Archive for Research

We’re In a Golden Age of the Lefty Fastball

The 2016 baseball season is well underway and we’re seeing an even more drastic version of the trends that we saw last year: There are more strikeouts, more home runs, and more challenges. And, notably, there has also been a steady increase in velocity across the league, assisted by the guys I’ll be highlighting here.

A “steady increase in velocity” might not be reason to stop the presses, but just soak in this Tweet real quick:


We’re basically seeing twice as many pitches thrown 95+ as we were in 2008. ¡2008!

Even left-handers, typically a step behind (always a bit of a quirky species, lefties), are chucking it. Across the league, lefties are throwing the ball 95+ mph just around 7.5% of the time. That’s way more often than the stereotype of the Tom Glavine-y, soft-tossing corner-nibbler would have you believe, but it’s 2016 and elite velocity isn’t just left to the elite pitchers anymore (Chris Sale is joined in that 95+ lefty fastball club by some guy named Buddy Boshers out of the bullpen for the Twins).

So…I’m not just interested in guys that throw hard; I want guys who throw hard and make the ball move, and I want them to be left-handed. (Truth: that lefty requirement is mostly an excuse so I can hopefully talk about Danny Duffy more, James Paxton for the first time, and because I already covered the right-handed side of things with my Charlie Morton post from the start of the season (The Unbelievable Emergence of Charlie Morton), and basically because lefties are more fun.)

A common refrain among pitching coaches is that movement is just as important as velocity. Velocity can get you to the majors, but big-league hitters will turn around 95+ fast if it’s straight. But when combined with some movement (and even better, control/command) 95+ is a high value commodity.

I’m after what I want to dub the best lefty fastball. Let’s start with the simple stuff: Who out there is throwing it 95+ most frequently? Note that the percentages here are for all pitches thrown, including the off-speed stuff.

Player Name Number of Pitches 95+ % of Pitches Thrown 95+
Zach Britton 152 93%
Sean Doolittle 118 84%
Aroldis Chapman 125 80%
Jake Diekman 111 73%
James Paxton 332 64%
Justin Wilson 81 62%
Josh Osich 58 57%
Enny Romero 86 54%
Tony Cingrani 102 53%
Jake McGee 32 51%
Danny Duffy 211 49%
Ian Krol 68 45%
Robbie Ray 208 41%
Felipe Rivero 61 35%
Sammy Solis 51 30%
Andrew Miller 52 30%
Andrew Chafin 6 26%
Carlos Rodon 90 23%
Blake Snell 45 23%

There are a number of relievers in there that I should probably get to know better. Zach Britton, Sean Doolittle, and Aroldis Chapman have all been flame-throwers for a while now; somehow their gas no longer brings the flicker to my eye that it once did. But Josh Osich and Enny Romero? Those are new guys that throw quite hard and are likely on their way to relevance.

The starters on the list are the most fun for me. James Paxton is there. Danny Duffy, too. But so are Carlos Rodon and Blake Snell. I’m not going to anoint any of these young guys just yet, but I’d venture that it’s been a long time since we’ve had four lefty starters out there throwing 95+ mph heaters at least 23% of the time. But…Carlos Rodon has a 4.16 ERA, and the other three all have fewer than 10 starts on the season. Let’s see if movement has anything to do with it.

We’re in search of the best lefty fastball and the best lefty fastball must move sideways, while also moving quickly. 10 inches of run seems like a pretty good place to set up camp.

Player Name Number of Pitches 95+
& 10+ inches of run
% of All Pitches
Jake Diekman 90 59%
James Paxton 171 33%
Josh Osich 24 24%
Cody Reed 18 20%
Chris Sale 90 16%
Sammy Solis 22 13%
Robbie Ray 58 11%
Brad Hand 26 11%
Clayton Richard 7 11%
Mike Montgomery 22 9%
Martin Perez 42 9%
Steven Matz 23 6%
Ian Krol 8 5%
Andrew Miller 9 5%
Ashur Tolliver 3 5%
Enny Romero 8 5%
Tony Cingrani 7 4%
Zach Britton 6 4%
T.J. McFarland 3 3%
Aroldis Chapman 4 3%
Sean Doolittle 3 2%
Carlos Rodon 8 2%

Look at that: Mr. Rodon and his 4.16 ERA bring up the rear, while Snell and Duffy dropped right off. But man, James Paxton is still up top there just behind Jake Diekman. Diekman is a very good reliever, who seems to be realizing his potential since his trade to Texas. Basically, by exclusively pounding the zone with that hard, running fastball, he’s posted an ERA below 2.00 since getting out of Philly.

Oh! Chris Sale, how did I forget to include him in my love fest of the young lefty starters in the league? Sale has thrown 110 pitches at least 95 mph, and of those, 90 have moved at least 10 inches. That’s nuts. His stuff is incredible.

We also see Steven Matz creep in there as 6% of his pitches are these 95 mph fastballs that move an unfair amount. Matz and his 2.96 ERA definitely belong in that quartet of young insanely talented left-handed starting pitching that I talked about before. He’ll be the fifth member of that group, and we instantly have to expand our Mount Rushmore of tantalizing excellence.

This is starting to feel a bit like the NBA where so much Amazing is happening. But it’s true: there’s a lot of amazing happening across the MLB landscape right now. These lefty fastballs are but one, tiny iota of all that is going on.

Let’s refine the batch of fastballs once more to include only those that have at least 10 inches of vertical movement, too. This admittedly feels like a laughable exercise. There’s no way that pitchers are actually throwing pitches that go 95 mph, while also running and rising that much….

Player Name Number of Pitches 95+
10+ inches of run
10+ inches of rise
Robbie Ray 15
James Paxton 14
Enny Romero 5
Rest of League 25

Oh. Damn. I see you Robbie Ray, James Paxton, and Enny Romero. I also see you Rest of League. That group included Danny Duffy, Sean Doolittle, Aroldis Chapman, Matt Moore, and Chris Sale. But really this is about those top three guys.

Ray was once a prospect known more for his feel and pitchability than a premier fastball. He’s starting for the Diamondbacks now and he’s striking out over 10 per game. His ERA sites at 4.59 and his WHIP is over 1.50, which are both significantly worse than his 2015 campaign, but still, if that pitchability from his earlier career outlook meets with his clearly impressive fastball, things could turn around quickly for the 24-year-old. I’m frankly surprised to see him here.

As for James Paxton, we know he’s throwing way harder now that he’s dropped his arm slot. I’ll save my full review of his stuff for the lengthier look that it deserves.

Then there’s Enny Romero. Romero isn’t well known in baseball circles just yet. He started a single game as a 22-year-old for the Rays back in 2013, spent 2014 throwing a 4.93 ERA in Triple-A, and hasn’t exactly torn things up in the majors since then. But he’s a young player, with a solid baseball name and a clearly electric fastball. He’s 25 and capable of figuring it out just like any other 25-year-old.

To be totally honest, I’m not entirely sure what to do with this group of pitchers. The guys atop this 95/10/10 club clearly have electric fastballs, but the electric fastball has not equated to big-league success so far. I guess that’s OK, and feeds back into the last bit of the the old pitching coach refrain: Velocity is nothing without movement…and control. But control is not sexy.

Speed is sexy, and all these guys throwing 95 are great, but Aroldis Chapman is the only one guy who’s ever thrown it 105 mph. He keeps the crown of best fastball. (All this talk of horizontal and vertical movement was really just an attempt to crown the best non-Chapman lefty fastball.)

So what is the takeaway?

This discussion mostly serves as a friendly reminder that we’re in the midst of a great revolution of left handed pitchers — all of whom make Clayton Kershaw old by comparison. These guys are throwing fastballs harder than we’ve ever seen before and there’s so many of them doing it.

Stat of the Day: I feel like I should also note that I unearthed an insane Andrew Miller pitch where he effectively threw a 95 mph slider on June 6th to some poor soul.


MLB’s Qualifying Offer: A King’s Ransom

With the MLB draft just past, I thought it would be appropriate to examine one of the most controversial topics surrounding the draft: the qualifying offer. Essentially, the qualifying offer intends to reward teams — presumably the small-market, low budget ones — that lose players in free agency. This reward comes in the form of an additional first-round draft pick for every player that signs with another team.

Only it isn’t that simple. Once a player reaches the end of his contract, the team can decide whether or not to offer the player a 1-year extension known as the qualifying offer. This new contract is equal to the average of the highest 125 salaries in MLB ($15.8 million in 2016). The player then chooses to either accept the qualifying offer or decline it — and thus, enter free agency with the assumption that he can earn more than a 1-year, $15.8 million contract. Once the player signs on with another team, his former team is awarded a first-round draft pick (to go along with the one(s) they already have, assuming they do) as compensation. Additionally, the player’s new team loses their first-round pick in the draft so long as it is outside the top 10 (in which case their second-round pick would be forfeited).

So, one would assume that, more often than not, a small-market team with a low payroll would benefit from this system. A budding star player reaches the end of his contract and commands a new contract worth hundreds of millions and spread over 5+ seasons. His current team does not have the financial resources to resign him, and another big-market team does. The cash-strapped team receives an additional first-round pick as compensation, while his new team willfully forfeits its first-round pick in exchange for his services over the next half-decade. And that’s that.

Not quite. I went back over the draft order for every year since 2013 (when the qualifying offer was first introduced) and summed the number of draft picks gained and lost. Results are shown below. I sorted the teams by their average payroll over the span in descending order. As you can see, the compensation is not in line with the assumption I presented above. In any way you shape it, the high-payroll teams are the ones benefiting from the current system. The 10 highest-payroll teams have received 19 additional draft picks over the four seasons — highlighted by the Cardinals who have gained four and lost none. The 10 teams with the lowest payrolls have received eight additional picks. The high payroll teams have a net draft pick gain of four, while the low payroll teams have a net loss of two.

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Now, I’m not coming up with any revolutionary solutions here — I’m not that smart and I don’t get paid enough. I am simply presenting data that supports that MLB’s current free-agent compensation system doesn’t benefit the teams that need it the most. In fact, this seems to be a story of “the rich are getting richer” — big-money teams are receiving the extra draft picks that were seemingly meant for the low-budget ones. Maybe MLB scraps the compensation system altogether, maybe they extend the time frame for when a player can accept the qualifying offer (they currently have seven days), or maybe they come up with some other solution. In any case, the current CBA ends after the 2016 season so us fans will likely know the answer before next year’s draft.


Hardball Retrospective – What Might Have Been – The “Original” 1984 Giants

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 1984 San Francisco Giants 

OWAR: 42.9     OWS: 294     OPW%: .508     (82-80)

AWAR: 27.7      AWS: 198     APW%: .407     (66-96)

WARdiff: 15.2                        WSdiff: 96  

The “Original” 1984 Giants ended the season with a winning record but merely earned a fifth place finish, 9 games behind the Astros. Gary “Sarge” Matthews established a career-best with 101 runs scored while pacing the circuit with 103 walks and a .410 OBP. Chili Davis contributed a .315 BA and merited his first All-Star invitation. Dave “Kong” Kingman walloped 35 four-baggers and knocked in a personal-best 118 baserunners. Bob Brenly achieved his lone All-Star nod with a .291 BA, 20 dingers and 80 ribbies. Jack Clark supplied a .320 BA with 11 long balls prior to a season-ending injury in mid-June. Dan “Dazzle” Gladden ignited the offense following his recall from the minor leagues in late June, posting a .351 BA and swiping 31 bags.

Jack Clark is ranked 27th among right fielders according to Bill James in “The New Bill James Historical Baseball Abstract.” “Original” Giants teammates listed in the “NBJHBA” top 100 rankings include George Foster (34th-LF), Gary Matthews (46th-LF), Garry Maddox (56th-CF), Chili Davis (64th-RF), Chris Speier (68th-SS) and Dave Kingman (98th-LF).  Al Oliver (31th-CF), Manny Trillo (49th-2B) and Dusty Baker (54th-LF) make the register for the “Actual” Giants. 

  Original 1984 Giants                              Actual 1984 Giants

LINEUP POS OWAR OWS LINEUP POS AWAR AWS
Gary Matthews LF 2.68 22.93 Jeffrey Leonard LF 2.38 20.37
Dan Gladden CF 2.81 16.46 Dan Gladden CF 2.81 16.46
Chili Davis RF/CF 4.19 21.58 Chili Davis RF/CF 4.19 21.58
John Rabb 1B -0.14 1.01 Scot Thompson 1B 0.35 6.89
2B Manny Trillo 2B 0.76 8.83
Johnnie LeMaster SS -0.47 7.23 Johnnie LeMaster SS -0.47 7.23
Chris Brown 3B 0.31 2.26 Joel Youngblood 3B -0.89 9.5
Bob Brenly C 3.58 21.32 Bob Brenly C 3.58 21.32
BENCH POS OWAR OWS BENCH POS AWAR AWS
Dave Kingman DH 2.49 21.48 Jack Clark RF 2.01 11.84
George Foster LF 1.16 18.27 Dusty Baker RF 1.19 8.81
Jack Clark RF 2.01 11.84 Al Oliver 1B -0.85 6.56
Bob Kearney C 0.26 8.63 Steve Nicosia C 0.79 4.99
Garry Maddox CF 0.53 6.67 Brad Wellman 2B -0.45 3.74
Chris Speier SS -0.24 2.96 Chris Brown 3B 0.31 2.26
Rob Deer LF 0.28 1.24 Fran Mullins 3B 0.24 2.08
Randy Gomez C -0.02 0.18 Gene Richards LF -0.04 1.92
Tom O’Malley 3B -0.5 0.03 Rob Deer LF 0.28 1.24
Jose Morales -0.19 0 John Rabb 1B -0.14 1.01
Casey Parsons -0.01 0 Duane Kuiper 2B -1.06 0.82
Randy Gomez C -0.02 0.18
Joe Pittman SS -0.17 0.12
Alejandro Sanchez RF -0.4 0.08
Tom O’Malley 3B -0.29 0.01

Bob Knepper rebounded from an 11-28 mark in the previous two campaigns to achieve a 15-10 record with a 3.20 ERA and 1.190 WHIP. Gary Lavelle notched 12 saves and fashioned a 2.76 ERA as the primary closer. Frank Williams collected 9 victories in a long relief role during his rookie year.

  Original 1984 Giants                                   Actual 1984 Giants

ROTATION POS OWAR OWS ROTATION POS AWAR AWS
Bob Knepper SP 2.16 12.43 Bill Laskey SP -0.02 4.8
Pete Falcone SP 0.91 5.33 Mike Krukow SP -1.04 3.94
John Montefusco SP 0.58 3.27 Jeff D. Robinson SP -0.67 2.84
Jeff D. Robinson SP -0.67 2.84 Atlee Hammaker SP 0.96 2.28
Mark Calvert SP -0.39 0.22 George Riley SP 0.19 0.98
BULLPEN POS OWAR OWS BULLPEN POS AWAR AWS
Gary Lavelle RP 1.78 7.85 Gary Lavelle RP 1.78 7.85
Frank Williams RP 0.43 5.7 Greg Minton RP -0.02 6.29
John Henry Johnson RP 1.22 4.39 Frank Williams RP 0.43 5.7
Scott Garrelts SW -1.13 0 Randy Lerch RP 0.11 2.58
Gorman Heimueller RP -0.7 0 Bob Lacey RP -0.07 1.51
Mark Grant SP -1.1 0 Renie Martin RP -0.09 0.99
Mark Calvert SP -0.39 0.22
Mark W. Davis SP -1.91 0.18
Jeff Cornell RP -1.25 0
Scott Garrelts SW -1.13 0
Mark Grant SP -1.1 0

Notable Transactions

Gary Matthews

November 17, 1976: Signed as a Free Agent with the Atlanta Braves.

March 25, 1981: Traded by the Atlanta Braves to the Philadelphia Phillies for Bob Walk.

March 26, 1984: Traded by the Philadelphia Phillies with Porfi Altamirano and Bob Dernier to the Chicago Cubs for Bill Campbell and Mike Diaz.

Dave Kingman

February 28, 1975: Purchased by the New York Mets from the San Francisco Giants for $150,000.

June 15, 1977: Traded by the New York Mets to the San Diego Padres for Paul Siebert and Bobby Valentine.

September 6, 1977: Selected off waivers by the California Angels from the San Diego Padres.

September 15, 1977: Traded by the California Angels to the New York Yankees for Randy Stein and cash.

November 2, 1977: Granted Free Agency.

November 30, 1977: Signed as a Free Agent with the Chicago Cubs.

February 28, 1981: Traded by the Chicago Cubs to the New York Mets for Steve Henderson and cash.

January 30, 1984: Released by the New York Mets.

March 29, 1984: Signed as a Free Agent with the Oakland Athletics.

George Foster

May 29, 1971: Traded by the San Francisco Giants to the Cincinnati Reds for Frank Duffy and Vern Geishert.

February 10, 1982: Traded by the Cincinnati Reds to the New York Mets for Greg Harris, Jim Kern and Alex Trevino.

Bob Knepper

December 8, 1980: Traded by the San Francisco Giants with Chris Bourjos to the Houston Astros for Enos Cabell.

Honorable Mention

The 1906 New York Giants 

OWAR: 65.9     OWS: 361     OPW%: .591     (91-63)

AWAR: 50.8       AWS: 287      APW%: .632    (96-56)

WARdiff: 15.1                        WSdiff: 74

The New York Giants secured the organization’s fourth consecutive pennant in 1906 with a record of 91-63, placing three games in front of the St. Louis Cardinals. Third-sacker Art Devlin pilfered 54 bases and delivered a .299 BA. Harry H. Davis topped the leader boards with 12 big-flies and 96 ribbies. Converted outfielder Cy Seymour nabbed 29 bags and drove in 80 baserunners while “Wee” Willie Keeler batted .304 with 23 steals. Christy Mathewson furnished 22 victories along with a 2.97 ERA. Left-hander Hooks Wiltse recorded 16 wins with an ERA of 2.27 and a WHIP of 1.143.

On Deck

What Might Have Been – The “Original” 2004 Royals

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


Who Has Performed Better In the Draft?

The MLB draft has passed but its impact will last. Some selections will go down as busts (e.g. Matt Anderson by the Tigers in 1997). Others will be real bargains such as Carlos Beltran with the 49th pick in 1995. I decided to look at the numbers in an attempt to answer the following questions I read over the last few weeks:

  1. How many Round 1 picks do end up in the big leagues? What’s the average impact of a Round 1 pick? How does that compare to Round 2? Are there differences between pitcher and batters?
  2. What has been the best draft class for the 1993-2008 period? (per three first rounds)
  3. What teams have done a better job?
  4. What is the best round (top 10 overall picks)?

As I usually do, let’s define the data sources and assumptions. First, my data source is Baseball-Reference. There are many assumptions and disclaimers in this process, but the most important ones are:

  1. I am using data from 1993 to 2008 to give ample time for players to reach MLB. As I am using career WAR, I don’t want to over-penalize players that have been selected in the recent years and therefore have not accumulated MLB service time.
  2. Organizations change and so do their ways of conducting business, which evidently includes draft strategy. We are looking at teams rather than specific front offices or general managers.
  3. WAR refers to Baseball-Reference WAR (i.e. bWAR).
  4. Teams may have more than one pick per round due to compensation and supplemental picks.
  5. This methodology does not take into account the overall quality of the draft pool i.e. total WAR per draft year is not constant.
  6. All WAR is allocated to the team that drafts the player. Understandably, that is not true but let’s toy with the idea through this post.

Let’s get to it.

Question 1 – How many Round 1 picks do end up in the big league? What’s the average impact of a Round 1 compare to a Round 2 pick? Are there differences between pitcher and batters?

The table below outlines how many players have been/were called up to the majors and how many actually have had a positive career WAR i.e. over 0.0. I have also added the average career WAR per player and I have broken down the data by round and by position (pitcher and batter) to grasp the differences easily. Just take a moment with this table:

 

Round Pos Total players Players that reached MLB % of Total players Positive WAR % of players who reached MLB Average WAR per player
Round 1
Pitchers 372 242 65% 161 67% 9.7
Batters 320 225 70% 157 70% 14.4
Sub-Total 692 467 67% 318 68% 12.1
Round 2
Pitchers 247 121 49% 60 50% 8.1
Batters 244 127 52% 70 55% 13.1
Sub-Total 491 248 51% 130 52% 10.8
Round 3
Pitchers 244 99 41% 59 60% 5.5
Batters 235 88 37% 50 57% 7.3
Sub-Total 479 187 39% 109 58% 6.3
Total 1662 902 54% 557 62% 10.6

 

Three things come to my mind:

First, this provides some empirical validation of what we intuitively thought: First-round picks produce greater WAR values than the others. While I only have data for the first three rounds, it’s worth noting that the gap between Round 1 to Round 2 (10%) is smaller than from Round 2 to Round 3 (41%).

Second, I actually found surprising that 67% of first-rounders reached MLB at some point. That is two players out of three and it’s a testament to how important raw skills are when it comes to moving up through the minors.

Lastly, the answer to the question of whether t draft pitchers or batters looks like an easy one. Batters not only reached MLB at a higher pace but delivered better results as a group and as individuals. While these results are not statistically significant, they provide a pragmatic answer to the question and suggest a sound strategy might be to draft batters and trade for pitchers later down the road.

Question 2 – What has been the best draft class for the 1993-2008 period?

This table should provide guidance on how to answer this question but does not fully explain it. If we think of it as the number of players that got to MLB, then 2008 is the best year. That year highlights Eric Hosmer, Buster Posey, Brett Lawrie, Craig Kimbrel and Gerrit Cole as the most prominent stars, but offers a very low career total WAR as most of its players are still playing – they’re the youngest generation of my sample. In this class, 27 out of the top 30 picks have reached MLB, though a few for a very short stint e.g. Kyle Skipworth or Ethan Martin.

Year Total war Total players that reached MLB Average WAR per player
1993 476.3 54 8.82
1994 243.4 54 4.51
1995 484.9 41 11.83
1996 280.0 45 6.22
1997 409.5 59 6.94
1998 397.6 53 7.50
1999 402.1 52 7.73
2000 236.8 47 5.04
2001 350.9 55 6.38
2002 508.1 54 9.41
2003 297.1 60 4.95
2004 393.2 63 6.24
2005 458.1 63 7.27
2006 282.7 62 4.56
2007 325.4 69 4.72
2008 213.2 71 3.00

 

If we think of the highest total career WAR, then the winner is 2002. This class is led by two of the best picks on the sample (Zack Greinke and Joey Votto) but also features Prince Fielder, Jon Lester and Curtis Granderson. If we think of highest concentration of skills, then the 1995 class has to be the first one with an average of 11.8 WAR per MLB player. On the other hand, only 41 players got the MLB call, the lowest among the sample. While Carlos Beltran and Roy Halladay are the most notable names in that draft, player such as Darin Erstad, Kerry Wood, Randy Winn and Bronson Arroyo enjoyed nice peaks.

 

Question 3 – What teams have done a better job?

Evidently, not every team has selected in the same combination of draft slots e.g. some teams have had the opportunity to choose top picks (Rays, for example), while other have frequently picked from mid-bottom draft slots (Yankees).  It would not be fair to compare total career WAR for players the Yankees has selected against those that the Rays has because the latter had more options and access to a different pool of players than that the Yankees had. How to fix that? I am comparing what each team did on the overall pick they were slotted. If we use 2016 as an example, I would be comparing how good Philadelphia was in choosing Mickey Moniak as pick 1 against the average of all other first picks in the timeframe (1993-2008). Once I know the WAR gap between a particular team and the average WAR per pick, I need to standardize that number by the standard deviation i.e. calculating Z scores. In simple terms, this is understanding how good or bad a pick was in relation to the entire distribution of a particular draft slot. The Z-score number allows us to compare how good a 14th pick was in relation to a third pick, for example. Finally, to identify which teams have fared better, I am calculating the average of Z-scores for all picks.

Again, there are many caveats here, but this should give us a ballpark estimate on how well teams have drafted from 1993-2008. Keep in mind, this methodology does not produce a linear WAR per draft slot. That would mean, for example, that overall pick 4 will produce greater WAR than pick 5. On average, the 4th pick has produced 6.2 WAR on average, while the 5th one has produced 14.3. While this might be counter-intuitive (it is at least for me), the empirical evidence of this sample size shows that.

 

Batter Pitcher    
Teams # of batters drafted Average of OvPck – Zscore # Pitchers drafted Average of OvPck – Zscore Total Count of Name Total Average of OvPck – Zscore
Phillies 26 -0.81 24 -0.46 50 -0.64
Nationals 9 -0.70 6 -1.14 15 -0.88
Athletics 40 -0.99 30 -0.75 70 -0.89
Twins 34 -0.57 32 -1.31 66 -0.93
Diamondbacks 18 -0.84 26 -1.06 44 -0.97
Angels 18 -1.10 27 -0.88 45 -0.97
Rays 14 -0.50 20 -1.31 34 -0.97
Rangers 26 -1.06 28 -1.05 54 -1.06
Cardinals 28 -1.03 34 -1.25 62 -1.15
Giants 34 -1.23 28 -1.10 62 -1.17
Braves 32 -1.24 35 -1.12 67 -1.18
Royals 25 -1.40 32 -1.04 57 -1.20
White Sox 24 -0.65 40 -1.54 64 -1.20
Reds 28 -0.73 27 -1.70 55 -1.21
Blue Jays 32 -1.46 27 -0.91 59 -1.21
Red Sox 29 -1.33 35 -1.14 64 -1.23
Brewers 26 -0.87 27 -1.72 53 -1.30
Dodgers 21 -1.13 32 -1.44 53 -1.32
Rockies 18 -0.85 33 -1.60 51 -1.33
Pirates 27 -1.72 23 -0.88 50 -1.33
Mariners 25 -1.33 20 -1.45 45 -1.38
Mets 17 -1.14 35 -1.61 52 -1.45
Tigers 20 -0.81 32 -1.88 52 -1.46
Orioles 28 -1.05 28 -1.88 56 -1.46
Padres 40 -1.47 24 -1.54 64 -1.49
Marlins 30 -1.59 23 -1.41 53 -1.51
Astros 23 -1.45 26 -1.61 49 -1.53
Expos 26 -1.30 22 -1.85 48 -1.56
Yankees 24 -1.94 29 -1.37 53 -1.63
Cubs 24 -1.46 29 -1.95 53 -1.73
Indians 33 -2.13 29 -1.49 62 -1.83
Total 799 -1.19 863 -1.35 1662 -1.27

 

Perhaps surprisingly, the Phillies come at the top of the list. The Phillies advantage came in three picks: First, Chase Utley was drafted in 2000 with the high 15th pick and has had a great career that is up to 63.4 WAR. Second, in 1993, the Phillies chose Scott Rolen (70 career WAR) with the 46th overall pick – which seems like a bargain now. Finally, Randy Wolf in 1997 was selected in the 54th position and went on to have a 23.1 career WAR. The Nationals have had very much success on their first few years as a franchise with both Jordan Zimmermann and Ryan Zimmerman. The sample size does not include Bryce Harper or Stephen Strasburg, which may push the Nats to the top of the list in the near future.

Astros, Expos, Yankees, Cubs and Indians are the bottom five teams. Coincidentally or not, these teams have long droughts (Yankees exempted). Interesting to see if there is a relationship between draft performance and wins but I guess that’s is another post.

We could go and dig deeper for each team into what they’ve done well and not so much but that would not make sense. Teams make mistakes and it looks like the draft selection is pretty damn hard with an extremely high WAR standard deviation (11.6 WAR through the first 30 picks).

 

Question 4 – What is the best round (top 10 overall picks)?

This question is about finding the best selection on each of the first 10 picks. I’ve used the Z-score which pick was really ahead of the curve.

OvPck Year Tm Player Pos WAR Average WAR of pick OvPck – Zscore
1 1993 Mariners Alex Rodriguez SS 118.8  22.73 3.16
2 1997 Phillies J.D. Drew OF 44.9  16.23 1.88
3 2006 Rays Evan Longoria 3B 43.3  9.00 2.46
4 2005 Nationals Ryan Zimmerman 3B 34.8  6.21 2.67
5 2001 Rangers Mark Teixeira 3B 52.2  14.26 2.02
6 2002 Royals Zack Greinke SP 52.3  4.76 3.63
7 2006 Dodgers Clayton Kershaw SP 52.1  11.86 2.42
8 1995 Rockies Todd Helton 1B 61.2  6.41 3.56
9 1999 Athletics Barry Zito SP 32.6  8.70 2.24
10 1996 Athletics Eric Chavez 3B 37.4  11.31 2.04

 

Well, this is quite a nice group of players. A-Rod is the WAR leader of our sample. Even as a first pick, which on average has yielded the highest WAR, he manages to be three standards deviations above the mean. Five other players are active and two of them (Greinke and Kershaw) still are among the best starting pitchers in the game. They will continue to cement their position as great draft picks for the Royals and Dodgers. Interestingly enough, Barry Zito and Eric Chavez were part of the A’s Moneyball team that frequently over-performed a few years ago — a reminder of how important it is to build a strong core of players.

As a bonus question – these are the top 10 picks, according to this methodology:

Year OvPck Tm Player Pos WAR Drafted Out of OvPck – Zscore
2002 44 Reds Joey Votto C 42.7 Richview Collegiate Institute (Toronto ON) 3.74
2007 34 Reds Todd Frazier 3B 16.8 Rutgers the State University of New Jersey (New Brunswick NJ) 3.71
1997 70 Rockies Aaron Cook RHP 15.9 Hamilton HS (Hamilton OH) 3.71
1995 69 Pirates Bronson Arroyo RHP 26.5 Hernando HS (Brooksville FL) 3.67
1995 53 Indians Sean Casey 1B 16.3 University of Richmond (Richmond VA) 3.67
2007 27 Tigers Rick Porcello RHP 12.2 Seton Hall Preparatory School (West Orange NJ) 3.63
2002 6 Royals Zack Greinke RHP 52.3 Apopka HS (Apopka FL) 3.63
1996 18 Rangers R.A. Dickey RHP 21.1 University of Tennessee (Knoxville TN) 3.61
1997 91 Royals Jeremy Affeldt LHP 10.5 Northwest Christian HS (Spokane WA) 3.61
1995 31 Angels Jarrod Washburn LHP 28.5 University of Wisconsin at Oshkosh (Oshkosh WI) 3.60
1998 33 Expos Brad Wilkerson OF 11 University of Florida (Gainesville FL) 3.60
1995 49 Royals Carlos Beltran OF 68.8 Fernando Callejo HS (Manati PR) 3.59

 

As always, feel free to share your thoughts and comments in the section below or through our twitter account @imperfectgameb.

Note: This analysis is also featured in our emerging blog www.theimperfectgame.com


Zack Cozart Probably Won’t Keep Scorching the Ball

Last week, August Fagerstrom handed Cincinnati fans an ice-cold Gatorade after we’d spent the better part of three months wandering through the Sahara that is the 2016 Reds baseball season.

After quickly touching on the wide range of sadness, of which there is no shortage—the historically bad bullpen, the woeful luck of one Joseph Votto, and oh, the losses—he rationally pointed to a reason for optimism: “[The Reds’] two most encouraging comeback stories, Zack Cozart and Jay Bruce, just so happen to be their two most sensible trade chips.”

Of Cozart, he wrote:

He leads the Reds in WAR. He enters his final year of arbitration next season. He’s always been a gifted defensive shortstop, something every team loves to have, but this year, he’s hitting at career-best levels…He’s become more aggressive at the plate, he’s hitting way more balls in the air than he did early in his career, and he’s hitting them with authority (emphasis mine).

This last piece is what’s so interesting. Watch any Reds game this year—or just stay through the leadoff hitter! I swear, that’s all I’m asking!—and you’ll hear announcers remark that Cozart is just hitting the ball differently this year.

As shocking as it may seem given recent broadcaster rankings, they’re right.

Cozart’s 2016 hard-hit rate of 33% is very good, if not earth-shattering. It ranks seventh amongst shortstops, and league-wide places him in the company of (slightly) more celebrated offensive names like Bogaerts, Kinsler, and Beltre.

But there’s a caveat to Cozart’s great contact. The number isn’t just a career high; it’s a massive outlier, sitting 8.5% above his career average. How many other hitters this year are enjoying similar spikes? I pulled all “jumps” above six points for 2016 qualified hitters, with a minimum of four seasons to ensure a stable career average:

Name Team Age 2016 Hard Hit % Career Hard Hit % “Jump”
Jose Altuve Astros 26 34.2% 24.5% 9.7%
Daniel Murphy Nationals 31 38.2% 28.7% 9.5%
Victor Martinez Tigers 37 41.5% 32.3% 9.2%
Matt Carpenter Cardinals 30 43.6% 34.7% 8.9%
Zack Cozart Reds 30 33.0% 24.5% 8.5%
Buster Posey Giants 29 40.8% 33.1% 7.7%
Joey Votto Reds 32 44.4% 37.0% 7.4%
Curtis Granderson Mets 35 40.2% 33.1% 7.1%
Salvador Perez Royals 26 35.6% 28.5% 7.1%
Chase Utley Dodgers 37 41.9% 35.3% 6.6%
Ben Zobrist Cubs 35 36.0% 29.4% 6.6%
David Ortiz Red Sox 40 46.9% 40.3% 6.6%
Josh Donaldson Blue Jays 30 40.5% 34.0% 6.5%
Yoenis Cespedes Mets 30 39.5% 33.2% 6.3%
Evan Longoria Rays 30 40.7% 34.6% 6.1%

This list shouldn’t be too surprising. While not a perfect indicator, we know that hitting balls hard is generally better than the alternative—and these guys, with one giant, Vottoian exception, are all in the midst of stellar years by more traditional metrics. Altuve owns baseball’s third-best WAR; Murphy remains one of baseball’s best bargains; Martinez and Ortiz continue to defy Father Time to the tunes of wRC+ of 142 and 194(!), respectively. Even Votto, recovering from a BABIP 60 points below his career average, is rapidly coming around.

The group presents club evaluators, though, with a very tough question: How “real” are these spikes in hard-hit rate, and by extension the jumps in offensive performance? For Cozart, the question for the Reds front office basically translates to: How likely is he to keep the hard contact up, and how quickly should we trade him?

Let’s start with what we know about hard-hit rate. It’s generally a repeatable skill, as a FanGraphs study from last year puts hitters’ YoY correlation at 0.69. We can also say that there’s no real drop in age to adjust for; I found the r-squared correlation between age and hard-hit rate to be 0.02.

It’s not crazy, then, to think that a career-high spike in hard-hit rate could be the start of long-lasting improvement. And if it is, we should see it in the years around the spike: an increase the year before that hinted at a breakout, or retaining/coming close to the same rate in the next few seasons.

But is that the case?

I pulled all hitters with a hard-hit-rate YoY “jump” above 9% since we began tracking the stat in 2002 to see how they fared in the years immediately before and after. In this chart, “Year Before” is the hard-hit rate versus career average for the season prior to the jump, with Y+1/Y+2 representing the two years following it:

Year Name Team Age Year Before “Jump” Year Y+1 Y+2
2007 Edgar Renteria ATL 30 2.2% 12.8% -3.7% -2.4%
2007 Derek Jeter NYY 33 5.1% 12.5% 0.7% -0.6%
2007 Ryan Howard PHI 27 2.0% 12.4% 3.5% 2.8%
2007 Jimmy Rollins PHI 28 3.5% 11.5% 3.7% -1.1%
2007 Carl Crawford TB 25 -1.8% 11.4% -3.6% 3.0%
2007 Coco Crisp BOS 27 2.5% 11.4% -0.4% -1.5%
2013 Marlon Byrd NYM/PIT 35 3.6% 11.1% 6.9% 1.3%
2007 Chone Figgins LAA 29 2.1% 10.9% -1.9% -0.5%
2007 Mark Teixiera TEX/ATL 27 -1.8% 10.7% 3.5% 1.0%
2009 Carlos Pena TB 31 -0.3% 10.3% 1.6% 6.4%
2007 Aaron Rowand PHI 29 -2.0% 9.7% -0.7% -2.5%
2010 Nick Swisher NYY 29 -0.8% 9.6% -3.4% 1.4%
2007 Michael Young TEX 30 0.4% 9.5% 0.2% 4.0%
2009 Raul Ibanez PHI 37 2.1% 9.5% 4.4% -4.0%
2007 Grady Sizemore CLE 24 0.5% 9.3% 1.4% -1.5%
2007 Ichiro Suzuki SEA 33 2.4% 9.2% 0.5% -1.4%

If you’re looking for a pattern, don’t bother: none really exist, aside from the observation that 2007 was, apparently, The Year of Hitting Baseballs Hard (a poorly anticipated sequel to The Year of Living Dangerously).

The “Jump” years make up a few of the better hitting seasons in modern history: Jimmy Rollins’ MVP campaign of 2007, Teixiera’s famous Rangers/Braves split season in which he posted a wRC+ of 146, even a Raul Ibanez “I’m Not Dead Yet” season with Philadelphia at age 37.

More importantly, though, surrounding these seasons on either side is case after case of regression—and not even particularly close regression at that. There doesn’t seem to be any ability to carry over a hard-hit-rate jump into the next year or beyond.

These seasons aren’t necessarily the same as those supported by BABIP-fueled mirages…but they are propped up by a contact rate that just doesn’t seem to hold up in any type of long run. It’s something that makes sense on an intuitive level: no one, even someone as skilled as a big-league hitter, wakes up and says, “Oh, yeah—that’s how I can hit the ball hard from now on,” then keeps it up for the rest of their career.

A 162-game baseball season may seem long, but it’s subject to many forms of chance, including the odds that some years you’ll strike the ball harder than others. For the purposes of evaluating our 2016 list, it’s info that is more “useful” than immediately actionable: every player on the list except Cozart is signed through at least 2018, while Ortiz, in a Breaking Bad-level identity switch, will hang up his spikes to try his skills as a masseur in Minneapolis.

But it’s a worthy piece of evidence that our protagonist will spend next year likely reverting to average or even worse at the plate—and that the Reds, by extension, should pursue every possible trading avenue for Cozart this summer while the hitting is hard.

 


Tyler Naquin’s Blossoming Power

Recently the Cleveland Indians were able to salvage their four-game series against the Seattle Mariners with a 5-3 victory, thanks to Tyler Naquin. In the top of the 8th inning with teammate Rajai Davis on first base, Naquin again found himself in an 0-2 count. Once again, it seemed that the rookie would strike out…especially because he was facing an excellent reliever in Joaquin Benoit. Going into the game, Benoit found himself with a respectable 3.27 ERA, 1.09 WHIP, and a BAA of just .154. But when Naquin came to the plate all of that was about to change. On an 0-2 pitch, Benoit threw Naquin a changeup down and in that he promptly golfed into the stands of Safeco Field giving the Tribe a 4-2 lead in the late innings. This advantage would end up sticking for the Tribe as they went on to split the four-game series and remain in first place in the AL Central.

Naquin is no stranger to hitting homers in the big leagues. In fact, at the time that was his fourth homer in his last six games. Before his most recent recall on June 1st, Naquin hadn’t yet hit one out of the park in the bigs. But now it appears that he has found his power stroke, and his team couldn’t be happier. Naquin always had a great swing; even looking back on his days at Texas A&M, that was more than apparent (he won two Big-12 batting titles). It appears now that he’s beginning to develop power. In the minors, Naquin managed just 22 homers in his 1542 plate appearances, a modest 70.1 PA/HR. In his short time in the majors this number has dropped significantly down to 22.3 PA/HR. In other words, around 27 HR in a 600 plate appearances. The power that he’s shown thus far has been quite impressive, and there’s a chance that it’s sustainable.

Naquin has shown the ability, throughout his minor and now major-league career, to possess a great swing with the ability to make good, solid contact which has translated well to this point. Naquin has a 41% hard-hit rate. Qualified players who have a hard-hit rate above 39% this season include the following list:

 # Player Team  PA  Hard%  HR  OPS  wRC+ wOBA
1 David Ortiz Red Sox 226 47.2 % 16 1.153 200 .470
2 Joey Votto Reds 248 43.5 % 11 .793 108 .338
3 Matt Carpenter Cardinals 255 43.2 % 9 .936 150 .394
4 Chris Carter Brewers 241 43.0 % 16 .803 105 .334
5 Trevor Story Rockies 258 43.0 % 16 .866 111 .362
6 Mike Napoli Indians 232 42.9 % 14 .799 115 .340
7 Chase Utley Dodgers 222 42.8 % 4 .748 110 .330
8 Michael Conforto Mets 211 42.8 % 9 .778 111 .330
9 Miguel Sano Twins 211 42.7 % 11 .799 116 .344
10 Yasmany Tomas Diamondbacks 208 41.1 % 7 .755 97 .324
11 Josh Donaldson Blue Jays 265 40.9 % 14 .890 139 .378
12 Victor Martinez Tigers 224 40.9 % 9 .925 149 .391
13 Khris Davis Athletics 215 40.8 % 14 .753 100 .316
14 Evan Longoria Rays 250 40.8 % 14 .865 134 .363
15 Curtis Granderson Mets 248 40.8 % 11 .742 102 .317
16 Buster Posey Giants 212 40.5 % 8 .766 108 .323
17 Giancarlo Stanton Marlins 214 40.4 % 12 .731 95 .315
18 Adam Duvall Reds 205 40.3 % 17 .902 135 .377
19 Jake Lamb Diamondbacks 225 40.3 % 11 .867 127 .368
20 Mike Trout Angels 263 39.8 % 13 .963 164 .405
21 Kris Bryant Cubs 257 39.8 % 14 .886 139 .380
22 Chris Davis Orioles 250 39.7 % 13 .795 114 .343
23 Corey Seager Dodgers 258 39.6 % 14 .869 135 .368
24 Mark Trumbo Orioles 251 39.0 % 20 .956 155 .403
25 Byung-ho Park Twins 201 39.0 % 11 .777 109 .334
26 Manny Machado Orioles 264 39.0 % 15 .968 155 .402

From the chart, 20 of the 26 players listed are in double digits in homers. If you take their ratio of HR/PA and multiply by 600 you find that they range anywhere from 27 HR to 48 HR potential. There’s no guarantee that any of these power hitters will keep their current pace, but one thing’s for sure, players who have a relatively high hard-hit rate are more likely to hit home runs and extra-base hits, and ultimately are more likely be more productive for their team. If we go back even further now, say the last three seasons (2013-2015), we get the following group:

 

# Name Team PA Hard% HR OPS wRC+ wOBA
1 Miguel Cabrera Tigers 1848 43.7 % 87 .981 168 .417
2 David Ortiz Red Sox 1816 43.7 % 102 .915 141 .382
3 Paul Goldschmidt Diamondbacks 1884 42.2 % 88 .968 159 .408
4 Giancarlo Stanton Marlins 1460 41.9 % 88 .915 150 .389
5 J.D. Martinez – – – 1447 40.9 % 68 .840 129 .359
6 Lucas Duda Mets 1534 40.6 % 72 .817 131 .355
7 Matt Kemp – – – 1537 40.0 % 54 .786 120 .341
8 Andrew McCutchen Pirates 2007 39.9 % 69 .917 157 .395
9 Chris Davis Orioles 1868 39.9 % 126 .891 140 .378
10 Jarrod Saltalamacchia – – – 1132 39.5 % 34 .746 104 .327
11 Pedro Alvarez Pirates 1550 39.1 % 81 .760 110 .327
12 Mike Trout Angels 2103 39.0 % 104 .973 172 .413

The chart says it all: the average HR% (HR/PA) of this group is 4.8%, or in other words about 29 HR per 600 PA. The average OPS of this group is an impressive .876, and even more impressive the average wOBA is .374. If Naquin can continue to make solid contact in his plate appearances, as he has proven throughout his career, he could be a very special player.

In the case of Tyler Naquin, he has: 99 PA, 41 Hard%, 4 HR, .870 OPS, 136 wRC+, and a .371 wOBA. His numbers correlate quite well to the rest of the group; in fact, his OPS, wRC+, and wOBA are all above or around the average in comparison. Obviously this is kind of a small sample size for Naquin. It’s nearly impossible to tell what kind of player Naquin will become with less than 100 major-league plate appearances, but there is definitely hope.


Success Rate of MLB First-Round Draft Picks by Slot

The MLB Rule 4 amateur draft was last week and fans will clamor for any sort of information regarding their team’s new, shiny, sometimes 18-year old future stars.  The draft gives fans a chance to dream on what will be in seasons to come, each team’s fans are hoping for their very own Mike Trout.  But for every Mike Trout, there are plenty of players like Hank Congers or Zack Cox who were also selected at pick number 25 and who aren’t exactly rewriting the record books.

In doing research for my latest post on the awful Jim Bowden, I found a concerning lack of recent research on draft success. We have plenty of anecdotes, and plenty of information on top prospects busting, but very little in the way of what to expect from a team’s first-round draft pick.  I found a good piece from 2012 from The View from the Bleachers on Success Rate of MLB Draft Picks by Slot and referenced that, but there’s definitely more here.

There have been nine drafts since the last draft referenced in that post.  Scouting, sabermetrics, and our general collective baseball knowledge feels like it has been increasing exponentially in that time.  Does draft success bear that out? Well, not exactly.

The first thing to set up here is to establish a “successful” player. I pondered it for a minute and settled on basically the same approach that Michael used way back in 2012. If the player hasn’t made the majors, or if they had a WAR of less than 1.5 per year when they got there, that first-rounder is a bust automatically. These players might be useful, but hardly the type that an organization should target in the first round. With that in mind, I established a simple calculation to assign a players success.

bWAR Per Season

(500 AB / 25 G for pitchers)

Under 1.5 Bust
1.5-2.5 Successful
Over 2.5 Superior

 

I likely should have built in a separate “World’s Best” category for those players who are averaging 8+ WAR.  Oh, that’s just Trout, OK.

The calculation feels like it makes sense on an anecdotal level, too.  Eric Hosmer, Yonder Alonso, and Wade Miley are labeled successful, but not superior.  That feels right.  These guys aren’t changing an organization.  They’re good major league players, but not great.

The trick comes in assigning busts, especially when considering players from more modern drafts.  Jameson Taillon has yet to achieve the mandatory 1.5 WAR, but he’s hardly a bust just yet. And what do we do with guys like Billy Butler? He’s officially a bust by my calculation, but that doesn’t feel quite right. Huston Street, James Loney, and Garrett Richards are all also busts.  Ike Davis, and Pedro Alvarez, too. But the formulas are sound.  A successful major leaguer should be able to produce 1.5 WAR per season. In 2015, Chase Headley, Nick Markakis, and Alcides Escobar all hit that threshold.  It shouldn’t be too much to expect a first-rounder to perform at that level.

Besides, this is baseball and statistics.  There’s no crying in baseball or statistics.

To the results!

First, how many of 1st rounders actually make the majors? That feels like some basic threshold of success. Is your organization capable of selecting a player in the first round that actually makes his way to the majors?

Draft Year 2000-2010
Overall Pick Average bWAR Number to Reach Majors Number Still in Minors
1-5 12.8 48 7
6-10 9.5 41 14
11-15 8.7 45 10
16-20 4.9 43 12
21-25 6.5 36 19
26-30 4.5 32 23

 

A few things jump out from the chart above. Of the 55 players selected in the top five between 2000 and 2010, 48 reached the major leagues. That seems like a really good rate. Teams were able to more or less successfully identify the best five players available in a given draft. Of course, there’s probably some bias here as teams are more likely to promote players they took at the very top of the draft to save face, even if they might not be perfectly qualified.

The pattern pretty much holds for the rest of the first round too. There’s more uncertainty as you get later and later in the draft but scouts seem to hit more than they miss. That’s a pretty low bar though. You would hope that scouts would be a bit better than 32/55 (58%) on picks 26-30, considering that there are hundreds and hundreds of players chosen.

Next, let’s look at the chance to find a successful player, as we defined it earlier, in the first round of the draft.

Chance to Find a Successful Player in the Draft
 Year pick 1-5 pick 6-10 pick 11-15 pick 16-20 pick 21-25 pick 26-30
00-05 2 5 5 3 4 1
06-10 4 3 1 0 2 2
All 6 8 6 3 6 3
Percentage 11% 15% 11% 5% 11% 5%

 

That’s pretty low. Our definition of a successful player was pretty narrow, to be sure, but it seems like 1.5 -2.5 WAR guys should be pretty prevalent. Guess not. Let’s see how front offices do on picking up superior players.

Chance to Find a Superior Player in the Draft
pick 1-5 pick 6-10 pick 11-15 pick 16-20 pick 21-25 pick 26-30
00-05 9 5 5 2 4 5
06-10 7 6 5 3 3 1
All 16 11 10 5 7 6
Percentage 29% 20% 18% 9% 13% 11%

 

Pretty well actually! Superior players should be pretty rare, at least if we set the criteria correctly, but more than a quarter of top five picks are in that category. That seems pretty good.

I’m starting to wrap my head around a theory, let’s see if this next chart bears it out…

Chance to Find a Bust in the Draft
pick 1-5 pick 6-10 pick 11-15 pick 16-20 pick 21-25 pick 26-30
00-05 19 20 20 25 22 24
06-10 14 16 19 22 20 22
All 33 36 39 47 42 46
Percentage 60% 65% 71% 85% 76% 84%

 

OK, here’s what I’ve got. It’s more likely than not that a first-round selection will be a bust. If he’s not a bust, though, it’s more likely than not that he’ll be a superior player. It seems like the chances of a first-rounder being merely successful — just a decent big-league player — are actually pretty small.

A reasonable conclusion then, is that scouts go for the proverbial home run in first-round selections. They take a bit more risk in order to try and unearth a truly unique talent. They then aim to fill out their system with more average players in the later rounds.

My research gives fans and scouts all the more reason to dream on their first-round picks from last week.

A last little bit of fun.  For the recent draft, I wanted to point out which organizations were selecting in a spot that may not yield quite the results that they are hoping for. Yankees fans, shield your eyes.

Overall Pick Who has it this year? Busts Successful Players Superior Players
1 Phillies 5 0 6
2 Reds 5 3 3
3 Braves 8 1 2
4 Rockies 9 1 1
5 Brewers 6 1 4
6 Athletics 8 0 3
7 Marlins 4 4 3
8 Padres 8 3 0
9 Tigers 9 0 2
10 White Sox 7 1 3
11 Mariners 7 0 4
12 Red Sox 8 1 2
13 Rays 8 2 1
14 Indians 10 0 1
15 Twins 6 3 2
16 Angels 9 1 1
17 Astros 9 0 2
18 Yankees 11 0 0
19 Mets 9 1 1
20 Dodgers 9 1 1
21 Blue Jays 9 2 0
22 Pirates 9 2 0
23 Cardinals 8 1 2
24 Padres 8 0 3
25 Padres 8 1 2
26 White Sox 11 0 0
27 Orioles 9 1 1
28 Nationals 8 0 3
29 Nationals 8 2 1
30 Rangers 10 0 1

 

So before you go getting all excited about the draft picks in the books, keep in mind that a majority of them are simply going to be busts. The ones that aren’t, though — they’ll probably be stars.


Hardball Retrospective – What Might Have Been – The “Original” 1975 Astros

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 1975 Houston Astros 

OWAR: 50.0     OWS: 291     OPW%: .535     (87-75)

AWAR: 28.7      AWS: 192     APW%: .398     (64-97)

WARdiff: 21.3                        WSdiff: 99  

The “Original” 1975 Astros fell six games short of the National League Western Division title as the Big Red Machine tallied 93 victories. Joe L. Morgan produced a .327 BA with 17 dingers, 94 ribbies and 107 runs scored to secure the NL MVP Award. “Little Joe” succeeded on 67 of 77 stolen base attempts and coaxed a League-leading 132 bases on balls. First-sacker John Mayberry racked up personal-bests in doubles (38), home runs (34), RBI (106), runs (95) and bases on balls (119). Rusty Staub swatted 19 big-flies and knocked in 105 baserunners. Cesar Cedeno swiped 50 bags and batted .288 while Bob “Bull” Watson delivered a career-high BA (.324) for the “Original” and “Actual” ‘Stros.

Joe L. Morgan is ranked as the top second baseman according to Bill James in “The New Bill James Historical Baseball Abstract.” “Original” Astros teammates listed in the “NBJHBA” top 100 rankings include Cesar Cedeno (21st-CF), Rusty Staub (24th-RF), Bob Watson (33rd-1st), John Mayberry (49th-1B), Doug Rader (64th-3B) and Jerry Grote (66th-C). “Actual” Astros outfielder Jose Cruz places 29th among left fielders.

 

  Original 1975 Astros                                    Actual 1975 Astros

LINEUP POS OWAR OWS LINEUP POS AWAR AWS
Greg Gross LF 1.91 14.4 Greg Gross LF 1.91 14.4
Cesar Cedeno CF 4.25 19.87 Cesar Cedeno CF 4.25 19.87
Rusty Staub RF 2.34 24.89 Jose Cruz RF 2.69 10.54
John Mayberry 1B 6.1 32.3 Bob Watson 1B 2.63 20.01
Joe L. Morgan 2B 9.44 43.74 Rob Andrews 2B 1.15 5.3
Enzo Hernandez SS -0.33 7.01 Roger Metzger SS 0.49 8.2
Doug Rader 3B 0.93 9.34 Doug Rader 3B 0.93 9.34
Jerry Grote C 2.15 17.24 Milt May C 0.6 7.5
BENCH POS OWAR OWS BENCH POS AWAR AWS
Bob Watson 1B 2.63 20.01 Cliff Johnson 1B 2.72 15.09
Derrel Thomas 2B 1.55 16.73 Wilbur Howard LF 1.52 9.93
Cliff Johnson 1B 2.72 15.09 Enos Cabell LF 0.34 7.12
Walt Williams DH 0.34 4.12 Jerry DaVanon SS 0.87 4.19
Fred Stanley SS -0.98 3.78 Ken Boswell 2B -0.11 3.51
Glenn Adams LF 0.61 3.63 Larry Milbourne 2B -0.25 1.31
Jack Lind SS -0.2 0.26 Tommy Helms 2B -0.32 1
Jesus de la Rosa 0.04 0.16 Skip Jutze C -0.5 0.88
Art Gardner RF -0.28 0.08 Jesus de la Rosa 0.04 0.16
Danny Walton 1B -0.55 0.07 Art Gardner RF -0.28 0.08
Ed Armbrister LF -0.46 0.03 Rafael Batista -0.01 0.07
Mike Easler -0.06 0 Mike Easler -0.06 0

Houston hurlers failed to generate much excitement during the ’75 campaign. Larry Dierker completed 14 of 34 starts and fashioned a record of 14-16 with a 4.00 ERA. Pat Darcy posted an 11-5 mark with a 3.58 ERA in his inaugural season. Dave Giusti furnished a 2.95 ERA and saved 17 contests despite accruing more walks than strikeouts.

 

  Original 1975 Astros                                    Actual 1975 Astros

ROTATION POS OWAR OWS ROTATION POS AWAR AWS
Larry Dierker SP 0.33 8.85 Larry Dierker SP 0.33 8.85
Pat Darcy SP 1.38 7.76 Ken Forsch SP 1.02 5.89
Ken Forsch SP 1.02 5.89 J. R. Richard SP -0.38 5.77
J. R. Richard SP -0.38 5.77 Dave Roberts SP -0.08 5.74
Roric Harrison SP -0.51 5.5 Doug Konieczny SP -0.92 3.17
BULLPEN POS OWAR OWS BULLPEN POS AWAR AWS
Dave Giusti RP 0.55 9.94 Joe Niekro RP 1.03 6.53
Tom Burgmeier RP 0.77 7.4 Mike Cosgrove RP 0.96 5.05
Mike Cosgrove RP 0.96 5.05 Jim Crawford RP 0.09 4.27
Jim Crawford RP 0.09 4.27 Wayne Granger RP -0.71 2.96
Bill Greif RP -1.04 3.26 Jose Sosa RP 0.26 2.12
Doug Konieczny SP -0.92 3.17 Jim York SW -0.04 2.07
Wayne Twitchell SP -1.37 3.05 Paul Siebert SP 0.17 1.09
Jose Sosa RP 0.26 2.12 Mike T. Stanton SP -0.55 0
Paul Siebert SP 0.17 1.09 Tom Griffin SP -1.38 0
Mike T. Stanton SP -0.55 0 Fred Scherman RP -0.41 0
Tom Griffin SP -1.38 0

 

Notable Transactions

Joe L. Morgan

November 29, 1971: Traded by the Houston Astros with Ed Armbrister, Jack Billingham, Cesar Geronimo and Denis Menke to the Cincinnati Reds for Tommy Helms, Lee May and Jimmy Stewart.

John Mayberry

December 2, 1971: Traded by the Houston Astros with David Grangaard (minors) to the Kansas City Royals for Lance Clemons and Jim York.

Rusty Staub

January 22, 1969: Traded by the Houston Astros to the Montreal Expos for Jesus Alou and Donn Clendenon. Donn Clendenon refused to report to his new team on April 8, 1969. The Montreal Expos sent Jack Billingham (April 8, 1969), Skip Guinn (April 8, 1969) and $100,000 (April 8, 1969) to the Houston Astros to complete the trade.

April 5, 1972: Traded by the Montreal Expos to the New York Mets for Tim Foli, Mike Jorgensen and Ken Singleton.

Honorable Mention

The 2013 Houston Astros 

OWAR: 26.6     OWS: 218     OPW%: .427     (69-93)

AWAR: 8.3       AWS: 151      APW%: .315    (51-111)

WARdiff: 18.3                        WSdiff: 67

Following a transfer to the American League West prior to the start of the 2013 campaign, the “Original” Astros finished dead last in the division. Nonetheless it represents a WSdiff of 67 and 18 additional wins compared to the “Actual” Astros from the same season. Hunter Pence established career-highs with 27 round-trippers and 22 stolen bases. Ben Zobrist laced 36 doubles and earned his second All-Star nod. Chris Johnson produced personal-bests in batting average (.321) and two-base hits (34). Jason Castro drilled 35 two-baggers and posted a .276 BA. Jose Altuve batted .283 and pilfered 35 bags.

On Deck

What Might Have Been – The “Original” 1984 Giants

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


How Plate Discipline Impacts wRC+

Like many of you, I spend hours on FanGraphs trying to take in as much information as possible. One of the more fascinating statistics to me is the category of plate discipline. This includes how often a batter will swing on a pitch inside or outside of the zone, how often a batter swings and misses, and many other variables that affect a player’s approach at the plate. While these numbers alone are a good indication on how a player acts at bat, I wanted to know how these numbers affected performance. For instance, it would make sense that a higher O-Swing% could lead to less-than-average hitting. The 2015 season had Adam Jones second in O-Swing%, swinging at 47% of pitches outside of the strike zone. Pablo Sandoval led the league in O-Swing% with a 48% rate. Jones recorded a 109 wRC+ while the Kung Fu Panda had a 75 wRC+.

By breaking down these Plate Discipline statistics for the 2015 season, I believe that we can get a good answer on which statistic leads to the best performance. For my methodology, I used the wRC+ and Plate Discipline leaderboard for the 2015 season. After breaking down each statistic, I compiled a top 10 and bottom 10 wRC+. Additionally, I grouped percentages to get the number of batters and average wRC+ for certain percentages.

O-Swing %

O-Swing% = Swings at pitches outside the zone / pitches outside the zone

Top 10 wRC+ Average: 97

Name O-Swing% wRC+
Pablo Sandoval 47.80% 75
Adam Jones 46.50% 109
Avisail Garcia 45.20% 83
Marlon Byrd 43.90% 100
Salvador Perez 42.50% 87
Kevin Pillar 40.10% 93
Starling Marte 39.40% 117
Gerardo Parra 39.40% 108
Freddy Galvis 39.20% 76
Nolan Arenado 38.50% 119

 

Bottom 10 wRC+ Average: 132

Name O-Swing% wRC+
Brett Gardner 22.90% 105
Ben Zobrist 22.60% 123
Matt Carpenter 22.50% 139
Paul Goldschmidt 22.40% 164
Jose Bautista 22.20% 148
Carlos Santana 21.10% 110
Francisco Cervelli 20.90% 119
Dexter Fowler 20.90% 110
Curtis Granderson 19.90% 132
Joey Votto 19.10% 172

 

Percentage Count Average wRC+
40%-48% 6 91
30%-39% 73 106
20%-29% 60 117
< 20% 2 152

O-Swing% gives us a pretty good indication of a player’s overall performance. It’s no surprise that patience and a good eye are part of a skill set that leads to a higher wRC+. For each 10-percent decrease of O-Swing percentage, batters see an increase of over 10 points for their wRC+. The top 10 wRC+ compared to the bottom 10 also tells a compelling story of what O-Swing tells us. In the top 10, we see a couple of above-average hitters like Starling Marte and Nolan Arenado. However, we also see five of the top 10 with a wRC+ under 100 and one hitter (Marlon Byrd) at 100. On the other side of the spectrum, there isn’t a hitter under 100 wRC+ in the bottom 10. The difference in wRC+ between the top and bottom 10 is 35, the biggest difference between all the statistics.

Let’s look at two very different extremes: Joey Votto and Pablo Sandoval. Sandoval had an O-Swing% of 48 percent while Votto had a 19 percent rate, which means that while Sandoval is swinging at almost half of the balls he faces, Votto is taking a little more than 80% of pitches out of the zone. Sandoval faced 1848 pitches (1287 strikes to 561 balls) while Votto faced 3020 pitches (1644 strikes to 1376 balls). Sandoval’s more than double strike-to-ball ratio and Votto leading the league in walks can both be explained by their O-Swing percentage.

Z-Swing %

Z-Swing%  = Swings at pitches inside the zone / pitches inside the zone

Top 10 wRC+ Average: 111

Name Z-Swing% wRC+
Marlon Byrd 83.20% 100
Brandon Belt 80.90% 135
Adam Jones 80.60% 109
Avisail Garcia 78.90% 83
Billy Burns 78.80% 102
Carlos Gonzalez 78.10% 114
Ryan Howard 77.80% 92
Starling Marte 77.50% 117
Kris Bryant 76.20% 136
Brandon Crawford 76.10% 117

Bottom 10 wRC+ Average: 115

Name Z-Swing% wRC+
Carlos Santana 57.90% 110
Logan Forsythe 57.70% 126
Joe Mauer 57.50% 94
Brock Holt 57.40% 98
Brett Gardner 55.80% 105
Brian McCann 55.80% 105
Mookie Betts 55.70% 119
Mike Trout 55.60% 172
Ben Zobrist 55.40% 123
Martin Prado 53.20% 100

 

Percentage Count Average wRC+
80%-83% 3 115
70%-79% 51 111
60%-69% 75 110
50%-59% 12 114

The first thing that I noticed when looking at the Z-Swing charts is the duplication of names from the O-Swing charts. Adam Jones, Avisail Garcia, Marlon Byrd, and Starling Marte showed up on both the O and Z Swing percentage top-10 while Ben Zobrist, Carlos Santana, and Brett Gardner appeared on both bottom-10 lists. This is a very mixed bag of players for both the top and bottom. Both have a 100 wRC+ hitter, the epitome of average. Both have seven hitters batting above 100 wRC+ meaning that both lists also have two hitters batting below 100. The top and bottom 10 averages are almost even. The one outlier that separates them is Mike Trout in the bottom 10 with a 172 wRC+. Seeing the same name on multiple lists can tell us a lot about a player. Someone like Marlon Byrd will swing at most of the pitches you send his way while Ben Zobrist will take a pitch outside of the zone about 77% of the time but will also take a strike 45% of the time as well.

O-Contact %

O-Contact% = Number of pitches on which contact was made on pitches outside the zone / Swings on pitches outside the zone

Top 10 wRC+ Average: 104

Name O-Contact% wRC+
Nick Markakis 86.10% 107
Michael Brantley 84.60% 135
Daniel Murphy 83.50% 110
Ender Inciarte 82.30% 100
Melky Cabrera 82.10% 91
Wilmer Flores 82.00% 95
Jose Altuve 81.70% 120
Ben Zobrist 80.90% 123
Angel Pagan 80.80% 81
Yadier Molina 80.20% 80

 

Bottom 10 wRC+ Average: 104

Name O-Contact% wRC+
Anthony Gose 55.00% 90
Avisail Garcia 55.00% 83
Nick Castellanos 53.20% 94
Ryan Howard 52.80% 92
Michael Taylor 52.10% 69
Justin Upton 51.50% 120
Addison Russell 51.10% 90
Chris Davis 50.90% 147
Kris Bryant 49.20% 136
Joc Pederson 49.00% 115

 

Percentage Count Average wRC+
80%-86% 10 104
70%-79% 46 103
60%-69% 60 118
50%-59% 23 105
< 50% 2 126

Similar to Z-Swing%, O-Contact doesn’t show much disparity between the top and bottom 10. In fact, they’re identical at 104 wRC+. A higher O-Contact gives a batter more balls in play, but doesn’t always lead to success. My initial thought was that swinging at a pitch way out of the zone can lead to weak contact, and usually an out. The fact the top and bottom are identical shows that this isn’t always the case.  It also makes sense why the middle of the pack (60%-69%) has the greatest wRC+ (besides the small sample size of < 50%). These batters are still able to make contact with pitches outside of the zone more than half of the time, but also miss the pitch enough of the time where they don’t make bad contact.

Z-Contact %

Z-Contact%  = Number of pitches on which contact was made on pitches inside the zone / Swings on pitches inside the zone

Top 10 wRC+ Average: 110

Name Z-Contact% wRC+
Daniel Murphy 97.50% 110
Ben Revere 96.70% 98
Michael Brantley 96.30% 135
Yangervis Solarte 95.50% 109
Martin Prado 95.40% 100
A.J. Pollock 94.60% 132
Jose Altuve 94.60% 120
Ian Kinsler 94.50% 111
Erick Aybar 94.30% 80
Ender Inciarte 94.20% 100

 

Bottom 10 wRC+ Average: 125

Name Z-Contact% wRC+
Mark Trumbo 80.90% 108
Brandon Belt 80.70% 135
J.D. Martinez 80.60% 137
Nelson Cruz 79.30% 158
Justin Upton 78.00% 120
Michael Taylor 77.40% 69
Joc Pederson 77.00% 115
Chris Davis 76.50% 147
Alex Rodriguez 76.50% 129
Kris Bryant 75.80% 136

 

Percentage Count Average wRC+
90%-98% 55 106
80%-89% 79 113
70%-79% 7 125

Z-Contact was the most surprising statistic in terms on its effect on wRC+, until you look at the names in the bottom 10. One would expect that hitters that hit more pitches in the zone would be the better performers. However, the bottom 10 is filled with power hitters, leading to the main difference in wRC+. Davis and Cruz were number one and two in terms of home-run leaders in 2015. In fact, besides Michael Taylor, the bottom 10 is all in the top 50 for home runs in the MLB. The list makes sense as players like Chris Davis are trying to “Crush” the ball out of the park and swing harder than someone in the top 10 like Martin Prado.

SwStrike %

SwStr% = Swings and misses / Total pitches

Top 10 wRC+ Average: 110

Name SwStr% wRC+
Avisail Garcia 17.30% 83
Marlon Byrd 17.20% 100
Ryan Howard 16.60% 92
Kris Bryant 16.50% 136
Michael Taylor 16.00% 69
Chris Davis 15.60% 147
Carlos Gonzalez 15.20% 114
J.D. Martinez 14.90% 137
Mark Trumbo 14.60% 108
Joc Pederson 14.00% 115

 

Bottom 10 wRC+ Average: 105

Name SwStr% wRC+
Ian Kinsler 5.20% 111
Ender Inciarte 4.90% 100
Andrelton Simmons 4.90% 82
Angel Pagan 4.40% 81
Martin Prado 4.30% 100
Ben Zobrist 4.20% 123
Nick Markakis 4.10% 107
Ben Revere 4.10% 98
Daniel Murphy 3.90% 110
Michael Brantley 3.10% 135

 

Percentage Count Average wRC+
15%-18% 7 106
12%-14.9% 20 107
9%-11.9% 36 117
6%-8.9% 50 114
3%-5.9% 17 106

Not surprisingly, the top 10 for SwStrike looks a combination of both the O-Contact and Z-Contact bottom 10. Obviously if your contact is low, you’re going to have more swings and misses. The main factor that stood out to me looking at the top and bottom 10 is the deviation of wRC+. The top 10 is all over the place, having players like Kris Bryant with a 136 wRC+, Michael Taylor with 69, and every level of player in between. The bottom 10 has less variation, providing a more consistent group of hitters.

Totals

Category Top 10 Bottom 10 Difference (Bottom to Top)
O-Swing% 97 132 35
Z-Swing% 111 115 4
O-Contact% 104 104 0
Z-Contact% 110 125 15
SwStr% 110 105 -5

 

As evidenced by the chart, the main statistic in regards to plate discipline to show a great change in performance that compares the bottom to the top level is O-Swing percentage. Z-Contact seems to also be relevant when evaluating and predicting a player’s performance.


The Tampa Bay Rays and the Advantages of Pulling the Ball

The Rays always seem to be at the forefront of sabermetric innovation. They employ an army of Ivy League baseball analysts in the front office, they fully embrace the shift, and they employ pitch-framing superstars. The Rays like to stay on top of the ball. For the Rays, sabermetric advancement is a means of survival. And for the Rays, in the powerhouse AL East, it is the only way to survive.

Over the past seven years, it seems the Rays have been on to something. Looking at FanGraphs team offensive data from 2009 to 2016, there is a clear pattern with the Rays. They are third in fly ball% at 37.5%. The team with the highest FB% during that time span is the Oakland Athletics. The A’s pursuit of fly-ball-happy hitters was pretty well documented. In a great article over at Deadspin from 2013, Andrew Koo (who now works for the Tigers) shows us the advantages of hitting fly balls. First, Koo highlights how fly ball rates have decreased in the league since 2009. With an increasing trend towards ground ball pitchers, Billy Beane made a clear effort to acquire fly ball hitters. Why? Because as Koo shows us, fly ball hitters are significantly better against ground ball pitchers compared to other batters.  Tom Tango, who is mentioned in the Koo article,  found that this platoon advantage is very minimal, and is really only realized and meaningful when the “advantage is multiplied through several hitters. This is exactly what the A’s and Rays have done over the past seven years. Both teams have stockpiled fly ball hitters.

The Rays have done something else too. They have stockpiled fly ball hitters that also have a knack for pulling the ball. Over the past seven years, they lead the league in Pull% at 42.8%. Looking at this year’s team, the strategy seems to be in full effect once again. Of all the Rays hitters with at least 100 PA this year, only three players (Miller, Forsythe, and Dickerson) are below the league average in Pull%. Now, it could be pure coincidence that the Rays pull the ball so much. But I think we all know this is no coincidence at all. They seem to be preaching the pull-happy approach.

When looking at offensive data on pulled balls vs. data on other batted ball directions, the strategy makes sense. Looking at league data from 2009 to 2015, the average wRC+ on balls hit to the pull side is about 157, compared to 112 on balls hit up the middle. Isolated power on balls hit to the pull side is over 100 points greater than on balls hit up the middle or to the opposite field. There is an offensive advantage to pulling the ball, when the ball is put in play. Given the clear advantage to hitting the ball to the pull side, one might ask why wouldn’t every team stockpile dead pull hitters?

One answer: conventional wisdom says dead pull hitters don’t have the right approach. From the time I started playing baseball, I have been told to hit the ball to all fields. And I don’t disagree with this philosophy. Staying back and being able to drive the ball to all fields definitely makes for a very productive hitter. But it also results in dead pull hitters being undervalued.

Another knock on pull hitters is that when they hit ground balls, they roll over the ball and commit easy outs.  Looking at the soft hit percentage vs ground ball percentage on balls that are pulled for all 30 teams from 2009-2016, I found this to be a valid concern about pull hitters. The data shows a positive correlation between ground balls and soft hit percentage. 

The Rays, however, have the fourth-lowest GB% on pulled balls. During that same time span, the Rays have the sixth-lowest Soft% on ground balls. They aren’t hitting weak ground balls. The Rays have made a concerted effort to pull the ball and they have avoided the weak contact that comes with pulling the ball on the ground.

Conclusion

The Rays have found and pounced on a market inefficiency. They have optimized their offense by targeting and developing players that consistently pull the ball in the air and avoid weak contact on the ground. Since these players aren’t the conventional hit-to-all-fields type of player, they can get these players for cheap. Simply put, the Rays have have capitalized on the offensive advantage of pulling the ball.

Food For Thought

Something to think about further is the trend of Pull% in the MLB from 2002-2016. It is down 5%. Intuitively, this makes sense, as velocity is way up over that time span. With velocity up, it is harder to pull the ball. This trend reminded me of a trend mentioned earlier in the article. As noted by Koo and Tango, ground balls are up around the MLB. As Tango found, fly ball hitters have an advantage against ground ball pitchers, and it is beneficial to utilize that advantage. What if there is a similar platoon advantage regarding pull hitters vs. power pitchers? In line with Tango’s logic, what if dead pull hitters have a platoon advantage against power pitchers? What if the Rays have figured out this advantage and have been exploiting it for years? The platoon advantage makes sense. Dead pull hitters, by nature, go up to the plate looking to pull the ball. Which means they are early on almost everything. As a result, they wouldn’t have as much trouble catching up to gas. This is definitely something to think about, and something I will be certainly researching in the coming weeks.