Say Goodbye To Dylan Bundy's Training Wheels

It was reported yesterday that Chris Tillman received a platelet-rich plasma injection in his right shoulder in December, and that he would not be the Orioles' opening day starter and could begin the year on the disabled list. That's not catastrophic news for the chances of the 2017 Orioles, but it's certainly not good.

In the short term, not that much changes. The Orioles could go out and sign a starter, but the free agent market was pretty already weak in starting pitching to begin with. And now, some available starters are Henderson Alvarez, Jorge de la Rosa, Doug Fister, Mat Latos, Jon Niese, and Jake Peavy. Clearly, they're all flawed in some way.

Then again, the O's internal options after Kevin Gausman, Dylan Bundy, Ubaldo Jimenez, and Wade Miley -- Tyler Wilson, Mike Wright, Jayson Aquino, Logan Verrett, T.J. McFarland, Chris Lee, Parker Bridwell, Joe Gunkel, and the newly acquired Gabriel Ynoa -- all have their shortcomings as well, without the benefit of much (or any) past major league success.

There's no real need to panic (yet, at least), both because Tillman is good but not great and that rest might do him well and he could be fine when the Orioles need him. Because of off-days in early April, the Orioles can get by without a fifth starter until mid-April. So who knows just yet if the Orioles choose to wait things out or make a minor signing or acquisition.

After trading Yovani Gallardo to the Mariners, the Orioles' rotation of Tillman-Gausman-Bundy-Jimenez-Miley, in some order, was pretty much set. Before Tillman's injury threw a wrench into those plans, the only real question was how the Orioles would use Bundy out of the gate.

Any reasonable expectation for Bundy's first major league work in four years would have included something around 40-50 average-ish innings out of the bullpen and simply getting through the year without any physical setbacks. Instead, Bundy threw almost 110 innings, started 14 games (all after the all-star break), and posted an overall ERA of 4.02 (3.08 in relief, 4.52 as a starter). And, again, he did all of that while throwing about 65 combined innings in 2014 and 2015 (after missing the entire 2013 season after undergoing Tommy John surgery). Bundy's numbers were more impressive out of the bullpen and his velocity dipped after moving into the starting rotation, but just getting him through the year unbroken was a moral victory.

As long as the O's had their five healthy starters to begin the 2017 season, there was some thought the O's could hold Bundy back a little at the beginning of the year and keep his innings down by using him in a relief role. With Tillman on the shelf, you can throw any chance of that happening out the window. The Orioles will need Bundy during the season's first week, and as long as he stays healthy, he could realistically pitch 160-plus innings in 2017. That's another large jump.

Still, even though there's nothing wrong with fans for being wary of Bundy breaking down, perhaps he's ready for this expanded, full-time role. He has the benefit of at least knowing he's going to be starting in the upcoming season. He's experimenting with his infamous cutter. He's committed to throwing his changeup, a welcome addition to his pitch arsenal. Whether Bundy's body is ready for an entire season in the rotation or not, we're going to find out.

Using Statcast to Project Isolated Power

Through my youth, there were basically only a few statistics one needed to know.  Homeruns, batting average, runs batted in, stolen bases, and, only if you were a bit wonky and had time on your hands, doubles and walks.  My youth was largely dictated by what someone back in the late 1800s who knew more about cricket than baseball thought newspaper readers would want to know.  In the past 16 years, though, we have experienced a renaissance as fans and clubs have begun to use computers, statistics, and approaches developed in other field to know more about the game.

Some of these new approaches required new technology.  One such approach is Statcast, which uses multiple cameras to identify elements such as players, the baseball, and a bat.  This is combined with radar data and the reward is a ton of data.  While one can use this data to evaluate pitchers, baserunners, and fielders, we will be using it in this column to discern ability in hitters.  Specifically, whether exit velocity of a batted ball can be related to the power metric, isolated power.  And, then, if one or two seasons of exit velocity data can be used to accurately project future performance.

Now, the first step in figuring out how useful these measurements might be is to compare them in season.  I only looked at player who had 300 plate appearances in both 2015 and 2016.  What we find is that average hit distance (forgive the error in the graphic below, it is average hit distance not home run distance) and barrel rate correlate very strongly with isolated power in the same year (p < 0.01 for both variables).  This means that these two ways to measure velocity and contact quality are related in-season to isolated power.  The regression model connecting those measurements to the metric isolated power was also significant (<0 .01="" p="">
Below is a graph comparing expected ISO with ISO for 2015 with the accompanying R2 value:

So all of this informs us that hit quality is connected to isolated power.  That should be obvious, but it is helpful to be able to see that here.  However, what we are really interested in is whether these values are meaningful from one year to the next.  In other words, is this simply a descriptive correlation or is it a predictive correlation. 

We will do a very simple comparison.  We will simply compare R2 values for expected ISO using the 2015 developed model vs. 2016's ISO.  This simple comparison will help show whether the formula using Statcast measurements better correlates with next season's values than simply using the actual ISO from the year before.  The comparison between 2015 ISO and 2016 ISO is not shown, but the R2 was 0.5026

What we find is that the Statcast method improves the predictive capability by about 15%.  That is remarkable, but is not earth shattering.  If your decision making process was simply finding the players with strong ISO, then this technique would help but it might take a decade or so for that to be able to be seen through the noise.  In general, I do not find this to be much of a silver bullet.  That said, it may be a hesitant flag for some players and suggest some players that should be expected to regress downward or upward.

Here is a list of players who the model thinks most underperformed.  In other words, who does this model think should have had a bigger 2016 than they actually did.

	2016 ISO	2016 xISO	Diff
Miguel Cabrera	.247	.295	.048
Josh Harrison	.105	.147	.042
Brandon Belt	.199	.239	.040
Howie Kendrick	.111	.149	.038
Kendrys Morales	.204	.242	.038
Buster Posey	.147	.184	.037
Albert Pujols	.189	.226	.037
Alex Gordon	.160	.197	.037
Adeiny Hechavarria	.075	.109	.034
Yonder Alonso	.114	.147	.033
Troy Tulowitzki	.189	.222	.033
Nick Markakis	.129	.161	.032
Mitch Moreland	.189	.220	.031
Yadier Molina	.120	.151	.031
Adam Jones	.171	.201	.030

One name that jumped out to me was Kendrys Morales.  He had a solid year last year, but the model thinks it should have been considerably better.  If the model better accounts for his talent, then we might see something closer to that expected isolated power.  It may well be that playing in Kansas City depressed his value a bit and some of his hard hit balls should have fallen in.  A different point of view would be that perhaps his isolated power was depressed because he is below average in converting singles into doubles.  That might explain why Pujols is up here as well.

Here is a list of players who the model thinks most overperformed:

Player	2016 ISO	2016 xISO	Diff
Brian Dozier	.278	.201	-.077
Nolan Arenado	.275	.224	-.051
Mookie Betts	.216	.170	-.046
Robinson Cano	.235	.190	-.045
Ryan Braun	.233	.189	-.044
Curtis Granderson	.228	.187	-.041
Jay Bruce	.256	.216	-.040
Edwin Encarnacion	.266	.229	-.037
Zack Cozart	.172	.137	-.035
Anthony Rizzo	.252	.217	-.035
Ben Zobrist	.174	.140	-.034
Carlos Santana	.239	.208	-.031
Didi Gregorius	.171	.140	-.031
Jose Bautista	.217	.187	-.030
Gregory Polanco	.205	.175	-.030
Josh Donaldson	.265	.235	-.030
Rougned Odor	.231	.201	-.030

I would have thought that the model would list speedster after speedster, guys who stretch singles into doubles.  That does not appear to be the case here.  Many of these players are rather plodding.  The closest Oriole on this list is Jonathan Schoop who comes in at a -.024, which is not a good thing to hear given how uneven and somewhat underwhelming his season was last year.

This made me wonder though about how things change over time.  For instance, is the over or under production from batted ball performance to expected batted ball performance a skill.  Are over producers always over producers.  What was remarkable was that the average difference between 2016's difference and 2014's difference was .011.  The greatest difference was .046.  This suggests that there is some element that I am missing.  The ability to over or under produce appears to be repeatable, so therefore likely having to do with a skill.  The next step is finding that skill.

The Bowie Boost for First Basemen

Joe Reisel's Archives

Christian Walker (2013-2015 Tides), first baseman on the All-Harbor Park team. Photo courtesy of Steven Goldburg / Norfolk Tides

After I wrote about the Norfolk Tides' catchers while they were an Orioles affiliate, I began to look at the Tides' first basemen of the same time period. I intended to do a similar survey of the first basemen, hoping to find general patterns among a long list of memorable and not-so-memorable names. (Anyone remember Robbie Widlansky?) But I re-discovered something much more interesting and potentially more important than a list of names and trivia. It involved four first basemen were unlike any of the catchers.

Brandon Snyder, Joe Mahoney, Christian Walker, and Trey Mancini arrived in Norfolk as highly-regarded, but not uber-regarded, prospects. (Among the catchers, Matt Wieters was an uber-prospect while none of the others were well thought of.) And all four of the first basemen were coming off outstanding performance at AA Bowie.

Brandon Snyder (facing camera) (2009-2011 Tide). Photo courtesy of Megan Morrow / Norfolk Tides.



The below table shows their Bowie performances.

	Age	G	PA	Slash	OPS
Snyder	22	58	233	.343/.421/.597	1.018
Mahoney	23-24	137	564	.300/.356/.518	.874
Walker	22-23	112	480	.292/.360/.488	.849
Mancini	23-24	101	429	.350/.399/.604	1.003

While Mahoney and Mancini were, perhaps, older than you'd like for AA players, all four put up some pretty impressive numbers. You might like them to take a few more walks, but these numbers are nonetheless impressive on the surface.

But when they were promoted to AAA Norfolk, all four looked a lot more ordinary, as shown in the below table.

		G	PA	Slash	OPS
Snyder	22-24	285	1167	.256/.317/.394	.711
Mahoney	25	132	536	.265/.319/.389	.708
Walker	23-25	313	1332	.260/.324/.429	.754
Mancini	24	125	536	.280/.349/.427	.775

Yes, I know that Norfolk is a level up from Bowie and that Norfolk is a terrible hitter's park. The fact remains that there have been four first basemen who performed very well at Bowie, achieved some prospect love, and then looked much less impressive at Norfolk. And Brandon Snyder and Joe Mahoney - whose careers are essentially defined - have been what we should have expected based on their Norfolk production, not their Bowie production.

Two years after starring at Bowie, Joe Mahoney was out of baseball. Photo courtesy of Allison Veinote / Norfolk Tides.

But this boost appears to be limited to first basemen. The statistics of other Orioles prospects such as Brandon Waring, Jonathan Schoop, L.J. Hoes, and Xavier Avery were not inflated (as much, at least) at Bowie. Obviously I don't believe that there is something special about first basemen that would cause their performance to be uniquely boosted by Bowie. I'm sure that a more detailed analysis would identify shared characteristics that Prince Georges Stadium favors.

As fans, we should learn this lesson - be skeptical of players in general, and first basemen specifically, who produce apparently dominant numbers at Bowie. Wait and see how they do at Norfolk before project them to be a future star. Brandon Snyder, Joe Mahoney, and (most probably) Christian Walker failed to live up to the promise of their Bowie performance.

But at least as compared to the others, Trey Mancini may still become a productive major-league player. Yes, his Norfolk performance dropped significantly from his probably-unsustainable high at Bowie. It's still better than the performance of the other three. While Mancini almost certainly won't be the star hinted at by his Bowie performance we can still be optimistic that he can become a useful major-league player.

Do The Orioles Need To Start The Season Strong?

The Orioles begin their 64th season in Baltimore on April 3, and a strong first month of the season could be very beneficial as they seek their fourth postseason berth in six seasons. The last time the Orioles had posted such an impressive postseason run was from 1969 through 1974, when they appeared in the American League Championship Series five-out-of-six years, and captured a World Series trophy in 1970. Since their run of postseason appearances began, the Orioles have played roughly 25 games before the calendar changes over to May. The exception to this was in 2015 when the team played just 20 games before May 1st, including an 8-2 victory over the Chicago White Sox in front of about 45,969 empty green seats, and two empty orange seats. 

I like to take the first 25 because that amount of games usually gets you through the first five weeks of the baseball season, and it also allows a television broadcast (MASN, for example) the ability to get away from the 13-for-46 graphic, for example, and begin showing the players' batting average on the season. Also, once a team has eclipsed their first 25 games, there's a pretty good idea of what a team needs to improve on, or what they need to continue to excel at.

Since their return to the postseason in 2012, the Orioles have compiled records through their first 25 games of 16-9, 15-10, 13-12, 12-13, and 15-10. Take notice of the 12-13 record in 2014. The year they won the AL East, and advanced to the ALCS, they were hovering around the .500 mark entering the first week in May. While still respectable, they've posted better records in the other two seasons that ended in a postseason berth (16-9 in 2012, and 15-10 in 2016). Analyzing historical results through Baseball-Reference, dating back to 1871, the top-200 performing teams have all won 17 games or more. Teams that have claimed 17 victories of the first 25 have made it to the postseason 98 times, and have won the World Series 29 times. 

The last time the Orioles won 17-of-25 was 12 years ago, back in 2005. That team held first place for 69 calendar days, and were as far as 14 games over .500 until June 21. A six-, eight-, and nine-game losing streak throughout the season's final months saw a collapse worse than a demolished building, and they finished 14 games under .500. That was the worst finish for a team that won 17 or more games in their first 25 since the 1978 Oakland Athletics. In that season, the A's won 19 games out of their first 25 and spent 57 calendar days in first place. They finished that season with just nine victories in their final 45 games, and an overall record of 69-93. 

So, what would happen to the 2017 Orioles if they struggle mightily through their first 25 games? While not completely impossible, a fourth postseason berth may not be in their future, and with their window of opportunity for a championship closing ever-so-quickly, they may be reduced to just one final opportunity to bring a title back to Baltimore. 

The bottom-200 team performances since 1871 have teams winning no more than eight games in their first 25. Out of those 200 teams, just two have made the postseason, and only one team went on to win the World Series. The 1981 Kansas City Royals made the postseason as a Wild Card team and were swept by the Athletics in the American League Division Series, while the 1914 Boston Braves were victorious in the World Series, sweeping the Philadelphia Athletics in four games. The 1988 Orioles finished the season with an overall record of 54-107, after beginning that year just 2-23, the worst overall record for a team, winning just twice in their first 25 games.

Alas, just because a team doesn't win 17 games doesn't mean their shot at the postseason automatically evaporates. Good teams, though, are expected to win games, regardless of what month it is. As you can see below, the previous 16 World Series Champions didn't all break 17 wins; however, they were all good teams, and all but two put up winning records in their first 25 games. 

Start strong. Finish strong. The formula for success.

A Look At Fangraphs Projections

Certain things always happen during the offseason.  Free agents are signed, players are traded for, tons of digital ink is spilled discussing all the possible rumors and Fangraphs predicts that the Orioles will be the worst team in the AL East. At this point, it probably isn’t very surprising to learn that Fangraphs expects the Orioles to win ten fewer games in 2017 than they did in 2016. It’s probably more surprising to learn that Fangraphs does project the Rangers to win twelve fewer games in 2017 than they did in 2016. But how accurate is Fangraphs exactly? They certainly missed on the Orioles last year, but did they do better predicting other teams’ results?

Fangraphs had a surprisingly good year from a wins perspective in 2016. They were off by roughly 5.6 wins per team in 2016. In contrast, presuming that teams that every team would win 81 games would have been off by slightly more than 9 wins per team making it roughly a 40% improvement than just picking each team to win the same amount of games.

While they successfully predicted the record of 11 teams within 3 wins of their 2016 results, they also were off by 9 or more wins for 6 teams. Interestingly, Fangraphs did a worse job projecting five other teams than the Orioles in 2016. Presuming that each team would go .500 would have resulted in being off by 9 or more wins for 14 teams, while being within 3 wins for only 6 teams. This indicates that while Fangraphs had poor predictions for a number of MLB teams, using these predictions is better than using nothing at all. It also shows that Fangraphs should be expected to have poor predictions for roughly 20% of MLB teams.

Fangraphs did a decent job predicting runs scored for each team as they were off on average by .266 runs per game. If someone knew that teams would average roughly 4.48 runs per game, and predicted that each team would score that amount, then they would have been off by roughly .289 runs per game. Given that it’s hard to know before the season how many runs will be scored, this shows that Fangraphs had some success at predicting runs scored. Fangraphs did better when it came to runs allowed. On average, Fangraphs was off by roughly .3 runs per game. If one presumed that each team would allow 4.48 runs per game, then presuming each team would allow the same amount of runs means that the average prediction would have been off by .36 runs per game. Clearly, using Fangraphs projections have some value.

Fangraphs does worse when trying to project earned and unearned runs. Fangraphs was off by an average of 59 earned runs per team. If one used the actual number of runs scored, than presuming that each team allowed the same amount of earned runs would have resulted in the projection being off by 51.4 runs. If one used the number of earned runs projected to be scored by Fangraphs, then the average team would have been off by 69.6 earned runs. This isn’t an impressive result.

Likewise, the Fangraphs projections predicted that each team would allow 75 unearned runs. In reality, teams only allowed 52.76 runs. This is a pretty big difference. On average, Fangraphs was off by 23.7 unearned runs per team. If one presumed that each team gave up an equal amount of the actual unearned runs allowed, then each team would have been off by 10.4 unearned runs. If one used the number Fangraphs projected, then each team would have been off by 22.9 unearned runs. This indicates that Fangraphs is unable to project unearned runs. It also suggests that Fangraphs is unable to successfully predict the number of runs allowed in a season, but has the ability to guess which teams will allow the most or least runs only in context. This isn’t as useful as how many runs a team will allow.

The Fangraphs projections undoubtedly have some predictive ability and are better than using nothing. Sometimes, that can be very valuable. For example, suppose someone builds a model that can successfully predict whether a stock on Wall Street will go up in value that works 5% more accurately than the current models. This model may only have minimal predictive ability but is still good enough to be worth billions of dollars. A small increase in predictive ability can be highly valuable.

However, it doesn’t change the fact that a small increase in predictive ability is still only a small increase. Sure, it tells us more than we knew before, but that’s not the same as calling it gospel. The model in the paragraph above will fail a large percentage of the time. Such a model can both have extreme value, but still be wrong a significant amount of the time.

What does this mean for Orioles’ fans? The Fangraphs projections do have some validity to them, but they’ve historically missed on a large percentage of teams. Their results should be taken seriously, but with the understanding that they aren't holy writ. They’re neither perfect nor worthless. The same is probably true for PECOTA. They’re better than nothing, but that doesn't make them great.

Throw (Good) Curveballs with Men on Base

Two weeks ago, I showed that pitchers can throw curveballs that reliably depress the exit velocities of hitters. There were many of directions I wanted to go with this, including determining whether the ability persists year to year (I believe it does, generally - a pitcher can always work on his curveball) and whether the ability presents itself in the minors (I imagine it would, but it's possible that pitchers really develop their curveball early at the MLB level rather than in the minors). Both pieces of follow-up research would offer strong opportunities for action at the Major League level; scouting and fairly shallow research could be used to identify pitchers with a top-notch second pitch early on. However, both pieces of follow-up research require something that doesn't exist: data. Baseball Savant doesn't have complete 2015 Statcast records, and no Statcast data is available for Minor League teams, at least publicly. If you're a professional baseball team that wants me to answer these questions...

Instead, I turned my attention to slices of data that did exist within the available Statcast data. Spin rate didn't show any correlation with the depression of exit velocity. This was a little surprising given what we know about spin rates and curveballs. But spin rate is not in and of itself a good thing; there can be meaningless spin on a ball, and curveballs are among the pitches that tend to have the largest amount of total spin being meaningless. Perhaps separating useful from meaningless spin on curveballs and comparing it to exit velocity depression would yield more valuable results.

I also looked at the bast state at the time of the pitch. considering any instance with at least one man on base to be an instance in which the pitcher would throw his pitch from the stretch. This is not always true; some pitchers pitch from the stretch in bases-empty situations, and some pitch from the windup regardless. Some mix it up! In general, my expectation was to find that pitchers would do worse from the stretch overall, whether by an inability to generate torque and spin with a faster move to the plate, or a push to reach back and throw harder curveballs that don't drop as much, and as such get hit for line drives more often.

The results were counter to my expectations: pitchers tended to depress exit velocities at the same rate with men on base than with the bases empty. Right handed pitchers shown because the Orioles only had righties throw curveballs in 2016:

It seems that, in general, pitchers that can throw good curveballs can throw them in any situation. Pitchers whose curveballs depress exit velocity do so consistently.

This is particularly useful for crafting pitch sequences with runners in scoring position. In some cases, fastballs are considered ideal to prevent the opportunity for runners to steal. However, with a pitcher on the mound whose curveball regularly depresses exit velocities (ie, induces ground balls), it may be advantageous to rely more heavily on the curve and ask the pitcher to generate outs rather than hold runners. At the very least, pitchers with good curveballs should not shy away from using them when the other team is threatening.

Rearview Mirror: A Look At The Chris Davis Contract, One Year Out

I bought my first, new car in 2011.

Nowadays, integrated-technologies for cars (Bluetooth, touch-screen displays, parking cameras, etc.) are included in manufacturers’ standard packages. That wasn’t quite the case six years ago, when you could still bypass most of the bells and whistles to save a buck.

To a poor, college student, it made financial sense. Besides, the word “Bluetooth” made me think of those awful people running around, wearing devices in their ear that made them look like a poor man’s Lobot.

I’d made up my mind that such things were just fads. As a result, my car’s present-day value is not as high as if it would have been if I had paid a bit more to get those features.  Failure to anticipate the market’s direction can leave you with an artifact bereft of the value you paid for it.

This leads me to the Baltimore Orioles’ first baseman, Chris Davis.

Just a year ago, he signed his name across the dotted-lines of a club-record, $161 million dollar contract that would keep him around through 2022.

Even at the time, it seemed overly-aggressive. The Orioles, despite their free-spending reputation, are the type of organization I’d imagine that if you borrowed a pen from, you’d expect to receive a bill for the ink.

In 1998, Baltimore led the league in player-spending with $71.8 million. In 2016, that number had grown to $115.6 million, a 61% increase. On its own, that sounds drastic. However, compare those numbers to that of the Tigers; their payroll has increased by a staggering, 805 percent.

The Orioles 2016 payroll ranked middle-of-the-pack, a number more consummate with their market-size (at least, according to this Bleacher Report article). This made the Davis deal so surprising.

Right off the bat, it put him among some elite company – at least, in terms of the overall dollar amount:

1) Giancarlo Stanton $325,000,000
2) Miguel Cabrera $248,000,000
3) Albert Pujols $240,000,000
4) Robinson Cano $240,000,000
5) Joey Votto $225,000,000
6) Joe Mauer $184,000,000
7) Jason Heyward $184,000,000
8) Buster Posey $167,000,000
9) Chris Davis $161,000,000*
10) Matt Kemp $160,000,000

*The contract does include a large amount deferred payments, which bring Davis’ annual, price tag down to $17,000,000.

The 30-year-old Davis is a menacing presence at the plate, but few would pencil him into their “top” lists. That is not a knock on “Crush,” it’s just that baseball has moved past the age when one-dimensional sluggers dominated the food chain.

For one thing, power isn’t the sacred commodity it was earlier this decade. 2016 saw an increase in home runs, with 2.31 home runs hit per-game (the first time that figure has been over two since 2009). Why pay Joe Gold the farm to hit 40 home runs, when Joe Silver can hit 30 – and you can pay him peanuts on the dollar.

This offseason, front offices adjusted to the changing tides. Reigning NL-home run champ Chris Carter is still unemployed. Ditto for Mike Napoli, who paced the Indians with 34 home runs and helped nearly lead them to a title.

Here’s how their 2016 resumes compare with Davis':

Carter: .820 OPS, .277 ISO, 112 wRC+*
Napoli: .800 OPS, .217 ISO, 113 wRC+
Davis: .791 OPS, .237 ISO, 111 wRC+

*Weighted runs created is a Fangraphs rate stat that measures the hitter's contributions at the plate, while adjusting for park and league factors.  It uses 100 as the base for a league average hitter   

In that last category, Napoli, Carter and Davis finished 14th, 16th, and 18th, respectively, among first basemen.  For Davis, that puts him just ahead of one, John Jaso.  That's not to say that wRC+ is the end-all-be-all, but anytime you're mentioned in the same breath as Jaso, it's probably not going to be a positive thing (Jaso, incidentally, made $4 million, last year - about what Davis pulled in a quarter of a season). 

OPS wasn't much more favorable to Davis, with the O's slugger finishing tied for 17th, in the category - below the average mark posted by first baseman in 2016 (.819 OPS).  The player he is tied with is the Angels' C.J. Cron - again, not exactly exclusive company. 

While it's true you can't measure a player's worth through the lens of a single season, it's telling that Carter and Napoli are still trudging down the unemployment line.  The market for such players has seen a cruel (at least from their perspective) adjustment. 

Injuries, opportunity, and/or desperation will set in, and the duo will find work.  And, when they do, I am willing to bet my outdated car that they won't be sniffing anything close to Davis money.  Think closer to one/two years guaranteed, at $8-12 million per.  Davis' teammate, Mark Trumbo, posted better numbers than any of them and only got three years, $37.5 million.

As for Davis himself, he will spend his Baltimore summers racking up massive home runs and equally-massive whiffs (league-high 32.9% K-rate in 2016), hoping the former occurs frequently enough to counterbalance the latter.  He's like a left-handed, slightly-wealthier man's version of Rob Deer. 

The Orioles need to be saving every penny for their own version of doomsday (2019, when Manny Machado can become a free agent).  To stay competitive during this process, they need to maximize the dollars they do spend.  This seems like a bad start. 

Figures obtained from Baseball-Reference, Fangraphs, and Spotrac

Kevin Gausman Is (Basically) The Ace You've Been Looking For

The Orioles have a starting pitcher who ranked in the top ten in the American League in K/9, K/BB, K%, xFIP, and SIERA in 2016.  He ranked in the top 15 in ERA, strikeouts, and fWAR.  He had the 18th best ground ball rate and the 15th best hard hit rate.  He threw his fastball harder than all but two other AL starters.  He was, by many measures, one of the 15 best starters in the American League.  That man? Kevin Gausman: ace pitcher.

Well, ok, maybe he’s not an “ace” ace.  He only threw 179 innings, had the 7th worst FB/HR and HR/9 ratio in the American League, stranded a likely unsustainable 81% of baserunners, and had relatively pedestrian FIP (4.10), WHIP (1.28), and BABIP (.308) marks.  In an American League that had a down year in terms of starting pitching, however, Gausman established himself as one of the league’s better arms despite a lack of fanfare.  Much of the hand wringing over the Orioles over the past five years has been over the lack of a top of the rotation starter, so it is odd that the emergence of that type of pitcher went largely unnoticed.  Perhaps it has to do with more traditional measures: a 9-12 record and a solid but unspectacular 3.61 ERA, or the fact that he had a middling 2015 after looking like a potential breakout candidate. Whatever the reason, though, there doesn't seem to be a ton of buzz about Gausman becoming a legitimate top of the rotation starter.

Gausman was more of a steady performer than a spectacular one in 2016 but, in the current run environment in the American League, steady is an important virtue.  From July 1 on, Gausman threw at least 6 innings in 14 out of 17 starts and gave up three runs or fewer in 11 of those starts. His best performance came at Boston on September 14 as the Orioles were pushing for a playoff berth.  He threw 8 shutout innings, allowing just 4 hits and 1 walk to go along with 6 strikeouts, capping off a dominant 5 game stretch where he allowed 3 total runs (all of which came in the same game).  While he faltered a bit in his next two starts, he rebounded with a Wild Card clinching victory over the Yankees in his final start of the season.  It’s always dicey to rely on second half breakouts to predict success in the following season, but Gausman certainly looked like he began to turn a corner and start to realize the potential the Orioles saw in him when they drafted him fourth overall in 2012.

So what can the O’s expect from Gausman in 2017?  Certainly, he needs to keep the ball in the yard. He has a career HR/FB mark of 12.7%, slightly above the league average since 2012, but his 15.4% mark in 2016 was nearly three percentage points above league average.  This was somewhat offset by his ability to strand baserunners, but it is unlikely he posts another 81% LOB mark again, especially since he hovered around 73% (almost exactly league average) in 2014-2015. Eno Sarris of Fangraphs categorized Gausman’s splitter as one of the best pitches in baseball in 2016, and heavier use of that pitch could certainly help bring down his sky high homer rate.  A league average home run rate and BABIP could also help offset any loss he will likely see in his strand rate.

The ZIPS and Steamer player projection systems do both forecast a bit of a pullback in terms of run prevention for Gausman, likely as a result of his artificially high LOB numbers in 2016, but still see the underlying skills of strikeout and walk rates as staying solid. I think it's more than reasonable to expect similar numbers to 2016 with the possibility for a true breakout. Given the uncertainty of every other starter in the rotation, aside from maybe Chris Tillman, that's exactly what the Orioles will need.

Chris Tillman Is Not Underrated

Yesterday, Steve Melewski of MASNsports.com wrote a post about Chris Tillman that included this cool fun fact right in the headline: "Orioles have won 64 percent of Chris Tillman’s starts since 2012." Hey, that's interesting, if you still care about pitcher wins. Some people do, and that's fine.

Baseball fans and analysts, though, are way past the point of putting much emphasis on pitcher wins. Many good starting pitchers collect plenty of wins, but starters are also reliant on their teammates to score runs and the bullpen behind them to shut the door. Anyway, the war over pitcher wins has already been fought and won by the sabermetric community (basically acknowledged by Melewski), so let's move along.

Later in the post, Melewski sums things up this way:

At a time when pitcher wins are valued less than ever (and probably rightfully so), can we deny that stellar win-loss percentage with Tillman on the mound? He probably flies under the national radar, but in Baltimore, we have seen that his starts consistently outperform the team as a whole.

I'm not sure what he means by denying how much Tillman has won, but it's definitely possible to put too much emphasis on it. And instead of focusing on how much Tillman has won, we can do a better job of comparing his performance to other starting pitchers.

Since 2013, which includes the four seasons in which Tillman has thrown at least 100 innings, here's how he ranks against 76 qualified American League starters:

• t-37th in ERA
• 56th in FIP
• 51st in K/9
• 64th in BB/9
• t-45th in HR/9
• 9th in IP
• t-3rd in wins

Those are the numbers that a sturdy, but perfectly decent starting pitcher would accumulate.  So if you think Tillman "flies under the national radar," then you're probably just talking about the Orioles in general, who somehow have stockpiled the most wins (444) in the American League since 2012 (and the fourth-most overall). 

Tillman gets credit for pitching against formidable AL East lineups. He gets credit for being the team's best starting pitcher in the Buck Showalter era. And he gets credit for staying relatively healthy and throwing a bunch of innings. Those things, though, don't make him an ace, or even a great pitcher. With Kevin Gausman's rise, Tillman may not even be the best starter in the O's rotation now (and Gausman was arguably better last season). 

But Tillman is a good pitcher, and that's important. The Orioles could use more competent starting pitchers, especially since Tillman and some others are impending free agents. But stop building up Tillman to be more than he is just because the Orioles have been one of the most successful teams since Showalter and Dan Duquette joined the organization. Tillman gets credit for a slice of that success, for sure, but let's not go overboard.