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Home Run Park Factors Part 2 – (Conversion to a Plus Metric, HRPF+)

In order to display my home run park factors in a way that is much more palatable for the readers, I’ve developed FreezeStats Park Factor for Home Runs (PFHR+) metric. It is used the same way other plus metrics are used such as ERA+ or wRC+. It measures how much better or worse a certain ballpark performs compared to the league average with 100 being average. We know if a player finishes the season with a 150 wRC+, he was 50% better than league average offensively. That’s the same premise behind my park factor metric. A park with a 150+ PFHR+ is 50% better than league average for home runs. 

All ballparks are not created equal, dimensions and irregularities within the same ballpark can vary quite a bit. So, I’ve broken the PFHR+ for each field or direction (Left-field, Center-field, right-field). The focus of directional park factors is important when evaluating a player’s tendencies and batted ball profile. It’s also interesting when looking at evaluating pitchers. I’ll analyze pitchers for my next article with respect to this metric in the next couple of weeks. For this article, I’ll cover nine hitters below who have changed teams. I’ll dive into the park change and what type of power output we can expect, both positive and negative based on the team/park change. 


First, I want to look at an example to help explain the park factors. Yankee Stadium is widely viewed as a great place to hit home runs. Part of this is true and part of it is not. It’s perception more than anything. The Yankees have some massive power bats including Aaron Judge, Giancarlo Stanton, and Gary Sanchez. These guys are mashers regardless of where they hit. As you’ll see below, right-field is extremely favorable for home runs at Yankee Stadium. In fact, it’s ranked number one in all of baseball based on my PFHR+ when compared to all right fields! This explains much of Brett Gardner’s late-career success and Didi Gregorius’s 20+ home run power seasons. These left-handed hitters pulled a high percentage of their fly balls to take advantage of the short right-field dimensions. However, Yankee Stadium grades out slightly below-average for home runs to center and left field respectively. 

The slightly unfavorable left-field dimensions don’t hurt the right-handed sluggers on the Yankees because a 450-foot fly ball is a home run anywhere. It actually helps when looking at Aaron Judge. He’s been hitting more and more opposite-field fly balls, up to 49.5% and 48% each of the last two seasons. His HR/FB% on opposite-field fly balls last season was an incredible 37.8% which was significantly higher than his HR/FB% to centerfield. These Home Run Park Factors+ (HRPF+) bare this out. If you take a look at the table below, you can see that Yankee Stadium has a 146 HRPF+ to right field and just an 83 HRPF+ to centerfield. That means Yankee Stadium is 46% better than league average for home runs to right field but 17% below the league average for home runs to centerfield.

To give you an example of the criteria I’m looking at to determine these home run park factors, here’s a three-year snapshot of right field at Yankee Stadium (NYY) and Oracle Park (SFG), the best and worse parks for home runs to right field respectively.

Venue (Rightfield) HR/BRL% (LHB) Non-BRL HR (LHB) HR/BRL% (RHB) Non-BRL HR (RHB)
Yankee Stadium 88.7% 73 77.4% 52
Oracle Park 48.7% 24 15.3% 8
League Average 73.6% 40 49.7% 13

Based on this information, you can see that both left-handed batters and right-handed batters benefit at Yankee Stadium when hitting the ball to right field and the opposite is true at Oracle Park. This is true based on the percentage of barreled balls that become home runs (HR/BRL%) and based on the total number of non-barreled home runs at each venue. The numbers seem a bit confusing and difficult to digest when displayed like this. That’s why I’ve created HRPF+. If you’re interested in the more granular data, feel free to DM me on Twitter or write in the comments below and I’ll share the Google Sheet.


Introducting HRPF+ (Home Run Park Factors Plus)

Park/VenueTeamLF - HRPF+CF - HRPF+RF - HRPF+
Oriole ParkBAL121134100
Comerica ParkDET1042897
T-Mobile ParkSEA97106103
Yankee StadiumNYY9183146
Rogers CentreTOR110101102
Target FieldMIN978294
Minute Maid ParkHOU13673129
Oakland ColiseumOAK9910184
Angel StadiumLAA8214799
Nationals ParkWSH10212485
Kauffman StadiumKCR886677
Fenway ParkBOS966875
Chase FieldARI1066897
Petco ParkSDP11011291
Citizens Bank ParkPHI11591114
Globe Life ParkTEX91110121
Citi FieldNYM110107105
Guaranteed Rate FldCHW110107113
Coors FieldCOL109134113
Dodger StadiumLAD9815095
Busch StadiumSTL8010581
GABPCIN121132136
Marlins ParkMIA868091
Tropicana FieldTBR1028295
SunTrust ParkATL88100100
Miller ParkMIL91134117
Wrigley FieldCHC10510679
Oracle ParkSFG896557
Progressive FieldCLE87108112
PNC ParkPIT7810596

Notes: Columns are sortable! Data for Globe Life in Texas is no longer valid. A new park will be used in 2020. 

Mookie Betts (OF – LAD) formerly with the Red Sox

Park (Team) LF HRPF+ CF HRPF+ RF HRF+
Fenway Park (BOS) 96 68 75
Dodger Stadium (LAD) 98 150 95

I don’t think people realize how much of a boost Betts could see in terms of his power with the move the LA. It’s important to note that while the left field HRPF+ is essentially the same in each park they play differently. Fenway allows more non-barreled home runs to left field (61 HR to 38 HR) where Dodger Stadium has a higher HR/BRL% (74% to 67.2%). That’s the Green Monster at play. The barreled balls with low launch angles smack off the high wall but balls hit at high launch angles that don’t qualify as barrels sneak over the monster. Right field is also more favorable but Betts does not have good power to right field so I don’t expect a huge boost in power production there.

Enough about left field, let’s talk about where Betts is really going to benefit. He’s going from Fenway where the HRPF+ was 38% below league-average to Dodger Stadium that plays 51% better than league-average to CF! Let’s try to quantify this. Betts has increased his fly ball% to centerfield each of the last five years (36.8% to 42.1%). I fully expect Betts, who has an elite hit tool to take advantage of centerfield. His HR/FB% to centerfield over the last three seasons is about 50% below the league average. However, when looking at his average exit velocity and average fly ball distance on fly balls to center, he falls in the top 30% of the league. That’s Fenway Park holding him back. Based on this information, I’d expect Betts to finish with a better than league average HR/FB% to center in 2020. To give some context, I’d expect somewhere between four and six more home runs to centerfield in 2020. 

Anthony Rendon (3B – LAA) – formerly with the Washington Nationals

Park (Team) LF HRPF+ CF HRPF+ RF HRF+
Nationals Pk (WSH) 102 124 85
Angel Sta (LAA) 82 147 99

Nationals Park plays surprisingly well, especially for right-handed batters, so Rendon takes a hit there. He should see some benefits to center and right field though. His batted ball profile on fly balls is pretty evenly distributed. He hit 23 of his 34 home runs to left field in 2019 with a career-best HR/FB% on fly balls to left field. I expect that number to drop However, he improved his quality of contact on fly balls to center and right, respectively but didn’t see many gains in 2019. So while I expect Rendon to hit more home runs to center and right, it should even out with a decline in homers to left. Expecting a repeat of 34 home runs is probably not wise but 28-30 seems like it’ll be in the cards.


Nick Castellanos (OF – CIN) – formerly with the Detroit Tigers

Park (Team) LF HRPF+ CF HRPF+ RF HRF+
Comerica (DET) 104 28 97
GABP (CIN) 121 132 136

I think the baseball world went nuts when they saw this overlay I Tweeted out including Castellanos’ line drives and fly balls over the GABP. 


It’s absolutely nuts. Some people were counting as many as 30 additional home runs based on the overlay. Obviously, that’s not how this works, plus he’s only playing half his games in the GABP. But, going from Comerica that plays like the worst park for home runs to centerfield at 72% below-league average to a top-five park to center is going to do wonders. Castellanos hit 41.5% of his fly balls to center last year but it’s fluctuated over the years. In the final two months of 2019, he benefited from playing in Wrigley which has a 106 HRPF+ to center, so he already took advantage over the final two months of last season. His HR/FB% has consistently been just under 14% for his career and there’s no doubt in my mind, he crushes that rate within a new career-high. I won’t peg him for a 20% HR/FB rate but would probably project him for something around 18% in 2020. Using his 2019 fly ball total, that would bring him to 34 home runs. 

Marcell Ozuna (OF – ATL) – formerly with the St. Louis Cardinals

Park (Team) LF HRPF+ CF HRPF+ RF HRF+
Busch Stadium (STL) 80 105 81
Suntrust Park (ATL) 88 100 100

I just found out that SunTrust Park had a name change and is now Truist Park. The park remains unchanged otherwise in terms of dimensions, so the park factors should be accurate. Overall, Ozuna will receive a park upgrade but it’s not as drastic as some of the players above. Ozuna was a massive underperformer based on my earned home run (eHR) metric last year, so I think he’s due for some positive regression regardless of his location. The park change just reiterates this point. His 22.1% HR/FB rate last year was the second-highest of his career but his barrel rate, hard hit%, expected metrics, etc were by far the best of his career. The question is whether or not he can keep his elite batted ball metrics for 2020. If he can, he should hit 35-40 home runs across 600+ PA, otherwise, he’s still a safe bet for 30 home runs.


Mike Moustakas (2B, 3B – CIN) formerly with the Milwaukee Brewers

Park (Team) LF HRPF+ CF HRPF+ RF HRF+
Miller Park (MIL) 91 134 117
GABP (CIN) 121 132 136

While Miller Park in Milwaukee is favorable for home runs, Cincinnati is simply the best park in baseball for home runs, as I discussed with Nicky C. Unfortunately, Moose bats from the left side limiting his overall benefit from the park change. Leftfield in the GABP is 30% better than Miller Park and right field is almost 20% better. Believe it or not, the slugger has just seven opposite-field home runs in his career. Four of those seven came last season. He did improve his hard contact on fly balls to left field, so if I was a betting man, I’d expect Mosse to hit more than four homers to the opposite field in 2020. But, where Moustakas makes his money is on pulled fly balls. His HR/FB% on pulled FBs typically sits around 35% but I have a feeling, it’ll push 40% next year. I’m beginning to think that Moustakas can hit 40-45 home runs next year. In fact, I’ll throw down a bold prediction about Moose & Casteallnos totaling a combined 80 home runs in 2020. This is bold because even if I combine both player’s career-high home run totals, we come up with 65 home runs (38 for Mosse, 27 for Castellanos). Combining for 15 home runs above their career-bests is a long shot but I think they have a chance. 

Starling Marte (OF – ARI) – formerly with the Pittsburgh Pirates

Park (Team) LF HRPF+ CF HRPF+ RF HRF+
PNC Park (PIT) 78 105 96
Chase Field (ARI) 106 68 97

Chase Field had the humidor installed before the 2018 season, so I’m not 100% confident in the data. However, one thing is for sure, Marte’s power will benefit to left field and is going to take a hit to center. Unfortunately, he regularly pulls fly balls at a below-average clip. However, he crushes pulled fly balls and line drives to the tune of 97.7 mph over the last two seasons. Those exit velocities on LD/FB put him in company with teammate Josh Bell, Edwin Encarnacion, and Khris Davis. If Marte can modify his approach and pull more fly balls, he could reach a new career-high in home runs. But, with a total of 20 pulled home runs over the last two years and 18 home runs to center, Marte’s move may just be neutral if his approach remains unchanged.

Didi Gregorius (SS – PHI) – formerly with the New York Yankees

Park (Team) LF HRPF+ CF HRPF+ RF HRF+
Yankee Stadium (NYY) 91 83 146
Citizen’s Bank (PHI) 115 91 114

We can completely ignore left field when discussing Gregorius’ power. He has NEVER hit a home run to left field and has hit just nine homers to centerfield. Now, he goes from a park that played 46% better than league-average to right field to a park that’s 14% better than league-average. Now that Didi is more than a year and a half removed from Tommy John surgery, I don’t have any doubts that he’ll enter 2020 healthy. Even in an abbreviated season, he was on pace for just under 30 home runs. The switch in his home park probably leads to three-four fewer home runs to right field. The difference to centerfield is about 3% in terms of a three-year HR/BRL%, so that’s relatively minimal. If Didi is a 25-homer hitter in New York, he’s a 22-homer guy in 2020 in Philly.

Avisail Garcia (OF – MIL) – formerly with the Tampa Bay Rays

Park (Team) LF HRPF+ CF HRPF+ RF HRF+
Tropicana (TBR) 102 82 95
Miller Park (MIL) 91 134 117

Let’s see, 11% worse to left field, 52% better the center, and 22% better to right. Is this not enough for you to buy into Garcia who reached 20 home runs for the first time in 2019? He actually earned 28 home runs based on eHR last year, so if he can maintain his impressive quality of contact, he’s a bargain in 2020. He’s notoriously a heavy ground ball hitter but as I highlighted in my potential power breakouts article on Pitcher List, Garcia has decreased his ground ball in four straight seasons. It’s interesting to note that Garcia doesn’t pull many of his fly balls. Will you look at that? Miller Park plays a little less favorably to left field. It’s almost as if the Brewers saw an advantage others didn’t. Nearly, 86% of his fly balls last year went to center or right field. Here’s the spray chart from last year overlayed at Miller Park.

Miller Park plays very favorable to LCF and RCF. I feel very strongly that Garcia improves significantly on his HR/FB% from 2019 and if given 550+ PA, he should hit 25 homers.



C.J. Cron (OF – DET) – formerly with the Minnesota Twins

Park (Team) LF HRPF+ CF HRPF+ RF HRF+
Target Field (MIN) 97 82 94
Comerica (DET) 104 28 97

Let’s address the elephant in the room. Cron’s move to Comerica Park is going to kill any power he has to centerfield. Not that Target Field was all that great for fly balls to centerfield but if you remember, Cron played for the Angels prior to 2018. We now know that Angels Stadium is a homer haven to centerfield. While Cron boosted his barrel rate and hard hit% in 2019, he’s trending in the wrong direction in terms of the percentage of pulled fly balls. His pulled FB% has dropped the last three seasons from 32.7% in 2017 to 24.2% last year. He’s going to want to adjust his approach back to the 2017 version of himself to take advantage of Comerica’s most favorable part of the park, left field. His range of outcomes in terms of home runs is huge. Fortunately, he should play every day because he’s basically the Tigers’ best hitter (at worst, second-best). If his pulled fly ball rate continues to drop and his fly-ball rate to center jumps to 40%, he could end up with a home run total in the low-20s. If he gets back to his pull-heavy approach, I could see him reach 30 home runs with the potential for even more.

If you prefer the color-coded version of the HRPF+, it’s below. Although, it’s not sortable like the table above.

Follow me @FreezeStats. Check out my work at FantasyPros and Pitcher List.





Photo Source : MLB and Lou Spirito

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2019 FreezeStats Hitter Projections Revisited – Fantasy Baseball

Every year I run as many player projections as I physically can given my personal time constraints. I then compare each player’s final results to my projections at the end of the season to see how accurate (or inaccurate) I was. It also helps me determine where and why I was wrong to help correct these issues for the future. Of course, projections are extremely difficult due to the countless number of variables and the sheer length of the season. For reference, here is the link to my article from last year comparing my 2018 FreezeStats Projections to the final 2018 results. Additionally, here is the link to the Google Sheet.


You’ll notice that I use all positive values when I run my Z-Scores which is not the way your statistics professor teach you to run them. However, in this case, I’m running Z-Scores compared to the difference in a statistical category from my projection to what actually happened. So, using the absolute value of the difference is the most accurate way to go if I want to compare the accuracy of each categorical statistic for each player. In addition to the standard 5-roto categories, I also include OBP (for you OBP leaguers out there) and plate appearances. Why? Because you can’t even start a projection for a hitter without determining his plate appearances. Thus, it may be the most important statistic to project and will help determine the validity of a projection. Here is the complete Googlesheet with all the data goodness from my 2019 FreezeStats Projections. Without further ado, let’s dive into the best or worst projections.

Projections with High Categorical Correlations

Adam Jones (OF – ARI)
As it turns out, my most accurate projection (by sum of Z-scores) was veteran outfielder Adam Jones. I suppose projecting a durable veteran with consistent year-to-year numbers isn’t all that surprising. However, I overestimated a little in plate appearances. I had him for 575 PA and he finished with 528 PA. The rest looked almost identical. I pegged his home runs and steals, missed his RBI by three, runs by two, AVG by .005 and OBP by just .001. 

Kris Bryant (3B – CHC)
I was down on Bryant coming into 2019 and nearly nailed his projections. He was dealing with injuries in 2018 so there was a high probability for a rebound but I didn’t see the superstar numbers coming back and I was right. My projections matched three of Bryant’s final numbers in AVG – .282, home runs – 31, and steals – 4. I missed his plate appearance total by just six and was very close on runs, RBI, and OBP. Being a Cubs fan, I’ve seen enough of KB to know who he is. The juiced ball dwarfed his numbers a bit even though he still managed a very productive season.



Ryan Braun (1B/OF – MIL)
What do you know, another veteran! Braun always misses time. You can bank of 125-135 games from him every year. The lower plate appearance projection actually allows me to provide more accurate projections. He still has some power, speed, and decent contact rates. As I mentioned earlier, the projection starts with the PA total and goes from there. 

J.D Martinez (OF – BOS)
Martinez’s numbers did not appear to be aided by the juiced ball. This helped my projections match his final numbers. With almost five years of consistent metrics from JDM, is a player I can count on and feel confident with where his numbers ultimately lie. His elevated BABIP and high home run rate helped me peg his AVG and OBP. I slightly over-projected his home run total but the runs and RBI are once again very high hitting cleanup for a great Red Sox lineup.

Domingo Santana (OF – SEA)
This one is interesting. Domingo was granted a fresh start in Seattle making him a prime bounce-back candidate in 2019. However, I was not projecting a career-year that matched his 2017. I thought he played over his head a little bit that season. So, I lowered his home run total based on his low fly ball rate but given his quality of contact, kept his BABIP elevated. That’s how I nailed his average and home run total. Not knowing exactly where he would hit in the order threw off the run and RBI totals a some, but still relatively close. 

Adam Frazier (2B – PIT)
I was a fan of Frazier as a deep league option for batting average and runs in 2019. Unfortunately, he did not take advantage of the juiced ball and took a step back in xwOBA. I just about nailed his PA and rate stats but inflated his home run and stolen base totals expecting a step forward in those departments. 

Brandon Crawford (SS – SFG)
I’m surprised I even projected Crawford. I thought he might be too deep but he plays every day because of his elite defense. I was not a fan of his heading into the season and he actually performed worse than my projections but is was close. His metrics are extremely underwhelming and his skills are declining. I don’t expect more than 500 PA for Crawford next year and he may be out of the league by 2021.

Justin Turner (3B – LAD)
Like Braun, Turner is a veteran talent who regularly misses time due to injury. Turner’s skills are strong and extremely consistent year-to-year. I’ve said it before, if Turner could get 650 PA, he would be a borderline top-25 player. His contact rates are strong as are his quality of contact skills, so he’d be a beast in four categories IF he ever stays healthy. So again, being accurate on his PA turned out to be the main factor in Turner’s projection. 


Michael Conforto (OF – NYM)
Did Conforto disappoint in 2019? Of course not. He hit 33 homers, drove in 92 runs, and stole seven bases. That’s a great year if it was 2018 or 2015 but it was 2019. Remember, my projections were made prior to the knowledge that the ball was juiced, so I was expecting a step forward for Conforto but he didn’t quite deliver the breakout some (including myself) were hoping for.

C.J. Cron (1B – MIN)
Other than an absurdly low run total for Cron in 2019, I just about predicted his season numbers to a tee. Again, thanks to an accurate plate appearance projection, the rest of the numbers fell into place. The home run and RBI totals were just a hair higher but that may have been juiced ball aided. He’ll be an interesting sleeper in 2020 after posting a career-best 15% barrel rate and cut his strikeout rate by nearly four percent. The lineup in Minnesota remains stacked but unfortunately for Cron, Cruz still occupies the DH. If Cron can get 140 games at first base, we could be talking about a career-year that looks something like .275-32-95.

Nick Ahmed (SS – ARI)
Um, so apparently, I projected Nick Ahmed’s mini-breakout? Had I known that I did this, I might have called it out on Twitter or something. I completely pegged his 19 homers (a career-high) and nearly nailed his AVG, OBP, and steals. He was coming off a career-high 16 home runs in 2018 at age-28 but he also cut his K-rate and improved his BB-rate with the metrics to back it up. There are two ways to project this type of performance. Call it career-year and negatively regress closer to the player’s baseline or trust the skills growth from the previous season and create a new baseline. I took the later. Maybe the juiced ball had something to do with his power but Ahmed took another step forward in terms of his plate approach as well. You better believe I’m expecting more of the same in 2020 from Ahmed at age-29.

Tyler Flowers (C – ATL)
T-Flow is an interesting case. It’s not difficult to project his stolen base total but I also nabbed his home run total and was very close on his OBP. My projections essentially had his playing time at a 50/50 split with declining skills, so the fact that this projection is a hit isn’t all that surprising from a 33-year-old catcher. 

Mitch Moreland (1B – BOS)
Moreland is another part-time veteran that is extremely consistent year-to-year. I was a little lower on his PA projection and the juiced-ball certainly helped aid in his 19 homers, but otherwise, this was a close projection. He’s been the same player for the last six years, so why would he change now? Same ol’ Mitch.

Mike Moustakas (2B/3B, MIL)
Unfortunately, Moustakas failed to reach the 40-homer plateau but still have a quietly productive season. I blame the juiced ball for the slightly inflated offensive numbers but you know what you’re getting from Moose. He had no business scoring 80 runs with under 600 PA and a .323 OBP but playing in Milwaukee with the juiced ball with do that for you. 

Projections with Poor Categorical Correlations

Travis Shaw (1B/2B/3B)
Boy was I off on this one. Not by a little but probably more than anyone was ever off about anyone. Who would have guessed that a player in his prime with back-to-back 30-homer seasons would end up with just seven! He only had 270 PA, was sent to the minors and hit an embarrassing .157. Wow, just wow. To be fair, no one could have projected a decline like this but I thought he would improve! Ugh, I apologize to anyone who listened to me on this one. 


Justin Upton (OF – LAA)
This can be chalked up to the toe injury Upton suffered literally right before the start of the season. Without a clear timetable, I only had him missing about two weeks. He ended up missing a total of almost four months between the toe injury and a knee injury that ended his season. He never really got going, but if you project his home run total out, you get very close to the 29 HR I projected. 

Pete Alonso (1B – NYM)
Here is an example of what a poor plate appearance projection can do. I never adjusted his plate appearances up after the Mets announced Alonso would start the season with the big club. I had him at 410 PA compared to his amazing 693! I projected Alonso for 24 homers in those 410 PA which projects out to 41 home runs in 693 PA. Considering my projection was pre-juiced ball, that isn’t an awful total. Also, I had his AVG in the .240s because I thought he would have a 29-30%% K rate in the majors. So kudos to Alonso for smashing even my relatively lofty expectations on the way to the 2019 Rookie of the Year.

Joey Gallo (OF – TEX)
Gallo is another injury case but also made a change in approach. He significantly lowered his launch angle (and fly ball rate as a result) which improved his BABIP and batting average. He maintained mammoth power and a strikeout rate far north of 30%. The injuries caused him to miss a ton of time so my projections pegged him for twice as many PAs. If you double his R, HR, and RBI, it’s a win on my end. I’ll take it, I guess. 

Aaron Hicks (OF – NYY)
This will be the last injury guy that I’ll talk about. Of course, I’m going to miss on guys that lose huge chunks of the season due to an injury. The difference between Hicks and players who were hurt after the season already began is number one, his history and number two, he was questionable to start the season due to a back injury suffered during spring training. Back injuries linger and I failed to adjust my plate appearance projection for Hicks docking him only two weeks of playing time. Going forward, in regards to players with injuries in the preseason (especially back, obliques, or arm injuries for pitchers) I’m going to downgrade and try to stay away from no matter how much I may love them. Other players I missed due to injury (after the start of the season) include Andrew McCutchen, and Mitch Haniger. 

Brandon Nimmo (OF – NYM)
I wasn’t expecting another step forward from Nimmo even though I projected his 2018 breakout. I thought he was good in 2018 but out-performed his metrics. Nimmo is technically an injury case but he was healthy through the first two months of the season and he was terrible. I expected a little bit of negative regression but what we got was a strikeout rate north of 30% and no power to speak of. He’s a curious case for 2020 as he’ll only be 27 and be dirt cheap. I suspect I may be back in after pick 250. 


Ian Kinsler (2B – SDP)
Nope, nope, nope! It’s safe to say Kinsler’s career is over. I’m not entirely sure what I saw in Kinsler’s profile that made me think Kinsler could hit .250 with 17 home runs at age-36. This was a poor projection and I’ll be the first one to admit it. 

Jorge Soler (OF – KCR)
Here is a projection that I was far too low on. I would imagine, most people were. I mean, he led the AL with 48 home runs for crying out loud! One issue for me was his strikeout rate that improved in 2018 but I wasn’t fully buying it. Also, his previous HR/FB rates were relatively pedestrian. There was nothing in his profile that showed an improvement that would result in a 20%+ HR/FB. Now, to my credit, I noticed his increased launch angle in the spring and I projected a potential power breakout, just nowhere near the final results. I guess I should have listened to myself but ended up with only one share.

Jose Peraza (2B/SS – CIN)
I was fading Peraza in 2019 and I owned him nowhere, that’s the positive side of things. His metrics were awful in 2018 and he “lucked” his way to 14 home runs. I dropped him to just nine HR which was correct but still projected him for 25+ stolen bases which is where I missed. That and the batting average. He just straight tanked. 

Cody Bellinger (1B/OF – LAD)
Ranking Rhys Hoskins over Cody Bellinger was a huge mistake. Where I missed with Bellinger is making the determination that his true skill level fell closer to 2018 than in 2017. I failed to realize that we were dealing with a 23-year-old phenom who hit 39 home runs as a rookie. He made strides from year-1 to year-2 by cutting his strikeout rate but made an unpredictable jump from year-2 to year-3. That’s my mistake. I projected him closer to a 25% strikeout rate and he finished with an impressive 16.3%! Amazing. That will add about 30 points to one’s batting average. Combine that with the juiced ball and you have the 2019 version of Cody Bellinger. I don’t expect 47 homers again, but 40 seems about right.

Rafael Devers (3B – BOS)
Devers is another young player where I failed to project significant improvements. While I did expect improvements in batting average and home runs, it was nowhere near the jump he made in 2019. So while I wasn’t completely out on Devers, I just missed on his superstar breakout. Oh well. My lesson learned is that maybe year-three is the time to buy into a young prospect who had high pedigree regardless of the previous year’s performance. 

Ryan O’Hearn (1B – KCR)
After a hot final two months of 2018, I expected better numbers from O’Hearn. He showed that his power was real even if it would come with a low batting average. His power was just OK and boy was he ever a batting average drain finishing below the Mendoza line. He’s a guy where I fell in love with the Statcast metrics (12.5% barrel rate, 44.2 hard hit%, solid batted ball profile, etc). I failed to notice that he was extremely poor against offspeed and breaking pitches where his whiff rate was north of 42% on both pitch types. A few good outcomes boosted the small sample numbers against those pitches for O’Hearn in 2018. In 2019, larger samples and regression set in. He actually made a few slight improvements and was unlucky against fastballs. He might just be a deep-league option in 15-team and deeper formats in 2020. Maybe.

Follow me @FreezeStats. Check out my work at FantasyPros and Pitcher List.




Photo by: Patrick Gorski-USA TODAY Sports