Archive for March, 2017

Matt Carpenter Makes Good Wood

Since earning a starting job with the Cardinals in 2013, Matt Carpenter has been one of the league’s best run producers, and one of the best OBP lead-off hitters. From 2013-2015, health was a staple for Carpenter, as he had 2109 PA (avg. 703), which ranked third, only behind Nick Markakis (which is a bit surprising) and Mike Trout. In 2016, Carpenter injured his right oblique on July 6th and was never quite the same after returning back from his DL stint. A lot of fans were surprised to see a power outbreak for him in 2015, Carpenter posting a career-high 28 home runs (in 665 PA) when he had only hit 25 homers in his previous 1766 PA. He made some changes to his approach at the plate in 2015 and strove to hit more fly balls, pull the ball more and to sacrifice some contact for some power.


2016 Avg. Launch Angle and Avg. Exit VelocityNow let’s jump to 2016. It was a tale of two halves. His offensive production was finally impacted by an injury which directly affected his swing and, more specifically, his new power-enhanced swing path.

In a recent article by Jeff Sullivan from FanGraphs, he references data from Baseball Savant which indicates that the optimal launch angle for slugging percentage is between 20-29 degrees. Carpenter has increased his average launch angle from 17.2 degrees to 18.2 between 2015 and 2016. Continuing to increase his launch angle while playing injured likely contributed to his plummeting batting average in the second half, as he continued to try to hit fly balls and line drives but simply couldn’t create the same bat speed and power to carry the ball into gaps and over the fence.

Below is a 15-Game rolling average of Carpenter’s Weighted On Base Average for the 2016 season. He got injured during game 78 of the season, which can easily be identified on the chart. He clearly never got back to form after hurting his oblique, but he did have the fourth-best wOBA before his injury, getting beat out by only David Ortiz, Josh Donaldson, and Mike Trout.

When playing healthy, his average swing speed was 62.7 MPH, but it dropped to 61.8 MPH after returning from the DL. His hard-hit rate also dropped by 6.7% and his soft-hit rate increased by almost the same amount. He clearly wasn’t the same hitter at the plate, and his numbers down the stretch took a massive hit.

If we focus on his 2015 and the beginning of 2016 production, we are looking at an elite run generator and on-base machine. He ranks ninth in OBP, 12th in BB% and wRC, had the same OPS as Nolan Arenado, had the same wOBA as Edwin Encarnacion (tied for 11th), and lastly he and Joey Votto were the only hitters in that timeframe to have a combined medium+hard-hit rate over 90%. That is some elite company.

Health will be imperative for Carpenter in 2017. If he is able to avoid a major injury this year and show no ill effects in spring training from his oblique injury from last season, we could be looking at someone who could shatter his current projections. His hitting tool and batted-ball profile are quite similar to Joey Votto and Freddie Freeman, with a high walk rate and hard-hit rate, a high line-drive rate, and power in the 25-30 home run territory.

The following stats are from 2015 – July 6th, 2016.

Advanced Stats:
Batted Ball Profiles:

Putting Carpenter in that category of hitter might be a stretch for some; however, since making adjustments to his swing path and approach at the plate, he really isn’t that far off, as Carpenter, for the majority of these metrics, falls below Votto but ahead of Freeman.

He has withdrawn from the World Baseball Classic due to a back injury that his manager has indicated isn’t too serious. Nevertheless, he is definitely worth monitoring in the coming weeks leading up to opening day, to make sure he looks like his hard-hitting normal self.

Basic Machine Learning With R (Part 3)

Previous parts in this series: Part 1 | Part 2

If you’ve read the first two parts of this series, you already know how to do some pretty cool machine-learning stuff, but there’s still a lot to learn. Today, we will be updating this nearly seven-year-old chart featured on Tom Tango’s website. We haven’t done anything with Statcast data yet, so that will be cool. More importantly, though, this will present us with a good opportunity to work with an imperfect data set. My motto is “machine learning is easy — getting the data is hard,” and this exercise will prove it. As always, the code presented here is on my GitHub.

The goal today is to take exit velocity and launch angle, and then predict the batted-ball type from those two features. Hopefully by now you can recognize that this is a classification problem. The question becomes, where do we get the data we need to solve it? Let’s head over to the invaluable Statcast search at Baseball Savant to take care of this. We want to restrict ourselves to just balls in play, and to simplify things, let’s just take 2016 data. You can download the data from Baseball Savant in CSV format, but if you ask it for too much data, it won’t let you. I recommend taking the data a month at a time, like in this example page. You’ll want to scroll down and click the little icon in the top right of the results to download your CSV.

View post on

Go ahead and do that for every month of the 2016 season and put all the resulting CSVs in the same folder (I called mine statcast_data). Once that’s done, we can begin processing it.

Let’s load the data into R using a trick I found online (Google is your friend when it comes to learning a new programming language — or even using one you’re already pretty good at!).

filenames <- list.files(path = "statcast_data", full.names=TRUE)
data_raw <-"rbind", lapply(filenames, read.csv, header = TRUE))

The columns we want here are “hit_speed”, “hit_angle”, and “events”, so let’s create a new data frame with only those columns and take a look at it.

data <- data_raw[,c("hit_speed","hit_angle","events")]


'data.frame':	127325 obs. of  3 variables:
 $ hit_speed: Factor w/ 883 levels "100.0","100.1",..: 787 11 643 ...
 $ hit_angle: Factor w/ 12868 levels "-0.01               ",..: 7766 1975 5158  ...
 $ events   : Factor w/ 25 levels "Batter Interference",..: 17 8 11 ...

Well, it had to happen eventually. See how all of these columns are listed as “Factor” even though some of them are clearly numeric? Let’s convert those columns to numeric values.

data$hit_speed <- as.numeric(as.character(data$hit_speed))
data$hit_angle <- as.numeric(as.character(data$hit_angle))

There is also some missing data in this data set. There are several ways to deal with such issues, but we’re just simply going to remove any rows with missing data.

data <- na.omit(data)

Let’s next take a look at the data in the “events” column, to see what we’re dealing with there.



 [1] Field Error         Flyout              Single             
 [4] Pop Out             Groundout           Double Play        
 [7] Lineout             Home Run            Double             
[10] Forceout            Grounded Into DP    Sac Fly            
[13] Triple              Fielders Choice Out Fielders Choice    
[16] Bunt Groundout      Sac Bunt            Sac Fly DP         
[19] Triple Play         Fan interference    Bunt Pop Out       
[22] Batter Interference
25 Levels: Batter Interference Bunt Groundout ... Sacrifice Bunt DP

The original classification from Tango’s site had only five levels — POP, GB, FLY, LD, HR — but we’ve got over 20. We’ll have to (a) restrict to columns that look like something we can classify and (b) convert them to the levels we’re after. Thanks to another tip I got from Googling, we can do it like this:

data$events <- revalue(data$events, c("Pop Out"="Pop",
      "Bunt Pop Out"="Pop","Flyout"="Fly","Sac Fly"="Fly",
      "Bunt Groundout"="GB","Groundout"="GB","Grounded Into DP"="GB",
      "Lineout"="Liner","Home Run"="HR"))
# Take another look to be sure
# The data looks good except there are too many levels.  Let's re-factor
data$events <- factor(data$events)
# Re-index to be sure
rownames(data) <- NULL
# Make 100% sure!

Oof! See how much work that was? We’re several dozen lines of code into this problem and we haven’t even started the machine learning yet! But that’s fine; the machine learning itself is the easy part. Let’s do that now.

inTrain <- createDataPartition(data$events,p=0.7,list=FALSE)
training <- data[inTrain,]
testing <- data[-inTrain,]

method <- 'rf' # sure, random forest again, why not
# train the model
ctrl <- trainControl(method = 'repeatedcv', number = 5, repeats = 5)
modelFit <- train(events ~ ., method=method, data=training, trControl=ctrl)

# Run the model on the test set
predicted <- predict(modelFit,newdata=testing)
# Check out the confusion matrix
confusionMatrix(predicted, testing$events)


Prediction   GB  Pop  Fly   HR Liner
     GB    9059    5    4    1   244
     Pop      3 1156  123    0    20
     Fly      6  152 5166  367   457
     HR       0    0  360 1182    85
     Liner  230   13  449   77  2299

We did it! And the confusion matrix looks pretty good. All we need to do now is view it, and we can make a very pretty visualization of this data with the amazing Plotly package for R:

# Exit velocities from 40 to 120
x <- seq(40,120,by=1)
# Hit angles from 10 to 50
y <- seq(10,50,by=1)
# Make a data frame of the relevant x and y values
plotDF <- data.frame(expand.grid(x,y))
# Add the correct column names
colnames(plotDF) <- c('hit_speed','hit_angle')
# Add the classification
plotPredictions <- predict(modelFit,newdata=plotDF)
plotDF$pred <- plotPredictions

p <- plot_ly(data=plotDF, x=~hit_speed, y = ~hit_angle, color=~pred, type="scatter", mode="markers") %>%
    layout(title = "Exit Velocity + Launch Angle = WIN")

View post on

Awesome! It’s a *little* noisy, but overall not too bad. And it does kinda look like the original, which is reassuring.

That’s it! That’s all I have to say about machine learning. At this point, Google is your friend if you want to learn more. There are also some great classes online you can try, if you’re especially motivated. Enjoy, and I look forward to seeing what you can do with this!

Catchers, Points Leagues, and Z-Scores


Catchers are undervalued in points leagues based upon their ADP compared to the relative replacement value to their position.


I play in a head-to-head points league with a pretty standard scoring system for hitters. Points leagues tends to be a little more straightforward than rotisserie leagues with projecting player value, because you can translate the projections directly into your scoring system. The end game is total points for the player, and it does not matter how they achieve it, whether through stolen bases or home runs etc. In an effort to gain a little insight into the total points rankings rather than just sort all players by points and draft off of that list, I’ve used z-scores to attempt to calculate the value of a player’s points relative to the positional average. I wanted to quantify how much value you may gain from drafting Carlos Correa at SS as opposed to Paul Goldschmidt at 1B, even though Goldschmidt is projected to score more points. This is not a particularly new concept, as there is  a great series articles written by Zach Sanders about it here: .

In calculating the z-scores based upon Steamer projections, I have found that the top three catchers (Posey, Sanchez, Lucroy) have a higher score than expected, and it would seem to place their actual value among the top 20 hitters overall, despite projected significantly fewer points than their peers, while maintaining an ADP anywhere from the 4th to 7th rounds. It would seem that it may be smart to exploit this value differential in points leagues.


Based upon Steamer projections and using a standard points-league scoring system, the z-scores for Posey, Sanchez, Lucroy put their top-end value with players such as Manny Machado and Paul Goldschmidt, and the low-end value with Xander Bogaerts. Buster Posey has a z-score of 2.31, Gary Sanchez has a score of 1.36, and Jonathan Lucroy has a score of 0.75. You may disagree with the ranking of Sanchez over Lucroy etc., but the main takeaway is that there is significant value with the top three catchers, as the next-highest projected scoring is Stephen Vogt, who has a z-score of -.05.

In rotisserie leagues, catchers do not carry as much value, because while Gary Sanchez may be projected for 28 HRs and cost a 6th-round pick, you can wait 6-8 more rounds and draft a Yasmani Grandal and only lose a projected 8 HRs. In points leagues, the difference between Sanchez and Grandal might be close to 100 points. That is the difference between having Paul Goldschmidt or Brandon Belt as your starting 1B. For reference, here are the projections with z-scores for 1B and 3B. I used replacement values of 23 for 1B and 17 for 3B based upon this article:

I am not advocating that, because of the numbers, you draft Posey over Rizzo or Sanchez over Bryant, because that would limit any potential value you may get by drafting Posey at his ADP. I also do not believe they carry as much value as those players despite the z-scores. I am just saying that, at least in points leagues, it may be time to reevaluate the value of the top catchers, compared to other positions. Having a Posey, Sanchez, or Lucroy in points leagues gives a significant value week to week in head-to-head leagues, or for total points. Additionally, there is added value in having a catcher like Posey or Sanchez, who also perform occasional 1B or DH duties, which increase their ABs and scoring potential. Ideally, with a top catcher, you are not playing musical chairs week to week at the position, hoping for good match-ups, only to end the week with a catcher who may have scored under 10 points.

Based upon projections and experience, points leagues tend to be fairly top-heavy with scoring, where the top five or so at each position hold significantly more value than they would in standard rotisserie leagues. That is because there are only 20-30 points separating the No. 6 3B from the No. 12 3B, and spaced out in 22 weeks in head-to-head points, it is a difference of 1-2 points per week.


I do not believe you should be drafting Posey or Sanchez in the first two rounds in fantasy points leagues, because it would not be the most efficient way to accumulate valuable players. I do think that catchers are particularly undervalued in points leagues relative to their draft positions. In points leagues, it is more valuable to have a top-three player at a weak position than having the No. 6 player at a strong to average position, even if traditional wisdom may say to draft Freddie Freeman over Gary Sanchez, because points leagues tend to equalize scoring after the top few players.

So my advice is to ignore conventional wisdom that says wait on catchers, and disregard ADPs that put players like Freddie Freeman, Jose Abreu, Jonathan Villar and Xander Bogaerts over Gary Sanchez or Jonathan Lucroy.

Hardball Retrospective – What Might Have Been – The “Original” 1999 White Sox

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.


OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams


The 1999 Chicago White Sox 

OWAR: 45.1     OWS: 289     OPW%: .504     (82-80)

AWAR: 28.5      AWS: 225     APW%: .466     (75-86)

WARdiff: 16.6                        WSdiff: 64  

The “Original” 1999 White Sox tied the Royals for second place in the American League Central, eight games behind the Indians. Robin Ventura (.301/32/120) established career-highs in batting average and RBI while earning his sixth Gold Glove Award at the hot corner. Randy Velarde (.317/16/76) rapped 200 base knocks and set personal-bests in almost every offensive category. Mike Cameron drilled 34 doubles and pilfered 38 bags. Harold Baines (.312/25/103) topped the century mark in RBI for the third time in his career during his age-40 season. Ray Durham registered 109 tallies and swiped 34 bags. Magglio Ordonez (.301/30/117) scored 100 runs and merited his first All-Star invitation. Frank E. Thomas clubbed 36 two-baggers and delivered a .305 BA. Chris Singleton (.300/17/72) placed sixth in the AL Rookie of the Year balloting and Paul Konerko contributed 24 dingers and 81 ribbies for the “Actuals”.

Frank E. Thomas rated tenth among first basemen according to “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” White Sox chronicled in the “NBJHBA” top 100 ratings include Robin Ventura (22nd-3B) and Harold Baines (42nd-RF).

  Original 1999 White Sox                          Actual 1999 White Sox

Carlos Lee LF -0.04 10.36 Carlos Lee LF -0.04 10.36
Mike Cameron CF 3.63 21.44 Chris Singleton CF 2.61 16.33
Magglio Ordonez RF 1.7 18.56 Magglio Ordonez RF 1.7 18.56
Harold Baines DH 1.7 12.96 Frank E. Thomas DH 2.2 17.07
Frank E. Thomas 1B/DH 2.2 17.07 Paul Konerko 1B 1.45 14.68
Randy Velarde 2B 5.23 24.19 Ray Durham 2B 3.63 20.45
Liu Rodriguez SS/2B -0.12 1.41 Mike Caruso SS -2.58 4.25
Robin Ventura 3B 5.1 28.27 Greg Norton 3B 0.06 12.36
Mark Johnson C 0.28 6.12 Brook Fordyce C 1.59 11.45
Ray Durham 2B 3.63 20.45 Mark Johnson C 0.28 6.12
Greg Norton 3B 0.06 12.36 Craig Wilson 3B -0.38 4.06
Olmedo Saenz 3B 1.35 8.68 Darrin Jackson LF -0.05 2.68
Craig Grebeck 2B 0.82 4.39 Brian Simmons LF -0.15 1.76
Craig Wilson 3B -0.38 4.06 Liu Rodriguez 2B -0.12 1.41
Brian Simmons LF -0.15 1.76 Jeff Liefer 1B -0.6 0.91
Jeff Liefer 1B -0.6 0.91 McKay Christensen CF -0.27 0.47
Norberto Martin 2B 0.09 0.44 Jason Dellaero SS -0.39 0.32
Jason Dellaero SS -0.39 0.32 Josh Paul C -0.09 0.27
Josh Paul C -0.09 0.27 Jeff Abbott LF -0.73 0.18
Robert Machado C -0.08 0.22
Chris Tremie C -0.18 0.18
Jeff Abbott LF -0.73 0.18
Frank Menechino SS -0.08 0.14
John Cangelosi LF -0.06 0.02

Mike Sirotka (11-13, 4.00) and James Baldwin (12-13, 5.00) labored through their second seasons in the Sox rotation. Alex Fernandez supplied a 7-8 record with a 3.38 ERA after missing the entire 1998 campaign due to injury. Bob Wickman notched 37 saves with an ERA of 3.39 for the “Originals” while Keith Foulke (2.22, 9 SV) and Bob Howry (3.59, 28 SV) secured late-inning leads for the “Actuals”.

  Original 1999 White Sox                       Actual 1999 White Sox 

Mike Sirotka SP 3.94 13.5 Mike Sirotka SP 3.94 13.5
Alex Fernandez SP 3.34 10.47 James Baldwin SP 2.19 9.47
James Baldwin SP 2.19 9.47 Jim Parque SP 1.26 6.82
Brian Boehringer SP 1.64 6.91 Kip Wells SP 0.79 2.93
Jim Parque SP 1.26 6.82 Jaime Navarro SP -1.15 2.16
Bob Wickman RP 1.33 10.19 Keith Foulke RP 3.86 16.7
Al Levine RP 0.77 6.84 Bob Howry RP 0.61 10.06
Pedro Borbon RP 0.36 4.11 Sean Lowe RP 1.58 7.94
Buddy Groom RP -0.27 3.49 Bill Simas RP 0.68 6.46
Steve Schrenk RP 0.54 3.04 Carlos Castillo SW 0.05 1.45
Kip Wells SP 0.79 2.93 John Snyder SP -0.97 1.22
Scott Radinsky RP 0 2.35 Tanyon Sturtze SP 0.48 0.91
Jason Bere SP -0.6 1.6 Pat Daneker SP 0.23 0.82
Carlos Castillo SW 0.05 1.45 Jesus Pena RP -0.27 0.42
Pat Daneker SP 0.23 0.82 Joe Davenport RP 0.13 0.25
Aaron Myette SP 0 0.11 Aaron Myette SP 0 0.11
Chad Bradford RP -0.5 0 Bryan Ward RP -1.15 0.09
John Hudek RP -1.04 0 Chad Bradford RP -0.5 0
David Lundquist RP -0.74 0 Scott Eyre RP -0.66 0
Jack McDowell SP -0.36 0 David Lundquist RP -0.74 0
Nerio Rodriguez RP -0.16 0 Todd Rizzo RP -0.11 0


Notable Transactions

Robin Ventura 

October 23, 1998: Granted Free Agency.

December 1, 1998: Signed as a Free Agent with the New York Mets. 

Randy Velarde

January 5, 1987: Traded by the Chicago White Sox with Pete Filson to the New York Yankees for Mike Soper (minors) and Scott Nielsen.

December 23, 1994: Granted Free Agency.

April 12, 1995: Signed as a Free Agent with the New York Yankees.

November 2, 1995: Granted Free Agency.

November 21, 1995: Signed as a Free Agent with the California Angels.

October 23, 1998: Granted Free Agency.

December 7, 1998: Signed as a Free Agent with the Anaheim Angels.

Mike Cameron

November 11, 1998: Traded by the Chicago White Sox to the Cincinnati Reds for Paul Konerko. 

Harold Baines

July 29, 1989: Traded by the Chicago White Sox with Fred Manrique to the Texas Rangers for Wilson Alvarez, Scott Fletcher and Sammy Sosa.

August 29, 1990: Traded by the Texas Rangers to the Oakland Athletics for players to be named later. The Oakland Athletics sent Joe Bitker (September 4, 1990) and Scott Chiamparino (September 4, 1990) to the Texas Rangers to complete the trade.

January 14, 1993: Traded by the Oakland Athletics to the Baltimore Orioles for Allen Plaster (minors) and Bobby Chouinard.

November 1, 1993: Granted Free Agency.

December 2, 1993: Signed as a Free Agent with the Baltimore Orioles.

October 20, 1994: Granted Free Agency.

December 23, 1994: Signed as a Free Agent with the Baltimore Orioles.

November 6, 1995: Granted Free Agency.

December 11, 1995: Signed as a Free Agent with the Chicago White Sox.

November 18, 1996: Granted Free Agency.

January 10, 1997: Signed as a Free Agent with the Chicago White Sox.

July 29, 1997: Traded by the Chicago White Sox to the Baltimore Orioles for a player to be named later. The Baltimore Orioles sent Juan Bautista (minors) (August 18, 1997) to the Chicago White Sox to complete the trade.

October 29, 1997: Granted Free Agency.

December 19, 1997: Signed as a Free Agent with the Baltimore Orioles.

Alex Fernandez 

December 7, 1996: Granted Free Agency.

December 9, 1996: Signed as a Free Agent with the Florida Marlins. 

Bob Wickman 

January 10, 1992: Traded by the Chicago White Sox with Domingo Jean and Melido Perez to the New York Yankees for Steve Sax.

August 23, 1996: Traded by the New York Yankees with Gerald Williams to the Milwaukee Brewers for a player to be named later, Pat Listach and Graeme Lloyd. The Milwaukee Brewers sent Ricky Bones (August 29, 1996) to the New York Yankees to complete the trade. Pat Listach returned to original team on October 2, 1996.

Honorable Mention

The 1932 Chicago White Sox 

OWAR: 21.5     OWS: 205     OPW%: .380     (58-96)

AWAR: 17.0      AWS: 147     APW%: .325     (49-102)

WARdiff: 4.5                        WSdiff: 58  

The cellar-dwelling “Original” 1932 White Sox fared better than their “Actual” counterparts in terms of team WAR, Win Shares and winning percentage. Although the “Actuals” recorded only 49 victories, the team finished in seventh place ahead of the miserable Red Sox (43-111). Willie Kamm clubbed 34 doubles, delivered a .286 BA and drove in 83 baserunners for the Pale Hose. Second-sacker Bill Cissell posted career-bests in batting average (.315), runs (85), hits (184), doubles (36), home runs (7) and RBI (98). Rookie right fielder Bruce Campbell (.286/14/87) contributed 36 two-baggers and 11 three-base hits. Smead “Smudge” Jolley (.312/18/106) drilled 30 doubles while outfield mate Carl Reynolds produced a .305 BA. Luke Appling aka “Old Aches and Pains” rewarded the Chicago brass with 20 two-base hits and 10 triples after achieving full-time status. Ted Lyons completed 19 of 26 starts and furnished an ERA of 3.28.

On Deck

What Might Have Been – The “Original” 2001 Rangers

References and Resources

Baseball America – Executive Database


James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive


Balancing the Realities of Michael Conforto’s Service Time

There’s no shortage of people who think Michael Conforto should never have been demoted last season. The thinking among members of this group is that the Mets messed around with Conforto’s development by twice transporting the 23-year-old outfielder to Las Vegas rather than allowing him to work through his struggles in the majors.

Whether you agree with this sentiment or not, there is no arguing that Conforto did struggle, especially against LHP. There is also no arguing that the acquisition of a left-handed RF at last year’s trade deadline was directly related to said struggles.

The presence of that left-handed RF, Jay Bruce — and maybe more importantly the $13-million 2017 salary associated with Bruce that has scared off potential trade suitors to date — leaves the current state of the 2017 Mets outfield quite complicated.

As it stands now, hundred-millionaire Yoenis Cespedes has permanent claim in left, and Curtis Granderson seemingly has permanent claim in center, leaving Bruce or Conforto to man right. The defensively-superior Juan Lagares could spell Granderson against lefties (if he makes the roster, that is), which would leave one of Granderson/Bruce/Conforto in right. Further, there is some talk of Jose Reyes getting time in the outfield. So yeah, pretty complicated.

A 2015 summer addition, Conforto never had a chance of being Super Two-eligible post-2017. But last season’s two Vegas vacations have left his current service time at 1.043. Stated another way, if Conforto starts 2017 in Vegas and spends the first 48 games there, the Mets gain another year of team control. Given the superfluous state of the Mets current OF, this scenario, which would have sounded outlandish in March of 2016, is now worth considering.

Michael Conforto Salary Chart

≥129 days, ≥114 games <129 days, <114 games
Year Age Salary Year Age Salary
2017 24 Team Control 2017 24 Team Control
2018 25 Team Control 2018 25 Team Control
2019 26 Arb 1 2019 26 Arb 1
2020 27 Arb 2 2020 27 Arb 2
2021 28 Arb 3 2021 28 Arb 3
2022 29 FA 2022 29 $10.7mil (Arb 4)
2023 30 2013 30 FA

While it’s impossible to predict Conforto’s future arbitration salaries, I arrived at this estimate using the general rule of a 50% increase in salary each year of arbitration. A low-ish estimate of $2 million for Conforto’s Year 1 arbitration salary would yield a $6.75 million Year 4 arbitration salary, while a high-end estimate of $4.5 million for Conforto’s Year 1 arbitration salary would yield a $15.2 million Year 4 arbitration salary. $10.7 million is not only pretty close to the exact middle of these two numbers, but also conveniently lines up with the value of 1 win in 2022 when using 5% inflation.

Year $/WAR (5% inflation)
2016 $8mil
2017 $8.4mil
2018 $8.8mil
2019 $9.3mil
2020 $9.7mil
2021 $10.2mil
2022 $10.7mil

If Conforto turns out to be just an average regular, the Mets would still gain $10.7 million in 2022 surplus value. If he’s a lot better than average while in the heart of his prime, then the Mets’ 2022 surplus value would be much greater. If you think Conforto will be below average in 2022, then what the Mets do with him in 2017 is mostly irrelevant. Any way you slice it, the potential long-term financial advantage is discernible.

It’s no surprise that Conforto’s playing time projections are all over the place — Depth Charts projects 245 PAs, Steamer projects 319 and ZiPS projects 558. 48 games equal 29.6% of the season, which equates to 73 PA using Depth Charts projections, 94 PA using Steamer projections, and 165 using ZiPS projections. Take the average of those three and you get 111 PA for Michael Conforto over the first 48 games.

Which brings me back to my original title of this piece — is the value of 111Michael Conforto PA in 2017 worth more than a one-year deal for ~$10.7mil in 2022, Conforto’s age-29 season?

There are plenty of variables to consider when answering this, but the most important is probably comparing the 2017 versions of Conforto and Bruce. While Conforto projects as a better hitter, fielder and runner than Bruce, Bruce did run a 124 wRC+ against RHP 2016 and holds a 115 career mark. No one is confusing Bruce for Bryce Harper, but he’s a perfectly suitable platoon option in RF.

Also relevant is the Mets’ schedule over the first 48 games.  Using FanGraphs projections, the weighted projected win percentage of the Mets’ first 48 opponents is .477 — roughly the equivalent of a 77-win team. Now of course these 48 games won’t count any less than the 114 that will follow, but if you truly think Conforto is a better option than Bruce AND you had to choose 48 games to play Bruce over Conforto, the first 48 would be pretty ideal.

While Conforto looked miserable at times last year, it’s impossible to ignore that he posted a 152 wRC+ from July 2015 – April 2016 at the ages of 22-23. While his 2016 Barreled Balls May Not Have Been Ideal, he continued to hit the ball hard amidst his struggles.

I hope Michael Conforto is in RF when Noah Syndergaard throws his first 100mph fastball against Julio Teheran and the Braves on Monday, April 3. But if he’s not, then he must be 2000 miles away, getting at-bats in Las Vegas, rather than a matter of feet away, wasting away in the dugout in Queens. The latter simply doesn’t pay.

Turning Nick Castellanos Into Nolan Arenado

Inspiration struck me after reading Jeff Sullivan’s piece yesterday on how Christian Yelich could morph into Joey Votto with continued changes, or shall we say improvements, to his batted-ball profile. Namely, hitting the ball in the air more. As Jeff rightly pointed out, Yelich hammers the ball as well as anyone in baseball; it’s just that, to date, he’s done so much more often on the ground. You know who doesn’t have Christian Yelich’s problem?  Nick Castellanos.

Castellanos has driven changes in his batted-ball profile, which were covered last May by Eno Sarris when he documented the change in Castellanos’ launch angles. Why should you care? Because he’s slowly morphing into Nolan Arenado, and now is the time to buy.

There have been only 10 players with at least 250PA each season since 2013 to grow their FB% year over year.

FB% 2013-2016
Player 2013 2014 2015 2016
Brian Dozier 41.3% 42.9% 44.1% 47.7%
Nolan Arenado 33.7% 41.8% 43.9% 46.7%
Yan Gomes 38.7% 39.4% 40.0% 45.1%
Matt Carpenter 34.0% 35.2% 41.7% 43.3%
Mark Trumbo 37.0% 40.2% 40.3% 43.1%
Bryce Harper 33.4% 34.6% 39.3% 42.4%
Adam Jones 32.0% 35.5% 36.3% 40.6%
Victor Martinez 35.4% 38.1% 38.7% 39.3%
Kendrys Morales 32.7% 33.3% 34.7% 35.7%
James Loney 27.9% 31.0% 33.0% 34.5%
Minimum 250 PA in each season 2013-2016.

Then there’s Nick Castellanos:

FB% 2013-2016
Player 2013 2014 2015 2016
Nick Castellanos N/A 36.5% 40.4% 43.0%

To be fair to Arenado, hitting more fly balls isn’t the only thing that’s made him the home-run king of the NL (now that Chris Carter has departed to the AL). It’s been his meteoric rise in HR/FB rate as well. There are 10 other players that would fit nicely on this table with Castellanos, but I’ll leave that as an exercise for the reader. Chances are, you’re already well aware of the other players that would join him on the list — I’m looking at you, Justin Turner.

HR/FB 2013-2016
Player 2013 2014 2015 2016
Nolan Arenado 7.1% 11.4% 18.5% 16.8%
Nick Castellanos N/a 7.5% 9.2% 13.7%

For fun, if we were to project out a full season of at-bats with some growth for Nick Castellanos, we get an interesting range of outcomes for his HR totals:

Castellanos HR Outcomes Given FB% and HR/FB
HR/FB 40% FB 41% FB 42% FB 43% FB 44% FB 45% FB
10% HR/FB 17 18 18 18 19 19
11% HR/FB 19 19 20 20 21 21
12% HR/FB 21 21 22 22 23 23
13% HR/FB 22 23 23 24 25 25
14% HR/FB 24 25 25 26 26 27
15% HR/FB 26 26 27 28 28 29
16% HR/FB 28 28 29 30 30 31
* Assumes 430 balls put in play

Much like the Yelich-to-Votto comparison, there are some things that keep Castellanos from becoming Nolan Arenado — namely his strikeout rate, which is 24.6% to Arenado’s 14.6%. This limits the number of balls he puts in play and thus the number of fly balls and homers he can hammer. However, with a little bit of health, growth and maturation in approach, we could see a 30HR season out of Castellanos this year.

Why Doesn’t Mauricio Cabrera Strike Out More Batters?

For many years, the undisputed king of velocity in Major League Baseball has been Aroldis Chapman, with his fastball that averages around 100 mph and regularly reaches higher. Few pitchers have even been able to approach the level of Chapman’s fastball since he came into the league, and none have surpassed him. However, in 2016, one pitcher finally did it. Mauricio Cabrera of the Atlanta Braves averaged nearly 101 mph on his fastball in 2016 and he regularly touched 103; but yet there was still a major difference between Cabrera and the incredible Chapman. Chapman struck out over 40% of the batters he faced last year, while Cabrera struck out less than 20%. Strikeouts are intuitively related to fastball velocity. The faster that a pitcher can throw the ball, the less time a batter has to react, making it harder to make contact. So how does a pitcher such as Cabrera, who throws as hard as anyone in the game, strike batters out at a well below-average rate?

I first thought that maybe his perceived velocity is not as great as his actual velocity, and sure enough Cabrera does gets very little extension toward the plate when he delivers the ball. He only extends about six feet toward the plate before he releases the ball, which is a full foot shorter than fellow reliever, Zach McAllister, and several inches shorter than average for fastball-heavy relievers. This lack of extension means that the velocity that the batter perceives is slower than the actual velocity coming out of Cabrera’s hand, because it has farther to travel before it gets to the plate. However, this is only a minor difference, as Cabrera’s perceived velocity is still above 100 mph. This is not a huge drop, but it does bring him closer to the pack, as many relievers get good extension that increases their perceived velocities above their actual velocities. Chapman, for instance, gets great extension toward the plate on his already incredible fastball, which results in his excellent perceived velocity of over 101 mph. Cabrera’s lack of extension is likely a contributing factor to his low strikeout numbers, but it does not seem to be the main culprit.

Next, I wanted to see if there was something about the spin rate on his fastball that doesn’t lend itself to strikeouts. Spin rates correlate quite strongly with strikeout rates. Pitchers with high spin rates on their fastballs typically generate more swings and misses, and thus more strikeouts. It turns out that Mauricio Cabrera does have a low spin rate on his fastball. His fastball spin rate of 2300 rpm is well below average for fastball-heavy relievers, which is probably a major reason why he doesn’t miss many bats.

While it makes intuitive sense that something like the amount of spin on his fastball could be the reason for his low strikeout totals, it is still puzzling to see that his spin rate is so low, because spin rate is typically correlated with velocity. For most pitchers, the harder you throw, the more spin you will put on the ball. Aroldis Chapman, for example, has one of the highest spin rates in the sample. In order to single out the spin rate from the velocity, I divided the spin rate by the velocity to find the Bauer Unit, named after Indians pitcher Trevor Bauer. Cabrera’s average Bauer Unit of 22.85 is one of the lowest in the entire sample of fastball-heavy relievers. This means that he has some of the lowest spin per MPH in the game. There must be something inherent in how Cabrera throws a baseball that just doesn’t allow him to generate the amount of spin that is typically commensurate of how fast he throws.

Cabrera’s low spin is not all bad, though. Just as high spin rates lead to strikeouts, low spin rates lead to ground balls. An average spin rate is really where you don’t want to be, as those are the pitches that get squared up more often. While Cabrera actually has an above-average spin rate for the entire population of major-league pitchers, his spin rate is one of the lowest in the league compared to his velocity. This effectively makes him a low-spin pitcher, and last year’s batted-ball numbers bear that out. Nearly 50% of the batted balls Cabrera gave up last season were on the ground, and he didn’t surrender a single home run all season despite giving up the hardest average exit velocity in the game last year on his fastball. Cabrera got away with that extreme exit velocity by only allowing an average launch angle of 5.9 degrees, which was one of the lowest among the fastball-heavy relievers. It is hard to do much damage on balls hit on the ground, even if they are hit 95 mph. While the myth that the harder the ball is thrown the harder the ball can be hit has largely been disproved, it is interesting to see that the pitcher who throws the hardest also gave up the highest average exit velocity.

Of course, strikeouts aren’t just about swinging strikes; you have to get called strikes as well. Throughout Cabrera’s minor-league career, he struggled to throw strikes consistently. So much so that many thought his strike-throwing ineptitude might prevent him from ever even reaching the big leagues. However, once he started pitching in the majors, he suddenly discovered how to find the strike zone. Of course, walking four and a half batters per nine innings is still poor, but that mark represented his lowest walk rate since rookie ball in 2012. Even with the high walk rate last year, he actually threw strikes at an above-average rate. His Called Strike Probability, according to Baseball Prospectus, was 47%, which is slightly above league average. For a guy like Cabrera who has always struggled with control, it is probably a good thing to see him filling up the strike zone at an above-average clip. However, the tendency to pitch within the zone could result in more contact and thus bring his strikeout numbers down. Since he doesn’t command his pitches well, he cannot nibble at the corners or trust himself to throw his pitches just off the plate to generate swings and misses. This allows hitters to either lay off pitches that are safely outside, or lock in to the pitches that are squarely in the zone. This could be another significant cause for his lack of strikeouts.

Another reason Cabrera doesn’t strike out many batters is because he doesn’t possess a bat-missing secondary offering. His secondary pitches are all used primarily to get hitters off of his fastball. He throws the hardest change-up in baseball at 91 mph, and a mid-80s slider with good depth. The change-up got squared up pretty often in 2016, which makes sense, seeing that he throws the pitch with the velocity of a league-average fastball. The slider also does not get many whiffs, but hitters were not able to do much damage off of it in 2016. Batters only slugged .136 off of his slider last season, and the pitch generated the highest rate of fly balls of any slider in the game. Perhaps what is even more significant is that hitters had an average exit velocity against his slider of 85 mph and an average launch angle of 30 degrees. For reference, hitters that hit the ball with an exit velocity of 85 mph at a 30-degree launch angle went 4 for 72. His slider may not be a swing-and-miss offering, but it sure seems to be a good out pitch for him.

It looks like Cabrera’s low spin rate on his fastball relative to its velocity is the main reason for his lack of strikeouts. However, it is also likely that that same low spin rate allows him to induce an extreme amount of ground balls, which helps him limit the damage from the opposing batter. His lack of extension toward the plate and his tendency to live in the strike zone are also contributing factors. He also doesn’t have a secondary offering that gets many swings and misses. His slider, however, does produce a great deal of pop-ups, which is another way he limits damage on his batted balls. A major reason for his success last season despite his low strikeout totals and high walk numbers was that he didn’t give up any home runs. While a complete lack of dingers is very unlikely to persist, the types of batted balls he allows on his fastball and slider make it difficult for batters to hit it deep off of him.

Cabrera walks too many batters, and while I wouldn’t be surprised to see some progression in his strikeout rate, I don’t expect him to ever strike out batters at the same rate as someone like Chapman. He should be able to persist for several years as a good late-inning reliever, but he probably will never reach the elite levels that his fastball might suggest.

Rick Porcello and Wins

Before spring training started, Scott Lauber at ESPN explored whether Rick Porcello could match his 22-win season from 2016. The short answer? No. Probably, almost definitely, not.

Conventional wisdom would swiftly say that, too, though. Three pitchers netted 20 wins last year, two in 2015, and three in 2014. And over those three years, none of the pitchers repeated the feat.

With wins speaking to much more than simply the pitcher on the mound, there are two things to consider when digging into the question: What could Porcello repeat, and what could the Red Sox offense?

Let’s start with the offense. Lauber’s article acknowledges that the Sox scored a league-leading 5.42 runs per game last year, and 6.83 per Porcello start. The biggest difference between this year’s and last year’s team is Mitch Moreland replacing David Ortiz. You could close your eyes and dip your hand into a bowl of cold spaghetti like it’s a Halloween Horror House and pull out the contrast between their production. As is, Moreland is projected to be worth about half a win next season. Alone, that suggests how the Sox could have struggles producing the same way in 2017.

But there are other questions to answer, too. How will top prospect Andrew Benintendi fare? Will Pablo Sandoval make any difference or continue to be negligible? I’m not suggesting the Sox won’t be good. It would be hard for them not to be. But they have enough variables going into the year that Porcello getting another 20+ wins is largely on him, which could be difficult for reasons beyond conventional wisdom.


These numbers tend to feed into each other, which is why they’re useful in seeing just how good Porcello was, and how well things broke for him last year. His pitching profile was relatively similar to past seasons, though. It’s not like Drew Pomeranz discovering a new pitch or Brandon Finnegan changing a grip. Porcello’s sinker (or two-seamer, depending which stat site you reference) gets a lot of the credit for his exceptional performance, but differences in his curveball may reveal reasons for it, too.


None of these changes are insignificant. The h-movement tells us Porcello’s curve ran away more from right-handed hitters and in on lefties. The v-movement tells us it dropped more. Add in how it was three mph slower and it rounds out how the pitch fell off the table more. He worked the zone more up and down over the plate than he did side to side in the two years prior, so it could have messed with batters more when the rest of his pitches moved as they have.

According to Lauber, Porcello mimicking anything close to 2016 will come down to “keeping hitters honest with his off-speed pitches.” Opponents hit .190 against his slider and .174 against his changeup. That could concern pitch-sequencing. Take a look at how he distributed his offerings in general, and then when ahead or behind in the count.


While the numbers don’t detail specifically when each pitch was thrown, they indicate that Porcello was eerily similar no matter what the count was. Sequencing isn’t about finding a magic combination of pitches; it’s about making sure a hitter can’t tell what’s coming. It certainly seems he was successful at it.

This data shines light on the tiny changes that might make a big difference in the game, which is one of the most fascinating aspects of baseball. But even more interesting is a quote from Dave Dombrowski in the ESPN piece, where he said, “I don’t think [Porcello] will try to do too much anymore.”

By itself, that reads like a generic sports-interview statement. But think about what the concept of “trying to do too much” really means in baseball: trying to do too much of one thing. A guy tries to hit a five-run homer or hit 100 on the gun every time; really tries to impose his will over the game by doing something impossible. Porcello wasn’t relying on any one pitch in 2016. And what Dombrowski is hinting at here, intentional or not, is there’s a certain amount of surrender that’s necessary for faring well in baseball.

Lauber tells how Porcello best explains his 2016 success by saying he “better understands what makes him effective.” Maybe that has to do with knowing how much the game controls versus how much he can, which let him harness his own abilities more.

I fear a lesser 2017 from Porcello could be called a disappointment by some, but an advanced understanding doesn’t always mean advanced success. The reality is it was a great year aided by good luck, probably buoyed by the cognizance that has allowed Porcello to be a contributing major-leaguer since he was 21. Maybe he isn’t as good this coming season, but it doesn’t take away from the player he is.

career and pitch movement data from FanGraphs; pitch usage from Baseball Savant

A New Option for the Nationals’ Closer

The Nationals have had what seems to be a perpetual issue at closer. They have churned through Drew Storen, Tyler Clippard, Rafael Soriano, Jonathan Papelbon, and now Mark Melancon. Some people have touted Koda Glover as the solution for the next half century, but he remains mostly untested. For a team with a great record of developing starting pitchers such as Jordan Zimmermann, Tanner Roark, and Stephen Strasburg, and a general manager in Mike Rizzo whose list of faults is one name long — Jonathan Papelbon (I’m still hopeful about Adam Eaton) — it is somewhat surprising that they have not been able to address the omnipresent glaring issue at the end of games. The potential solution might be in the starting rotation: Joe Ross.

It may not seem obvious, but Ross is a perfect candidate to be moved to the bullpen. Ross has never pitched a full season as a starter. He pitched 105 innings this most recent season, and missed the middle of the season sidelined with a shoulder ailment. The slider that he threw 39% of the time this season is known to wear down a pitcher’s arm, and it did wear down his brother Tyson’s. A move to the ‘pen might save Joe Ross’s arm.

Ross’ numbers are far superior his first time through the order. As Eno Sarris detailed in his article “Who Needs a New Pitch the Most,” Ross’s velocity decreased a full mile per hour during his average start, his strikeout rate dropped by over 10 percent, and his wOBA against shot up from .248 his first time through the order to .371 his second time through.

Most importantly, Ross really only throws two pitches, a slider and a sinker. Two pitches are typical of a reliever, but a solid third option is often required to stick in the rotation. His sinker currently averages about 93 mph, so a move to the end of games could see that number rise to 95. He might also be able to get away with throwing his slider, which batters have hit just .173 against, more often. That combination is tantalizing.

It doesn’t make sense to give up on Joe Ross as a starter just yet, but if his arm fizzles out yet again this season, the Nats should give him a shot in the ‘pen.

MLB to Across the Pacific and Back

The player that all Milwaukee Brewers fans, and baseball fans for that matter, should be watching most closely this spring is Eric Thames. Thames, after three incredible seasons in the KBO, signed a three-year, $16-million deal to man first base for the Brewers. The front office likes what they see from the 2015 KBO MVP, but admittedly did not scout him in person while he was playing overseas; instead, they relied on video to make their assessment of his game. I’ll admit, I can’t wait to see Thames play this year; the mystery, concerns, and potential all make for great theater, but there is one question that keeps haunting me at night: How do former MLB payers fare when they play overseas and then return? As much as this post is about Thames, it is also about those few players who have done what he is doing.

I approached this by looking at all the major-league players who have played in both Korea and Japan over the past 10 years. I could have gone further back to the days when Cecil Fielder was playing in Japan, but the game, both in North America and across the Pacific, has changed significantly since then. The argument could be made that the game has changed significantly over the past 10 years — it changes every season — but that is the beauty of baseball.

I wanted to isolate Korea only, but, perhaps not surprisingly, there were too few players to make anything of that. Out of the several hundred total players in both these leagues over the past 10 years, only a total of 11 players who began their career in MLB returned to MLB after an overseas hiatus. That’s 11 between the KBO AND NPB. 11! Four players from the KBO and seven from NPB. Here’s a graph that shows their names and WAR before and after their careers in Japan and Korea:

Pre WAR MLB Season(s) Pre Post WAR MLB Season(s) Post
Joey Butler 0 2013-2014 0.5 2015
Brooks Conrad -0.1 2008-2012 -0.5 2014
Lew Ford 8.4 2003-2007 0 2012
Andy Green -1.2 2004-2006 0 2009
Dan Johnson 4.0 2005-2008 -0.8 2010-2015
Casey McGehee 1.6 2008-2012 -0.4 2014-2016
Kevin Mench 5.8 2002-2008 -0.4 2010
Brad Snyder -0.1 2010-2011 0.1 2014
Chad Tracy 5.7 2004-2010 -0.3 2012-2013
Wilson Valdez 0.7 2004-2005, 2007 -1.1 2009-2012
Matt Watson -0.5 2003. 2005 0.1 2010
Total WAR: 24.3 -2.8
Eric Thames -0.6 2011-2012 ? 2017-?

(Numbers courtesy of

The outcome for these players is, well, not good. A select few players like Lew Ford and Chad Tracy carry the “pre-Japan/Korea WAR” section thanks to longer, successful careers in MLB before they changed leagues. It also seems unfair to compare these players to each other due to their careers, or lack thereof, upon their return. For example, Ford’s 79 plate appearances are incomparable to Wilson Valdez’s 966. But, in every case, the story arch is the same: Begin their professional baseball career in North America, make it to the majors as a 20-something, decline at the major- and minor-league level, go to Japan/Korea, return to North America in a very limited capacity and fail to make an impact with a major-league-affiliated team.

If the careers of these 11 players is a trend, then Eric Thames is in for a lot of trouble.

But there is reason to believe that Thames is the exception to the rule. Will Franta wrote a convincing Community Research article about the reason to believe that Eric Thames will do well. Additionally, various projections believe that Thames could be anywhere from a 1.2 to 2.2 WAR player with mid- to high-20 home-run totals and an above-average wRC+. Dave Cameron wrote an article analyzing the projections for Thames and concluded that he has the potential to be “the steal of the winter,” and for three years and $16 million, that could very well be true.

But there are factors going against Thames. It isn’t all too often professional players find their footing at the major-league level in their 30s (Thames will be 30 on Opening Day). Plus, with several other corner infielders in the form of Hernan Perez, Travis Shaw, Jesus Aguilar and others who could fill in at first if need be such as Ryan Braun and Scooter Gennett, a team in the middle of a rebuild might not completely be opposed to disposing the incumbent starting first baseman if another star emerges. Even comparing career KBO and NPB players to their transitions to MLB, we can see that there are a lot more Tsuyoshi Nishiokas than Jung-ho Kangs, which is why players like Kang, Ichiro Suzuki, Hideo Nomo, and Yu Darvish are lauded when they succeed in the majors.

I believe that Eric Thames will not be like the 11 others who, by and large, failed in their returns. Thames is intriguing and there is a lot to like about him — and a lot to worry about with him. There are pros and cons to his game. I believe that he will be a great addition to a team that, honestly, could afford to wait for him to assimilate completely to the game.