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, Minnie Minoso is listed on the Indians roster for the duration of his career while the Giants declare Hack Wilson and the Mariners claim Ichiro Suzuki. 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 1946 Detroit Tigers         OWAR: 58.3     OWS: 303     OPW%: .599

GM Jack Zeller acquired 42.5% (17/40) of the ballplayers on the 1946 Tigers roster and fellow front office executive Mickey Cochrane added 35% (14/40). Based on the revised standings the “Original” 1946 Tigers topped the Junior Circuit in OWAR but finished two games behind the Red Sox.

The Tigers’ ferocious rotation featured future Hall of Famer Hal Newhouser (26-9, 1.94). “Prince Hal” led the circuit in victories for the third consecutive season, collected his second straight ERA title and paced the League with a 1.069 WHIP. Newhouser finished runner-up in the MVP race following back-to-back MVP Awards in 1944-45. Dizzy Trout recorded 17 wins and fashioned an ERA of 2.34. Johnny Sain (20-14, 2.21) returned from military service and notched at least 20 victories in four of the next five campaigns. Fred Hutchinson (14-11, 3.09) and Virgil Trucks (14-9, 3.23) bolstered the back-end of the rotation. Schoolboy Rowe contributed an 11-4 record with a 2.12 in 16 starts.

In his penultimate campaign Hank Greenberg clubbed 44 circuit clouts and knocked in 127 runs to lead the American League in both categories for the fourth time. Rudy York (.276/17/119) eclipsed the century mark in RBI for the sixth time in his career. Roy Cullenbine posted a .335 BA with a .477 OBP while fellow outfielder Barney McCosky batted at a .318 clip.

Greenberg placed 8^th among first basemen according to Bill James in “The New Bill James Historical Baseball Abstract.” In addition to “Hammerin’ Hank,” seven ballplayers from the 1946 Tigers ballclub registered in the “NBJHBA” top 100 rankings including Hal Newhouser (36^th-P), Rudy York (56^th-1B), Virgil Trucks (61^st-P), Birdie Tebbetts (64^th-C), Roy Cullenbine (68^th-RF), Barney McCosky (70^th-CF) and Hoot Evers (100^th-LF).

The “Original” 1946 Detroit Tigers roster

Honorable Mention

The “Original” 1915 Tigers     OWAR: 52.4     OWS: 299     OPW%: .598

Detroit edged Boston by a single game to secure the American League pennant in 1915. Ty Cobb (.369/3/99) swiped a career-high 96 bases while accruing 51 Win Shares and 9.5 WAR. “The Georgia Peach” claimed his ninth consecutive batting title and topped the leader boards with 144 runs scored, 208 safeties and a .486 OBP. Bobby Veach (.313/3/112) delivered League-bests in RBI and doubles (40). Ossie Vitt registered 116 tallies. Hooks Dauss established a personal record with 24 victories.

On Deck

The “Original” 1983 Cardinals

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’m not going to tell you anything you can’t find out for yourself.  This is just a little research on Mr. Chen.  Alternative title would’ve been Chen Music, but I couldn’t find proof of an increase in high and inside fastballs.  Anyways:

Wei-Yin Chen’s surface level numbers have been great this year:

18 GS,   2.86 ERA,   1.12 WHIP,   93 K/116.1 IP

The thing is, he’s been just as good dating back to Jul 1^st of 2014:

33 GS,   2.88 ERA,   1.14 WHIP,   164 K/209.2 IP

His peripherals over that time have declared him lucky and say that this success in unsustainable.  His FIP, xFIP, and SIERA for each half have been quite different from the ERAs he’s put up.

Look, I get it, he doesn’t strike out even 20% of the batters he faces and he can struggle with the long ball.  But the Orioles’ defense is ranked 3^rd in the league by UZR, and 3^rd by UZR/150.  Ahead of the Orioles are the Rays and the Royals.  Each of these teams are outperforming their ERA indicators by a decent amount.

This does not mean that every pitcher on each of these teams is outperforming their peripherals but it’s obvious (and not because of that table) that defense helps pitchers’ numbers.  I also understand that Camden Yards is a little bit more of a hitters’ park than Kauffman and Tropicana, but that shows up in Chen’s numbers as he has surrendered HR at the rate of 1.29/9 IP at home and 0.89/9 IP on the road (July 3rd 2014 – present).  To be fair, I don’t know if 112 IP and 97.2 IP (home and away, respectively) are large enough sample sizes compared to his full body of work to be worth anything, but let’s say they are, and let’s see what Chen has done differently over his last 209.2 IP compared to his first 422 big league innings.

(209.2 denotes the last 209.2. IP by Chen, spanning from July 3rd, 2014 to his last start against the Yankees, and the 422 is the 422 IP prior to July 3rd of last season, which encompasses the rest of his career)

Even though his ground ball rate doesn’t lead to much confidence in terms of sustainability in that soft contact management, he still is inducing pop-ups at an above-average rate.  So whether it’s a change in sequencing or it’s just as easy as working ahead in more counts, there has been some variation in his pitch usage…another table.

Obviously he’s traded some curveballs and change-ups for sliders.  His fastball has become increasingly more valuable in 2015 at 8.9 runs above average, compared to 3.3 runs above average from 2014 which was his previous high.

The last thing he’s done better is pound the zone early in counts which has led to a slight decrease in batters’ plate discipline against him.

(Almost) Everywhere you want to see improvement there is improvement even if you have to look through a magnifying glass.  Granted, this could be Chen adjusting to the league and now the league will adjust to him.  It would be perfect for him to just cleanly split from the success he’s been having after the all star break and after this piece.

In conclusion, it’s hard to know what to make of Chen as a fantasy option in the long term because he is experiencing a deflated BABIP and a higher LOB% than he has in the past.  Is it all about the luck??  I’m not too bullish on him; the tweaks he has made, while they have led to some slightly positive results, do not warrant picking him up in a dynasty league, but if you’re behind in starts or innings Chen seems to be a solid option for QS/ERA/WHIP this season if he can thwart off the regression monster.  After all that, I did not recommend him in his start against the Yankees and their .325 wOBA (results on that game were meh – it was a QS, but he gave up 10 H in 6.1 IP, 3 ER, and struck out 3) but he’s at Tampa (94 wRC+) after that.  Projecting ahead, he’d face the Tigers (113 wRC+ which is best in the majors, but they could be selling some pieces and they will still be without Miguel Cabrera), and the Athletics (99 wRC+)who are also sellers.  After that it’s likely the Mariners and their 92 wRC+; I’d take that 4 start stretch.  Something to scratch your Chen about.

Contact quality is a recurring sabermetric theme.  Much discussion over the last decade has centered around how we interpret Voros McCracken’s groundbreaking analysis, where he showed that the majority of variance in a pitcher’s ERA was driven by the rates at which he recorded strikeouts, walks, and home runs allowed.  This led to the conclusion by many that the batting average on balls in play (excluding homers) was largely outside of a pitcher’s control, and further research has probed the influence of team defense, home ballpark, and other outside factors on differences in BABIP.

Nevertheless, pitchers like Dallas Keuchel and Chris Young seem to have above-average success in “pitching to contact”,  even after allowing for outside factors.  To better understand such outliers from the standard fielding-independent pitching model, I have developed a new bottom-up  framework to analyze the quality of contact allowed, using the newly-available batted-ball data from MLB Advanced Media (via Baseball Savant).  This model takes all batted balls (including homers) and calculates the expected run value based upon how hard the ball was hit (“exit velocity”) and the estimated angle at which it left the bat (“vertical angle”).  In addition to the contact quality model, I’ve also developed a parallel model to estimate the defense-independent expected run value from batted-ball data (yes, contact quality and defense-independent run value are two different things.)

Relationship to FIP

The key difference between the Comprehensive Contact Quality Model and FIP is the integration of expected home runs allowed into the analysis.   Various metrics such as xFIP have attempted to account for the volatility in HR% by normalizing this rate as a fixed percentage of fly balls allowed.  A different perspective is to treat home runs as one extreme in a broad spectrum of contact quality:

           Swinging strike < Foul tip < Weakly-hit fair ball < Well-hit fair ball

This spectrum ranks how well the hitter has “squared up” on the ball, with better-struck balls further to the right. Home runs can be considered a subset of well-hit fair balls, where the likelihood of actually becoming a four-bagger depends primarily upon the distance travelled, which itself is a function of exit velocity, vertical angle, and a host of other factors.   So, when we talk about a pitcher’s ability to limit the long ball, what we’re really talking about is his ability (if any) to prevent the ball from being hit hard at an optimum angle to leave the park.

With that brief introduction, let’s outline the framework for valuing the contact quality on any batted ball.  First, for balls hit in the air:

Step 1  – Estimate the Probability of a Home Run

For this first iteration of the model, I made the following simplifying assumptions:

• Exactly 1/30 of all outfield fly balls are hit in each MLB ballpark
• The direction of these balls is distributed 20% LF to LC, 30% LC to CF, 30% CF to RC, and 20% RC to RF
• Outfield dimensions are as currently posted in Wikipedia

Also since distance in the MLBAM data is measured to the assumed landing point, we also need to adjust for the height of the outfield wall.   To do this, I used Dr. Alan Nathan’s excellent trajectory calculator to estimate the complete distance traveled by a ball that is W feet above the ground when it passes over the outfield wall, where W is the height of the wall.   Note that this distance will be further for line drives than it will be for high flies, so the necessary distance for a home run will depend upon both the listed distance to the wall and the vertical angle of the batted ball.

[Caution – next section is somewhat technical; you can safely skip and not miss the gist of this article]

One problem with the MLBAM data found on Baseball Savant is that batted-ball angles are only available for home runs.  For other batted balls, we can use the fact that we have both the batted-ball velocity and distance to back-solve for the vertical angle:

1.  Make grid of distance = f(exit vel, angle), using the default settings in Dr. Nathan’s trajectory calculator:

(Key values shown below – columns are vertical angle, rows are exit velocity)

2.  Distance peaks at a certain “optimal” vertical angle then decreases.  This means that there are 2 possible solutions for the vertical angle when doing a lookup based upon distance and exit velocity.  Lacking any other information, I used the batted-ball type recorded by the Baseball Scoresheet stringers to guide which value to use:

LD uses lower of the two angles, PU uses higher of the two, FB uses mean of the two

This becomes our estimate of vertical angle on the batted ball.

[End of technical note]

Now, for each of the 30 MLB ballparks, we can use the combination of distance and vertical angle to estimate the probability of a homer, assuming the pull/center/opposite mix assumed above (note – version 0.0 of this model does not reflect batted ball direction).  After averaging across all ballparks, we get a grid of home run probabilities for any outfield fly ball:

Step 2 – Estimate BABIP if Not a Home Run

One big benefit of hitting the ball over the fence is that virtually no chance of making an out.  For balls hit in the air to the outfield, however, there typically three guys whose goal it is to catch the ball in order to get the batter out.  Now while a little bit of extra loft on a hard-hit OF fly can improve the chance of a dinger, for balls that stay in play the relationship between BABIP and vertical angle is essentially linear (using first-half 2015 data):

    BABIP if hit in the air to OF = .9698 – .0256 * MIN(37.5, angle)

We will use this in conjunction with the next step to determine the run value of a non-homer fly/popup/liner.

Step 3 – Estimate Expected Run Value If A Hit (Non-HR)

For balls not caught by the outfielder, the chances for an extra-base hit vary by vertical angle and also increase for higher exit velocities.  Regressing the first-half 2015 data (using hits to the outfield only) results in this estimate:

RV if hit to OF =  -1.06 + 0.0206*velocity – 0.00006*velocity^2 + 0.0223*angle – 0.000318*angle^2

We can now calculate the contact-quality run value as:

     CQRV = (1.38 x HR Probability) + (RV if hit to OF x (1 – HR Probability))

Contact Quality Run Values for Ground Balls

For ground balls, the expected run value increases with increasing exit velocity.  We can estimate the CQRV directly from the following regression equation:

CQRV = 0.35-0.0174*velocity+0.00014*velocity^2, if velocity > 65; else CQRV = -0.19

Note that the expected run value is set to -0.19 for velocity less than 65 MPH.  This is because the run expectancy actually improves for grounders hit at a very low speed (basically dribblers and slow rollers).  Because this is a model of contact quality, we are not going to penalize the pitcher for poor batted-ball luck when the actual quality of contact is low.

This leads us to a discussion of the last key feature of the model….

Contact Quality vs. Expected Batted-Ball Result

The CQ model is designed to produce higher run values for better quality of contact.   However, as discussed in Tony Blengino’s enlightening series on batted-ball outcomes, real-life BABIP doesn’t improve continuously with higher batted-ball velocity, but instead actually decreases over the stretch between balls hit relatively shallow and balls hit to the deeper parts of the outfield.  The CQ model calculates BABIP as a function of vertical angle in order to avoid rewarding pitchers for the better-struck balls that fall into the “donut hole” near the depths where outfielders normally position themselves.

I chose vertical angle to model BABIP for the CQ framework because of its close relationship to hang time, which in turn is a key component of the likelihood of the outfielder making the putout.  In reality, batted-ball location also plays an important role in determining whether a fielder can range into position to catch the ball.  To model this more realistic BABIP, I estimated what proportion of balls hit a certain distance would be reachable by one of the three outfielders, given a certain amount of hang time (note – hang time can be estimated by Dr. Nathan’s trajectory calculator based upon exit velocity and vertical angle).    For example, an arc 320 feet from home plate is roughly 502 feet long from foul line to foul line.   If we assume that each outfielder can cover 52 feet in 3.0 seconds, then we can draw a circle with a 52 foot radius from each fielder’s initial position and estimate the overlap between the arc and these circles to be about 237 feet.  So we assign a 47% chance (237 divided by 502) of catching a fly ball hit 320 feet with a 3.0 second hang time.  If we increase the hang time to 4.0 seconds, the coverage circles now have an 87 foot radius, and 479 feet of the arc are covered, for a 95% chance of an out.

Here is how the more realistic BABIP varies based upon both batted-ball distance and hang-time.  Note the “donut hole” for balls hit around 300 feet with hang times in the neighborhood of 4 seconds.

This neatly explains why fly balls hit at 85 MPH often result in an out, while line drives hit that hard are most often base hits.

If we substitute the hang-time based BABIP for the vertical-angle based BABIP used in the CQ model, we obtain a batted-ball-data expected run value that is more realistic and truly fielder-independent.  Unfortunately, this metric (let’s call it BBRV) doesn’t do as well as CCRV in measuring the actual quality of contact, since it rewards a pitcher allowing an 85 MPH/25 degree angle fly (.032 expected BABIP) more than a pitcher who gives up a 75MPH/25 degree bloop (.537 expected BABIP).

In short, we can see that fielding-independent pitching consists of two parts:  contact quality allowed, and batted-ball luck.

Some Actual Results…

Well, with all that said, what does CCRV version 0.0 tell us about pitchers so far in 2015?

First, let’s look at the actual run expectancy above average allowed on batted balls (using linear weights).  Here are the top 5 and bottom 5 through the first half of 2015:

No real surprises for those who’ve followed this year’s FIP/BABIP outliers (though Greinke’s never been this successful on batted balls – maybe he’s the guy who’s heisted Kyle Lohse’s secret formula for contact management.)

Now, let’s look at CQRV:

The only mildly interesting name in the bottom five is Phil Hughes, who has returned to allowing a high HR% after conquering the gopher ball in 2014.  In the top five, we see saber-fave Brad Ziegler, whose ridiculous .177 BABIP/0.45 HR/9 combo is driven far more by low contact quality than by batted ball/defensive luck.  We also see two very surprising names at #4 and #5.   Brandon Maurer has allowed a .238 BABIP along with just 1 HR in 44 innings, thanks to a career high 27% soft hit percentage alongside a career low 21% hard hit percentage.  Alex Wilson has likewise improved his contact management numbers (25% soft hit/21% hard hit) to drive a .270 BABIP with just 2 longballs allowed.

Finally, it’s interesting to note Clayton Kershaw’s numbers.  Despite having a BABIP north of .300 for the first time since his rookie season, Kershaw has been well above average in terms of stifling contact quality.  But, between having fewer fly balls than average dying in the outfield “donut holes” (3 runs) and other batted-ball/defensive factors (10 runs), Kershaw has been a few runs worse than average on balls in play. (Not that he needs any help to remain brilliant).

Conclusion

I have chosen to call this version 0.0 of the CCQM framework because in essence this is as much a “proof of concept” as a potential tool.   Two key areas will require continuous research and review to fully power up this model.

First, the raw data used to develop the model is new and evolving.  As more MLBAM data becomes publically available, there will be a more robust historical track record of fundamental physical stats behind every play made, which will improve the reliability of the model.

Second, the framework itself needs to be tested further to make sure that any variables that truly affect contact quality are considered.  For example, I consciously chose to not include batted-ball direction as a factor for this first version of the model in order to avoid extra complexity.  In effect, this was equivalent to a null hypothesis that pitchers cannot influence batted-ball direction.  It would be foolish not to test the validity of this assumption for future iterations of the model to see if there are pitchers who consistently show the ability to improve their performance by influencing the batted-ball direction, all other factors being equal.

My hope is that the CCQM model sparks a fresh round of discussions on the whole notion of contact quality, leveraging this whole new generation of metrics at our disposal.

When preparing for the baseball season we will practice by playing intersquads to ensure we get as many live at-bats and innings as possible. Since it would not be affordable to hire umpires for our daily practices our assistant coaches will rotate umpiring behind the pitching mound. We have a big squad, I am talking 31 pitchers alone on the team, so in the interest of not playing until the sun rises the strike zone will expand quite a bit. It is easy for me to look great when our coaches will call strikes the hitters normally take. Offense can be limited during these practices as pitchers tend to dominate and hitters often are walking away frustrated.

Following the Nationals and Dodgers matchup on Sunday, Bryce Harper expressed his displeasure with umpire Bill Miller’s strike zone. In a recent ESPN article Harper explained “when you’re getting 6 inches off the plate, its tough to face” (Zack Greinke). Was Harper just trying to downplay the performance that Greinke put on or is there merit to the comments Harper said?

During the July 19^th game between the Dodgers and Nationals, Zack Greinke had 10 pitches called for strikes outside the strike zone.

Here is Greinke’s pitching plot courtesy of Brooks Baseball:

So Harper is not incorrect by saying that Greinke was the beneficiary of some balls being called strikes. This year, Greinke has thrown 1905 pitches according to Baseballsavant.com and of those 142 pitches (7.45%) have been called strikes outside the strike zone. Currently, Greinke has the 5^th most called strikes outside the strike zone only behind Dallas Keuchel, Jon Lester, Yovani Gallardo and Mike Leake.

Looking at the man behind the plate, Bill Miller, he has the highest percentage of called strikes outside the strike zone at 17.5%. Since 2010 Bill Miller has ranked in the top two for umpires in called strikes outside the strike zone four times with an average of 16.9%.

Well if that wasn’t enough to convince you that Bryce Harper was on to something, let’s look at Yasmani Grandal. Grandal, according to StatCorner.com, and taking catchers who have caught over 2000 pitches, has the 3^rd highest percentage of strikes called outside.

Possibly Greinke and catcher Yasmani Grandal game-planned knowing Miller was behind the plate so they exploited his tendency. It could possibly be that on that day Greinke was a beneficiary of his normal game plan. This year of the 1905 pitches 1262 of them have been outside the strike zone. An umpire with a large zone, a fantastic pitch-framer behind the dish and a pitcher who lives outside the zone sounds like a recipe for strikes being called outside the zone.

In the end, sorry Bryce, that is just how baseball works — the zones are never the same.

The 2015 MLB season has been filled with plenty of surprises thus far. The Twins have maintained their hot start, and currently hold the first wildcard spot in the AL. The NL Central has been highly competitive, with three teams in position to make the playoffs should the season end today. The Padres have been a huge disappointment (although I never fully bought into them), and may be sellers at the deadline just seven months after making the biggest splashes of the winter. Sleeper picks Cleveland and Seattle might as well have been asleep the first half of the year, both putting together extremely underwhelming performances and effectively ending their postseason hopes.

But no over- or under-achieving organization quite took the league by storm like the Houston Astros. They began the year hotter than any team in baseball besides St. Louis, finishing May with a record of 32-20 and a four-game lead over the second-place Angels. In their last 43 games, however, it has been a much different story. They have gone 20-23, enduring one seven-game losing streak in June, and they began the second half on a six-game losing streak (also losers of 8 of their last 9). Back in mid May, amidst all the frenzy over the already anointed playoff bound Astros, I began to wonder what was propelling this team to victory. It was clear that although their starting pitching doesn’t blow anyone away with immense velocity or stuff, they had a set of guys who were displaying that they knew how to pitch and could hold their own in a MLB rotation. Here are the splits on the Astros’ starting pitching, using May 31^st as the divider.

I made sure that only active players on the Astros’ roster were included due to the fact that there are many insignificant players whose numbers would have been included in the splits because of spring training. The ERA and FIP numbers are very similar, as are the BABIP and LOB% stats. The two interesting changes are the increase in strikeouts and the simultaneous increase in walks. Walks lead to runs, and since the Astros have not been great offensively, the more free passes given out the more likely they will be playing from behind in games. While Dallas Keuchel has been extraordinary, and Lance McCullers has been solid as a rookie, their rotation doesn’t seem to have enough to strike fear into opposing team’s hearts (see 1990’s Atlanta Braves).

They do however appear to have a solid bullpen, possessing the 4^th best ERA at 2.67 and the 4^th best LOB% at 80%. Their recent scuffles have them at an overall record of 49-42, currently a half game back of the Angels. According to the computers, they have a 55.3% chance of making the playoffs in some capacity this year, and are expected to finish with a record of 84-78 — good enough to win a wild card spot. The computer’s calculations aren’t always perfect, so it is safe to say that there is definitely a margin of error here, although I cannot say for certain what that number might be (probably ±3-4 wins). Regardless of what the analysts are saying, I believe they WILL NOT make the postseason. Why? Because history is not on their side.

After a lot of hard work entering in all of the numbers by hand, I finally have created a table that houses several statistics from all playoff teams starting in 1995 when the wild card was introduced. Take a look at these numbers:

If the season were to end today, and the Astros made the playoffs, they would have some heavy outliers among the last 20 years worth of playoff teams. They would have the second lowest batting average, the highest K%, and the lowest OBP. Their ISO is well above average, but the fact that they run such a low OBP means that those extra base hits won’t increase their expected run totals very much; you need guys on base to score runs. Their average walk rate is to be expected based on the lineup they have assembled. They have a lot of what I would call “hackers,” guys who go up and take massive cuts trying to crush the ball — Chris Carter, Evan Gattis, Luis Valbuena, and Colby Rasmus to name a few. The only way this lineup gets worse in the ‘K’ department is if you bring Adam Dunn back from the dead and trade for Mark Reynolds. My point is simple: there has never been such a boom or bust type of team to make the playoffs, at least not one this extreme. Even if they were to acquire a frontline starting pitcher like Johnny Cueto or Cole Hamels, I do not believe that their lineup would be able to support the pitching staff enough to catapult them into the postseason.

Only time will tell what happens with the darlings of the MLB this season. They have a strong core, and a bright future, with many top prospects making their debut this season and even more right on the doorstep. Jeff Luhnow has done an incredible job building this team, and there is no doubt that they will be contenders in the AL West for many years to come. Yet, while they are not the same Astros of recent memory, they are not quite ready to make the postseason. This may not be a bad thing, though. As Yogi Berra once said, “You can observe a lot by watching.”

(This is Part 4 of a four-part series answering common questions regarding starting pitchers by use of discrete probability models. In Part 1 we explored perfect game and no-hitter probabilities, in Part 2 we further investigated other hit probabilities in a complete game, and in Part 3 we predicted the winner of pitchers’ duels. Here we project the probability of scoring at least one run in various base runner and out scenarios.)

V.  I Don’t Know’s on Third!

Still far from a distant memory, the final out of the 2014 World Series was preceded by an unexpected single and a nerve-racking error that brought Alex Gordon to 3^rd base with two outs. Closer Madison Bumgarner, who was on fire throughout the playoffs as a starter, allowed the hit but would be left in the game to finish the job. There is some debate as to whether Gordon should have been sent home rather than stopped at 3^rd base , but it would have taken another error overshadowing Bill Buckner’s to get him home; also, next up to bat was Salvador Perez, the only player to ever ding a run off Bumgarner in three World Series. So even though the Royals’ 3^rd Base Coach Mike Jirschele had to make a spur of the moment critical decision to stop Gordon as he approached 3^rd base, it was a decision validated by both statistics and common sense. We will show our own evidence, by use of negative multinomial probabilities, of how unlikely the Royals would have scored the tying run off of Bumgarner with a runner on 3^rd with two outs and we will also consider other potential game-tying or winning situations.

Runs are generally strung together from sequences of hits, walks, and outs; in the situations we will consider, we will only focus on those sequences that lead to at least one run scoring and those that do not. Events not controlled by the batter in the box, such as steals and errors, could also potentially reshape the situation and lead to runs, but we’ll take a very conservative approach and assume a cautious situation where steals are discouraged and errors are extremely unlikely.

Let A and B be random variables for hits and walks and let P(H) and P(BB) be their respective probabilities for a specific pitcher, such that OBP = P(H) + P(BB) + P(HBP) and (1-OBP) is the probability of an out; we combine the hit-by-pitch probability into the walk probability, such that P(BB) is really P(BB) + P(HBP) because we excluded hit-by-pitches from our models, P(HBP) > 0 against Bumgarner in the 2014 World Series, and the result on the base paths is the same as a walk. The first negative multinomial probability formula we’ll introduce considers the sequences of hits, walks, and an out that can occur after two outs have been accumulated, setting the hypothetical stage for the last play in Game 7 of the 2014 World Series.

In the 2014 World Series, Bumgarner’s dominantly low P(H) and P(BB) were respectively 0.123 and 0.027 and his (1-OBP) was 0.849; by applying these values to the formula above we can generate the probabilities of various hit and walk combinations shown in Table 5.1. The yellow highlighted cells in the table represent the combination of hits and walks that would let Bumgarner escape the inning without allowing the tying run (given a runner on 3^rd with two outs and a one run lead). By combining these yellow cells, we see that the odds were overwhelmingly in in Bumgarner’s favor (0.873); all he had to do was get Perez out, walk Perez and get the next batter out, or walk two batters and get the third out.

Table 5.1: Probability of Hit and Walk Combinations after 2 Outs

The Royals could have contrarily tied the game with a simple hit from Perez given the runner on 3^rd and two outs, yet this wasn’t the only sequence that would have kept the Royals hopes alive. Three consecutive walks, one walk and one hit, or any combination of walks and one hit could have also done the job; examples of these sequences are shown in the graphics below:

Generally, any combination of walks and hits not highlighted yellow in Table 5.1 would have tied or won the World Series for the Royals. This glimmer of hope was a quantifiable 0.127 probability for Kansas City, so it was justified that Gordon was kept at 3^rd rather than sent home after shortstop Brandon Crawford just received the ball. It would have taken an error from Crawford or Buster Posey, with respective 0.033 and 0.006 2014 error rates, to get Gordon home safely. The probability 0.127 of winning the game from the batter’s box is noticeably three times greater than the probability of winning it from the base paths (where Crawford and Posey’s joint error probability was 0.039).

We should note that the layout in Table 5.1 is a simplification of what could occur with a runner on 3^rd, two outs, and a one run lead, because it only applies to innings where a walk off is not possible. In innings where a walkoff can occur, such as the bottom of the 9th, the combinations of walks and hits captured in the red highlighted cells are not possible because they would occur after the winning run has scored and the game has ended. However, Bumgarner was so dominant in the World Series that these probabilities are almost non-existent, thereby making our model is still applicable; we would otherwise exclude these red-celled probabilities for less successful pitchers.

The next probability formula considers the sequences of walks, hits, and outs that can occur after one out has been accumulated, which is situation definitely worth examining if there is a lone runner on 2^nd base.

Once again we’ll use Bumgarner’s 2014 World Series statistics to evaluate this formula and insert the probabilities into Table 5.2. According to the sum of the yellow cells, Bumgarner would be able to prevent the tying run from scoring (from 2^nd base with one out) with a probability of 0.762 and would otherwise allow the tying run with a probability of 0.238.

Table 5.2: Probability of Hit and Walk Combinations after 1 Out

To get out of the inning unscathed, Bumgarner would need to prevent any further hits or allow fewer than 3 walks given a runner on 2^nd with 1 out; it would be possible to advance the runner to on 3^rd with 2 walks and then sacrifice him home in this situation (with no hits), but this probability is insignificantly tiny especially for a dominant pitcher like Bumgarner. Once again we depict these sequences that could get the tying run home from 2^nd with 1 out, with the second out inserted randomly.

A runner on 2^nd base with one out is a scenario commonly manufactured in an attempt to tie the game from a runner on 1^st with no outs situation. The logic is that if the hitting team is down by one run and the first batter leads off the inning with a single or walk, the next batter can control getting him into scoring position and hope that either of the next two batters knocks the run in with a hit. However, this method of control, a bunt, sacrifices an out to move the runner from 1^st to 2^nd. The defense will usually allow the hitting team to move the runner into scoring position for an out, but the out wasn’t the only sacrifice made. The inning is truncated for the hitting team with one less batter and the potential to have more hitters bat and drive in runs is reduced. Indeed, against a pitcher like Bumgarner, the out is likely not worth the meager 0.238 probability of getting that runner home.  We’ll see in the next section what exactly gets sacrificed for this chance at tying the game.

We should note that in this “runner on 2^nd with 1 out” model we added few more assumptions to those we made in the prior “runner on 3^rd with 2 outs” model, neither of which should be farfetched. The first assumption is that with the game close and the manager intent on tying the game rather than piling on runs, he should have a runner on 2^nd base fast enough to score on a single. Another assumption is that the base runners will be precautious enough not to cause an out on the base paths, yet aggressive enough not to get doubled up or have the lead runner sacrificed in a fielder’s choice play. Lastly, we assume that the combinations of hits, walks, and outs are random, even though we know the current state of base runners and outs can have a predictive effect on the next outcome and the defensive strategy used. By using these assumptions we simplify the factors and outcomes accounted for in these models and reduce the variability between each model.

The final probability formula considers the sequences of walks, hits, and outs that can occur when we start with no outs accumulated; this allows to forge situation will allow us to forge the outcomes from a runner on 1^st with no outs scenario and compare them to a runner on 2^nd with 1 out scenario.

Table 5.3 below uses Bumgarner’s 2014 World Series statistics, the same as before, although in this model we deal with more uncertainty because the sequences captured in each box are not as clear cut between run scoring or not given a runner on 1^st with no outs. The yellow and non-highlighted cells are still the respective probabilities of not allowing and allowing the tying run to score, however, we now introduce the green probabilities to represent the hit and walk combinations that could potentially score a run but are dependent on the hit types, sequences of events, and the use of productive outs. These factors were unnecessary in the prior two models because in those models any hit would have scored the run, the sequence of events was inconsequential, and the use of productive outs was unnecessary with the runner is already on 2^nd or 3^rd base (except when there is a runner on 3^rd and a sacrifice fly or fielder’s choice could bring him home).

Table 5.3: Probability of Hit and Walk Combinations after 0 Outs

We must break down each green probability into subsets of yellow probabilities representing the specific sequences that would not score the tying run from 1^st base with no outs; we depict these sequences below, but for simplicity, not all are depicted.

Now that we know the conditions when a run would not score, we take the probabilities from the green cells in Table 5.3, narrow them down according to the proportion of sequences and the proportion of hit types that would not score the run, and separate them based on the usage of productive and unproductive outs; the results are displayed in Table 5.4. For example, there are 6 possible combinations for 1 hit, 1 walk, and 3 outs and 3 of these 6 combinations would not score the tying run on a single, where P(1B | H) = 0.755, with unproductive outs; yet, the run would score with productive outs, with unproductive outs on a double or better, or with unproductive outs and the other 3 combinations. When we finally sum these yellow cells, they tell us that an aggressive manager would score the tying run against Bumgarner with a 0.370 probability and Bumgarner would escape the inning with a 0.630 probability. Otherwise, a less aggressive manager would score the tying run with a mere 0.154 probability and Bumgarner would leave unscathed with a significant 0.846 probability.

Table 5.4: Probability of No Runs Scoring after 0 Outs

We summarize the results from Tables 5.1-5.4 into Table 5.5 from the perspective of the hitting team.  We compare their chances of success not only against Madison Bumgarner from the 2014 World Series but also against Tim Lincecum, Matt Cain, and Jonathan Sanchez from the 2010 World Series.

Table 5.5: Probability of Allowing at least One Run to Score

Let’s return to the scenario that is the launching point for this study… The hitting team is down by one run and there is a runner on 1^st base with no outs. If the game is in its early innings, where it is not mandatory that this runner at 1^st gets home, the manager will likely decide against being aggressive and avoid sacrificing outs in order to increase his chances of extending the inning to score more runs; there are several studies supporting this logic. Yet, if the game is in the latter innings and base runners are hard to come by, the manager should lean towards utilizing productive outs and intentionally sacrifice the runner from 1^st to 2^nd base. His shortsighted goal should only be to tie the game.  By forcing productive outs rather than being conservative on the base paths, his chances of tying the game increase significantly (between 0.216 and 0.271) against our four pitchers given a runner on 1^st and no outs scenario.

However, the if the manager does successfully orchestrate the runner from 1^st to 2^nd base with a productive out, he does still lose a little bit of probability of tying the game; between 0.132 and 0.215 of probability is lost against our pitchers. And if he decides to sacrifice the runner further from 2^nd to 3^rd base with another out, his team’s chances would decrease again by a comparable amount; this decision is ill-advised because a hit is likely going to be needed to tie the game and the hitting team would be sacrificing one of two guaranteed chances to hit in this situation. In general, the probability of scoring at least one run decreases as more outs are accumulated, regardless of the base runners advancing with each out. The manager could contrarily decide against sacrificing his batter if he has confidence that his batter can hit the pitcher or draw a walk, yet the imperative goal is still to tie the game. The odds of tying the game actually favor an aggressive hitting team that is able to get the runner to 2^nd base with one out, by an improvement ranging from 0.012 to 0.084, over a less aggressive team with a runner at 1^st with no outs. Thus, even though sacrificing the runner from 1^st to 2^nd base does decrease the chances of tying the game, it would be worse to approach the game lifelessly when the situation demands otherwise.

Joe DiMaggio’s foot was “parboiled” by a trainer, delaying his Major League debut in 1936. This footnote, no pun intended, occurred in St. Petersburg, Florida and was immortalized with a plaque along Baseball Boulevard.

For some baseball fans, seeing a game in each of the 30 Major League ballparks is a dream. Others take it to the next level, creating checklists that contain minor league ballparks, spring training facilities, museums and historical markers. While professional baseball stadiums are relatively easy to find, baseball-themed museums and memorials are often tucked away in locations far from the beaten path. Frankly, the number of baseball museums and historical markers across the United States and Canada is staggering.

One of the most ambitious baseball historical marker projects ever was installed in St. Petersburg in 1998. The Jim Healey and Jack Lake Baseball Boulevard was named for the two men who campaigned for a Major League team in St. Petersburg. Costing over $47,000, home plate-shaped plaques were installed in chronological order listing a significant event from each year of St. Petersburg baseball from 1914 through 1998. These highlights often had a humorous tenor, recalling not only the cooking of Joltin’ Joe’s foot but Babe Ruth having been chased off of Crescent Lake Park field by an alligator in 1925, a 1940 game played by men riding donkeys and the infamous Sidd Finch hoax in 1985. The trail culminated with a plaque celebrating the arrival of the Devil Rays as the city’s first year-round home team in 1998.

Recently, this author took in a Tampa Bay Rays game at Tropicana Field and set out the next day to walk Baseball Boulevard. Armed with an article from the Tampa Bay Times detailing the route, the course was plotted – at nearly a mile and a half in length – the plaques were to be found at intervals starting at Al Lang Stadium, up 1st Street and traveling down Central Avenue before turning towards Tropicana Field at 13th Street.

Arriving at the starting point, it was apparent that remodeling work was underway at Al Lang Stadium. Named for the former mayor who championed spring training in St. Petersburg, Al Lang Stadium was the spring training home for Major League teams from 1947 through 2008. It is now the home field for the Tampa Bay Rowdies, a professional soccer team with a loud green and yellow color scheme.

The first of the commemorative plaques was easily found, highlighting the first spring training game ever held in St. Petersburg, a Chicago Cubs 3-2 victory over the St. Louis Browns at Coffee Pot Park in 1914. Also easy to spot was a marker honoring Al Lang, himself, and the trailhead legend, which reads:

Florida’s love affair with baseball began in St. Petersburg in 1914 when the city’s former mayor, Al Lang, convinced Branch Rickey to move his St. Louis Browns to the Sunshine City for spring training.

For the next 84 years, St. Petersburg collected grand springtime memories. Then, in 1998, the spectrum changed as the Tampa Bay Devil Rays began play and made St. Petersburg their year round home.

We invite you to stroll along Baseball Boulevard and relive a colorful history that highlights the time spent in St. Petersburg by some of the sport’s greatest and most exiting players-stars such as Babe Ruth and Joe DiMaggio of the Yankees, Stan Musial and Bob Gibson of the Cardinals, Tom Seaver of the Mets and Cal Ripken of the Orioles.

The Boulevard also honors the contributions of local heroes who worked tirelessly to bring Major League Baseball to St. Petersburg.

It is named in honor of Jim Healey and Jack Lake, both of whom were instrumental in the construction of Tropicana Field and the city’s success in securing a Major League Baseball franchise for Florida’s West Coast.

Walking north on 1st Street, however, no other plaques were found. Turning on Central to head west, the markers were nowhere to be seen. Having continued to 5th Street, it seemed rather unusual that no plaques or pedestals had yet to be encountered. People on the street were not much help. The first four folks had never even heard of Baseball Boulevard. The fifth person knew that “they moved ‘em” and that was about it.

Accessing the newspaper article again by iPhone revealed, unseen at the bottom of the page, that it had originally been published in the St. Petersburg Times on September 16, 1998. A previously undiscovered article explained that Baseball Boulevard did not garner the attention the planners had hoped and by 2011 several of the concrete pedestals had fallen into disrepair. Faced with costs of repairing and replacing the pedestals, the city decided instead to relocate the plaques to Al Lang Stadium.

Now having returned to Al Lang Stadium, it was clear that no other plaques were on display there. A walk around the entire exterior did not reveal any commemorative markers. Peering into the concourse did not yield any signs of relocation. This was now a full-blown mystery.

A couple of tradesmen were bothered with questions about Baseball Boulevard and neither had any information about the plaques. Just about to leave, with more questions than answers, a carpenter appeared out of nowhere and pointed at the stadium’s façade. “They’re right there, don’t you see them?” in a clear attempt to poke fun at a tourist. “Huh?”

“Come closer,” he said, “you can kinda see the outline of the plaques under that banner.” It was true, the home plates that had been lovingly relocated to Al Lang Stadium were now ingloriously covered by a Rowdies banner.

It was perfectly clear that the Rowdies did not hold the plaques or the area’s baseball history in high regard. What a shame. A must-see destination for baseball history buffs is now just a shadow of its former self, cloaked in garish green and yellow. Hopefully we will live to see Baseball Boulevard resurrected.

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, Mark Grace is listed on the Cubs roster for the duration of his career while the Dodgers declare Frank “Hondo” Howard and the Diamondbacks claim Dan Uggla. 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 La Russa 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 1948 Cleveland Indians         OWAR: 57.9     OWS: 291     OPW%: .606

GM C.C. Slapnicka acquired 52.5% (21/40) of the ballplayers on the 1948 Indians roster. Based on the revised standings the “Original” 1948 Indians outpaced the Red Sox, taking the American League pennant by eight games.

Lou Boudreau set personal-bests with a .355 batting average, 199 base hits, 18 dingers and 106 ribbies. “Old Shufflefoot” earned the 1948 American League MVP award and made his seventh All-Star appearance. Tommy “Old Reliable” Henrich (.308/25/100) topped the circuit with 138 runs scored and 14 triples. Ken Keltner posted career-highs in home runs (31), RBI (119) and runs (91). Larry Doby contributed a .308 BA with 83 tallies in his first full season. Jeff Heath swatted 20 big-flies and hit .319 for the Tribe while Dale Mitchell supplied a .336 BA with 204 base knocks and 30 two-baggers. Five-time All-Star backstop Jim “Shanty” Hegan clubbed 21 doubles and 14 taters.

Larry Doby placed 11^th among center fielders according to Bill James in “The New Bill James Historical Baseball Abstract.” Thirteen teammates join him in the top 100 rankings including Lou Boudreau (12^th-SS), Bob Feller (12^th-P), Al Rosen (14^th-3B), Sherm Lollar (31^st-C), Tommy Henrich (34^th-RF), Ken Keltner (35^th-3B), Jeff Heath (44^th-LF), Jim Hegan (44^th-C), Bob Lemon (48^th-P), Ray Boone (69^th-3B), Eddie Robinson (86^th-1B), Phil Masi (93th-C) and Dale Mitchell (95^th-LF).

Bob Lemon (20-14, 2.82) paced the Junior Circuit with 293.2 innings pitched, 20 complete games, 10 shutouts and a 1.226 WHIP. Johnny “Bear Tracks” Schmitz earned his second All-Star nod with a record of 18-13 along with a 2.64 ERA while Joe “Burrhead” Dobson (16-10, 3.56) made his lone appearance in the Mid-Summer Classic. Allie Reynolds (16-7, 3.77) battled control problems as he yielded over 100 walks eight times in his first nine seasons. Bob Feller (19-15, 3.56) led the League in strikeouts.

The “Original” 1948 Cleveland Indians roster

Honorable Mention

The “Original” 1999 Indians   OWAR: 55.5     OWS: 298     OPW%: .555

The Tribe outdistanced the Royals and White Sox by eight games en route to claiming the 1999 American League pennant. Manny Ramirez (.333/44/165) secured his lone RBI title and finished third in the MVP balloting. Brian S. Giles slammed a career-best 39 circuit clouts, knocked in 115 baserunners and registered 109 tallies while batting .315. Jim Thome topped the leader boards with 127 bases on balls and slugged 33 four-ply swats. Albert Belle walloped 37 round-trippers and plated 117 baserunners. Sean Casey aka “The Mayor” achieved All-Star status with a .332 BA, 42 doubles, 25 home runs and 99 ribbies. Bartolo Colon fashioned a record of 18-5 with a 3.95 ERA.

On Deck

The “Original” 1946 Tigers

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

We see it every night. A weak groundball to a defensively incapable player, a broken-bat roller behind the mound into no-man’s-land, a slap hit into the vacated area caused by the shift, a tomahawk chop resulting in a dirt-bounce that goes 20 feet upward. Not good enough to be a true hit, not bad enough to be an error. Infield hits are awkward.

“It’ll look like a line drive in the box score,” the broadcasters chirp happily. And while that’s very true, I would argue that infield hits are ESPECIALLY demoralizing for pitchers. Usually, the pitcher made a quality pitch, got the groundball he was looking for, and had little control over the infield defensive positioning or assignments. But because the official scorer ruled the play too difficult for a fielder to make, any runs driven in by the infield hit or resulting later in the inning will be earned.

Infield hits are the result of bad defensive skill, poor defensive positioning, poor use of the shift, sloppy weather conditions, speedy runners, jittery infielders, and/or good old fashioned bad luck. So which pitchers have been victimized the most by infield hits? Let’s look at the numbers for each league.

American League pitchers have allowed 9,650 hits, including 1,166 infield hits (as of June 29). The infield hits/hits rate in the American League, therefore, is 12.1%.

The Athletics’ defense ranks worst in the American League with a -23.9 UZR, and the team’s two best starters suffer a plethora of infield hits allowed. Take out Gray’s 17 infield hits allowed, and his already pristine 0.99 WHIP falls to 0.84 WHIP. Without the infield hits, Chris Sale of Chicago would also see his WHIP drop to a crazy 0.82 WHIP. (The ChiSox need to figure out how to shift.) Keuchel is the king of groundballs (64.5% GB), so infield hits are only natural to him. Same goes for Madson and his 56.4% GB rate. The Yankees’ middle infield has been miserable this year, and the team doesn’t know how to shift properly. Warren, Rogers, and Betances have been the poor-luck “beneficiaries.”

Nate Karns (45.4% GB) and Brad Boxberger (36.6% GB) are the real enigmas here, as the Rays have the second-best defense in the AL. Bad luck? Infielders hate them? Poor use of the shift by Tampa Bay coaches? According to Inside Edge, Rays defenders make only 4% of very difficult plays, labelled “remote.” Since these plays are too difficult to be ruled an error if the defender miffs, these balls in-play are often ruled infield hits (if, of course, they occur on the infield). For the curious, the Yankees are dead last (1.2%), and the Blue Jays are first (19%).

Zach Britton’s rate really jumps out, but it is most likely a result of very few hits allowed overall and, as with all the relievers, a small sample size. Britton has only allowed 28 hits on the season, and only 17 have left the infield. Dominant.

National League pitchers have allowed 9,892 hits, including 1,174 infield hits (as of June 29). The infield hits/hits rate in the National League, therefore, is 11.8%.

Noah Syndergaard (16.6% infield hits/hits) just missed this list, so that’s three Mets starters who have allowed way more infield hits than the average NL starter. The Mets have already taken Wilmer Flores off shortstop, but Eric Campbell (-1.1 UZR) and Daniel Murphy (-2 UZR) aren’t helping either. Brett Anderson (68.7% GB rate) is the most predictable pitcher on this chart, but Alex Wood and Shelby Miller are not, especially since 2B Jace Peterson and SS Andrelton Simmons flash the leather on a nightly basis. (Do the Braves  suffer from the Dee Gordon effect or just from poor use of the shift?)

The Cardinals infield has been below average defensively (Matt Carpenter -1.6 UZR; Mark Reynolds -1.6 UZR; Jhonny Peralta -1.1 UZR), which partially explains Lynn and Rosenthal. Starlin Castro (-3.4 UZR) and Arismendy Alcantara (-2.0 UZR) have not helped out Hendricks or Strop defensively either. Benoit is on the wrong team defensively to have a career-high ground ball rate (43.6%).

Finally, who has been stingy with infield hits? For the American League:

And for the National League:

Just something else Max Scherzer has been amazing at in 2015.

 2^nd Base hasn’t been a particularly stacked position in the major leagues in the past five years. Entering the 2015 season, the 2^nd base position was headlined by Jose Altuve and Robinson Cano. The second tier arguably consisted of Ben Zobrist, Neil Walker, Dustin Pedroia, and Ian Kinsler, and maybe Brian Dozier. Then the next level housed names like Jason Kipnis, Daniel Murphy, and maybe DJ LeMahieu. I’m here to analyze what has possibly happened to this group of baseball players in the past few months.

According to the Depth Charts pre-season projections, the top eight second basemen ranked by wOBA were Robinson Cano, Neil Walker, Ben Zobrist, Jose Altuve, Dustin Pedroia, Ian Kinsler, Howie Kendrick, and Chase Utley. The projections are usually somewhat accurate, but if you’ve been following baseball at all this season, just by looking at those names, you know that we’ve found an exception to that.

These are the top 10 second baseman thus far in the 2015 season ranked by wOBA:

If I told you in April that Logan Forsythe would be the 3^rd best second baseman in the league, you would think I’m ridiculous. He came absolutely out of nowhere to raise his BABIP nearly 60 points and raise his ISO 55 points! Joe Panik’s beautiful swing has moved him up to be the 4^th best-hitting 2^nd baseman. Jason Kipnis has shut up all the critics. He took his .310 2014 OBP as confidence going into this year, and now has a wOBA over .400. Danny Espinosa, who has been previously known as a ‘defensive’ second baseman, has skyrocketed his offensive production into a player who Matt Williams is comfortable having run onto the field every day. Cardinals 2B Kolten Wong is pulling the ball more and more every season. He’s also upped his LD% from 19% to 25%. Braves utility-infielder Jace Peterson is doing a bit of hitting in his rookie year, after being traded from San Diego (who, it turns out, could really use him) to the Braves in December. Think back to when I mentioned the tiers up top. Where is Robby Cano on the list above? Where’s Altuve? I don’t see Zobrist, Walker, or Kinsler on this list either. It is not an error.

So we talked about the breakouts at 2nd; now lets talk about the guys who haven’t or haven’t yet lived up to expectations.

Lets start with the guy who all of his fantasy owners hate this year. Robinson Cano. Yeah, the six-time All-Star Robinson Cano. The 32-year-old — the guy who has a wRC+ of 76. This is easily, by far, his worst season of his 11-year career. Why? Lets talk about it.

Cano has raised his Hard% and his Pull% over 4% each! What does jump out at you is that he’s making a ton less contact than he did last year. Actually, the least of his whole career. His Contact% has plummeted down almost 5%. Along with a raised K%, his BABIP has jumped down nearly 50 points.

The next guy is Altuve. Altuve hasn’t been that bad this year, but compared to his 2014 campaign, he’s not playing like Jose Altuve. He’s even fighting with a mild hamstring injury, but in his 287 PA’s, every single one of his numbers are down. His wOBA has decreased .363 to .304. BB% is down, strikeouts are up, OBP and SLG are both way down. He’s swinging more, and making less contact which isn’t a combination that pulls you in a positive direction.

Same can be said for Neil Walker. Almost all his numbers are down. One of the positives that I found, though, is that he’s hitting the ball harder. My prediction is that the .303 wOBA will start to show positive regression. Ian Kinsler isn’t having a horrible season. He’s raised his OBP a bit, but he’s becoming more of a singles hitter, dropping his SLG from .420 to .338.

Almost every starting second baseman in the big leagues has totally changed their style of hitting this year. Guys like Forsythe and Panik, who were projected to be replacement level or below, have made their names rise to the top of many leaderboards. Cano and Altuve’s value have fallen. Here’s your homework: Think of all the 2nd baseman in the major leagues. How many of them have close to similar stats from their projections? Comment down below.