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MLB Directional Home Run Park Factors Using Statcast (Updated)

Last April, I developed home run park factors based using a combination of home run per barrel rate (HR/BRL%) and non-barreled home runs. The data I used was from Baseball Savant. I gathered the data from each season 2015 through 2018 at each ballpark. Essentially how it worked was any park that allowed higher than league-average HR/BRL rates and allowed more non-barreled home runs were more favorable and vice-versa for parks that scored below-average. This was relatively simplistic but it allowed me to determine that Great American Ballpark in Cincinnati was the most friendly park in MLB for home runs and that Fenway Park in Boston is indeed a poor park for home runs. Naturally, the next step was to breakdown each park directionally (left field, centerfield, right field).


I pulled data from the last three seasons to determine directional home run park factors. I choose a three-year sample for two reasons. First, some of the sample sizes seemed a little small using just a single season of data. Second, combining two juiced ball seasons with one “dead ball” season may be a good way to aggregate how the 2020 ball might respond if there is a slight adjustment to the ball. Of course, it’s anyone’s guess as to how or if the properties of the ball will change, but at minimum I’m accounting for the range of possibilities here. Before I get down into the final park factors, below are the directional HR/BRL% for both right-handed hitters and left-handed hitters.

Left Field Centerfield Right Field
Right-Handed Hitters 75.82% 42.3% 49.65%
Left-Handed Hitters 46.80% 43.2% 73.55%

Not surprisingly, pulling the ball yields a much high home run percentage compared to balls hit to center or balls hit to the opposite field. Based on this information, I separated right-handed and left-handed hitters when determining the directional park factors due to the large discrepancies in HR/BRL%. For example, I ran home run park factors to left field for pulled fly balls by right-handed hitters and opposite-field fly balls hit by left-handed hitters. Then, I created a formula to combine the two for a final left-field park factor. I did the same thing for right field park factors. Hopefully, this makes sense. Just to be clear, these park factors are for home runs only. OK, enough of the boring explanations, let’s get to the Home Run Park Factors.


Note: 1.0 is neutral, less than 1.0 is below-average, over 1.0 is above-average

Home Run Park Factors Using Statcast (FreezeStats)

Venue/ParkTeamLF PFCF PFRF PF
GABPCIN1.1071.1361.176
Oriole ParkBAL1.1131.1441.012
Miller ParkMIL0.9841.1451.108
Coors FieldCOL1.0081.1441.055
Guaranteed Rate FldCWS1.0511.0321.114
Dodger StadiumLAD1.0041.2150.976
Citi FieldNYM1.0641.0271.057
Minute Maid ParkHOU1.1020.8861.155
Citizens Bank ParkPHI1.0790.9651.084
Angel StadiumLAA0.9111.1971.010
Petco ParkSDP1.0771.0550.981
Globe Life ParkTEX0.9731.0481.087
Yankee StadiumNYY0.9480.9311.212
Nationals ParkWSH1.0201.1020.936
Progressive FieldCLE0.9561.0311.054
T-Mobile ParkSEA0.9881.0261.006
Rogers CentreTOR1.0121.0060.995
Oakland ColiseumOAK1.0251.0080.943
SunTrust ParkATL0.9650.9991.003
Chase FieldARI1.0730.8611.006
Tropicana FieldTBR1.0180.9270.985
Wrigley FieldCHC0.9921.0270.909
PNC ParkPIT0.8801.0220.962
Target FieldMIN0.9660.9250.954
Busch StadiumSTL0.9141.0250.887
Marlins ParkMIA0.9290.9170.961
Kauffman StadiumKCR0.9550.8560.874
Comerica ParkDET1.0070.6920.958
Fenway ParkBOS0.9120.8620.844
Oracle ParkSFG0.9400.8540.717

Some things that jumped out at me upon seeing the results is that both Los Angeles ballparks are extremely favorable to centerfield. Dodger Stadium and Angel Stadium rank one and two, respectively for home runs to centerfield based on my HR Park Factors. Without diving in too deep, I noticed that Angel Stadium is perfect for Shohei Ohtani (the batter). Ohtani hits nearly 37% of his fly balls to centerfield and he absolutely crushes balls up the middle. It partially explains how he has maintained an insanely high 40.4% HR/FB on fly balls to center compared to league-average 10.5%. Another player who benefited from playing half his games in Angel Stadium over the last couple of seasons is Justin Upton (2019 injury notwithstanding). He’s hit a whopping 45.4% of his fly balls to centerfield since the start of 2018. There’s a reason that his HR/FB rate jumped once he was traded from Detroit to LA (23.4% w/ LAA compared to his career 16.6% HR/FB%).

On the flip side, centerfield at Comerica Park in Detroit is where fly balls go to die. That tweet was from back in April, so I had a feeling Detroit was awful to center but it’s worse than I thought compared to other parks. Consider this, since the start of 2017, no park has seen more barreled balls to centerfield than Comerica Park (404 barrels), but only 12.13% of those barreled balls turned into home runs (49 home runs). That is the fewest number of barreled home runs to centerfield since 2017 in all of baseball. That’s crazy! Just for fun, if Comerica played neutral to center, there would have been an ADDITIONAL 127 home runs hit over the last three seasons. If it played as favorable as Dodger Stadium has over that time frame, we would have seen a whopping 222 additional home runs to centerfield alone! It’s amazing Miguel Cabrera surpassed the 40-homer plateau multiple times while playing in Detroit despite hitting 35-40% of his fly balls to center. Nick Castellanos gets a huge boost wherever he lands in 2020 because he hit 41.5% of his fly balls to center in 2019.  


A few other interesting observations that jumped out at me is that Oakland Collusiem and Petco Park in San Diego actually play somewhat favorable for home runs. Both play above-average to centerfield and left field. So, let’s give Manny Machado another chance to bounce back in 2020 even though Petco is still a downgrade compared to Oriole Park. I’ll touch on Yankee Stadium’s right field but the park is below-average to center and left field. I’m beginning to understand why Aaron Judge hits so many balls to the opposite field. Citi Field, the other park in New York, ranks as the seventh most favorable park for home runs by my park factors. If you recall, they moved the fences in before the 2015 season, so that modification has done wonders for their hitters. It also makes what Jacob deGrom’s done over the last two seasons extremely impressive.

Oriole Park, Great American Ballpark (GABP), and Minute Maid Park are the top three parks for home runs to left field. I’m not surprised, because GABP is favorable to all fields and Minute Maid has the short porch in left thanks to the Crawford Boxes (84.14% HR/BRL for pulled FB to left). Although Minute Maid is even better for left-handed pull power but below-average to center. Oriole Park has proven to be more favorable for right-handed pull power and straight-away center but plays neutral to right field. We should shift our analysis for left-handed pull hitters and right-handed hitters who favor the opposite field in Baltimore as they may not see a boost in power numbers. PNC Park in Pittsburgh is the worst for home runs to left field but is OK to center and right. More on this in a future article.

Oracle Park is a nightmare for power hitters who favor right field. That’s a well-known fact of course. However, the fences are indeed coming in as the bullpen is now moving behind the right field wall! It’s hard to say how much this will improve the home run park factors in Oracle because the entire park plays unfavorable. Either way, I’m intrigued by Brandon belt (if he stays in SF), Mike Yastrzemski , and Alex Dickerson. In fact, one of my bold predictions involves Alex Dickerson surpassing 20 home runs in 2020. The number-one venue to right field is Yankee Stadium. Along with the juiced ball, it helped boost Didi Gregorius’ power numbers and resurrect Brett Gardner’s power. Great American Smallpark comes in at number two and how about Minute Maid Park ranking third to right field. It’s actually MORE favorable than left field with the Crawford Boxes! 

I had to dig a little deeper to find out why Minute Maid was so favorable to right field. It ranked second in HR/BRL% to right field and allowed the fourth-most non-barreled home runs. Minute Maid is only 326 feet down the right field line which is 11 feet deeper than the short porch in left field, however, the height of the wall is only seven feet high in right field as opposed to the 19 and 25-foot walls in left and left-center. In the power alley (right-center), the fence is 373 feet from home plate and 10-feet in height. Again, this is 11 feet further than left-center but with a much shorter wall. In other words, batted balls with a lower trajectory have a higher probability to be a home run to right field than to left field in Houston. Meanwhile, non-barreled fly balls with high launch angles to left field have left Minute Maid 113 times in three seasons, most in MLB.


My next article will look at hitters and some pitchers who are changing parks and how we should evaluate each player based on the park change. Obviously, we need to see more signings before that happens. To reiterate, these park factors do not consider singles, doubles, or triples, so they are not complete park factors. They are strictly measuring how favorable/unfavorable each park is for home runs to each part of the field using Statcast metrics (barrels and on-barrels). ESPN and FanGraphs along with several other sites have overall park factors, but we care about the long ball!

This metric can be extremely helpful for the evaluation of certain players who have extreme pull or oppo tendencies on their batted balls. Heavy pull hitters or hitters with a higher percentage of opposite-field fly balls can be analyzed and projected more accurately. I could also see where this metric could provide value for DFS purposes. For example, imagine righty-masher Joc Pederson in Yankee Stadium against a right-handed pitcher. That’s easy money right there. I’m open to any questions or ideas you may have as well. Feel free to hit me up on Twitter @FreezeStats.


 Photo courtesy of southerncal88
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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

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2018 FreezeStats Hitter Projections Revisited

(Cover image courtesy of Star Tribune)

This past season was the second time I did my own full projections covering over 300 players. In total, that came out to approximately 225 hitters and 100 pitchers. I wanted to get an idea of the overall accuracy of my projections, which of course is difficult if I don’t compare them to other project systems. The problem is, I didn’t project enough players to accurately compare them to the major projection systems. What I did do, is run my projections against each player’s final statistics and calculate the z-Scores for each statistic. For hitters that’s Runs, HR, RBI, SB, AVG, OBP, & Plate Appearances; for pitchers, it’s IP, W, K, ERA, and WHIP. I also eliminated any player that had under 300 PA or pitcher with less than 90 IP.  For this article, I’ll only touch on the hitters. I’ll follow up with pitchers in a day or two.


The link to each projection spreadsheet is below.  I’ve used conditional formatting for the Z-Scores where Dark RED is very poor accuracy (high Z-Score), white is an average projection, and dark green is very accurate. I’ll highlight a few from both ends of the spectrum below, but make sure to take a look at the link to see the results of the rest of the projections. In the meantime, I’ve already started my projections for 2019 and plan on doing well over 400.

2018 Hitter Projections vs Actual

A few players I basically projected to a “T” were:

Andrelton Simmons (SS – LAA)

Actual 68 11 75 10 0.292 0.337 600
Proj 65 12 72 12 0.278 0.332 612

Simmons hit for a higher average than I projected thanks to yet another improvement in contact rate. Simmons rarely swings and misses, but he’s more of a compiler than anything else. If Simmons hit my 612 PA, he may have gone 12-12 as I projected.

Nelson Cruz (DH – SEA)

Actual 70 37 97 1 0.256 0.342 591
Proj 87 35 104 1 0.264 0.345 635

Not surprising that I hit on Nelson Cruz. The elder statesman has been a model of consistency for the better part of the last decade. I projected a decrease in power and batting average due to natural age-progression, and that’s exactly what happened. Going into 2019, Cruz will turn 39 during the season, so it’s difficult to project better than .250-34-90 this coming year as he hits free agency.

Eddie Rosario (OF – MIN)

Actual 87 24 77 8 0.288 0.323 592
Proj 74 24 84 8 0.273 0.316 592

Rosario had a nice breakout in 2017 at age-26, so naturally, he should continue to improve, right? Instead, he basically finished with the same results he had in 2017. My projection for plate appearances (592), home runs (24), and steals (8) all were a direct hit! I liked Rosario’s value coming into 2018 but didn’t expect a skills bump. For 2019, I see regression for Rosario due to a decrease in plate discipline and I’m staying away.

Rhys Hoskins (1B/OF – PHI)

Actual 89 34 96 5 0.246 0.354 660
Proj 81 37 95 3 0.256 0.345 609

Talk about projections that were all over the map for Hoskins. After bashing 18 homers in 50 games at the conclusion of 2017, I saw anything from mid/upper 20 homers to 40+ homers from Hoskins. There was also talk of a higher batting average given his elite plate skills. The problem was, he hits far too many fly balls and doesn’t run well, limiting his BA upside. I had Hoskins at .256 which turned out to be HIGH and almost nailed his HR projection with 37 but he had 50 more PA than my projection. I’ll be cautious with Rhys for 2019 and don’t think he’s a lock to be a top 50 player.


Jean Segura (SS – SEA)

Actual 91 10 63 20 0.304 0.341 632
Proj 86 11 64 20 0.282 0.328 622

Jean proved me wrong with a .300+ batting average, but everything else worked out pretty nice. Whether it seems like it or not, Jean is becoming more consistent but his upside is relatively limited at this point. Still, a solid player giving you speed which continues to decrease league-wide without complete lack of power. Segura should hold some value for 2019 as flashier players begin to move ahead of him.


Justin Upton (LAA – OF)

Actual 80 30 85 8 0.257 0.344 613
Proj 83 30 95 11 0.254 0.336 625

After blasting a career high in home runs and RBI in 2017, I figured Upton was due for some regression. Well, duh. Even getting to play a full season hitting behind Mike Trout, Upton’s rate stars were a bit out over their skis in 2017. In addition to the HR/RBI regression, I knew that Upton could maintain another .270+ batting average given his high-20s K rate. Going forward, Upton’s speed s dwindling and he is looking more like a .250-28-90-7 guy which is useful but could be overvalued in drafts for 2019.

Now for the projections that were so far off, it’s hard to fathom how I got there. I’ll give it a shot to figure this out as I recap.

Carlos Correa (SS – HOU)

Actual 60 15 65 3 0.239 0.323 468
Proj 94 29 103 10 0.295 0.378 637

Injuries. It’s not just that he missed time due to his injured back, he also recently had offseason surgery to repair a deviated septum. In other words, he couldn’t breathe. OK, he could breathe, but not well. So, Correa went from hitting .315 in 2017 to a meager .239 in 2018. I think one thing I’m going to do with Correa’s 2019 projection is to limit his plate appearances to around 550-575. I see a big bounce-back in average and power but the speed isn’t coming back friends.


Javier Baez (2B/SS – CHC)

Actual 101 34 111 21 0.29 0.326 645
Proj 62 21 67 9 0.251 0.299 465

On the other end of the poor projection spectrum, we have Javy Baez. One of my bust picks finished second in NL MVP voting. Yikes. Well, I discussed Baez’ awful plate discipline which he has embraced. I also factored in Manager Joe Maddon‘s decisions to move players around the field, in the lineup, etc. I figured Baez would see the bench during slumps and that Ian Happ would see more time at 2B. Whoops. The lesson for 2019, never bet heavily against power/speed talent.

Lewis Brinson (OF – MIA)

Actual 31 11 42 2 0.199 0.24 406
Proj 73 18 65 12 0.256 0.315 565

Speaking of players with the talent of power and speed… Well, I figured the move to Miami would allow Brinson to play every day without an OF roster crunch like there was in Milwaukee. As it turns out, if you hit .199 with an OBP that’s below Giancarlo’s weight, you don’t get to play every day. Oh well. My projections weren’t even that optimistic, Brinson was just straight BAD.

Logan Morrison (1B – MIN)

41 15 39 1 0.186 0.276 359
68 26 77 2 0.243 0.328 548

After a late breakout in 201, Logan Morrison was in the spotlight for less time than his great-uncle Jim. (That’s a Doors reference for those of you who aren’t 60 years old). Not much to say here. I knew that the 36 bombs he hit in 2017 wasn’t for real but come on Lo-Mo! 15 homers and a .186 batting average?!? Who are you, Chris Davis? It’s safe to keep Morrison out of my projections for 2019 and for everyone’s sake, hopefully, he retires. Thanks for reading! I’ll continue my projections for 2019 riiiiiiiiight now!

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