Ryan Howard doesn’t like infield shifts. I’m not making this up, or just surmising it. He said so after recording four outs on balls that probably would have gotten through an unshifted infield:

 “No, I don’t like it at all,” said Howard, who has grown accustomed to seeing four infielders on the right side of the infield when he steps to the plate. “That’s four hits. I mean, again, it’s nothing that I’m doing wrong, I’m hitting the ball hard. It’s just right at guys playing shifts. So, all you can do is continue to swing.”

Back in May, Craig Edwards noted that Jason Kipnis had a rotten 2014 (86 wRC+), likely in part due to an oblique injury in April that affected his performance all year. Kipnis’s 2014 was characterized by an increase in grounders, weak contact, and pulled balls. This season, of course, he’s been a monster (164 wRC+ through June 21, fourth in the American League), in part because, with a healthy oblique, he’s pulling less.

Why, fans and sportswriters ask, can’t more hitters do what Jason Kipnis has done by going the other way? Proliferating infield shifts are taking hits away from the likes of Howard, David Ortiz, and Mark Teixeira. So why don’t those players just hit the ball where they ain’t? I’m not talking about bunting. This is just about pull-happy hitters go the other way, guiding batted balls to the opposite field.

I think pretty much everyone reading this knows the answers why. First, infield shifts are effective against grounders and soft liners, and a batter with fly ball tendencies hits the ball over the shifted infielders more often than not. The Indians’ Brandon Moss is a pronounced pull hitter (48% of batted balls hit to right field) who also hits the ball in the air a lot (0.67 ground ball/fly ball ratio, fifth lowest in the majors). So he goes to the plate trying to hit over the shift. Second, and more significantly, I think, hitting, one hears, is hard. Granted, I never played professional baseball like the people on sportstalk radio who insist that Chris Davis would be a much more productive player if he’d just shorten his swing and go with where the ball’s pitched, dumping singles through the vacated space on the left side of the infield. Changing one’s approach to the plate would seem to have a concomitant risk of decreased production.

But players can and do make adjustments. And, as the case of Kipnis illustrates, those adjustments can lead to better outcomes. How often do batters rely less on pulling the ball, and what does it do to their performance?

To answer this, I looked at every batter who was a semi-regular (which I arbitrarily defined as compiling two-thirds the plate appearances necessary to qualify for the batting title) in 2014 and 2015. (My criteria worked out to 334 plate appearances in 2014 and, depending on the team, 140 or so plate appearances in 2015.) There were, through games of June 21, 186 such players. I calculated their “pull tendency” by subtracting the percentage of balls they hit to the opposite field from the percentage they hit to the pull field. Ryan Howard, for example, has a pull tendency of 31.9% this year, having hit 48.8% of batted balls to right field and 16.9% to left. I then subtracted each player’s 2015 pull tendency from his 2014 pull tendency to arrive at the change, i.e. how much more he’s going the other way in 2015. To determine the effect of the change, I calculated the change in the player’s wRC+ from 2014 to 2015 as well. Here are the 15 players who have most dramatically changed from pulling to going the other way in 2015:

Well now. That’s quite a mix. There’s probably the 2015 poster child for going the other way, Mike Moustakas, leading the way with a vast improvement in wRC+. But the median wRC+ change on the list is negative five points–players who are pulling markedly less are having less, not more, success at the plate. Seven of the fifteen players are having a better year in 2015, while the remaining eight aren’t. Of course, as with any list like this, there are caveats. The change for Moustakas is evidence of a well-executed plan. But you can make a pretty good argument that Carlos Ruiz is pulling less because,at 36, he can’t get around on pitches as he used to, and a fairly airtight argument that Victor Martinez is pulling less because he’s been playing hurt. Danny Santana, he of the .405 BABIP in 2014, was at the top of the 2015 regression list regardless of where he’d hit the ball–a lot more of them were bound to find their way into fielders’ gloves. Josh Harrison is coming off a career year. Alcides Escobar, who cares, the guy’s an All-Star Game starter! But if there’s a pattern there of pull less, hit more, I’m not seeing it.

How about players who have done the opposite–in the face of shifts, they’re pulling the ball more now than they did last year? Here’s the list:

Another mixed bag, with a negligible median change in wRC+ (-2 in this case; seven players doing better, eight doing worse). Pulling the ball more certainly hasn’t hurt Todd Frazier (leading all third basemen in wRC+) or the first two second basemen on the list. By the same token, it’s one of many symptoms of the woes of Robinson Cano (discussed in length by Jeff Sullivan here). As with players pulling less, if there’s a pattern here, that pulling more has a systemic impact, I’m not seeing it.

Admittedly, there are limits to this type of analysis. Hitting isn’t just a product of where the ball is hit. It’s affected by whether it’s hit on the ground or in the air, whether it’s hit hard or soft, whether it’s hit by a healthy batter or an injured one, whether it’s hit off a fastball or an off-speed pitch, and whether it’s hit at all or missed. I looked only at the first comparison: pulling the ball vs. going the other way, because that one characteristic has been harped on by fans and some in the media. And the data indicate that changing one’s approach–going from pulling to hitting to the opposite field, or, for that matter, vice-versa–does not appear to have a systemic change in batting outcomes. It works for some players. It doesn’t work for others. For every Mike Moustakas, it seems, there’s a Todd Frazier, or a David Peralta. When Ryan Howard hits four grounders to Kolten Wong in short right field, turning singles to right into 4-3 putouts, it may frustrate him, but that’s not to say that trying to hit the ball to left wouldn’t frustrate him even more.

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. Therefore, Roy Halladay is listed on the Blue Jays roster for the duration of his career while the Brewers declare Gary Sheffield and the Cardinals claim Mordecai Brown. 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 finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “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. Additional information and 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

Assessment

The 1953 Milwaukee Braves         OWAR: 52.2     OWS: 300     OPW%: .664

GM John Quinn acquired 88% (22/25) of the ballplayers on the 1953 Braves roster after assuming the reigns from his father Robert Quinn in 1945. Based on the revised standings the “Original” 1953 Braves secured the National League pennant by a 16-game margin over the Brooklyn Dodgers. Thus began a streak of seven consecutive National League titles while pacing the Senior Circuit in OWAR and OWS.

Eddie Mathews (.302/47/135) established career-bests in home runs, RBI and SLG (.627) during his sophomore year. The slugging third-sacker placed runner-up in the 1953 NL MVP race and led the circuit in round-trippers. Al Dark (.300/23/88) rapped 194 base hits, clubbed 41 doubles and scored 126 runs from the leadoff slot. Johnny Logan slashed 27 two-base hits and registered 100 tallies. Del Crandall walloped 15 dingers and earned the first of eight All-Star invitations. Earl Torgeson aka “The Earl of Snohomish” drove in 64 baserunners while batting .274. Bill Bruton placed fourth in the 1953 NL Rookie of the Year balloting after collecting 14 triples and leading the League with 26 stolen bases.

Mathews is listed as the third-best ballplayer at the hot corner according to Bill James in “The New Bill James Historical Baseball Abstract.” Five teammates join him in the top 100 rankings including Warren Spahn (5^th-P), Dark (27^th-SS), Logan (39^th-SS), Crandall (30^th-C) and Bruton (73^rd-CF).

Warren Spahn (23-7, 2.10) flummoxed opposing batsmen as he completed 24 of 32 starts and paced the National League in ERA, victories and WHIP (1.058). Hoyt Wilhelm aka “Old Sarge” provided 7 wins and 15 saves in 68 relief appearances. Returning from two years of military service, Johnny Antonelli delivered a record of 12-12 with a 3.18 ERA.

The “Original” 1953 Milwaukee Braves roster

Honorable Mention

The “Original” 1902 Beaneaters         OWAR: 44.1     OWS: 314     OPW%: .580

Vic Willis (27-20, 2.20) shouldered a massive workload, completing 45 of 46 starts and leading the National League with 410 innings pitched and 225 strikeouts. Alas, Boston (81-59) finished three games behind Cincinnati. Togie Pittinger (27-16, 2.52) matched Willis’ win total and registered 36 complete games. Charlie “Piano Legs” Hickman (.361/11/110) led the circuit with 193 hits and 288 total bases. Chick Stahl, Jimmy Collins, Fred Tenney, Patsy Donovan, Kitty Bransfield, Joe Kelley and Dan McGann exceeded the .300 mark in batting average.

The “Original” 1983 Braves    OWAR: 51.0     OWS: 293    OPW%: .568

Dale Murphy (.302/36/121) received his second straight NL MVP award. “Murph” topped the charts in RBI and SLG (.540) while earning the second of five successive Gold Glove Awards. Brett Butler led the League with 13 triples and Glenn Hubbard (.263/12/70) received his lone All-Star nod. Craig McMurtry (15-9, 3.08) merited a runner-up finish in the 1983 NL Rookie of the Year balloting. Larry McWilliams (15-8, 3.25) whiffed 199 batters and set career-bests in virtually every pitching category as he placed fifth in the Cy Young voting.

On Deck

The “Original” 1948 Indians

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

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

The Cleveland Indians can be a frustrating team to follow.  As a small to mid-market ball club, the Indians do not have the luxury to spend big in free agency, and when they do, they usually have to spend extra to bring in overrated players (see Kerry Wood, Nick Swisher, and Michael Bourn).  As a result, the Indians hedge their success on taking risks.  This process started in the early ’90s when they signed their young players with big potential to long-term contracts.  As a result, the Indians turned into an offensive juggernaut with stars such as Jim Thome, Sandy Alomar Jr., Kenny Lofton, Omar Vizquel, Carlos Baerga, and many others.  However, many people do not realize that the Indians also lost on some of the risks that they took in that era with cases such as Eric Plunk, Herb Perry, and so on.

This strategy has continued to this day, and has resulted in a very promising, yet frustrating team.  Carlos Santana has been productive with his high OBP, but never the player the team envisioned.  Jason Kipnis has had up and down years.  Lonnie Chisenhall has shown moments of brilliance, but has been unable to sustain those moments, resulting in his demotion to Triple-A Columbus last week.

While the offense has been widely inconsistent, the rotation has, for the most part, been brilliant.  Corey Kluber is second in the league in strikeouts with 111.  Danny Salazar, Carlos Carrasco, and Trevor Bauer have been inducing a lot of long walks back to the dugout as well, ranking tied for sixth, seventh, and ninth, respectively.  These four have a combined 3.65 ERA, which is quite remarkable considering that the most respected starting rotation in the Majors (the Nationals) has logged a 3.60 ERA.

However, this is not where the story ends.  As everyone (hopefully) knows, major-league rotations consist of five starting pitchers (unless you’re the Mets for a few weeks).  With the fifth starter added in, the Cleveland Indians rotation has an ERA of 4.37, almost three-quarters of a run higher.  This is absolutely startling.  By themselves, the fifth starters in the rotation have gone 3-8 with an 8.33 ERA.  I’ll repeat that, an 8.33 ERA.  The only fifth starter to win at all has been Shaun Marcum, the man who had not pitched in the major leagues since halfway through 2013.  In the games that a fifth starter has pitched in, the Indians have been outscored 88-48.  Opponents have nearly doubled the Indians run output when a fifth starter has taken the hill.

Are there games that the Indians should have won when a fifth starter has pitched? Absolutely.  But in order to have any shot of making the playoffs, someone has to step up in the back of the rotation and close up the black hole.  A .375 winning percentage does not a contender make.

In some ways, baseball is a simple game. For a hitter, it boils down to: see ball, swing at ball, hit ball hard, jog around bases (the Frankenstein approach). Of course, it’s not really that simple and there are many other variables involved. Still, a simple goal for a hitter would be to hit the ball hard as often as possible. With that in mind, I thought I’d investigate what happens when a batter hits the ball hard on a regular basis.

I took all players with 150 or more plate appearances so far in 2015. All data in this article is through June 14. Using players with more than 150 plate appearances gave me 236 players.

To start off, I looked at the correlation between the old-school statistic of batting average with hard, medium, and soft hit percentage. You may think that players who hit the ball hard more often would have higher batting averages. This is true. The correlation between batting average and hard hit percentage for these 236 players was .18. For both medium and soft hit percentage, the correlation was negative. The more often you hit the ball hard, the higher your batting average. The more often you hit the ball medium or soft, the lower your batting average.

Here is a table that shows the correlation for other metrics:

The statistic that correlates most with hard-hit percentage (Hard%) is Isolated Slugging (ISO), with a correlation of .73. Hitting the ball hard more often leads to getting extra-base hits more often. The top three metrics in the table—ISO, HR/FB, and SLG—are all measures of power and correlate quite nicely with Hard%. The two measures of overall hitting production—wRC+ and wOBA—also score high on this chart.

Both strikeout rate and walk rate correlate positively with Hard%. When you swing hard, you are more likely hit the ball hard and also more likely to miss, so hitters who have a higher Hard% also have higher strikeout rates, in general. Hitting the ball hard also correlates with walking more often. Perhaps pitchers a more careful to hitters who can beat them with one swing of the bat.

Hard% has a positive correlation (0.36) with fly-ball percentage, no correlation with line-drive percentage, and a negative correlation with groundball percentage.

With all of that in mind, I decided to separate hitters into groups based on hard-hit percentage and compare their composite batting lines. Consider the charts below. There’s a great deal of information here, but if you go down the column as Hard% goes down, you can see the effect on other statistics.

The group of hitters who have a hard-hit percentage of 35% or higher have combined to hit .276/.356/.491. Their .276 batting average is 9 points higher than the next-highest group, their OBP is 22 points higher, and their slugging percentage is 49 points higher. They have a .215 ISO, a solid 10.2% walk rate, and the best HR/FB rate, at 17.5%.

As the hard-hit percentage goes down, the other numbers go down also. By the time you get to the bottom group, those with Hard% below 23%, the composite batting line is .252/.303/.332, with an ISO of .080 and a HR/FB rate of 3.4%. This group strikes out less often than any other group, has the highest rate of soft-hit balls, and the lowest rate of fly balls. These are your typical light-hitting shortstops (Alcides Escobar, Elvis Andrus) and speedy outfielders (Billy Burns, Sam Fuld).

The three wRC+ columns show the number of hitters with a wRC+ at 100 or higher, the number below 100, and the percentage of above-average hitters in each group. For example, 86% of the hitters with Hard% above 35% have been above-average hitters this year, while just 19% of the hitters with Hard% below 23% have been above-average. If you’re not hitting the ball hard with some frequency, you are unlikely to be productive. The break-even point where half the players are above average hitters and half are below is in the range of 27% to 28% hard hit balls.

Let’s take a closer look at these groups.

Diamond Group (35% and higher Hard%)

.276/.356/.491, .321 BABIP

.215 ISO, 17.5% HR/FB

10.2% BB%, 22.3% K%

WAR/600 PA: 4.0

wRC+ >100: 43 players (86%)

wRC+ <100:   7 players (14%)

Best Hitter: Bryce Harper, 216 wRC+

Median Hitter: George Springer, 131 wRC+

Worst Hitter: Matt Kemp, 78 wRC+

• Top five hitters in this group, by wRC+: Bryce Harper, Paul Goldschmidt, Miguel Cabrera, Mike Trout, Anthony Rizzo.
• Middle five hitters in this group, by wRC+: Andrew McCutchen, Jose Abreu, George Springer, Adam Lind, Seth Smith.
• Bottom five hitters in this group, by wRC+: Mark Trumbo, Will Middlebrooks, Matt Adams, Steve Pearce, Matt Kemp.
• Bryce Harper is the top performing player in this group, hitting .333/.469/.721 with a Hard% of 40.4% and a 216 wRC+. He’s the Hope Diamond of Major League Baseball right now.
• Giancarlo Stanton has the highest Hard%, at 51%. Stanton’s .341 ISO is second in baseball to Harper’s .388. Stanton’s Hard% is 5% higher than the next-highest player in baseball, Brandon Belt.
• Nine of the top ten hitters in baseball by wRC+ are in this group (Nelson Cruz missed the cut with a Hard% of 32.9%.
• The seven below-average performers with a 35% or higher Hard% are Jorge Soler (96 wRC+), Jay Bruce (96 wRC+), Mark Trumbo (93 wRC+), Will Middlebrooks (80 wRC+), Matt Adams (79 wRC+), Steve Pearce (79 wRC+), and the enigmatic Matt Kemp (78 wRC+) bringing up the rear. These players are hitting the ball hard at a high rate but have still been below average hitters.
• Speaking of Kemp, what is up with this guy? After a bounce-back year in 2014, when he hit 25 home runs in 599 plate appearances, Kemp has just two homers so far in 274 plate appearances. He’s the Robinson Cano of the National League. His 35.4% Hard% isn’t bad, but it’s not as high as last year’s 40.3%. He also pulled the ball more often last year (43.8%) and his ground ball rate is at a career high (48.7%, career rate is 41.9%). He is struggling big time on fastballs after crushing fastballs in 2014. It’s been an ugly start to the year for Kemp, just ask Bud Black.
• Two players in this group who are very close to a 100 wRC+ are Ryan Howard (100 wRC+ and Pedro Alvarez (104 wRC+). They both have been terrible against left-handed pitchers, having struck out at least 30% of the time against lefties. Also, when they do make contact against southpaws, they aren’t making good contact, with identical 25% Hard% versus lefties.

Quartz Group (31% to 35% Hard%)

.265/.334/.442, .300 BABIP

.177 ISO, 14.1% HR/FB

8.6% BB%, 19.4% K%

WAR/600 PA: 2.9

wRC+ >100: 39 players (75%)

wRC+ <100: 13 players (25%)

Best Hitter: Nelson Cruz, 175 wRC+

Median Hitter: Evan Longoria, 115 wRC+

Worst Hitter: Christian Yelich, 68 wRC+

• Top five hitters in this group, by wRC+: Nelson Cruz, Mark Teixeira, Andre Ethier, Albert Pujols, Stephen Vogt.
• Middle five hitters in this group, by wRC+: Danny Espinosa, Carlos Santana, Evan Longoria, Khris Davis, Mark Canha.
• Bottom five hitters in this group, by wRC+: Justin Maxwell, Luis Valbuena, Robinson Cano, Michael Taylor, Christian Yelich.
• The magic pixie dust that Nelson Cruz was sprinkled with in April (206 wRC+) and May (188 wRC+) seems to have worn off in June (78 wRC+). His ISO has dropped from .402 to .262 to .000 by month (although he has continued to be fortunate on balls in play with a .393 BABIP in June). He’s also seen a big drop in the percentage of hard hit balls, from 40.6% in April to 30.1% in May to 21.4% this month. This has coincided with a drop in fly ball rate, from 55.1% to 27.4% to 21.4%. Cruz was never going to keep up his torrid early-season pace but he’s also not as bad as he’s looked recently.
• The player at the bottom of this group, Christian Yelich, has an above average Hard% of 34.2%. Unfortunately, his sky-high ground ball rate (69.1%) and miniscule fly ball rate (15.4%) mean those hard hit balls are not providing much production (68 wRC+).
• Another player in this group, Luis Valbuena, is having a very peculiar season. He’s hitting the ball hard (31.5% Hard%) and hitting a ton of fly balls (50.3%), which has resulted in 14 home runs in just 234 plate appearances. His career high was set last year when he hit 16 dingers in 547 plate appearances. Valbuena has seen his fly ball rate increase in each of the last three seasons from 35.4% in 2012 to his current rate of just over 50%. That all sounds very good until you look at his ugly .185/.256/.412 batting line, good for an 86 wRC+. With all of those balls flying over the wall for home runs, Valbuena has a .169 BABIP. It’s surprising that a player could hit 14 home runs in 234 plate appearances and be a below-average hitter but Valbuena is doing it.

Apatite Group (28% to 31% Hard%)

.267/.325/.417, .307 BABIP

.150 ISO, 10.8% HR/FB

7.3% BB%, 18.4% K%

WAR/600 PA: 2.5

wRC+ >100: 30 players (60%)

wRC+ <100: 20 players (40%)

Best Hitter: Jason Kipnis, 160 wRC+

Median Hitter: Torii Hunter, 110 wRC+

Worst Hitter: Alexei Ramirez, 51 wRC+

• Top five hitters in this group, by wRC+: Jason Kipnis, Josh Reddick, Justin Turner, Russell Martin, Brian Dozier.
• Middle five hitters in this group, by wRC+: Edwin Encarnacion, Trevor Plouffe, Torii Hunter, Daniel Murphy, Brad Miller
• Bottom five hitters in this group, by wRC+: Aaron Hill, Nick Castellanos, Ryan Zimmerman, Mike Zunino, Alexei Ramirez.
• Jason Kipnis tops this group of players with a 160 wRC+, 50 points higher than the median player in this group. He’s having his best season. Looking at his numbers, he’s striking out less often than he ever has and has a .375 BABIP that is 68 points higher than his career mark. He’s also hitting fewer fly balls than ever (26.9% FB% compared to a career mark of 30.8%). He’s replaced those fly balls with line drives. His current Hard% almost exactly matches his career rate, but he’s done it in an interesting way. In three of his first four seasons, his Hard% was around 27% (2011, 2012, 2014). In 2013, his Hard% was 35.3%, which would put him up in the elite group (the Diamonds). That 2013 season was his best before this year’s revival. Kipnis is not going to OBP over .400 and likely won’t slug over .500, but he’s looking more like the 2013 version of himself than last year’s colossal disappointment. Maybe this is what a healthy Kipnis looks like.
• The two players at the very bottom of this group in wRC+ are Mike Zunino and Alexei Ramirez. Zunino just strikes out way too much (36.7% K%). He has power (.159 ISO, 13.0% HR/FB) because he does hit the ball hard, but he doesn’t hit the ball hard often enough to be productive.
• Alexei Ramirez is actually hitting the ball hard more often than he has in any season in his career. His current 28.4% Hard% is quite a bit higher than his career mark of 23.7%. His batted ball profile hasn’t changed, with a similar rate of line drives, ground balls, and fly balls. He does have the lowest BABIP of his career, at .256 (career mark is .293) and he’s walking at the lowest rate of his career, although he’s never been one to walk much.

Calcite Group (25% to 28% Hard%)

.262/.313/.378, .303 BABIP

.116 ISO, 7.4% HR/FB

6.3% BB%, 16.9% K%

WAR/600 PA: 1.8

wRC+ >100: 16 players (30%)

wRC+ <100: 37 players (70%)

Best Hitter: Mike Moustakas, 133 wRC+

Median Hitter: Jean Segura, 91 wRC+

Worst Hitter: Danny Santana, 42 wRC+

• Top five hitters in this group, by wRC+: Mike Moustakas, Dustin Pedroia, Brandon Guyer, Cameron Maybin, Rajai Davis.
• Middle five hitters in this group, by wRC+: Cory Spangenberg, Juan Uribe, Jean Segura, Martin Prado.
• Bottom five hitters in this group, by wRC+: Dustin Ackley, Lonnie Chisenhall, Chris Owings, Jordy Mercer, Danny Santana.
• The Bizarro World version of Mike Moustakas tops this group. This is the first time Moustakas has ever been an above average hitter in his major league career, but it doesn’t look like Hard% has much to do with it. His Hard% of 25.6% is right in line with most of his career and close to his career average (last year’s 31.7% is an outlier). The rest of his batted ball profile is quite different, from the lowest fly ball rate and highest ground ball rate of his career, to the direction he’s hitting the baseball. After never hitting the ball to the opposite field more than 22.7% of the time in a season, Moustakas’ 33.3% Oppo% this season is a career high. That may explain some of his elevated .346 BABIP (.270 career mark). He’s also striking out less frequently than he usually does (11.1% K% to 16.1% career mark). On the other hand, looking at his monthly splits throws up a big red flag. Moustakas went opposite field 39% of the time in April, 30.7% of the time in May, and is at 26.3% in June and has seen his wRC+ drop from 170 in April to 112 in May and June (aided by a .368 BABIP in June). Whatever changes he made in April don’t seem to be sticking, as his batted ball locations in June look much more like his career marks than they did in April. This Tiger may be reverting back to his original stripes. It seems strange that he would consciously make that change despite being so effective in April, so it could be that pitchers have adjusted and are pitching him differently.
• Among this group of 53 players with Hard% between 23% and 28%, Salvador Perez has the most home runs, with ten. This is interesting because Perez has had much higher Hard% rates over the last three years, when his lowest mark was 29.8%. This year, he’s hit fewer hard hit balls but has the highest HR/FB of his career. That doesn’t seem like something that can continue going forward.

Talc Group (23% and below Hard%)

.252/.303/.332, .290 BABIP

.080 ISO, 3.4% HR/FB

6.4% BB%, 14.8% K%

WAR/600 PA: 1.1

wRC+ >100:   6 players (19%)

wRC+ <100: 25 players (81%)

Best Hitter: Nori Aoki, 131 wRC+

Median Hitter: Eric Sogard, 69 wRC+

Worst Hitter: Rene Rivera, 24 wRC+

• Top five hitters in this group, by wRC+: Nori Aoki, Jacoby Ellsbury, Jose Iglesias, Billy Burns, Dee Gordon.
• Middle five hitters in this group, by wRC+: Alcides Escobar, Elvis Andrus, Eric Sogard, Freddy Galvis, Jimmy Rollins.
• Bottom five hitters in this group, by wRC+: Melky Cabrera, Chase Utley, Jose Ramirez, Omar Infante, Rene Rivera.
• If you aren’t hitting the ball hard on a regular basis, you better find some holes. The top 12 hitters by wRC+ in this group have BABIPs at .311 or higher and the top five are significantly higher than that: Aoki–.344 BABIP, Ellsbury–.379, Iglesias–.367, Burns–.366, Gordon–.418.
• On the other hand, the players at the bottom of this group in wRC+ are not only struggling to hit the ball hard but also struggling to get those balls to drop in for hits: Utley–.189 BABIP, Jose Ramirez–.205, Infante–.241, Rene Rivera–.198. Of course, this may not stop Infante from starting the All-Star game, but that’s a whole different topic.
• These players don’t hit home runs, for the most part. Of this group of 31 players, just two have more than four home runs and 25 of the 31 have 0 to 2 home runs.
• Stephen Drew leads this group with 9 home runs, despite a Hard% of 20.4%. His career rate is 30.6% and he had a 38.8% Hard% in 2013, the last year he was an above average hitter (109 wRC+). In that 2013 season, Drew had a fly ball rate of 41.6%. Drew then went unsigned prior to the 2014 season and missed spring training and the first two months of the year before joining the Red Sox in early June. He appears to be a very different hitter than he’d been before. His Hard% has been 23.2% and 20.1% in 2014 and 2015 after regularly being around 30% in previous seasons. He’s also greatly increased his rate of fly balls, from a consistent 40-42% from 2009 to 2013 to around 50% the last two seasons. Along with the increase in fly balls is an increase in the number of balls he pulls. His career rate is 41.2%. Over the last two seasons he’s pulled the ball over 50% of the time. Along with these changes in batted ball profile, Drew has a .182 BABIP since joining the Red Sox in early June of 2014. It’s hard to believe that missing a half season could result in such a dramatic change in a player’s batted ball profile, but it may have happened to Drew and it’s not a good thing for him.
• Jimmy Rollins is the other hitter in this group with more than four homers. He currently has seven. His HR/FB rate is 10.6%, which would be the highest he’s had since 2007. Unfortunately, that’s about all he’s done well on offense, as he is hitting .199/.260/.336 (.265 wOBA, 68 wRC+).

Delino DeShields currently has a .395 BABIP en route to a .291 average. A .395 BABIP is probably unsustainable, but I was shocked when I saw the Steamer projection of .287 BABIP for DeShields, going forward. A .287 BABIP for a guy like DeShields is just unreasonable. He’s one of the fastest guys in the league, and his baseline BABIP should be well above .300 as he can turn groundouts into infield singles.

So I decided to crunch some numbers, which ultimately confirmed my suspicions. A BABIP of .286 is too low.  Looking at batted ball data, .315 is what I calculated his expected BABIP to be going forward. I’ll explain below:

DeShields has 13.6% infield hit%.

League average is 6.7%.

DeShields is more than twice as likely to get an infield hit, which is 6.9% more likely than average to get a hit in general. As a side note, he’s also 50% on bunting for hits, which is astounding (also more than twice league average).

Baseline BABIP for groundballs is .232

Add a DeShields speed .069 infield groundball advantage, and therefore you’re looking at a DeShields baseline groundball BABIP of .301.

Line drives are the best — .690 baseline BABIP according the source above. Fly balls have .218 baseline BABIP. Speed shouldn’t have much of an effect on these so I’m not adjusting them, other than accounting for infield fly balls which are guaranteed outs.

I’m going to calculate the expected BABIP for DeShields based on the above data. The expected BABIP will equal the summation of the following:

Flyballs — .218 x .261 (26.1% FB, minus the difference between DeShields IFFB and league average, which is .111 minus .095 = .016; .261-.016=.245) = .05341

Groundballs — .301 x .638 (63.8% GB) = .192038

Line Drives — .690 x .101 (10.1% FB) = .06969

= .315 BASELINE BABIP

We can take that average and take away his strikeouts/walks to determine his expected batting average/OBP going forward.

22.1% Ks. So we’ll take the baseline BABIP multiplied by .779. = .245 expected batting average.

13.1% BBs. So we’ll take the baseline BABIP multiplied by .648 (Ks and BBs out) = .204. Add back the BBs. = .335 expected OBP.

I haven’t even gotten into directional placement of grounders, so it could be true that DeShields is even better than these projections I just calculated.

Regardless, league averages are .252 average and .314 OBP. DeShields is proving to be roughly a league-average hitter by expected batting average, and clearly above-average hitter if you’re looking at expected OBP.

In other words, DeShields is here to stay.

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. Therefore, Jim Edmonds is listed on the Angels roster for the duration of his career while the Astros declare Rusty Staub and the Athletics claim Lefty Grove. 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 finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “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. Additional information and 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

Assessment

The 2002 Toronto Blue Jays         OWAR: 51.4     OWS: 312     OPW%: .572

GM Pat Gillick acquired 65% (35/54) of the ballplayers on the 2002 Blue Jays roster. 43 team members were drafted by the club. Based on the revised standings the “Original” 2002 Blue Jays captured the American League Eastern Division title by nine games over the New York Yankees and topped the Junior Circuit in OWAR and OWS.

The middle of the Blue Jays’ batting order was stacked. Shawn Green (.285/42/114) scored 110 runs and placed fifth in the MVP balloting. Jeff Kent (.313/37/108) laced 42 doubles and recorded a career-best in home runs. Carlos Delgado tallied 103 runs scored and blasted 33 round-trippers in the midst of a ten-year streak with at least 30 home runs per season (1997-2006). John Olerud (.300/22/102) rapped 39 two-base knocks and garnered his second Gold Glove Award. Shannon Stewart contributed a .303 BA and registered 103 tallies from the leadoff spot. Alex S. Gonzalez slashed 27 doubles and clubbed 18 circuit clouts while fellow shortstop Chris Woodward batted .276 with 13 dingers. Vernon Wells produced a .275 BA with 23 four-baggers and 100 ribbies.

Kent placed 48^th at the keystone position in “The New Bill James Historical Baseball Abstract” and Olerud ranked 53^rd among first sackers.

Roy “Doc” Halladay (19-7, 2.93) led the American League with 239.1 innings pitched and merited the first of eight All-Star invitations. David “Boomer” Wells equaled Halladay’s win-loss record. Billy Koch amassed 11 victories and saved 44 contests while Jose Mesa closed out 45 games with a 2.97 ERA. Steve Karsay (3.26, 12 SV) and Ben Weber (2.54, 7 SV) provided solid relief in the late innings.

 The “Original” 2002 Toronto Blue Jays roster

Honorable Mention

The “Original” 2001 Blue Jays           OWAR: 51.5     OWS: 297     OPW%: .547

Toronto outpaced Boston to claim the A.L. East by a four-game margin. Shawn Green dialed long distance 49 times and plated 125 baserunners. John Olerud (.302/21/95) earned his second All-Star nod. Carlos Delgado launched 39 moon-shots and Jeff Kent drilled a career-high 49 two-baggers.

On Deck

The “Original” 1953 Braves

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

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

I’ve been thinking about this one a lot, and I think people in general still view Price as a top-10 pitcher. I’ve seen him appearing in expert lists as such, and that’s the general vibe I’ve gotten from the fantasy community. I just think top-10 at this point is too high, especially when we’ve got such talented young stars ranked below him, both according to the expert lists and public perception (I’m talking about guys like Archer, deGrom, and Cole).

I’d actually have him closer to top 20-25 at this point (there are so many great pitchers). His K/9 has plummeted to 7.6 and K-BB% has fallen nearly nine percentage points to 14.3%.

At 14.3%, David Price is the No. 39 pitcher in the league in K-BB%.

Am I putting too much stock into a small sample, or has the decline begun, but people haven’t realized it yet (he still sports a solid 3.15 ERA)?

His peripherals also support his regression, as his xFIP is 3.94 and SIERA is 3.87.

Encouraging signs: FIP still has him at 3.27. Swinging strikes are similar to last year at 10.4% (only 0.2% difference). No velocity loss — in fact, his fastball is faster this year than last year.

Over his career, however, Price has been only slightly better than average at giving up/suppressing home runs, so I think xFIP and SIERA are the better ERA estimators than FIP. League average HR/FB is 10.8%, and Price was at 9.7% last year, 8.6% in 2013, 10.5% in 2012. So he may be slightly better than average, but unlikely to maintain 6.6% going forward.

It’s also worth noting that last year’s 9.8 K/9 was a career high. In 2013, he had a 7.3 K/9. From 2010-2012 his K/9 hovered in the 8s (and in 2008 and 2009 his K/9 was also sub-8, although I don’t give any weight to that at all as he was still developing as a pitcher). It could be that his high K/9 last year was an aberration.

I’m choosing to give weight to his current K-rate and peripherals (the sample size is now significant), while accounting for some improvement (this is David Price after all). Doing that, by my rough calculations, I’m looking at about a 3.5+ ERA ~8 K/9 pitcher going forwards.

Those are quality numbers, but not top-10 numbers, which is where people still value him. I’d flip Price for any top-20 pitcher with upside in an instant.

I don’t have an answer as to why the K-rate has plummeted so far. I did take a look at his usages, and he seems to have reduced the usage of his two-seam fastball. His entire career, that has been his most-used pitch. Last year he used it 40%. This year, he’s only throwing it 23% of the time, instead favoring a four-seam fastball as his dominant pitch. I believe this *may* be related to his K-rate drop, but it’s just an observation at this point. Regardless, we’ve reached the point in the season where it might be wise to be proactive.

Imagine a batter hits a long fly ball that’s destined for the right-field seats only for the outfielder on the other team to clear the wall and rob him of his home run. In traditional stat sheets, this is treated the same way as any other out and there’s no real way of distinguishing that from a dribbler down the third-base line. But intuitively we know that these are two very different things, and a batter who does more of the first is going to end up being more valuable than one who does more of the second. Thus, if we wanted to truly measure how well a player has performed, we need to separate the performance from the results. The best way of doing that is to break down a batted ball in the most granular way possible and look at the average performance for similar batted balls, and today I’ll reveal a personal tool to do this. This work was inspired by Tony Blengino’s terrific posts on batted-ball data, and I suggest reading his introductory post as background on the theory and methodology that I employ.

This tool uses information on the type, velocity, direction, and distance of a hitter’s batted balls to calculate an expected AVG, OBP, and SLG for him. It divides batted balls into buckets based on the type (GB, FB, LD, PU) and either the direction and velocity or the direction and the distance and calculates the resulting AVG and SLG for all batted balls that meet that criteria. It then goes through all of a batter’s plate appearances and uses these data to calculate both the observed and expected AVG/OBP/SLG for each PA. The table below shows the top 30 hitters by Expected wOBA (xwOBA) as of 5/26/2015.

The tool uses the velocity and direction, rather than the distance and direction, of a batted ball to calculate the expected values with a few exceptions. If the velocity is not available for a fly ball or a line drive, it uses the distance and the direction of the batted ball to calculate the expected values. If the velocity of the batted ball is not available for a ground ball, the tool assumes it was of average velocity and only considers the direction it was hit when calculating the expected values. It does not consider distance for ground balls, as the distances are calculated using where the ball was fielded, so using distance would be describing what actually happened rather than what we expected to happen. For all line drives and fly balls hit over 375 feet it uses distance and direction rather than velocity and direction. The reason for this is that I do not have information on the hang time of batted balls, and in going through the data I found that fly balls and line drives that traveled over 375 feet but weren’t hit very hard were being severely underrated by the tool. As an example of the underlying data, the table below shows the reference data for fly balls hit to center field.

I’m providing a link to a Google Sheets document with a leaderboard for all qualified batters, along with leaderboards broken down by each batted ball type. The document also contains a reference page that contains all the information for how batted balls performed in each bucket based on 2015 StatCast data for velocity references and 2014-2015 MLBAM data for distance references. The numbers in the reference page will continue to be updated as more data becomes available from StatCast. Feel free to look through this section and point out any inconsistencies you may see, and note that all data comes from BaseballSavant.

I’ve also provided a Methodology Example in the document so you can dig through what the behind the scenes data looks like as it’s being processed. Note that you may see some discrepancies in a player’s actual AVG seen here and his AVG seen elsewhere, as I treat sac flies as regular outs. The “Notes” tab gives a general outline of the procedure, and also contains a link to an Excel sheet that you can download to perform these calculations on your own.

https://docs.google.com/spreadsheets/d/1-XohbJlWIceDS2Rc8_7-rOxv9avU3IwMCecPkUNxlYU/edit?usp=sharing

Before I wrap up, I should also mention the limitations. It’s been noted elsewhere on FanGraphs that the StatCast data isn’t always completely accurate. Also, the tool currently doesn’t incorporate a player’s speed in any way, so guys like Dee Gordon are going to be fairly underrated in terms of their ground ball performance. I’ve been brainstorming ways to incorporate this and am open to any input you may have. Furthermore, I’ve noticed the tool can be pretty stingy with labeling balls as pop-ups and occasionally pretty generous with labeling them as line drives. I’ve noticed some fly balls with velocities over 95 MPH that only traveled 300 feet, indicating they were hit almost straight up in the air. Unfortunately, without data on the vertical angle of the ball off the bat or on the hang time of the ball in play, it will be difficult to fix this issue.

Even with these limitations, the tool works extremely well at determining how well guys have been hitting the ball and identifying who has been helped or hurt by factors beyond their control. Take the time to dig through the data and the code and point out areas for improvement, and I’ll incorporate them in future versions.

Previously, I looked at hitters and how their current in-season performance has changed their rest-of-season projections. Here, I do the same for pitchers.

I limited my analysis to starting pitchers. For starting pitchers, I compared their preseason Depth Charts projections to their rest-of-season Depth Charts projections, then found the five starters with the biggest positive and negative differences in their projected ERA. All statistics are from May 27^th.

The Biggest Losers

Adam Warren (+0.29 in ERA)

50.7 IP, 3.91 ERA, 4.54 FIP, 4.42 xFIP, 1.24 WHIP—current

166 IP, 3.81 ERA, 1.28 WHIP—preseason

115 IP, 4.10 ERA, 1.33 WHIP—rest-of-season

Used exclusively as a reliever in 2014, Adam Warren had a career-high 8.7 K/9 and career-low 0.5 HR/9. As a starting pitcher this year, Warren has seen his K/9 drop to 5.7 and his HR/9 go up to 1.1. Even though he has a 3.91 ERA, his 4.54 FIP and 4.42 xFIP explain why his rest-of-season ERA projection has increased by 0.29 from his preseason projection. He’s lost a little more than two miles per hour on his fastball, from 94.2 to 91.9 but has increased the use of the fastball at the expense of his slider and has seen his contact percentage go up from 76% in 2014 to 81% this year. Warren is a good illustration of the difficult transition from reliever to starter. As a reliever, he threw harder and used his slider more and struck out more batters in short stints. As a starter, he’s throwing his slider less, using more two-seam fastballs and generating fewer strikeouts.

Phil Hughes (+0.29)

64.7 IP, 4.59 ERA, 4.60 FIP, 4.16 xFIP, 1.29 WHIP—current

205 IP, 3.66 ERA, 1.15 WHIP—preseason

144 IP, 3.95 ERA, 1.19 WHIP—rest-of-season

Phil Hughes had an unreal 1.9% walk rate last year that was coupled with the best strikeout rate he’s had as a starting pitcher (21.8%). This year, he’s still been quite stingy with the walks (2.2% BB%) but his strikeout rate has dropped to 15.1%, his lowest mark since 2011. The biggest change in his pitch types (per PITCHf/x) has been a dramatic rise in the percentage of two-seam fastballs, from 5.2% last year to 23.9% this year, with a drop in four-seam fastballs (61.8% to 50.2%) and cutters (16.7% to 12.8%). He’s also lost about one mile per hour on his fastball. Batters are making more contact on his pitches and they are doing damage with the long ball, which had long been a problem for Hughes before last season. From 2010 to 2013, Hughes allowed 1.4 HR/9 and had a HR/FB rate of 10.8%. Last season, those numbers dropped to 0.7 HR/9 and 6.2%. This year, he’s back to his old ways, giving up 1.6 HR/9 and a 12.3% HR/FB. Looking at Hughes’ last six years as a starting pitcher, it’s clear that his 2014 season was the aberration.

Matt Garza (+0.29)

57 IP, 6.00 ERA, 5.31 FIP, 4.37 xFIP, 1.58 WHIP—current

185 IP, 3.96 ERA, 1.26 WHIP—preseason

124 IP, 4.25 ERA, 1.32 WHIP—rest-of-season

Garza came into this season with a walk rate consistently in the range of 7.5% over the previous five seasons but has seen that rate jump to 10.5% so far this year. He also may be experiencing age related decline as his strikeout rate is on pace to decline for the fourth straight year right along with his fastball velocity, which has also gradually declined over that time from 93.8 miles per hour in 2011 to 92.5 this year. Like other pitchers in the “biggest losers” category, Garza has had trouble with home runs, giving up 10 so far in 57 innings with a career-high 18.2% HR/FB rate. Last year, pitching for the same team in the same ballpark, Garza allowed just 12 home runs in 163 1/3 innings (7.0% HR/FB). He’s also had trouble stranding runners for the second year in a row (67.5% LOB% this year, 66.6% last year).

R.A. Dickey (+0.28)

64 IP, 5.77 ERA, 5.70 FIP, 4.80 xFIP, 1.33 WHIP—current

209 IP, 4.08 ERA, 1.28 WHIP—preseason

140 IP, 4.36 ERA, 1.33 WHIP—rest-of-season

Dickey’s preseason Depth Charts projection called for a 7.1 K/9, which was reasonable considering he’d been over seven strikeouts per nine in each of the last three seasons. Unfortunately, the knuckleballer has seen his strikeout rate plummet this year. Through 10 starts, he’s at 4.9 K/9. Dickey has also seen his walk rate go up in each of the last three seasons and he’s giving up more homers than he has since establishing himself as an above-average starting pitcher with the Mets in 2010. So, strikeouts down, walks and homers up, that’s a recipe for disaster.

Taijuan Walker (+0.28)

43 IP, 7.33 ERA, 5.48 FIP, 4.84 xFIP, 1.84 WHIP—current

158 IP, 3.98 ERA, 1.30 WHIP—preseason

116 IP, 4.26 ERA, 1.34 WHIP—rest-of-season

Taijuan Walker pitched well in 11 games and 53 innings over his first two partial seasons in the major leagues at 21 and 22 years old (2.89 ERA, 3.28 FIP, 1.21 WHIP). He then ramped up the hype machine with a sterling spring training this year that saw him give up just two runs in 27 innings with 26 strikeouts and five walks. He had a nice projection for a 23-year-old pitcher coming into this season. Unfortunately, he’s been lit up so far in 2015, posting a 7.33 ERA and 1.84 WHIP. His .356 BABIP and 63% LOB% are a big part of the problem, as are the eight home runs allowed in 43 innings (1.7 HR/9, 14.5% HR/FB). His FIP (5.48) and xFIP (4.84) are much better than his actual ERA, but still nothing to be excited about, which is why his rest-of-season projection for ERA is 0.28 higher than it was before the season started.

That 14.5% HR/FB should come down but Walker has also allowed many more fly balls than he did last season, up from 26.2% to 39.0% so he is likely to give up more home runs than he has in the past even with regression in his HR/FB rate. He’s also giving up a higher percentage of hard-hit balls, increasing from 23.8% last year to 34.3% this year. On the bright side, his fastball velocity has stayed at the same level since his rookie year. Walker still has the raw skills to be a good major-league pitcher but a combination of giving up more fly balls and seeing more fly over the fence, along with a below-average left-on-base percentage has really hurt him this season.

The Biggest Winners

Michael Pineda (-0.40)

64.3 IP, 3.36 ERA, 2.54 FIP, 2.52 xFIP, 1.14 WHIP—current

142 IP, 3.74 ERA, 1.20 WHIP—preseason

117 IP, 3.34 ERA, 1.12 WHIP—rest-of-season

Last year, Phil Hughes had the second-lowest walk rate of any pitcher who qualified for the ERA title since 2001. He walked just 1.9% of the batters he faced. Only Carlos Silva in 2005 (1.2%) has had a better rate over the last 15 years. This year, Michael Pineda is walking even fewer batters than Hughes did last year, allowing just 1.7% of the batters he’s faced to reach via the base on balls. Pineda has the added bonus of a 25% strikeout rate to go with that miniscule walk rate. Along with elite strikeout and walk rates, Pineda has added the ability to induce groundballs to his arsenal. He came into this year with a career groundball rate of 37.2%. This year, his groundball rate is up to 52.4%. He’s turned into Felix Hernandez Lite. Imagine if the two of them were on the same staff. Jesus (Montero) that would be nice for Mariner fans. Pineda’s 3.34 ERA is actually higher than you’d expect based on his peripherals, mainly due to a .339 BABIP and 68.3% left on base percentage.

Jake Odorizzi (-0.24)

66.3 IP, 2.31 ERA, 2.52 FIP, 3.61 xFIP, 0.96 WHIP—current

178 IP, 4.05 ERA, 1.31 WHIP—preseason

136 IP, 3.81 ERA, 1.27 WHIP—rest-of-season

Jake Odorizzi has seen his strikeout rate drop from last year’s 24.2% to 20.2% this year but he’s offset that with a walk rate that has dropped at an even greater rate, from 8.2% last year to 4.6% so far this year. According to PITCHf/x Pitch Types, he’s changed his pitch arsenal in a number of ways. Consider the chart below:

Odorizzi has dropped his usage of the four-seam fastball and almost completely eliminated his slider and increased the use of his two-seamer, cutter, and splitter. The result has been a lower strikeout rate but better control and an increase in ground ball percentage, from 29.9% last year to 41.2% this year. Last year, Odorizzi gave up the second-highest percentage of fly balls of any pitcher who qualified for the ERA title, at 48.7%, behind only Chris Young (58.7%). This year, he’s 34^th out of 109 pitchers who qualify. Fewer fly balls generally mean fewer home runs allowed and Odorizzi has seen a big drop there also. Last year, he gave up 20 homers in 168 innings. This year, he’s allowed just two long balls in 66 1/3 innings, although a very low 2.8% HR/FB rate is part of that decrease and he’s not likely to sustain a home run per fly ball rate that low for the whole season. Still, despite the lower strikeout rate, Odorizzi has been even more effective this year than previously because he’s made up for it with better control and more ground balls.

Jake Arrieta (-0.23)

58 IP, 2.95 ERA, 2.39 FIP, 2.68 xFIP, 1.09 WHIP—current

173 IP, 3.62 ERA, 1.25 WHIP—preseason

127 IP, 3.39 ERA, 1.21 WHIP—rest-of-season

Over the first four years of his career, Jake Arrieta had a 5.23 ERA (4.75 FIP) and 1.43 WHIP. He struck out 17.5% of the batters he faced, walked 10.2%, and induced ground balls on 43.3% of his balls in play. Since the beginning of 2014, Arrieta has a 2.64 ERA (2.29 FIP), and 1.02 WHIP. He’s struck out 27.1% of the batters he’s faced, walked 6.5%, and has a 49.8% ground ball rate. All of the most important things you want a pitcher to do better, he’s done better. He’s done this by changing his pitch arsenal. If you go by Baseball Info Solutions pitch types, Arrieta started throwing cutters nearly 30% of the time in 2014 and has continued to do so this season after not throwing any cutters from 2010 to 2012 and throwing it 6% of the time in 2013. According to PITCHf/x pitch types, he went from throwing sliders roughly 14% of the time prior to the 2014 season to nearly 30% of the time this year and last. I don’t know enough about the difference in classification between the two sources but Arrieta made a change of some sort and it is working. It may be more cutters, it may be more sliders. Maybe it’s a slutter. Whatever it is, this change in pitch type that has resulted in much improved numbers since the beginning of last season would suggest to me that even that improved rest-of-season projection is likely going to come in on the high side for Arrieta.

Aaron Harang (-0.22)

65 1/3 IP, 1.93 ERA, 2.86 FIP, 4.30 xFIP, 1.03 WHIP—current

155 IP, 4.51 ERA, 1.40 WHIP—preseason

125 IP, 4.29 ERA, 1.37 WHIP—rest-of-season

Based on some metrics, Aaron Harang is off to a terrific start. He’s currently seventh in WAR among all pitchers with a sub 2.00 ERA. His 2.86 FIP is also quite good but that comes with a caveat—Harang’s 2.3% HR/FB rate, which would be much better than anything he’s ever done before. In his career of over 2215 innings, Harang has allowed 9.9% of his fly balls to leave the yard, so it’s hard to believe he has somehow magically developed the ability to limit home runs on fly balls. He has improved his walk rate but is also striking out fewer batters and has a .258 BABIP, which is much lower than his career .304 BABIP. All signs point to regression from his current numbers, but his rest-of-season projection doesn’t think he’ll be as bad as his preseason projection thought he’d be. That’s still not good, of course.

Chris Archer (-0.21)

59.3 IP, 2.12 ERA, 2.46 FIP, 2.59 xFIP, 0.99 WHIP—current

183 IP, 3.69 ERA, 1.29 WHIP—preseason

138 IP, 3.48 ERA, 1.26 WHIP—rest-of-season

Chris Archer was good in 2013 and 2014 but has turned it up a notch this year. Over the first two years of his career, Archer had a 21.2% strikeout rate, 7.9% walk rate, and 46.3% ground ball rate. This year, he’s increased his strikeout rate to 30.7%, dropped his walk rate to 7.5%%, and increased his ground ball rate to 52.5%. Archer has increased his slider usage by about 10% over last year (based on BIS and PITCHf/x) and is generating more swinging strikes than he ever has, up to 12.1% this year compared to 9.3% last year.

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. Therefore, Roy Halladay is listed on the Blue Jays roster for the duration of his career while the Braves declare Joe Torre and the Brewers claim Prince Fielder. 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 finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “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. Additional information and 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

Assessment

The 2005 Los Angeles Angels      OWAR: 48.2     OWS: 309     OPW%: .560

GM Bill Bavasi acquired 43% (17/39) of the ballplayers on the 2005 Angels roster. 32 team members were drafted by the club. Based on the revised standings the “Original” 2005 Angels battled the Athletics down to the wire as Oakland seized the pennant by a lone game. The Halos earned the Wild Card entry as a consolation prize.

Jim Edmonds (.263/29/89) anchored the third slot in the lineup and played flawless defense in center field to earn his eighth Gold Glove Award. Troy Glaus manned the hot corner and batted cleanup for the Halos, thumping 37 round-trippers and knocking in 97 baserunners. Bengie Molina supplied a career-high .295 batting average along with 15 jacks and 69 ribbies. Darin Erstad triggered the offense with 33 two-base hits from the leadoff slot.  Garret Anderson (.283/17/96) cracked 34 doubles and merited his third All-Star selection. Eduardo Perez (.255/11/28) and Mark Sweeney (.294/8/40) provided additional power potential while platooning as the designated hitters.

John Lackey compiled a record of 14-5 with a 3.44 ERA and a personal-best 199 strikeouts. Jarrod Washburn fashioned a 3.20 ERA but failed to earn a decision in 13 of his 29 starts. Ervin Santana accrued 12 victories in his rookie campaign despite an ERA of 4.65.

Francisco J. Rodriguez (2.67, 45 SV) captained the Halos’ sensational bullpen. “K-Rod” whiffed 91 batsmen in 67.1 innings. Roberto M. Hernandez (8-6, 2.58) and Scot Shields (10-11, 2.75) locked down the late-inning threats. Matt Wise contributed a 3.36 ERA and a WHIP of 0.964 to round out the relief corps along with Bobby Jenks (50 K’s in 39.1 IP) and Scott Schoenewis (3-4, 3.32).

The “Original” 2005 Los Angeles Angels roster

Honorable Mention

The “Original” 1997 Angels               OWAR: 40.3     OWS: 313     OPW%: .547

Los Angeles outdistanced Seattle by a seven-game margin, taking the Western Division title with a record of 89-73. Tim “Kingfish” Salmon (.296/33/129) recorded a personal-best in RBI. Damion Easley joined the 20-20 club as he swiped 28 bases and belted 22 long balls while scoring 97 runs. Roberto M. Hernandez registered 10 victories and saved 31 contests with a 2.45 ERA. Darin Erstad posted a .299 BA, tagged 34 doubles and pilfered 23 bags. Jason Dickson (13-9, 4.23) made his lone All-Star appearance and finished third in the 1997 A.L. Rookie of the Year balloting.

On Deck

The “Original” 2002 Blue Jays

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

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive