Stat Line:  6 IP, 0 R, 3 H, 6 SO, 2 BB

In the New York Mets’ 2-1 victory over the Miami Marlins, Logan Verrett showed why he would be in the starting rotation for 29 of 30 Major League teams.  Verrett attacks opposing lineups differently than the Mets’ big three power pitchers, looking to induce poor contact early in at-bats rather than inducing a high whiff/miss and strikeout rates.

Logan Verrett continues showing more than scouts prepared us for, exhibiting an above-average breaking ball while keeping four pitches low in the strike zone.

Repertoire

Verrett’s breaking ball (slider and curveball) is a viable MLB strikeout pitch, inducing a strong 16.6% whiff/miss rate (swing and miss rate).  His breaking ball, particularly his slider, shows sharp, late, downward movement and has enough velocity to deceive the hitter into thinking the pitch is a four-seam fastball.  The reason behind defining it as a breaking ball is because it’s tough to decipher the difference between his slider and curveball.

Verrett’s four-seam fastball sits 90 to 93 mph while his two-seam fastball, also referred to as a sinker, dials in at 88 to 92 mph.  At times, Verrett’s two-seam fastball/sinker seemed to move 6 to 10 inches with sharp 10-to-5 downward movement (think of 10 to 5 on a clock).  Although he didn’t show consistent fastball command on the corners of home plate, Verrett kept his pitches between ankle and thigh high.

Staying low in the strike zone with two pitches having sharp downward movement makes it nearly impossible for opposing hitters to lift the baseball for hard hits and extra base hits.

Verrett’s Four Keys to Success

Verrett has to focus on four aspects of pitching to be successful with a fastball/sinker primarily sitting 88 to 92 mph:

1. Command fastball low in the strike zone because any misses in the strike zone will be hit hard.
2. Rely more on a two-seam fastball/sinker with downward movement rather than straighter four-seam fastballs further reducing hard contact and naturally helping keep the ball down.
3. Throw many off-speed pitches (45%-50% of total pitches) making his fastballs appear harder than reality.
4. Throw at least 70% to 75% first-pitch strikes otherwise Verrett will be forced to throw predictable fastballs to climb back even in counts.

Verrett commanded his fastball thigh-high or below on 46% of fastballs but excluding the five intentionally thrown high four-seam fastballs the percentage moves to a respectable 52%.  However, Verrett only threw 34% two-seam fastballs/sinkers, another reason his “fastball low in the strike zone” percentage wasn’t higher.  Verrett threw 52% off-speed pitches at an outstanding 70% strike rate.  Lastly, Verrett threw 77% first-pitch strikes.  Three out of four isn’t bad for his first spot start of the 2016 season.

Cause for Concern

Verrett showed a stronger out pitch than scouts reported but didn’t exhibit fastball command on each corner of home plate needed for a pitcher throwing in the low 90s.  In fact, he threw 27 of his 85 pitches (31%) on the inner half of home plate or inside to hitters but only eight of those were commanded well on the inside corner.  Understandably, Verrett lives on the outside corner but must learn to throw inside with a purpose and control.  Lacking control and a presence on the inside corner allows MLB hitters to feel comfortable in the batter’s box and gives them the ability to look for predictable outside pitches.  When an MLB hitter is able to predict or feel comfortable guessing a certain pitch type or pitch location, the more aggressive and confident their swings become.  This makes Verrett vulnerable to higher home run, hard contact and walk rates.

I wrote the article below before the news that Hector Olivera had been arrested on suspicion of domestic assault.  Obviously, if true, those allegations are horrible, and take precedent over any analysis as a player. 

As you may know, the Atlanta Braves have entered a full-scale rebuild.  Nearly every player of note from the 2014 Braves has been shipped out of town: Justin Upton, Jason Heyward, Evan Gattis, Andrelton Simmons, Melvin Upton, Craig Kimbrel, Alex Wood, etc.  Most of the transactions the team has made can be characterized as typical for a rebuilding club — exchange short-term assets for long-term assets with a focus on youth.  You can argue the emphasis on stockpiling pitching is unique, but the general idea of the Braves rebuild fits the standard template.  That is, with the exception of one transaction.

Just before the 2015 MLB Trade Deadline, the Braves sent 24-year-old left-hander Alex Wood and organizational top infield prospect Jose Peraza to the Los Angeles Dodgers for 30-year-old Cuban rookie third baseman Hector Olivera.  The teams exchanged other pieces in the deal (including a 2016 draft pick headed to Atlanta), but the backbone of the trade was Wood and Peraza for Olivera.  In making the deal, the Braves bucked the conventional rebuild philosophy (particularly theirs) in sending out young, cheap, controllable assets while acquiring a more expensive player who was already 30 years old.  It was a bold move that made Olivera and his development hugely important in making the tear-down to build-up strategy a success.  So, eight plus months later, what do the Braves have in Hector Olivera?

The short answer is no one knows.  There simply is not enough of a sample to have any confidence projecting Olivera.  When the Braves acquired him, Olivera was nursing a hamstring injury, so he began his Braves career with a rehab stint in the middle of August.  After a combined six games between the Braves’ rookie and Single-A affiliates, Olivera played another 10 games at Triple-A before making his major-league debut September 1.  He finished 2015 with 87 plate appearances and has added 21 more thus far in 2016 for a grand total of 108 major league PAs.  While 108 plate appearances is not much to go on, this is FanGraphs, so we can do better than shrugging and throwing our hands in the air until the sample grows.

Plate-discipline numbers are some of the first to stabilize after a player is called up.  During his time in MLB, Olivera has walked less than average (BB% of 5.6%) while also striking out less than average (K% of 15.7%) and making contact at a rate just above league average (Contact% of 81%).  A low walk rate combined with a low strikeout rate and near average contact rate means he must be swinging the bat.  Sure enough, that is what is shown on his player page.

Olivera swings at over 4.0% more pitches than the MLB average player.  That alone would not be concerning, except the reason that his Swing% is elevated is mainly because he is swinging at pitches outside of the strike zone, as evidenced by an O-Swing% 6.6% above the league average.  These are the hardest pitches to get the barrel of the bat on, and Olivera’s batted-ball numbers show the effects of swinging at balls outside the zone.

Despite showing the ability to hit the ball hard with a maximum exit velocity of 110 according to Baseball Savant (approximately 86th percentile thus far in 2016), Olivera has posted ISO and BABIP figures well below the MLB average.  His struggles to make consistent solid contact show up throughout his profile with a low LD%, high IFFB% (a BABIP killer), and low HR/FB ratio.  Perhaps the best summary of Olivera’s MLB batted-ball authority is found within his soft/medium/hard contact percentages.  His medium contact rate is nearly identical to the league average, but Olivera’s hard contact percentage is well below league average with the entire difference and more being accounted for in his soft contact percentage.  Essentially, Olivera’s offensive output has been sunk by a poor approach.  He has swung at too many pitches outside the strike zone, leading to weak contact and therefore poor production on balls in play. 

I haven’t yet touched on his fielding and baserunning numbers.  The Braves were not confident in his ability to stick at third base, so they moved him to left field this past offseason.  Obviously that does not suggest much confidence in his fielding ability, but it remains to be seen how he will perform as an outfielder.  The early returns are not promising as both DRS and UZR have him rated negatively (-2 and -3.7 respectively) in an admittedly microscopic sample of 43 innings.  As for his baserunning, BsR numbers of an exactly average 0.0 leave little reason to expect him to contribute or hurt much on the base paths.  It seems safe to say the bat will be what determines Olivera’s future success. 

Fortunately, the potential in that bat is obvious given the hype surrounding him and ultimately the contract he received coming out of Cuba.  He has also shown the ability to hit the ball hard on occasion at the major-league level, but particularly given the Braves decision to move him from third base to left field, Olivera will need to learn to make much more consistent hard contact to post acceptable offensive numbers.  For the Braves, there is plenty left to see to determine if this trade was a wise investment, but the early returns are not promising. 

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 TuataraSoftware.com.

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.

Terminology

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

Assessment

The 1919 Philadelphia Athletics

OWAR: 33.3     OWS: 224     OPW%: .381     (53-87)

AWAR: 9.0       AWS: 107     APW%: .257   (36-104)

WARdiff: 24.3                        WSdiff: 116.4  

The “Original” 1919 Athletics outperformed the “Actual” squad by 17 victories with a staggering WSdiff of 116.4. The “Actuals” were reduced to a shadow of their former dynasty due to a variety of factors, primarily financial. The “Originals” featured second-sacker Eddie Collins (.319/4/80), the League-leader with 33 stolen bases. “Shoeless Joe” Jackson supplied a .351 BA with 31 doubles, 14 triples and 96 ribbies in his penultimate season. Their counterparts, Whitey Witt (.267/0/33) and Merlin Kopp (.226/1/12) were barely adequate. In addition to left field and second base, the “Originals” surpassed the “Actuals” at catcher and third base. Wally Schang furnished a .306 BA and pilfered 15 bags while Steve O’Neill contributed a .289 BA with 35 doubles. Home Run Baker (.293/10/83) bested Fred Thomas (.212/2/23) at the hot corner.

Eddie Collins placed runner-up to Joe Morgan in the All-Time Second Basemen rankings according to Bill James in “The New Bill James Historical Baseball Abstract.” “Original” Athletics teammates listed in the “NBJHBA” top 100 rankings include Baker (5^th-3B), Jackson (6^th-LF), Wally Schang (20^th-C), Jimmie Dykes (52^nd-3B), Steve O’Neill (54^th-C), Stan Coveleski (58^th-P), Stuffy McInnis (68^th-1B), Charlie Grimm (85^th-1B), Joe Dugan (88^th-3B), Jack Barry (90^th-SS), Bob Shawkey (95^th-P) and Amos Strunk (100^th-CF). George H. Burns (79^th-1B) and Terry Turner (92^nd-SS) round out the roster for the “Actuals”.

  Original 1919 Athletics                                                       Actual 1919 Athletics 

Stan Coveleski averaged 23 victories per season over a four-year stretch (1918-1921). “Covey” delivered a 24-12 mark with a 2.61 ERA for the “Originals” staff. Bob Shawkey fashioned a 2.72 ERA and a 1.186 WHIP to complement his 20-11 record. Herb Pennock aka “The Squire of Kennett Square” added 16 wins with a 2.71 ERA. The “Actuals” countered with Walt Kinney (9-15, 3.64), Jing Johnson (9-15, 3.61), Rollie Naylor (5-18, 3.34) and Scott Perry (4-17, 3.58).

  Original 1919 Athletics                                                       Actual 1919 Athletics

Notable Transactions

Shoeless Joe Jackson

July 30, 1910: the Philadelphia Athletics sent Shoeless Joe Jackson to the Cleveland Naps to complete an earlier deal made on July 23, 1910. July 23, 1910: The Philadelphia Athletics sent a player to be named later and Morrie Rath to the Cleveland Naps for Bris Lord.

August 21, 1915: Traded by the Cleveland Indians to the Chicago White Sox for a player to be named later, Ed Klepfer, Braggo Roth and $31,500. The Chicago White Sox sent Larry Chappell (February 14, 1916) to the Cleveland Indians to complete the trade.

Eddie Collins

December 8, 1914: Purchased by the Chicago White Sox from the Philadelphia Athletics for $50,000.

Home Run Baker

February 15, 1916: Purchased by the New York Yankees from the Philadelphia Athletics for $37,500.

Wally Schang

December 14, 1917: Traded by the Philadelphia Athletics with Bullet Joe Bush and Amos Strunk to the Boston Red Sox for Vean Gregg, Merlin Kopp, Pinch Thomas and $60,000.

Morrie Rath

July 23, 1910: Traded by the Philadelphia Athletics with a player to be named later to the Cleveland Naps for Bris Lord. The Philadelphia Athletics sent Shoeless Joe Jackson (July 30, 1910) to the Cleveland Naps to complete the trade.

September 1, 1911: Drafted by the Chicago White Sox from Baltimore (Eastern) in the 1911 rule 5 draft.

August 23, 1913: Purchased by Kansas City (American Association) from the Chicago White Sox.

September 20, 1917: Drafted by the Cincinnati Reds from Salt Lake City (PCL) in the 1917 rule 5 draft.

Steve O’Neill

August 20, 1911: Purchased by the Cleveland Naps from the Philadelphia Athletics.

Stan Coveleski

December, 1912: Purchased by Spokane (Northwestern) from the Philadelphia Athletics.

November 27, 1915: Sent from Portland (PCL) to the Cleveland Indians in an unknown transaction.

Bob Shawkey

June 28, 1915: Purchased by the New York Yankees from the Philadelphia Athletics for $3,000.

Herb Pennock

June 6, 1915: Selected off waivers by the Boston Red Sox from the Philadelphia Athletics.

Honorable Mention

The 1998 Oakland Athletics 

OWAR: 41.6     OWS: 306     OPW%: .510     (83-79)

AWAR: 28.7     AWS: 222     APW%: .457   (74-88)

WARdiff: 12.9                        WSdiff: 84.8  

Mark McGwire launched 70 four-baggers and drove in 147 runs for the “Original” 1998 Athletics. “Big Mac” placed runner-up in the MVP balloting while his protégé Jason Giambi (.295/27/110) completed his third season for the “Actuals”. Scott Brosius (.300/19/98) socked 34 two-base hits and earned his lone All-Star invitation as he outclassed Mike Blowers, who batted .237 with 11 dingers in his solitary campaign for the green-and-gold crew. Darren Lewis posted career-bests in runs scored (95) and RBI (63), a significant upgrade over “Actuals” center fielder Ryan Christenson (.257/5/40). Rickey Henderson, a member of the “Original” and “Actual” A’s roster in ’98, notched the American League stolen base title for the twelfth time in his career. “The Man of Steal” tallied 101 runs scored and a League-leading 118 bases on balls.

On Deck

What Might Have Been – The “Original” 1905 Beaneaters

References and Resources

Baseball America – Executive Database

Baseball-Reference

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 “www.retrosheet.org”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

It’s like Rob Deer and Gorman Thomas don’t even know this franchise anymore.  What happened to our free-swinging Brewers, the same ones that just two seasons ago had Carlos Gomez remarking, “It has to be, like, wayyy a ball for us to not swing…everybody here has the green light?”

Well, for one, a small sample size.

But, through mid-April in 2016, the Brewers have swung less than any other team in baseball.  This, after swinging the second-most in each of the past two seasons.  They’re swinging less at pitches out of the zone, and they’re swinging less at pitches in the zone, leading to sequences like this from Monday:

And then Domingo Santana also struck out looking to lead off the sixth!

So, to recap: we’re less than two weeks into the season and not even at the point where swing rate has stabilized.  But it sure looks like the Brewers are making a concerted effort to swing less, given the drastic fluctuations in their swing rates from the past couple of years:

Part of that must be the overhaul the organization has gone through in the past year.  Jean Segura and Carlos Gomez both swung at over half the pitches they saw in 2015, and their O-Swing% was north of the team average.  Adam Lind and Gerardo Parra also chased and drove the team’s O-Swing% up.  So it’s partially a function of a new team with a new front office that may place a higher premium for on-base guys.

But the holdover hitters from last year have also seen their swing rates decrease both outside the zone and overall.  Ryan Braun, Scooter Gennett, and Domingo Santana have thus far decreased their O-Swing% from last year’s totals by 10% or more in the early going.  Brewers beat reporter Tom Haurdicourt reported Manager Craig Counsell saying, “It’s an everyday message (to the hitters) and it’s really about swinging at good pitches.  It’s discipline. (Hitting coach) Darnell (Coles) is preaching that every day.”

This mix of the front office acquiring more on-base players and the on-field management working with the players on adjustments seems to be making an impact.  The Brewers are fifth in walk rate, after finishing in the bottom third each of the previous three seasons.

Whether this is an organizational philosophy change, or more a function of the players on the current roster remains to be seen.  Or, given the small sample size, this could look completely different in May, with the Brewers back to their free-swinging ways and me wondering why I didn’t use this time instead to plant those jalapenos I’ve been meaning to get around to, but now it’s too late and the harvest won’t come until at least September.

In the meantime this is something to watch for a young team with more organizational talent than Milwaukee has seen in a while, and that is sure to go through rough stretches in a rebuilding year.  The new and veteran Brewers are watching pitches, and we’ll watch with them, watching pitches.

Most FanGraphs readers know that even the fastest-stabilizing statistics take almost a quarter of a season to mean anything. With the availability of PITCHf/x data, we can look at individual pitch data, which can give us hundreds of data points for an individual pitcher just from one start. Instead of waiting until near the All-Star break to see if Aaron Sanchez has really made a leap forward or if the league has adjusted to Dallas Keuchel, we can use statistics that stabilize quickly (both “approach” stats and “results” stats) to guide these decisions.

The “results” stats that I used are:

• Zone Contact%
• Zone Whiff%
• Zone Take%
• Out-of-Zone Contact%
• Out-of-Zone Whiff%
• Out-of-Zone Take%
• First Pitch Strike%

First, I used a regression model to create a formula that used only these statistics to produce an expected ERA (or SIERA, actually, as I wanted to filter out any BABIP and HR/FB luck).

The formula ended up as: -3.11 + (12.48 * Z-Con%) + (3.08 * Z-Take%) + (11.96 * O-Con%) – (14.19 * O-Whiff%) + (13.06 * O-Take%) – (3.46 * F-Strike%)

Using 2015 data (and only pitchers who threw more than 1,500 pitches), I get an r-squared of 0.68. I’m going to call this statistic “PD-SIERA” since it uses only plate-discipline data to produce an expected SIERA.

The PD-SIERA leaders for 2015 were:

1. Clayton Kershaw, 2.47
2. Chris Sale, 2.75
3. Max Scherzer, 2.78
4. Carlos Carrasco, 2.78
5. Chris Archer, 2.92

The r-squared is good enough, and those names pass the sniff test, so I’m pretty comfortable that this produces a good approximation of pitcher performance.

I will use this to calculate a Results Change% = (year2_PD-SIERA – year1_PD_SIERA)/(year1_PD_SIERA). For example, Drew Smyly had a 3.73 PD-SIERA in 2014 (year2) and a 2.33 PD-SIERA in April of 2015 (year1). The calculation would then be: (3.73 – 2.33) / (3.73) = +37.5%

[This number can be positive or negative to indicate a positive or negative change in results]

Now, just looking at the plate discipline statistics isn’t enough. We need to see if there was a reason for a pitcher to have a better or worse PD-SIERA than he had the previous year. PITCHf/x to the rescue again, as we can look at what I will call “approach” stats: a pitcher’s pitch mix and velocity. Since these are things almost completely under the pitcher’s control, they should stabilize quickly.

In order to calculate a pitcher’s “Approach Change%,” I calculate the change in his pitch mix + the percentage of velocity change from the previous year. An example of the calculation is below:

• Drew Smyly, 2014 (full): 89.9 mph, 51.9% FB, 15.9 % CT, 28.5% CB, 3.8% CH
• Drew Smyly, 2015 (April):  90.2 mph, 46.4% FB, 30.1% CT, 23.5% CB, 0.0% CH

Velocity change = (year1_velo – year2_velo)/(year2_velo) = (90.2-89.9)/89.9 = 0.3%

[If this value ended up negative, we would use the absolute value, as we are only interested in the amount of change, not positive/negative change]

Pitch Mix change = -5.5% FB, +14.2% CT, -5.0% CB, -3.8% CH = (take the absolute value of all of these changes and then divide by two) = (28.5%) / 2 = 14.3%

[Dividing by two makes sure that each percentage change is only counted once – a +1% increase in FB% combined with a 1% decrease in CH% equals only a 1% chance in pitch mix]

Approach Change% = Velocity change + Pitch mix change = 14.3% + 0.3% = 14.6%

In order to see if this formula would work for 2016, we can look backwards to see how it would have done predicting 2015 breakouts/blow-ups.

Looking at the data from 2014 (full season) to 2015 (April only), we can multiply Approach Change% * Results Change% to see if we can identify early-season breakout/blow-up candidates. The three highest rated “breakout” candidates in April 2015 were:

1. Drew Smyly: 14.6% Approach Change%, +37.5% Results Change%… Improved SIERA from 3.69 (2014) to 3.25 (2015)
2. Chris Archer: 13.7% Approach Change%, +36.1% Results Change%… Improved SIERA from 3.80 (2014) to 3.08 (2015)
3. Dillon Gee: 13.4% Approach Change%, +36.6% Results Change%… SIERA increased slightly from 4.30 to 4.41 (groin injury in May, lost his rotation spot, and ended up in the minors for most of the second half)

Not bad – two of the clear top three breakout candidates actually improved their SIERA by over 10% from 2014. How about the bottom of the list? We have a clear top four:

1. Homer Bailey: 14.2% Approach Change%, -34.7% Results Change%… SIERA jumped from 3.60 to 5.65 (injured after two starts)
2. Jake Peavy: 21.9% Approach Change%, -14.9 Results Change%… SIERA increased slightly from 4.11 to 4.33
3. Tyler Matzek: 23.9% Approach Change%, -13.6% Results Change%… SIERA jumped from 4.08 to 6.45 (injured after five starts)
4. Wade Miley: 10.2% Approach Change%, -31.5% Results Change%… SIERA jumped from 3.67 to 4.24

Bailey and Matzek were both headed for season-ending injury (maybe this formula is a good predictor of an aching arm?), Miley went from above-average to below-average, and Peavy got a bit worse.

To show why we need both the Approach and Results Change%, consider these two pitchers:

• James Shields: 5.5% Approach Change%, +26.5% Results Change%… SIERA increased slightly from 3.59 to 3.72
• Edinson Volquez: 5.2% Approach Change%, +23.5% Results Change%… SIERA increased slightly from 4.20 to 4.35

Both pitchers had significantly better results in April of 2015 than they did in 2014, but their approach barely changed at all. As the change in results was not backed by any change in approach, they both ended up being essentially the same pitcher for the remainder of 2015 as they had been in 2014.

I’ve run the numbers for the first week of 2016, but will wait until we get about a month’s worth of data before releasing the actual numbers. For those that would like a sneak peak (caution: most of these are using ONE game’s worth of data!):

Breakout candidates: Alfredo Simon, Wade Miley, Jose Fernandez, Jacob deGrom, Noah Syndergaard, Aaron Sanchez

Blow-up candidates: Dallas Keuchel, Stephen Strasburg, Jerad Eickhoff, Chris Sale, Taijuan Walker, Masahiro Tanaka, James Shields

Steven Matz’s season debut quickly went south, with him giving up seven earned runs in the second inning en route to a 10-3 New York Mets loss against the Miami Marlins.  Matz exhibited one mechanic flaw leading to his lack of command, particularly in two-strike counts, resulting in the Marlins’ second-inning carousel around the bases.

Steven Matz (L, 0-1) 1.2 IP, 7 R, 6 H, 1 SO, 2 BB

Arrows were pointing up after watching a 1-2-3 first inning with fastball velocity at 95 mph, culminating in striking out one of Major League Baseball’s best power hitters, Giancarlo Stanton.

Unfortunately, Matz started the second inning breaking one of the cardinal rules of pitching: walking leadoff man Martin Prado.  When a leadoff hitter makes it to first base, he scores approximately 35% of the time.  Making matters worse, Matz walked the following batter, Chris Johnson.  Five of the next seven batters ground out hits, including a Giancarlo Stanton man-bomb home run to left field, knocking Matz out of the game.

The frustrating aspect for Matz and Mets fans alike was during all three Marlins RBI single at-bats, Matz was ahead in the count 1-2 or 2-2.  Matz proceeded throwing hanging curveball after hanging curveball.  Even in Marcell Ozuna’s pop-out, both two-strike pitches were hanging curveballs which thankfully Ozuna missed (Side note: Ozuna can’t hit an outside pitch….at all).  Obviously, Matz’s objective was not to hang curveballs or throw hittable two-strike pitches.  The reason lies in Matz’s release point.

Simply, Matz’s throwing arm lagged behind his body forcing his release point to be late, rushed and higher than normal as opposed to his normal release point out in front of his body.  This results in an inability for Matz’s throwing fingers to stay on top of the baseball.

Matz, and every other Major League pitcher, needs his fingers on top of the baseball at release, allowing his fingers to stay on top of the seams on the baseball.  This allows his fingers to manipulate the spin of the baseball at release, whether ripping down on the seams for a four-seam fastball or getting over the top of the baseball throwing a curveball.

Consequently, Matz’s fingers come around the seams of the baseball instead of over the seams of the baseball when throwing his curveball, resulting in a weak spin rate and a floating or hanging curveball.  Additionally, fastballs tend to sail high and towards the throwing arm side (up and to the left for Matz) out of the strike zone as seen in four straight fastballs during Prado’s leadoff walk.

Why Matz’s arm lagged is a separate question I couldn’t unveil through the TV broadcast.  Originally, Matz appeared throwing across his body, meaning his stride/planting leg lands too far left towards first base, blocking off his arm from releasing the baseball out in front of his body.  But this was not the case as Matz was landing with good alignment towards home plate.  Other reasons could include rushing his motion or opening his front shoulder and glove hand too soon but I didn’t see those either.

Whatever the reason, pitching on ten days’ rest is very difficult, especially for a starting pitcher in a rhythm and routine created during spring training.  Do not put too much stress in the results of this start.

I’ve been wondering to myself lately: “Self, what would it take for another Rockies player to win an MVP?” and yeah I know that whole winning thing goes a long way but I’m fairly unfamiliar with that as a Rockies fan. This has been in my mind all offseason long after the Rockies’ Nolan Arenado could barely break the top 10 after hitting 42 HR (22 on the road) and knocking in 130 runs and hitting .287. Oh yeah — and getting a Gold Glove as the hard-to-argue best defensive third baseman in the NL if not all of MLB. To try and figure this out I decided to see what makes an MVP using stats since I can’t quantify the minds of the writers that vote.

Since I wanted to see the Coors Field stigma that is placed on players statistically I chose wRC+ because it’s one of the best park-adjusted stats to see how much better than the rest of the league a player was. Then I tallied the wRC+ and WAR for the top five players in each league from 2009 – 2015. I stopped at 2009 because it became apparent before 2009 that these stats would not somewhat closely represent the best players vs the vote-getters. For the years in which pitchers won/made the top five I used FIP-.

At this point I’ve got all my players and their respective stats. To even things out a bit between wRC+ and FIP- I subtracted 100 from wRC+ and inverted FIP- and subtracted 100 so it became a points system essentially with 0 being average and seeing just how far from average players were. I then averaged out the points and WAR needed for 1st place, 2nd place, 3rd place and so on and here is what I found.

So I now had my “baseline,” per se, of what to look for in past Rockies seasons to see what a season would look like that is good enough for the stigma to break and a player have a chance to win the MVP. First-place vote-getters in the NL average right at a 170 wRC+ (170-100=70 points in this article), 2nd place averaged 160 wRC+. By the way if we take away Bryce Harper’s insane season in 2015 with his wRC+ of 197 it drops to 165 wRC+ average; his season was 17 points higher than the next-highest in the NL in the past seven years. We will look into that later but for now I need to look for Rockies players with a 170 wRC+. Well that was easy, there is only one, and it’s the only Rockies MVP ever.

In 1997 Larry Walker won the Rockies’ only MVP award ever with a wRC+ of 177, so what were his stats that year? He had a line of .366/.452/.720  with a 1.172 OPS. He had 49 homers and 130 RBI along with a Gold Glove in right field (which helps me wonder what a guy like Arenado would need to do). So what are the odds of reaching those types of numbers? Since 2009 let’s see how many players have hit those numbers at all let alone together. Minimum 500 PA

.366 Average = None (Joe Mauer hit .365 in 2009)

.452 OBP = Two, Bryce Harper (.460) and Joey Votto (.459) both in 2015

.720 SLG = None (highest is Albert Pujols in 2009 with .658)

1.172 OPS = None (highest is Bryce Harper in 2015 with 1.109)

49 HR = Two, Jose Bautista (54 in 2010) and Chris Davis (53 in 2013)

130 RBI = Seven (highest is 141 both Prince Fielder and Ryan Howard in 2009)

So…wow, that seems pretty unlikely to reach those levels. I did however mention what happens if we remove Harper’s 2015 season — the average wRC+ drops to 165. In the Rockies’ history they have four seasons within two points of 165 (excluding Walker’s 1997). Those seasons:

1999 Larry Walker = 167 wRC+ – .379/.458/.710,  1.168 OPS, 37 HR  115 RBI  –   Finished 10th in NL voting for MVP

2004 Todd Helton (Post-humidor!) = 166 wRC+ –  .347/.469/.620, 1.088 OPS, 32 HR 94 RBI  –   Finished 16th in NL voting for MVP

2001 Larry Walker = 163 wRC+ – .350/.449/.662,  1.111 OPS, 38 HR  123 RBI –   Tied for 24th in NL voting for MVP

2003 Todd Helton (Post-humidor also) = 163 wRC+ – .358/.458/.630,  1.088 OPS, 33 HR 117 RBI  –   Finished 7th in NL voting for MVP

So those number are a tad more reasonable. So is it possible for a Rockies player to ever win the MVP again? Absolutely, but this was written to show not whether or not the Rockies can have another MVP someday but more what numbers it may take to get the votes and erase the Coors Field stigma in voting, if for just one season. Which I think may never happen again without one of the best seasons we’ve ever seen.

It was supposed to be the new cast of characters that stirred the pot on the south side. Who would have guessed that the preseason drama would emanate from an old war horse? The 36-year-old Adam LaRoche walked away from $13 million after White Sox management asked LaRoche to “dial it back a bit” and stop bringing his son Drake to the ballpark. Apparently, Drake had spent 120 games with the White Sox in 2015, and had already been a mainstay at the spring training facility in 2016.

Many of the White Sox players, including stars like Chris Sale and Adam Eaton, have publicly displayed their discontent with White Sox management, siding with both Adam and Drake LaRoche. Eaton was adamant enough to say that the White Sox “lost a leader” in Drake LaRoche [1] – a comment that he directed at team president, Kenny Williams. With so many players openly expressing their opposition to the removal of Drake LaRoche, it’s interesting to note that the issue arose from a small group of anonymous White Sox players who privately reported their distaste of Drake’s omnipresence.

Adam LaRoche was clear with his teammates – if there was ever an issue about his son Drake’s presence in the clubhouse, let him know about it:

“Though I clearly indicated to both teams the importance of having my son with me, I also made clear that if there was ever a moment when a teammate, coach or manager was made to feel uncomfortable, then I would immediately address it. I realize that this is their office and their career, and it would not be fair to the team if anybody in the clubhouse was unhappy with the situation. Fortunately, that problem never developed [2].”

Unfortunately, things didn’t exactly play out that way, as no one brought it up to LaRoche personally:

“Apparently, no one ever told LaRoche. These players and staff members didn’t feel comfortable even sharing it with their own teammates, with several White Sox players saying they never heard a complaint. But they did express their views to management [3].”

It’s not that LaRoche was an outcast. From the reaction of many of the players, it seems like primary players on the White Sox (if not large swaths of the team) were cool with Drake hanging around as much as he did. So, if a few people had a problem, why didn’t they speak up to LaRoche? Conversely, why couldn’t LaRoche sense that Drake was weirding some of his teammates out?

Let’s talk about feelings

 Average sensitivity is the ability of members within a team to sense how other team members are feeling by observing their facial expressions, body language, and other behavioral cues. Average sensitivity is an aspect of a broader construct called psychological safety, which helps to explain how and why team members speak up, exchange information, and their general willingness to be open with other teammates (Edmonson & Lei, 2014). There has been extensive research on the relationship between psychological safety and performance (Baer & Frese, 2003; Edmondson & Lei, 2014; Collins & Smith, 2006; Schaubroeck et al., 2011); broadly, this research indicates that open communication between team members is related to team performance.

How important is psychological safety in terms of group performance, you ask? Google’s Project Aristotle explored the characteristics that make the perfect team. After four years, hundreds of experimental teams, thousands of people, and 50 years’ worth of academic literature, the critical variable in predicting a successful team was…psychological safety [4].

Now, we haven’t measured the White Sox’ sensitivity or psychological safety directly, so the suggestion that these things played a role in the LaRoche situation is more of an educated guess than an empirical observation. Also, much of the research on the influence of psychological safety has been done in organizations, as opposed to sports teams. But it isn’t difficult to imagine that if there was a greater emphasis on psychological safety, a situation like this might not have arisen. It’s not too far of a jump to say that higher-performing teams should:

• Place a premium on speaking up with ideas, without fear of punishment or ridicule. Don’t stifle effective collaboration regardless of the topic.
• Promotion of safety is key – the research has shown that it does not arise naturally, but should be discussed and fostered.

Imagine if LaRoche’s teammates might have felt comfortable going directly to him instead of circumventing him. Would the White Sox still be in their current state of disarray? It could be less about this particular incident, and possibly more indicative of a greater, team-wide issue of communication.

Sooner or later, a situation similar to the Drake and Adam LaRoche situation is going to happen again. There’s also plenty of other team level constructs to explore, such as team chemistry. The broader point, though, is that these scenarios are likely somewhat avoidable: Teams can work to increase sensitivity and psychological safety. It won’t be easy, but research suggests that things like players-only meetings to air out grievances, establish lines of communication, and solidify roles might be a place to start. For the White Sox, it’s a rough way to begin the season, but fortunately these issues are fixable – and it’s certainly a helluva lot better to address these issues now rather than at the All-Star break.

[1] http://heavy.com/news/2016/03/adam-laroche-son-drake-retire-ken-williams-comments-family-wife-jenn-daughter-montana/

[2] http://thebiglead.com/2016/03/21/adam-laroche-son-anonymous-white-sox-teammates-complained/

[3] http://www.foxsports.com/mlb/story/adam-laroche-chicago-white-sox-ken-williams-retire-drake-laroche-son-chris-sale-teammates-031916

[4] http://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html?_r=3

Positional adjustments are a tricky subject to model. It’s obvious that an average shortstop should get more credit for defense than an average first baseman, but there are a wide variety of methods to calculate this credit. Some methods use purely offense to calculate the adjustments, while others have used players changing positions as proxy for how difficult each area is.

We’ll use a simplified version of the defense-based adjustments (which I’ll propose a change for later) for Part 1. This model looks at all players who have played two positions (weighted by the harmonic mean of innings played between the two). Then, it produces a number for how much better an average player performed at a certain position than another. After doing this for all 21 pairs of positions, we combine the comparisons into one scale, weighted by which changes happen the most often.

Example: the table below shows how all outfielders in 1961 performed when changing positions within the outfield (using Total Zone per 1300 innings):

• LF/CF: 14.5 runs/1300 better at LF, 4028 innings
• LF/RF: 10.4 runs/1300 better at LF, 9487 innings
• CF/RF: 7.4 runs/1300 better at RF, 6025 innings

After weighting each transition by the number of innings, we get an estimate that the LF adjustment should be -8.3, RF should be 1.0, and CF should be 7.3. (We’re assuming that players being better at a position means that that position is easier.)

I performed this calculation for all seven field positions (1B, 2B, SS, 3B, LF, CF, RF) for all years between 1961 and 2001. While using only seasons from the same year does away with any aging issues, the big problem with this analysis is that it doesn’t adjust for experience, as very few managers, ever, send full-time first basemen to play the outfield. This experience issue will be addressed in Part 2, but for now we just have to keep it in mind.

Finally, while I could have expanded this to 2015, the difference between UZR/DRS and TZ is so massive that using both would have created a lot of error in the graphs below.

The graphs (using loess regression to smooth the yearly data):

With yearly data:

With error bars:

Less smooth version:

Less smooth version with points:

A lot of positions have 4-run error bars, so it would be wise to take some jumps and drops with a grain of salt. However, it is interesting to note that corner outfielders (especially left fielders) appear to get much better at defense since the 1960s, while the right side of the infield has seemed to drop in quality. Also, for whatever reason, center field had a huge dip during the 1970’s.

During Part 2, I’ll analyze these graphs in depth, and propose adjustments to this simple model.