential that his profile translates well to Comerica Park, a place he has never played a regular season game prior to 2016. I’d recommend setting up a custom trend to track Upton’s 2016 performance in home games so that you will have this information at your fingertips as the games get underway.