Although the Nationals had a disappointing 2013 season overall, Tanner Roark (RHP) was one of their more pleasant surprises. The Nats brought him up in August, as injuries and performance problems created openings for several pitchers in their minor league system.

While Taylor Jordan also performed well, I think it’s fair to say that Roark had the most impressive and intriguing debut for the big-league team. Roark accumulated excellent “traditional” stats, and he did so at least in part by exploiting an unusual but highly effective talent: making batters not swing at good pitches. This post explores Roark’s story, and opens up the question of how his distinctive forte, zone-swing rate, contributes to effective pitching.

To recap, Roark finished 7-1 with a 1.51 ERA over 53 2/3 innings. He allowed only 1 home run in total, or 0.17 home runs per 9 innings; and the league batted .197 against him (– “batting average against” or “BAA”). The Nationals’ ace, Stephen Strasburg, allowed 0.79 home runs per 9 innings, with a BAA of .205. Roark was comparable in BAA to Strasburg, and much, much better at preventing home runs.

Of course, Strasburg reached his figures in 183 innings of pitching as compared to Roark’s 53 innings of pitching. This is what is sometimes described as a smaller sample. But we should not discount Roark’s performance too quickly. His 53 innings involved five starts and nine relief appearances, and a total of 12 appearances with at least two innings pitched. This is considerably more than, say, one start and no relief appearances. Roark played for the Nationals for the last two months of the season. His stint in the majors last year was substantial enough, I think, to merit serious interest.

Roark’s 2013 performance was surprising in part because of his pedigree. In 2012 Roark was 6-17 as a starter in Triple-A, pitching for the Nationals. His 2012 ERA in Triple-A was 4.39 (although his FIP [Fielding Independent Pitching rating] of 3.85 was better). Providing more background, Adam Kilgore wrote in September 2013 that

Roark has never been regarded as a star or a significant prospect. In 2008, the Rangers drafted him in the 25th round. The Nationals acquired him and another minor league pitcher for Cristian Guzman at the 2010 trade deadline. Last winter, the Nationals left Roark unprotected from the Rule 5 draft for the second straight year. They invited him to major league spring training this year, and shipped him out in the first round of cuts.

(Washington Post, Nationals Journal, 9/17/2013; http://www.washingtonpost.com/blogs/nationals-journal/wp/2013/09/17/tanner-roarks-incredible-start-built-on-command-feel-for-pitching/)

Roark’s 2013 performance was also surprising because, with a fastball averaging 92.6 mph, he had good but not overwhelming velocity.

Going back to FIP and similar topics, another reason why Roark’s 2013 performance was surprising was because of some relationships between his statistics. For instance, although his 2012 Triple-A ERA (4.39) was higher than his 2012 Triple-A FIP (3.85), this relationship reversed itself last year in the majors, with Roark posting a 1.51 ERA and a 2.41 FIP. In addition, his xFIP (“expected Fielding Independent Pitching”) was 3.14, significantly higher than the FIP.

“ERA < FIP < xFIP” spreads of this size are not unheard of, but they are rare, especially when your ERA is less than 2.00. In fact, ERA < FIP < xFIP distributions of this type suggest that you are identical to Clayton Kershaw (1.83 ERA / 2.39 FIP / 2.88 xFIP) and that you have just signed a contract worth 215 million dollars!

These observations about Tanner Roark’s performance and pedigree raise several questions:

How did he perform so well in 2013?

What is going on with his ERA<FIP<xFIP distribution?

What can we say about his future performance?

Taking a quick initial look at the ERA<FIP<xFIP distribution, a “negative” delta between ERA and FIP is often attributable to the pitcher having a low Batting Average on Balls in Play (BABIP). Roark’s BABIP was indeed very low, at .243. (Kershaw’s was .251).

Also, although this might sound odd, Roark’s extremely low HR rate (0.17 per 9 innings) pushed his ERA below his FIP, even though home runs are a fielding-independent matter. Roark was fine (league average or better) on the other FIP elements — walks, K’s, HBP’s. But combining these normal-range statistics with his homer rate produces a compromise number and some information loss.

Turning to xFIP, this calculation substitutes out the pitcher’s own homer rate for the league average homer rate. As we might expect, the league average homer-rate was much higher than Roark’s, and this explains the FIP < xFIP delta, while also contributing to the delta between his ERA and his xFIP.

These observations tend to intimate that some of Roark’s statistics are not likely to repeat themselves. Before turning to the “future performance” question identified above, I want to look more at the first question of trying to understand Roark’s 2013 success. There are aspects of Roark’s pitching last year which suggest that his strong performance numbers were not an accident, and that his apparent prowess is not simply overmagnified by the small prism of his innings total.

The first statistic of interest is that Roark was seriously good at throwing pitches in the strike zone which batters did not swing at. This is the Z-Swing% statistic recorded on FanGraphs and other places. Roark’s Z-Swing rate in 2013 was 54.8% (per Baseball Info Solutions [BIS]), or 55.9% per PITCHf/x. This means that batters only swung at Roark’s pitches in the strike zone about 55% of the time.

(BIS and PITCHf/x converge around 55% for Roark’s Z-Swing%. These systems actually diverge, or report different percentages, for some other stats which are not independent of Z-Swing%. Although this is interesting, the differences do not materially affect our evaluative questions. I will cite the BIS plate discipline statistics throughout and compare them to PITCHf/x at various points below).

The complement of Z-Swing% is what I will call “Z-pass” — the phenomenon of non-swings on pitches in the strike zone. Tanner Roark’s Z-pass rate last year was 45% — batters passed on about 45% of his pitches in the strike zone.

This was a very high Z-pass rate. In fact,

• It was the highest Z-pass rate on the Washington Nationals, by about 5 percentage points, among Nationals pitchers with at least 50 innings.

• It also was more or less the highest Z-pass rate in all of major league baseball, again among pitchers with at least 50 innings. Roark came in first in Z-pass rate according to BIS. According to PITCHf/x, Roark was tied for sixth-best in Z-pass rate, behind Sonny Gray with a 47% Z-pass rate.

A high Z-pass rate is indicative of several good pitching qualities. Z-passes are good because they mean that batters are laying off a higher number of pitches which damage their cause and advance the pitcher’s cause. A high Z-pass rate indicates that the pitcher is accumulating strikes while maintaining an atypically lower risk of allowing a hit. (This is true if the pitcher is hitting the strike zone at a reasonable rate. More on this below). Tactically speaking, the Z-pass is the best outcome on the swing v. strike zone matrix below.

Swings on pitches in the zone and out of the zone can lead to hits, and worse. By contrast, if we assume that non-swings in the zone lead to strikes, the Z-pass simply constitutes a good outcome for the pitcher.

How often did Roark throw strikes? In 2013 Roark hit the strike zone 47.7% (BIS) of the time. This was about 3 percentage points ahead of major league average (44.9%). 3 percentage points comes out to about one standard deviation above average. (PITCHf/x reports a higher league-wide strike-zone rate — 49.4% — and a higher strike-zone rate for Roark as well, at 53.8%. PITCHf/x appears to have a larger strike zone than BIS).

It therefore appears Roark was exploiting his elite Z-pass rate often enough for it to be useful, and indeed for him to have an advantage over hitters. Roark accumulated strikes at a good rate; and, by strongly suppressing swings at pitches in the zone, he lowered the risk of allowing a hit. It appears this dynamic was a main factor in Roark’s success in 2013. That’s part of the answer to our “How did he perform so well” question.

Another factor which stands out from Roark’s strike-zone data is that he threw first-pitch strikes 70.6% of the time. This tied for third in major-league pitchers with at least 50 innings in 2013. Consistently gaining an initial advantage over hitters, and doing so at an elite rate, was another main factor in Roark’s success.

Other discussions of Roark have cited his command, his aggression, and an improved mental approach. Going back to Adam Kilgore, he writes:

Roark’s ascension began last season, when he told himself he would not allow his temper to control him on the mound. He would not the things out of his control – fluky hits, errors, whatever – distract him. He would throw strikes. He would be confident. He would attack, above all else.

“I feel that last year is when I had my, I guess, mental turnaround,” Roark said. “That was the biggest thing for me.”

(Washington Post, Nationals Journal, 9/17/2013; http://www.washingtonpost.com/blogs/nationals-journal/wp/2013/09/17/tanner-roarks-incredible-start-built-on-command-feel-for-pitching/)

We can certainly see command at work in Roark’s low homer rate, and his low walk rate (5.4%). We can see both command and aggression at work in his first-pitch strike rate. Roark’s league-leading Z-pass rate substantiates the command/aggression understanding of his performance, and also adds to this understanding.

A pitcher who suppresses swings on pitches within the zone is presumably hitting unattractive parts of the zone, but he may also be throwing in-zone pitches which do not present to hitters as strikes. This sounds like a pitcher on whom it is difficult to make good contact. This is a third idea, beyond Z-pass rate and first-pitch strike rate. One way, however, to be averse to good contact is to be a high Z-pass pitcher.

Being a high Z-pass pitcher does not entail being a high strikeout pitcher. Roark’s strikeout rate was only one percent below major-league average (again, among pitchers with 50 innings and up). Of course, on other measures, like ERA, Roark was much better than league average. I think that connecting Z-pass rate with suppression of good contact can help us understand why.

Z-passes represent hittable pitches – pitches in the zone – which were not hittable enough to induce a swing. Poetically speaking, Z-passes involve real visual ambiguity: since they end up in the strike zone, they can’t look that bad; but they do not look good enough to induce a swing.

How well does this characterization actually apply to Roark’s pitches? On this question, we have the following from the Atlanta Braves:

“He wasn’t missing with any pitches over the plate, it seemed like,” said Braves catcher Gerald Laird. “When he was going away, he was throwing that little two-seamer back door, when he was coming in he was running that two-seamer in on your hands, and he had that little slider working.

“Tonight it seemed like he was hitting his spots and wasn’t making any mistakes. I know (Freddie Freeman) was saying he was starting it at him and running it back over. When he’s doing that it’s hard to pull the trigger.”

(http://www.washingtontimes.com/blog/nationals-watch/2013/sep/17/tanner-roark-shines-nationals-complete-doubleheade/#ixzz2prxGGOUh)

Of course, these descriptions of visual ambiguity — or of evidence which shifts within a fraction of a second — presumably apply to all or most of a high Z-pass pitcher’s offerings, not just to his pitches in the strike zone which do not elicit a swing. The image that emerges is of a player whose whole volume of pitches is tough to react to in a manner that creates good contact.

Roark was actually pretty good at inhibiting contact of any kind, especially on pitches within the strike zone. However, a look at his contact numbers does not immediately confirm this interesting and important point. As we see in the table below (from BIS by way of FanGraphs, again looking at 50+ IP), many of Roark’s contact rates were actually above league average, sometimes by more than one standard deviation.

Before turning to contact rates, you will have noticed that this table also gives us a look at how Roark’s Z-swing rate compared to the rest of baseball. According to BIS, Roark was 3 standard deviations above average on a positive pitching statistic which is completely independent of fielding. He was two standard deviations (56% Z-Swing%, as opposed to 63% league average) ahead according to PITCHf/x — this is still pretty good for a former 25^th-round pick! Some other observations:

• O-contact. Here Roark was much higher than average, but this may not be a bad thing, since contact outside the zone is less likely to be productive for the hitter.

• Z-contact. Roark again was higher than average. But this somewhat unsettling number should not be digested outside of its relevant context, which is helpfully provided by Roark’s Z-swing rate. Looking at Z-contact multiplied by Z-swing yields the interesting result that Roark allowed contact on 51 percent of his strike zone pitches, as opposed to a league average of 57 percent, with a standard deviation of 3 percent.

(PITCHf/x condenses this gap, in much the same way that it condenses the gap between Roark and MLB on Z-pass. PITCHf/x reports Roark at 52.2% contact on all pitches within the zone, and MLB at 54.6%. Thus, if we switch from BIS to PITCHf/x, Roark’s contact rate goes up, and MLB’s goes down.

However, as noted above, PITCHf/x appears to be working with a larger strike zone than BIS (MLB-average Zone% of 49.3 vs. MLB-average Zone% of 44.9). This point complicates Roark’s apparent movement back towards league average. In brief, the fact that Roark’s swing rates go up — while the MLB average goes down — on larger renditions of the strike zone may be a testament to his effectiveness, rather than a knock against it.

• SwStr (swinging strikes/total pitches). Since Roark did a good job suppressing contact within the zone, Roark’s low swinging-strike number does not seem to be an especially important piece in his overall puzzle.

The standard contact rates reported by BIS and PITCHf/x do not do a good job of communicating how well a pitcher actually prevents contact, because these contact rates only look at swings. Since you can suppress contact by suppressing swings, multiplying the contact rate by the swing rate provides a better view of how a pitcher is actually doing along this dimension. Despite a “zone-contact” rate which was higher than league average, Roark was very good to excellent at suppressing contact within the strike zone.

We are exploring a clue provided by Roark’s excellent Z-pass rate that Roark was good at inhibiting solid contact. This clue was supported by our look at Roark’s contact rates, which indicate that he was pretty good at suppressing contact flat out. The idea that Roark’s pitches were visually ambiguous enough to limit good contact receives further confirmation from his batted-ball statistics. In addition, looking at these statistics (2013, 50+ IP) will bring us around nicely to the question of how well Roark might sustain his performance in future seasons.

Roark’s ground-ball, fly-ball, and infield-fly rates combine to indicate a strong bias against good contact. Roark had a somewhat high line drive rate, and, admittedly, line drives are a form of good contact. For instance, I suspect it’s unusual to have a somewhat high line-drive rate and a markedly low BABIP. Roark’s line-drive rate provides one specific indication that his BABIP is due to increase. However, a low line-drive rate is not entirely at odds with the idea that a pitcher is suppressing good contact — especially if we are thinking about home runs. Since most line drives are not home runs, a slight tendency towards line drives is a small but genuine homer-prevention measure.

In this way, Roark’s line drive rate coheres with his ground-ball, fly-ball, and infield-fly rate statistics. All of these rates, and especially their combination, suggest a low-homer pitcher. Why didn’t Roark give up a lot of home runs? Well, he got a lot of grounders and infield flies, while limiting his fly balls overall, and he gave up a somewhat high proportion of line drives. It is very plausible to suppose that Roark’s extremely low HR/FB rate overshoots the anti-homer bias suggested by his other batted-ball rates. Equally, however, the other rates tell a clear enough story that a low homer rate is not at all a surprise. Roark was very good at inhibiting good contact.

How will he do in the future? A nice way to frame this question is in terms of Roark’s ERA, FIP, and xFIP numbers mentioned earlier. And, leading up to that, I think it’s helpful to assess the respective importance of two things: (1): the overall coherence of Roark’s 2013 statistics; and (2) the sample sizes in which they were achieved.

In terms of coherence, Roark’s statistics tell a consistent story:

• Looking at Z-pass, Roark was very good at limiting swings on good pitches

• Looking at Z-swing * Zone%, Roark was very good at limiting contact within the zone

• Looking at his batted ball rates, Roark was very good at limiting good contact

I could be wrong about this, but I do not see relationships among Roark’s 2013 statistics which point to trouble looking ahead. These statistics tell a consistent story of effectiveness. You can focus on his low swinging-strike rate if you like, but this rate was consistent with Roark being at least one standard deviation (two sd’s according to BIS) better than average on limiting contact within the zone.

In addition, there are pockets within Roark’s portfolio where some stats are very good and others are even better, like the HR/FB rate relative to Roarks other batted-ball statistics. However, this type of overshooting is a good problem to have. To the extent that the non-harmonic components of Roark’s statistical portfolio are extremely good statistics, this relates to the issue of our expectations for future years. A version of Tanner Roark based on 2013, but without the extra anti-homer overshooting, would still be above MLB-average.

As noted above, Roark only pitched 53 innings, and that’s a much lower total than what a starting pitcher would typically accumulate over a full year. Although we intuitively regard this as a small sample, it does not follow that Roark’s performance is without predictive value. As is often pointed out on the pages of FanGraphs, statistics stabilize, or acquire predictive value, at different thresholds (http://www.fangraphs.com/library/principles/sample-size/). Generally speaking, fielding-independent stats stabilize more quickly for pitchers than fielding dependent stats; this is a helpful point in assessing the forward relevance of Roark’s 53 innings.

Some of Roark’s relevant statistics are above their stabilization thresholds. Roark allowed 153 balls in play (BIP), which puts him above the stabilization points for groundball rate and flyball rate:

70 BIP: GB rate

70 BIP: FB rate

Roark faced 204 batters, which is above the stabilization points for walks and strikeouts:

70 BF: Strikeout rate

170 BF: Walk rate

However, Roark was league-average in K’s and was “only” one standard deviation above average in walks; these numbers are not as good as Roark’s plate discipline statistics like Z-pass and suppression of contact within the zone. So it’s not clear whether Roark reached the stabilization points for key parts of his performance.

But this is more or less where I will have to leave it. Figuring out the stabilization point for Z-pass is beyond the scope of the present study. Indeed, my post has probably pushed us to near overload regarding things that we ever wanted to know about Tanner Roark! By the same token, it’s not clear that learning more about Roark’s statistical profile would shift our opinion much about his prospects for future performance. This is what I think we have to consider:

In an intuitively small sample size, Roark put up a consistent portfolio of excellent fielding-independent stats: on limiting zone-swings, limiting contact in the zone, and limiting good contact. Very broadly, the size of a sample has to be balanced with the consistency of the evidence within it. Just imagine watching a one-round boxing match in which one competitor knocks the other one down three times. This is a small sample which tells a very compelling story about the respective abilities of the boxers. Roark’s sample size is larger, of course, and his performance was not as dominant. Nonetheless, his limited 2013 season is packed with a lot of positive indicators.

Here are a few final comments about what Roark might do in the future, framed in terms of his ERA, FIP, and xFIP:

As we discussed above, the delta between Roark’s ERA and FIP is primarily a matter of his low BABIP and his very low homer rate. Although Roark’s BABIP will probably go up, there are signs he may be better than average at suppressing hits: he showed a tendency to induce ground balls and infield flies; the latter especially inhibit BABIP.

Roark’s very low homer rate pulls down both his ERA and his FIP. Although his .17 homers per 9 innings will almost certainly go up, there are signs he may be better than average at suppressing home runs…signs which are distinct, that is, from his one homer allowed in 53 2/3 major league innings!! Roark’s tendencies toward ground balls, infield flies, and line drives are all anti-homer measures. These tendencies flow, by hypothesis, from his ability to inhibit good contact by throwing visually ambiguous pitches.

The most eligible view by far is that Roark will regress towards league average in future years. But accepting this view should not deprive us of optimism. Roark could go back at least one standard deviation on each of the ERA-like measures and still be at league average or better than league average. That’s a good position for any pitcher. It’s a great position, albeit a paradoxical one, for a pitcher who is currently slated to compete for no better than the 5th spot in the Washington Nationals’ 2014 starting rotation!! Suffice to say I think that Roark ought to receive full consideration for the opportunities available to him.

Who can the Orioles rely on in their bullpen?
The Orioles bullpen was the lynch pin of their success during the 2012 playoff season and the normalized regression of the bullpen was the difference in the 2013 Orioles season. Coming into 2014, the Orioles are working on a bullpen without a proven closer which may cause even bigger issues than those from 2013. This offseason, the Orioles tried to save some money and traded away their closer, Jim Johnson, to the Athletics and then were on the verge of signing Grant Balfour, only for a physical to go awry. This may not be a great thing for the Orioles, but when you look back at the teams that have made the playoffs in the past, there are a lot of good examples of teams that have had lackluster closer experience.

Where those teams were successful was in correctly platooning relievers and making sure that the right pitchers were pitching in the right scenarios. This is where the Orioles may have some issues; the Orioles may be a bit light in their bullpen. Darren O’Day is an above average middle reliever but he has no closer experience and his stuff may not translate to the ninth inning. Ryan Webb and Brad Brach are nice additions, but they may not be able to make the difference of the Orioles competing or not.

At the end of the day, all analysis of the Orioles bullpen depends on Tommy Hunter. The former starter for the Texas Rangers has transitioned into a bullpen role for the Orioles since the middle part of the 2012 season and has been a solid contributor. Hunter has struck out more and walked less since moving to the bullpen and has focused more on working the zone with his fastball, which has gained more than 4 mph since moving to the bullpen. What the Orioles bullpen comes down to is if Hunter can make that jump from the 7th and 8th inning to the 9th. He has a lot of things that work in his advantage, but there is also the fact that he just moved to the bullpen over the past couple years and that he has changed his approach to pitching a bit. This is not to say that either of those are bad things, but it may be a big jump for Hunter considering that he does not have a lot of experience to begin with.

It is a comfortable assumption that the Orioles will not have a very long leash with Hunter, especially if the AL East gets off to a good start, but he should be able to get by his hiccups and be the Orioles closer throughout the season.

When does Adam Jones get the respect that he deserves?
Adam Jones might be one of the most underappreciated stars in the major leagues. His lack of appreciation may be from his nonchalant attitude in the outfield with blowing bubbles with his bubble gum while trying to catch a ball; it may be that he does not hit a ton of home runs or that he is not very flashy; or it may just be that he is not that interested in the limelight. Chris Davis’ huge 2013 season did not do very much to help Jones either, as Jones was seen as the sidekick to the titanic efforts of Davis. Adam Jones is a star and should be treated as such.

When you look at Jones, the issue with him is what makes him so great; that he is very solid at almost everything while not being truly elite at anything. His streaky nature of hitting and mental lapses may also be detractors, but he is very valuable in the fact that he can do almost everything that the Orioles ask of him. Over the past five years, Jones is basically the poor man’s version of Ryan Braun: combining speed, power, and durability. Unlike Braun, Jones plays a premier defensive position and adds value to the team. There are not very many center fielders in baseball that have 30 home run power, in fact, there is only one other center fielder with multiple 30 home run seasons in the last five years and that is Curtis Granderson who played in the home run haven of Yankee Stadium. Jones was properly respected by the Orioles with the $85 million deal he inked in 2012 and soon the whole baseball world will see the value of the Orioles’ star.

How do the Orioles fill the void of Manny Machado?
Manny Machado is everything that the Orioles could ask out of a 21 year old shortstop. He is versatile enough to play third base, in fact at a Gold Glove caliber, and has even become a better hitter since he started professional ball. His arm strength is elite and the 51 doubles that he turned in last year will quickly turn into home runs as his swing matures. Unfortunately, this season may be a wipe out for Machado because of the gruesome leg injury he got running down the first base line in a September game against the Rays. Machado is going to try to play and is cleared to hit down in Sarasota, but has yet to be cleared to run.

Given this, Orioles fans should get used to Ryan Flaherty at third, which in turn makes Jemile Weeks the starting second baseman. Neither of these are good things for the impending future of the Orioles. Both Weeks and Flaherty are subpar offensively and the advantage of Flaherty’s defensive skill at second will probably be lost at third while he is filling in for the rehabbing Machado.

For the Orioles, they should not rush Machado as his better years are to come and if a leg injury like his is not properly rehabbed, he may lose some of that elite range. There are a lot more Gold Gloves in Machado’s future and it is important for the Orioles to be patient than rush him back. Although the Orioles would be much better off with Machado at third for the duration of the season, they may be able to patch up the infield with a combination of Weeks, Flaherty, and Jonathan Schoop to fill the void left by Machado.

The most optimistic view of the situation would be that Machado is able to take the field by the middle to end of April, but a more realistic view would have him being a designated hitter for a bit and taking over at third by mid May. This may be optimistic for the Orioles considering how bad the injury looked at first glance, but his being cleared for baseball activities is a good sign.

What will the Orioles do with Dylan Bundy this year?
Dylan Bundy was all the rage during the 2012 season, making it from Single A to a September call up in Baltimore. There were still big questions about his workouts and labor throughout high school and the Orioles took it very slowly with him, as he only was allowed to go once through the lineups and was instructed to not throw breaking balls as to not harm his arm and to work on his control. All of the talent was there; the Orioles just wanted to preserve the 19 year old prospect that they drafted 4th in 2011.

In the beginning of the 2013 season, Bundy was having arm trouble that shut him down. By the summer, Bundy had undergone Tommy John surgery and the Orioles were trying to figure out what to do with their prized possession. There is a long history of pitchers coming back stronger from Tommy John, but Bundy is not a normal case. It is common knowledge that Bundy is a fan of long toss to warm up (like Trevor Bauer with the Indians) and he was used to pitching 100+ pitches from a very young age in Oklahoma, throwing hundreds of pitches a weekend even. Given all of this, there was not normal wear and tear on Bundy’s arm as to what you would expect from a 20 year old.

The issue now is for the Orioles not to be scared to let Bundy pitch. The fear for every major league team is that a pitcher gets injured and then they lose him forever because they wanted to stretch those extra 15 pitches out of him; this should not be the case for Bundy and the Orioles did him a disservice in the minor leagues in 2012. The team should not run him out there for 85 pitches, especially not during his rehab, but they need to let him pitch. Bundy’s numbers were outstanding during the 2012 season, but most of them were accumulated while he was only facing the lineup in one turn. The hitters were not getting a chance to adjust to what he was throwing and there was very little to show for Bundy’s stamina in a high pressure situation. In fact, once Bundy did get the opportunity to go a bit deeper, there were a few times when he allowed runs when the pressure was there.

It is best for the Orioles to let Bundy recover from this surgery and not let him pitch until the end of the season, but when they let him pitch again, give him the opportunity to stretch his arm a bit and let him work his whole arsenal of pitches. In the long run that will be best for the Orioles and for Bundy. For the 2014 season, it would be best to keep Bundy in the minor leagues and let him work on extending his arm and arsenal against minor league talent.

Why are the Orioles going to win 84 games?
This team is very strong and has a bright future, but the way that the 2014 season lays out does not look very good for the Orioles. Sadly, teams may only get little opportunities to be competitive and hopefully this is not one of the better chances for the Orioles being lost, but there are too many big questions left unanswered. Who is going to close? Will we see first half of 2013 Chris Davis or second half of 2013 Chris Davis? Will Nick Markakis stay healthy? Will the Orioles add another starter? All of these are massive questions that could not have even been touched on in this article because they are very fluid.

The injuries of Manny Machado and Dylan Bundy do not help either. Judging by the talent level of each of these players, the Orioles had to have hoped that they would be key contributors for the 2014 season and, quite frankly, the team is quite barren and two positions that these talented young players would be outstanding fits for. There is a lot of room for improvement with this team and, fortunately for them, it can be made from inside of the organization but until the team is a whole rather than a bunch of incongruent pieces, the playoffs are not in the near future for the Orioles.

5 You Know:
1. Adam Jones
2. Chris Davis
3. Chris Tillman
4. Nick Markakis
5. Matt Wieters

5 You Will Know:
1. Dylan Bundy
2. Kevin Gausman
3. Eduardo Rodriguez
4. Jonathan Schoop
5. Henry Urrutia

5 You Should Remember:
1. Hunter Harvey
2. Tim Berry
3. Zach Davis
4. Chance Sisco
5. Josh Hart

Earlier today, I was looking at trends and projections for some Cubs prospects and looked up Starlin Castro.  A trend immediately struck me: his 2010 batted ball statistics are nearly identical to his 2013 peripherals.

Stat:      ISO         LD%        GB%       FB%      IFFB%

2010:   .108      19.5%      51.3%     29.2%     7.0%

2013:   .102      19.9%      50.7%     29.4%     7.6%

These two seasons are closer than any of his other seasons in batted ball numbers.  A key difference?  2010 BABIP was .346, 2013 BABIP was .290.  His career BABIP is .323.  So is it we assume some good luck in 2010 and bad luck in 2013?

It should be noted that his BB% in 2013 was his career low, and his K% was his career high mark.  So can we expect some regression in those numbers as well?

I think the answer is yes to both questions.  In 2012, his BABIP was .315.  Even if Castro could return to that level (right around his career average), he looks much better than the .245 hitter we saw in 2013.

Additionally, his K% in 2013 was 3.8% higher than his previous career high, so I tend to expect a slightly lower rate in 2014 (though his contact rate in 2013 was also the lowest in his career, so if that is a trend, it is possible the K% could stay).

I’m still a firm believer in the idea that the past management, while trying to teach Castro to be selective and patient, actually taught him to take pitches for the sake of, well, taking pitches.  This could also potentially explain the low contact rate.  The numbers indicate that he didn’t learn to distinguish balls from strikes any better, and that maybe for him, the best approach is to swing at whatever looks good.

Given the striking similarities between his rookie season in which he hit .300 and garnered national attention as an upcoming star and 2013, it’s easy to dream about a bounceback 2014 season.  Only time will tell if that’s a reality, but I believe that Cubs fans have reason to be optimistic.

(I posted this earlier at the-billy-goat.mlblogs.com.  For more Cubs news and analysis, feel free to check out the blog.)

Below are two tools for Ottoneu FGPoints players to be used for the 2014 MLB season.  The first tool is a roster building tool that will provide 2013 statistics, including platoon splits, for offensive players.  Ottoneu players can use this tool to construct their team and prepare for 2014 auction drafts.

The second tool is a 2014 player projection tool that Ottoneu players (and commissioners) can use to estimate player projections for the 2014 season.  The tool incorporates Steamer, Oliver, and 3 Year Average stats for each player and then allows you to enter your own projections for the 2014 season.  Your own projections (will auto-populate FanGraph’s “Fans” projections as of 2.8.14…you can override these projections by entering your own) will load the team dashboard at the top of the tool and provide you with a summary of what you can expect from your Ottoneu team in 2014.

Roster Breakdown w/platoon splits:

http://bit.ly/1iCKkvl

2014 Team Projections Tool:

http://bit.ly/1eh614z

One of the great things about baseball is that it’s played in so many different ballparks, each with their own quirks and different dimensions. Much has been written about how different ballparks affect the game: the different distances of the fences, the size of the foul area, the altitude, and even what days the locals hang their laundry outside. These various park factors affect more than just the results of batted balls. They also influence the number of walks and strike outs. I want to take a look at a more esoteric park factor that has to my knowledge been ignored up to this point. Gravity. In high school you were probably told that gravity on Earth was a constant 32 ft/s² (or 9.8 m/s²), which was actually a white lie.  To be exact, the Earth’s gravity is 32.1740 ft/s² (or 9.80665 m/s²), but more importantly gravity is not constant.

There are several reasons the Earth’s gravity as we experience it is not constant. First, the Earth is not a perfectly uniform sphere. When mathematically approximating gravity we make the assumption that the Earth is a perfectly uniform sphere. But, since the Earth is not perfectly round and uniform, this assumption leads to a small error in the approximations and does not account for gravitational variations in different locations.

Second, gravity is dependent on your distance from the center of the Earth. Gravity is inversely proportional to the square of the distance between two objects, say between you and the Earth. The further away from the Earth you are, the weaker gravity is, g = g₀ (r_e /(r_e+h))²  where r_e is the radius of the Earth, g₀ is gravity at sea level, and h is how high you are above sea level. For example, at Coors Field g=g₀(20,925,524.9/(20,925,524.9+5,219.82))²  this equation tells us that gravity is 32.157913 ft/s²  at Coors Field, or  0.05% less than gravity at sea level (32.1740 ft/s²).

The third reason why the Earth’s gravity as we experience it is not constant is related to the centrifugal forces caused by the Earth spinning. The fact that the Earth is rotating does not actually change gravity (well this is a lie according to relativity there will be some rotational frame dragging but this effect is extremely hard to detect and surely won’t have a measurable effect on baseball). Centrifugal forces appose gravity and make items feel lighter. These forces are strongest near the equator (where you are the furthest from the Earth’s axis and therefore moving the fastest) and weakest near the poles (where you are closer to the Earth’s axis and rotating more slowly).  An easy way to remember this is gravity will be weaker the closer you are to the equator.

Let’s take a break from all this math for a bit. Here is the juicy part, the table below shows the gravity at all the different major league ballparks and the percent increase or decrease in gravity compared to the average gravity at all the ballparks (this is based on EGM2008, made easily available thorough wolfram alpha). Negative percentages indicate a decrease in gravity, while positive percentages indicate an increase in gravity.

(If you are paying close attention: 1) you might have noticed the average gravity in the table is lower than our conventional constant for gravity, 32.1740 ft/s². The average gravity in the table above is the average gravity at major-league ballparks only, not  the average gravity of all points around the world. 2) The table value for gravity at Coors Field does not exactly match what we calculated earlier. This is because the measure we calculated earlier did not account for centrifugal force or the effects of a non-uniform Earth. The gravity for Coors Field in this table allows for those factors.

The difference between the two most extreme ballparks is 0.07649 ft/s².   Alone this number seems small and is hard to conceptualize. I’ve gone ahead and explored a few different baseball scenarios to illustrate its effects.

So, what does 0.07649 ft/s²  really mean for the game of baseball?

1. Players are measurably lighter at lower gravity ballparks.

CC Sabathia feels just a little lighter while pitching in Miami than when in Seattle, a whole whopping 0.69 lbs lighter!  (Perhaps this is why when so many players travel to Florida for Spring Training they report feeling in the best shape of their life…)

2. An outfielder will have slightly longer to catch a fly ball in a lower gravity ballpark.

A fly ball with 4.5 second hang time at an average park would stay in the air 5.7 milliseconds longer in Miami, and in Seattle it would be in the air for 5 fewer milliseconds. That almost 11 millisecond difference in hang time between Miami and Seattle would mean that a running out fielder might cover 2 more inches in Miami, not enough to make any reel difference but interesting nonetheless.

3. Pitches will sink less in a lower gravity ballpark.

Pitches will sink less in Miami than they will in Seattle, but how much less? On a 65 mph slow curve it takes the ball about 0.650 seconds to reach the plate. This ball will drop 0.2 inches lower in Seattle vs. Miami. An average pitch taking 0.45 seconds to reach home plate, will only drop an addition 0.09 inches in Seattle vs. Miami. For comparison the diameter of a baseball bat is 2.6 inches or less.  A 0.2 inch difference is 1/13 the diameter of a baseball bat, which is too small of a difference to turn a hit into a swing and miss.

4. Home runs will travel farther in a lower gravity ballpark.

When it comes to home runs one would think differences in gravity would start to play a bigger role. Because home runs are in the air longer, gravity is bound to have a greater effect on them than it does on pitched balls. The hang time of a home run is usually a full order of magnitude longer than that of a pitch. Assuming identical weather conditions, a baseball hit 120 MPH at a 26^o angle would travel 13 inches (THAT’S MORE THAN 1 FOOT!) farther in Miami than it would in Seattle. That could make a difference, not in the actual score, but in what seat in the bleachers the ball would land. Although a foot is the largest difference we have talked about so far, practically it doesn’t really matter much for a no-doubt home run that’s traveling over 460 feet.

5. Just for Fun…

On the surface of the Earth if we wanted to look for extremes we would see the highest gravity at the South Pole, which would be 32.26174 ft/s² or 0.335% higher than the average gravity at a major league ball park (this and a few other factors would lead me to believe that playing in the South Pole would really suppress home runs). The other extreme would probably be in Quito, the capital of Ecuador (there is actually a volcano in Ecuador with slightly lower gravity but let’s look at one plausible hypothetical) where gravity is 32.04248 ft/s² or -0.347% below average. In Quito Sabathia would be 1lb lighter than he would at an average ball park and 1.3 pounds lighter than he would in Seattle. That same hypothetical 120 mph home run would go 0.9 feet farther in Quito than it would a an average ball park, and 1.3 feet shorter at the South Pole. This is of course completely hypothetical because we are assuming all other conditions are the same at these two ball parks such as air density and temperature, and this definitely not the case.

Thanks to

National Geospatial-Intelligence Agency for publicly releasing the Earth Gravitational Model EGM2008

Alan Nathan for providing the trajectory calculator tool, which I used to calculate difference in batted ball distances, the calculator can be found on his website http://baseball.physics.illinois.edu/trajectory-calculator.html

Who is Masahiro Tanaka?
This has been the question that baseball has been asking since there was a buzz created around his coming to the United States; buzz that probably started around the 2009 World Baseball Classic. Tanaka is a 25 year old Japanese pitcher with a stunning arsenal of pitches, especially his split-finger, who has had quite a bit of success in Japan since his debut in 2007 at 18. In looking at his abilities, it is best to look at his NPB statistics against those of his two best contemporaries, Yu Darvish and Daisuke Matsuzaka. It is fair to compare Tanaka to each of these pitchers because they were all similar ages when they started in the MLB (Darvish was also 25 and Matsuzaka was 26) and each was a top of the line pitcher in Japan.

For measurement’s sake, this will look at a couple key stats: innings pitched per start, WHIP, and strikeout to walk ratio. In Tanaka’s 7 year career in Japan, he averaged 7.6 innings per game started as compared to 7.7 for Darvish and 7.3 for Matsuzaka. When analyzing WHIP, Tanaka posted a 1.11 WHIP, while Darvish was at .985 and Matsuzaka was at 1.14. Finally, in the ever important category of strikeout to walk ratio, Tanaka was at 4.5, while Darvish marked at 3.75 and Matsuzaka was at 2.7. As we have seen, Darvish has rounded into a pretty good pitcher in the big leagues, even with some walk issues, and Matsuzaka was a solid part of the Red Sox rotation until his own pitch count issues did him in with Boston. Given these comparisons and the trends of statistics for each of these players, it is fair to say that Tanaka may not be as explosive as Darvish, but he is a very solid pitcher that will work the zone effectively and get the team deeper in the game.

Both Darvish and Matsuzaka had some walk issues as they transitioned to the MLB, as there is a huge difference between MLB players and NPB players in pitch recognition, and this may be a problem for Tanaka. If one were to hypothesize a reason for the walk issues for both Matsuzaka and Darvish, it was that they had such a huge gathering of pitches and it was tough to grab the strike zone with all of them, particularly their split finger fastballs which had a lot of NPB hitters swinging and missing as they dove out of the strike zone. As the splitter is a key pitch for Tanaka, this is absolutely something to watch during the 2014 season.

The good thing for Tanaka, though, is that he does not have the crazy assortment of pitches like Matsuzaka and he is more like Darvish with the basic four pitch arsenal. Once Tanaka is able to grasp the difference between the MLB and NPB strike zone, there is nothing to keep him from being a solid pitcher in the big leagues. Maybe he does not have the upside of Darvish, but it is not outlandish to predict that he will be a solid number two or fringe number one starter in the big leagues.

When will the Yankees realize how much they miss Mariano Rivera?
Mariano Rivera was the rock and foundation of the back end of the Yankees for the better part of two decades. It would be foolish to say that there will not be a difference made by his retirement, but the impact of his retirement will not be as great as one would assume, particularly for the closer position. In fact, when the Yankees lost Rivera for the 2012 season, they were fine with Rafael Soriano as an All-Star closer. David Robertson may or may not have as much of an impact as a veteran closer like Soriano, but it would be within the realm of possibility that the All-Star reliever Robertson can translate into the All-Star closer Robertson.

That being said, Robertson’s departure to the closer role leaves a large gap in the middle relief and set up roles. Both the inconsistent Joba Chamberlain and the ever reliable Boone Logan leaving will not help the 7th and 8th inning situation for the Yankees as well. The good news for the Yankees is that they signed Matt Thornton to take Boone Logan’s role and Shawn Kelley looked good in spurts while at the end of the game. The big unknowns are two young pitchers that may have a huge impact for the Yankees bullpen in 2014 and beyond. Cesar Cabral is a hard throwing lefty that the Yankees selected in the Rule 5 Draft in 2012 and lost for that season due to Tommy John surgery. Fortunately for the Yankees, the 23 year old came back during the 2013 season and was a strikeout machine in the minors, leading to a September call up to the Yankees, where he was solid in an 8 game audition. If he is able to work on his control, the 24 year old Cabral would be a huge boost to the bullpen.

Another young pitcher that the Yankees need to have make strides is Dellin Betances. The former top 50 prospect as a starter has bounced around a bit and had found a niche in the Scranton bullpen during the 2013 where he allowed one run and struck out 30 while minimizing his walks in his final 19 innings in the minor leagues. The imposing Betances should be able to fill the void left by Chamberlain in the Yankees bullpen and may even be a set up man by the time the stretch run comes around. The impact of Mariano Rivera’s retirement is may not be felt in the closer’s role, but the Yankees will need to shuffle around some players and hope for their younger pitchers to continue their development to fill the void left by the Hall of Fame closer.

How will the big spending of the Yankees affect the development of the younger players?
The Yankees were lauded in the late 1990s and early 2000s for having a seemingly never ending farm system that was fruit for big league stars and young players to involve in the blockbuster trades that the Yankees made. For a long time now, though, this well has dried up and the Yankees farm system is decent at best. There is a ton of opportunity in the minors, though, and the Yankees farm system could bloom into a top farm system if things go right. At the same time, players like Tyler Austin or Mason Williams could continue to regress and Michael Pineda or Manny Banuelos could stay injury prone and the farm system could be even worse off than they are now.

In answering the question posed above, the big spending allows the Yankees to let all of this play out. There will not be a ton of pressure on the younger players to move up the ladder quickly and, frankly, other than middle infielders and relievers, the Yankees do not have pressing needs at the big league level. This is not to say that the Yankees could not use a player like Pineda or Sanchez or Williams at the big league level, but rather it is that the Yankees have spent a lot of money on their big league roster and would like to see return on their investment. There are a lot of players in the minor league system for the Yankees that need a big 2014 season after disappointing 2013 seasons and the spending spree that the Yankees went on this offseason will allow these players to develop at a steady pace rather than feel the pressure of an imminent big league promotion.

What will the twilight of Derek Jeter’s career look like?
As with every person, at some point in life, your skills diminish and you have to walk away from what you were once good at. For Derek Jeter, this realism has to occur quite soon. In almost every way you look at it, Jeter has become weaker and his skill base is eroding. At the best point of Jeter’s career, he was a hitter that could control the field and spread the ball all over the place with his patented inside out swing. Now, he has lost a bit on his swing and cannot get around on the inside pitch as well as he did even two or three years ago and his contact has become weaker, with ground ball rates in the 60% range. Since his speed has also disappeared, this is a bad omen for the soon to be 40 year old Jeter.

What is even worse for Jeter is that his hitting is the reason that he is still playing baseball, as his range is nearly non-existent. It is sad to see the greats go out like Jeter will, but he needs to realize that his time has come to an end. The Yankees need to work diligently at finding a replacement for Jeter in the minors, as the free agent market for shortstops is usually thin, and it was good thing that the Yankees used an early pick on Gosuke Katoh who may be able to bridge the gap. As for the twilight of Jeter’s career here in 2014 and, possibly 2015, expect that he plays about 100 games at shortstop, another 20-25 at designated hitter, and is cautiously used in a way that can optimize what skills he does have left. If Jeter is able to keep his batting average in the high .270s or .280s, the Yankees will be able to accept that along with his leadership and knowledge of the game.

Why are the Yankees going to win 93 games?
The prediction on the Yankees is strongly based in the fact that the past two years that the Yankees have not had superb seasons and have had very good outputs. It is shocking to say that the Yankees have not had a great amount of success considering how much money they spend on their team, but that is the truth. At some point, Joe Girardi may need to be given some credit for managing the egos that the Yankees have and for making sure that they are at the top of their games. Last year’s team had no reason to win 85 games and there is more talent on this team. There are many that are not fans of the Yankees having a lineup that is full of so many older players and, at my count, five different players that will need to play DH this year for some reason or another, but there is a lot to like about this Yankees team. Although Jacoby Ellsbury was a very big reach, all of the other pick ups that the Yankees made this offseason were smart in a financial and player personnel way. This year, a lot of the holes that were there with the Yankees of 2013 should be filled and the Yankees will return to the playoffs.

5 You Know:
1. Alfonso Soriano
2. CC Sabathia
3. Hiroki Kuroda
4. Carlos Beltran
5. Jacoby Ellsbury

5 You Will Know:
1. Masahiro Tanaka
2. Jose Ramirez
3. Mark Montgomery
4. Slade Heathcott
5. Zolio Almonte

5 You Should Remember:
1. Eric Jagielo
2. Tyler Austin
3. Gary Sanchez
4. Mason Williams
5. Ian Clarkin

What will the Red Sox get from Xander Bogaerts this year?
Right now, there are a lot of good things that people are saying about Xander Bogaerts and there is a lot of reason for that. He is a big, strong kid (yes, kid — he is only 21) and he will only grow into his body more and more as time goes on. Many can say that Bogaerts strikes out way too much for a middle infielder, but he is also not your typical middle infielder, as people see 25-plus home run potential from Bogaerts. Also, his walk rate has stabilized in the 10% range, and that is good for a young hitter. As for this year, Bogaerts should grab the shortstop position from the departed Stephen Drew. An average around .270 and somewhere between 15-20 homeruns with a very incongruent fielding season should be a good rookie campaign out of Boegaerts. That would make him about the same value to the Red Sox in 2014 as Drew was in 2013, but in the grand scheme of things, a top 3 Rookie of the Year performance will be a huge boost to the future of the Red Sox.

Who will be the 5th man in the Red Sox rotation by the end of the season?
On the onset of the season, the Red Sox have a very volatile rotation other than Jon Lester. Between the inconsistency of John Lackey and Ryan Dempster and the injury history of Jake Peavy and Clay Buchholz, it is very difficult to say if the Red Sox will have an elite staff like the one that led them to a World Series title or if the injuries and inconsistency will lead to a lot of round trip journeys to Pawtucket. By the end of the season, for one reason or another, Matt Barnes will sneak into a consistent fifth starter in the rotation. The first pick by the Red Sox in the 2011, Barnes has had some issues with walks throughout his minor league career, but he has blown hitters away at each level since being drafted and will prove his worth in AAA before he makes it up to the Boston roster. This is not an indictment of Allen Webster or Henry Owens, but rather it is an endorsement of the skills of Barnes over them. As stated previously, the Red Sox are set up very favorably in the near future with those three ready to join the rotation with Lester and Buchholz.

Will the Red Sox miss Jacoby Ellsbury?
This could be very simple and to the point, Jackie Bradley Jr. should be worth about two wins less than Jacoby Ellsbury this year. That is very cut and paste and that should be enough to say that the Red Sox will miss Ellsbury. This is not the whole story though. There is the fact that Ellsbury has been hurt throughout his career very frequently and his production has been incongruent. Considering the amount of money that the Yankees paid to get him to come to New York, it is not a shock that the Red Sox let him leave. In a vacuum, the Ellsbury move was one that was bad for Boston, as they do not have a sure thing in Bradley and there is nothing in Bradley’s history that shows that he will be anything better than just above average.

When you look at all of the factors, though, the move is a bit better for Boston. The easiest reason to say that the Red Sox will be fine is that all of the money that would have been spent on Ellsbury can now be given to other players and that the Red Sox do not need to pay an aging veteran a lot of money in the next five years. Also, even though the Red Sox are coming off of a World Series win, the team is looking to build for the future with guys like Bradley and Bogaerts and want to see what they have for the future and want to see if they have in house players that could fuel another run and a profitable future.

What should the Red Sox expect out of Clay Buchholz?
A couple times in this post, I have mentioned Clay Buchholz and I feel like I could write 2500 words just explaining him and the enigma that he is as a player. Throughout his minor league career, Buchholz was a big time strikeout guy and looked that way during his brief call up in late 2007. He also pitched a no-hitter late in the 2007 World Series winning season. Since that time, Buchholz’s entire career has been an elevator and at any time that he seems to figure it out, bigger questions are created; specifically looking at his two best seasons, 2010 and 2013.

In 2010, Buchholz was 17-7 and had a 2.33 ERA which were stellar numbers for a 26 year old, making the Red Sox look at him as the ace for the future. He also, though, only had 6.22 K/9 and 3.38 BB/9. There were good numbers that led to the solid “baseball card” numbers of 17 wins and a 2.33 ERA, but none of that was sustained in 2011 and 2012, although there were moments in 2011 when Buchholz was a good player before he got injured.

Suddenly, in 2013, Buchholz was better than ever, posting a career high in K/9, a career low in BB/9, and minimizing home runs, leading to a sub-2 ERA. Unfortunately, this was done in just over 100 innings pitched and his strand rate was at a career high while his BABIP was at a career low. For the 2014 season, the median should be the norm, as Buchholz’s ERA should be in the mid 3’s and he should be able to contribute 25-28 starts for the Sox. As for the walk and strikeout rates, it is probably best for Buchholz to pitch to contact a bit more and let that walk rate get into the high 2’s per 9. A wise suggestion for his future would be to get a bit more sink on his fastball, as his ground ball rate is alarming low for a pitcher obviously focusing on pitching to contact a bit more.

Why are the Red Sox going to win 86 games?
The 2013 Red Sox were a team on a mission, both to run the table in the AL East and to win the World Series. This year, though, there are some big question that are still similar from the onset of the 2013 season. No one knows about the health of Clay Buccholz or Jake Peavy or even Shane Victorino or Mike Napoli and a team with those many injury questions cannot be seen as a force going forward. That being said, there is a very strong case for the Red Sox exceeding what the predictions say, as John Farrell is a very good manager. As shown last year in the juggling that was done and all of the correct platoons that Farrell played, there is no reason to expect that the Red Sox will be under 90 wins. It is a catch-22 to say that the same reasons that the Red Sox may succeed is why they may fail, but the Red Sox cannot expect guys like Jonny Gomes, Mike Carp, and Daniel Nava to perform at the same level that they were at during the 2013 season and that is why there is a dose of pessimism in the the forecast for the Red Sox.

5 You Know:
1. David Ortiz
2. Dustin Pedroia
3. Mike Napoli
4. Jon Lester
5. Clay Buchholz

5 You Will Know:
1. Matt Barnes
2. Henry Owens
3. Rubby De La Rosa
4. Allen Webster
5. Brandon Workman

5 You Should Remember:
1. Bryce Brentz
2. Garin Cecchini
3. Blake Swihart
4. Trey Ball
5. Mookie Betts

What is the impact of Evan Longoria on the 2014 Rays?
This is a tricky question to answer, as he is the most important player on the team and he makes this team run smoothly. That being said, he has had some injury and consistency issues in the past and it is very possible that those same issues will plague him during the 2014 season. The positive things about Longoria abound: he fields a tough position very well, he hits for power, he balances the lineup, and he walks a good amount for a power hitter. Yet there are still some questions with the young star. First off is the strikeout rate, which has consistently been in the 20% range, except for the 2011 season. This is interesting to look at because the 2011 season, Longoria had outliers in the positive rate for walk rate and for strikeout rate, yet his patient approach lead to a career low in batting average, most attributed to his ridiculously low BABIP.

This season should be a year for Longoria to really break out and that should bode very well for the Rays. Longoria needs to focus on getting the ball in play, though, because that 2011 season was very fluky and should be looked at as an outlier. As Longoria focuses on stretching out the count and shortening his swing when the count is in the pitcher’s favor, his numbers will get even better. For the 2014 season, an average in the mid-280s with 35-40 home runs and elite defense will make Longoria an MVP candidate.

Is Wil Myers going to turn into a megastar for the Rays?
The 2013 AL Rookie of the Year was everything that the Rays could have expected from a first year player, other than the sometimes suspect defense (see ALDS vs. Red Sox). Considering that, Myers has big expectations for the 2014 season and beyond. When you look at Myers, there is a lot of reason to see a very good player and a few reasons to see just a solid starter. First off, there is the fact that he has jumped around position wise in his time in professional baseball. His days as a catcher and third baseman are behind him, but there was still reason to question his ability to play defense leading to him jumping around. He does have a strong arm, but some of the angles he takes to the ball can be a bit off and that leads to some issues.

Secondly, he strikes out too much. This is an issue for most young hitters so it would be unfair to characterize this as an issue that just plagues Myers, but it is something to look at as he progresses throughout his career. There is a bit of a hitch in his swing, so the strike out issues may not be something that go away. Although I do not see Wil Myers becoming megastar for the Rays, I do see him as a solid contributor, someone that will have upper 20 home run power, play a sufficient right field, and make a couple of All-Star games along the way.

How are the Rays going to manage Matt Moore and Chris Archer?
This question needs to taken in two different ways. First off: are either Chris Archer or Matt Moore that big in the grand scheme of the Rays’ plan and if so which one and how much? In watching Chris Archer, I see stardom in his pitches, his focus, and his delivery. There are certain players that have that IT and Archer has it; his only questions are if he will be able to focus his aggression and emotion on the mound and if he can keep his walk rate near his 2013 MLB level. He needs to focus more on his offspeed pitches, particularly his cutter, and he will be fine.

The analysis of Matt Moore opens up the second question: what can they get from these players? For Archer, they will be getting a decade of advanced pitching. There is no such thing as a sure thing, but looking at Archer, one can see that the moment does not scare him. As for Moore, he will be the next target of a big time trade, either with David Price being traded or Price not being traded. I do not have a huge issue with Moore other than the walk issues, but I feel that there are other teams that may value Moore more (see the pun there!) than he actually should be valued. There are a lot of parallels between Moore and Myers, sadly in a bad way, and I feel like the Rays moreso than any other team in baseball will optimize the value of Moore.

What will be the impact of the next wave of young talent for the Rays?
The Rays are a very solid team that has turned into a superb team by good drafting and developing of players. At this point, though, the well is a bit dry. When you look at the ten prospects below, there are a couple good players that the Rays have in the MiLB, maybe a starter or two, but not that true impact player like the Rays have been rolling off. Going from Longoria to Price to Moore to Myers to Romero/Lee is quite the drop off and the Rays will remedy that accordingly. There will be more than one team that will overspend on David Price and the Rays will make sure to get top flight young talent for him. A team like the Rockies or Phillies, that may be fringe playoff teams, might overspend greatly on Price and fix the Rays minor league issues.

That being said, Hak Ju-Lee should be the shortstop of the future for the Rays and should be a 30 steal player with average hitting and fielding and Taylor Guerreri and Nick Ciuffo are very interesting because they are so young and talented. When you have those three players as middle of the road prospects for the Rays after the big Price trade yields them a big name (see: Eddie Butler from the Rockies or Maikel Franco from the Phillies or a huge package from the Rangers), the Rays will yet again have a top five farm system.

Why are the Rays going to win 89 games?
The Joe Maddon Rays always find a way to be in the conversation to win the division or make the playoffs. He has changed the entire culture of the organization and made it one of the best run teams in the league. Those are the exact same two sentences from the 2013 preview and I do not plan on changing those sentences until Maddon retires. It is nearly unprecedented in the history of baseball that a manager and executive have changed the fortunes of a franchise in the ways that Friedman and Maddon have. The only thing that is missing for the Rays is a World Series title and I have a feeling that there will be a championship in the Rays future soon, as they only get better. All the team does is reload and utilize the players that they have to their maximum utility. Talent wise, this may be the best Rays team ever, so it is not crazy to think that this team could be closer to 95 than 90 wins.

5 You Know:
1. David Price
2. Evan Longoria
3. Ben Zobrist
4. Matt Moore
5. Wil Myers

5 You Will Know:
1. Enny Romero
2. Hak Ju-Lee
3. Alex Colome
4. Jake Odorizzi
5. Kevin Kiermaier

5 You Should Remember:
1. Taylor Guerrieri
2. Andrew Toles
3. Ryan Brett
4. Nick Ciuffo
5. Richie Shaffer

Is positional versatility underutilized? What does it cost for a player to transition from one position to another? MLB rules state that players currently in the game may switch positions at any dead ball, so why don’t teams shift their stronger fielders around the diamond based on batted ball profiles? Would it be worth it, in terms of runs, to try to have players play multiple positions and shift around the diamond? These are the questions that the following research attempts to answer.

I. The cost of transitioning between positions

The first thing that must be evaluated is what a player gains or loses when moving from one position to another. To do this, I looked at a player’s Total Zone and Defensive Runs Saved numbers, on a per inning basis, for each position they played at least 500 innings at. I did this for every player that met this minimum during the years from 2003-2013 (2003 was chosen as the cutoff because that is the first year DRS numbers are available). After data collection, for each position I took the total per inning number, subtracted from the position they were moving to, multiplied by 1200 innings for roughly a full season. I did this for every position, but I will only list the important positions for the purposes of this research. Since teams would most likely be shifting based on handedness and pull rates (though they theoretically could shift based on other things like GB/FB ratio if they had an outfielder who played a fantastic infield position or vice versa), this makes the important transitions ones shifting between the right and left side of the diamond. Those transitions are as follows:

(Note that due to how this was calculated, the inverse transitions, like 2B-SS, are the same number, but negative. This data was all gathered from Baseball Reference.)

SS-2B: 2.32 TZ runs for a season

SS-2B: 1.82 DRS

3B-1B: 4.68 TZ

3B-1B: 4.41 DRS

LF-RF:  -1.03 TZ

LF-RF: -2.05 DRS

(Personally, I had thought left field was more difficult, though maybe that is a result of mostly watching games in PNC park. It is also worth mentioning that on an individual basis, LF and RF are where Total Zone and Defensive Runs Saved had the largest disagreements)

So, as most people would expect, shortstop came out to be the most difficult position on the field, followed by second base and center field, third base and right field, left field, and first base. So, now that we’ve established that baseline for players transitioning between positions, we can move on to how many runs they would gain or lose in the process.

II. Estimating the number of fielding opportunities

Initially, I could not find detailed batted ball information broken down by handedness. So I attempted several methods of quantifying the impact, using the Cubs fielders as an example, and continually came up with the Cubs gaining 3-6 runs over the course of a season while shifting 20-30% of the time. However, those methods will not be discussed here. This is because Tony Blengino posted this wonderful article yesterday, complete with a batted ball breakdown for left and right handed hitters. So, it was revision time.

Step one was to take the number of fielding opportunities (also from Baseball Reference) for each of the examined positions, so I could get TZ/Fld and DRS/Fld numbers. This was also done with the transitions applied, to get TZ/Fld and DRS/Fld numbers for when they were playing the alternative position. Then, Blengino’s breakdown was combined with the average GB%, FB%, LD%, and IFFB% for left and right handed hitters. This gave a more specific batted ball breakdown for each area of the field. This breakdown is as follows:

With this information, I could get to work on estimating the number of fielding opportunities for each position. The first thing to do was to find the number of balls put in play against the Cubs for their 6149 PAs. For right handed batters I took the 6149 PAs * 58% (percentage of RHH) * 68.77% (percentage of balls put in play by RHH). For left handed hitters it was 6149 * 42% * 67.76%.

Unfortunately, this is where I ran into a small problem. I don’t know which balls hit in an area are attributed to which fielding position. For example, I don’t know what proportion of line drives to right field are caught by the first baseman, and what proportion is considered a ball the right fielder should field. This information is likely available, but I do not have it, and could not find it. If someone does find it, I would love to be able to do this more accurately. As it stands, I made educated guesses. The estimated fielding opportunities for each position, broken down by handedness, are as follows for Cubs fielders:

(Percent chance a ball in play was hit into that position’s area, and actual total number of fielding opportunities from last season in parenthesis)

1B: 93.88R (3.83%), 244.35L (13.96%)

1B Total:  338.23 (333 actual)

2B: 223.67R (9.12%), 273.44L (15.63%)

2B Total: 497.11 (496 actual)

3B: 351.59R (14.34%), 69.12L (3.95%)

3B Total: 420.71 (424 actual)

SS: 415.05R (16.92%), 170.37 (9.74%)

SS Total: 585.42 (584 actual)

LF: 459.42R (18.73%), 217.04L (12.40%)

LF Total: 676.46 (676 actual)

RF: 280.80R (11.45%), 331.27 (18.93%)

RF Total: 612.07 (662 actual)

(Estimations attempted to keep close to the actual number and proportion of fielding opportunities. I could not get it to happen properly for RF. It will have to be ironed out at a later date.)

III. Estimating the number of fielding opportunities and runs when shifting

The first thing worth mentioning is the total number of additional runs saved depends entirely on how often a team chooses to run this particular shift. When estimating for the Cubs, I chose to run this shift 25% of the time against all batters (Normally, one might only shift against left handed hitters, but the data suggests that Darwin Barney may be better off playing shortstop than Starlin Castro, so the Cubs will be shifting 25% of the time against all hitters). The first thing to do is to find out a position’s number of fielding opportunities when it is shifting to cover someone else 25% of the time, and when it is covered 25% of the time.

When covering, this is done by taking the number of fielding opportunities when the ball is more likely to be hit at them (like when a 1B is facing a LHH) + 25% of the position being switched to (3B against RHH) + 75% of opportunities when the ball is less likely to be hit at them (1B against RHH). So, a 1B would be playing 1B against every LHH, 3B against 25% of RHH, and 1B against the other 75% of RHH. For being covered, it is the opposite. All fielding opportunities when it is less likely to be hit at them (1B against RHH) + 25% of the alternative position (3B against LHH) + 75% of their original opportunities (1B against LHH). The new total number of estimated fielding opportunities for covering and being covered is as follows:

1B

Original: 338.23

Covering: 402.66

Covered: 294.42

2B

Original: 497.11

Covering: 544.95

Covered: 471.34

3B

Original: 420.71

Covering: 464:52

Covered: 356.28

SS

Original: 585.42

Covering: 611.19

Covered: 537.58

LF

Original: 676.46

Covering: 705.02

Covered: 631.80

RF

Original: 612.07

Covering: 656.72

Covered: 583.51

Essentially, this would get your strongest fielders more fielding opportunities, provided they are still strong after making the transition. Converting the previous formula to runs is simple, since we took both the regular and alternative position’s TZ and DRS runs per fielding opportunity. So for covering this becomes the more likely side * TZ(or DRS)/Fld + 25% of the alternative position’s strong side * AltTZ(or AltDRS)/Fld + 75% of the original weaker side * TZ/Fld. For being covered, the runs per fielding opportunity are added into that previous formula in the same way. That gives us the total number of runs for covering and being covered as follows:

When optimizing the lineup, since one of each pairing (1B-3B, 2B-SS, LF-RF) must be covered, both Total Zone and Defensive Runs Saved agree that 1B should cover for 3B (due to a love of Rizzo’s defense. TZ would disagree if Valbuena had played the whole year) and 2B should cover for SS (both metrics love Barney and dislike Castro). They disagree on RF and LF, where TZ thinks LF should cover, and DRS thinks RF should cover.

If optimized for Total Zone runs, shifting 1B-3B, 2B-SS, and LF-RF 25% of the time results in a total TZ runs for these positions of 7.81, which is a 2.81 run improvement over the original lineup.

If optimized for Defensive Runs Saved, shifting 1B-3B, 2B-SS, and RF-LF 25% of the time results in a total DRS of 22.31, which is a 2.31 run improvement over the original lineup.

IV. Conclusions

Running this shift for the Cubs 25% of the time resulted in a gain of 2-3 runs over the course of the season. This is not an insignificant amount of runs, but there are some things that need to be mentioned.

1. This shift is run 25% of the time against the average for left and right handed hitters. If a team is really going to shift 25% of the time in this method, they will do it against the 25% most extreme pull hitters for each handedness. I do not know the batted ball profiles of the most extreme pull hitters, but it would result in more fielding opportunities when covering, and fewer when being covered. This would likely increase the total number of optimal runs gained significantly. Since I do not have those profiles, I am unsure by what specific margin, but I would love to be able to know.

2. This enables you to somewhat “hide” a poor fielder, particularly at first base. The greatest difference in the odds of a ball being hit at them is between first and third base. If one fielder was particularly poor, you could make sure the odds of a ball being hit to him were always low. The greater the difference between the positions being switched, the greater the overall runs gained are for the season.

3. The Cubs were a terrible team to choose. I initially thought of this idea as I was speaking with a member of their front office, so I did this work on their team specifically. The reason the Cubs are a poor team to choose is because the disparity between the positions being switched is relatively small, except for 2B-SS which has a smaller impact. As mentioned above, this results in a smaller amount of runs gained. A team with a large disparity between first and third would see a far greater impact, particularly with a very good third baseman and poor first baseman due to the transition between positions. I will likely do this with additional teams in the future.

4. As mentioned, this was only run 25% of the time. The more often it is run, the more total runs will be gained.

5. This could be done far more accurately. I do not have all the information I would like available to me right now. I know that an entity like Baseball Info Solutions already records batted ball data to a large number of vectors on the field, as that is how DRS is calculated. That information could be used to come up with far more accurate results in terms of the exact likelihood a batted ball will be fielded by a specific position.

6. The transitions between various positions vary widely on an individual basis. I used the average numbers over a very large sample, so it should be a decent approximation, but every player is different. For every player that went from a very poor shortstop to an excellent second baseman, there is one who performed worse in the same transition. However, due to the transition values roughly lining up well with the positions that are generally known as being difficult, I have no issue with using them.

7. I did not look into whether shifting defensive positions could come with a reduction offensively. Theoretically, a player may slide a bit if he has to focus more attention on fielding multiple positions. I have not yet looked into this. If such a reduction exists, it could possibly be neutralized by an organizational philosophy embracing positional flexibility as players develop.

Overall, the Cubs could likely gain around 3 runs by shifting 25% of the time. If a team has a greater difference between fielders, and shifts with greater frequency, I don’t think it’s unreasonable to expect that team to improve by 1-2 wins over the course of the season. Shifting has grown far more popular lately, and it has been demonstrated to improve overall defense. I believe this is an extension of shifting. It makes sense to shift your fielders to where the other team hits the ball most. It also makes sense to shift players in this manner, and give your better fielders more opportunities to field the ball while giving your poorer fielders fewer opportunities. If you’re going to put a fielder where they hit the ball most, you might as well make it the fielder that is most likely to make a play.

V. A more extreme example

When I wrote this article a few days ago (but hadn’t decided to post it yet) I mentioned that the Cubs were not the greatest choice of team. So, I ran it on a more extreme example, and with greater frequency. As far as frequency is concerned, I upped it from 25% of the time to 50% of the time. For the team, I needed a team with an excellent third baseman, and below average first baseman. The first team that I thought of was the Orioles, so that is the team I used. Considering this is just a quick example to demonstrate the top end of the spectrum rather than the bottom, and the process was not changed, I will not walk through the process in detail again and will just provide the total runs.

If optimized for Total Zone runs, shifting 3B-1B, 2B-SS, and RF-LF 50% of the time results in a total TZ runs for these positions of 49.34, which is a 15.34 run improvement over the original lineup.

If optimized for Defensive Runs Saved, shifting 3B-1B, SS-2B, and LF-RF 50% of the time results in a total DRS of 44.65, which is a 14.65 run improvement over the original lineup.

(For reference, the Orioles when run 25% of the time were approximately an 8-9 run improvement)

With the same potential improvements and diminishments as mentioned in the first example, this is more of an idea of the top end of the spectrum. The Orioles, already a strong defensive team, could potentially gain about 1.5 wins by shifting in this manner 50% of the time. There are definite caveats to consider and improvements to make, but shifting like this could have an extreme defensive impact.

Welcome to the 3^rd annual forecast competition, where each forecaster who submits projections to bbprojectionproject.com is evaluated based on RMSE and model R^2 relative to actuals (see last year’s results here).  Categories evaluated for hitters are: AVG, Runs, HR, RBI, and SB, and for pitchers are: Wins, ERA, WHIP, and Strikeouts. RMSE is a popular metric to evaluate forecast accuracy, but I actually prefer R^2.  This metric removes average bias (see here) and effectively evaluates forecasted player-by-player variation, making it more useful when attempting to rank players (i.e. for fantasy baseball purposes).

Here are the winners for 2014 for R^2 (more detailed tables are below):

Place

Forecast System

Hitters

Pitchers

Average

1st

Dan Rosenheck

2.80

2.50

2.65

2nd

Steamer

1.60

6.00

3.80

3rd

FanGraphs Fans

5.80

2.75

4.28

4th

Will Larson

6.60

3.00

4.80

5th

AggPro

6.40

4.25

5.33

6th

CBS Sportsline

5.40

8.00

6.70

7th

ESPN

6.60

7.50

7.05

8th

John Grenci

8.00

8.00

9th

ZiPS

9.80

7.25

8.53

10th

Razzball

6.80

10.25

8.53

11th

Rotochamp

8.60

9.00

8.80

12th

Sports Illustrated

8.80

12.00

10.40

13th

Guru

10.60

12.00

11.30

14th

Marcel

11.20

12.50

11.85

And here are the winners for the RMSE portion of the competition:

Place

Forecast System

Hitters

Pitchers

Average

1st

Dan Rosenheck

2.60

2.00

2.30

2nd

Will Larson

3.60

2.50

3.05

3rd

Steamer

1.80

5.00

3.40

4th

AggPro

4.00

3.00

3.50

5th

ZIPS

6.00

5.75

5.88

6th

Guru

4.80

7.25

6.03

7th

Marcel

6.20

8.50

7.35

8th

John Grenci

7.50

7.50

9th

Rotochamp

9.40

9.00

9.20

10th

ESPN

9.20

10.50

9.85

11th

Fangraphs Fans

11.80

8.75

10.28

12th

Razzball

9.40

11.25

10.33

13th

Sports Illustrated

10.60

11.75

11.18

14th

CBS Sportsline

11.60

12.25

11.93

I’m beginning to notice some trends in the results across years.  First, systems that include averaging do particularly well.  This is pretty well established by now, but it’s always useful to reflect upon.  It’s been asked in the past to perform evaluations separating forecasts computed by averaging with those that do not include information from others’ forecasts (more “structural” forecasts). I decided not to do this because the nature of the baseball forecasting “season” makes it impossible to be sure forecasts are created without taking into account information from others’ forecasts. This can include direct influence (forecasting as a weighted average of others’ forecasts), but can also occur in more subtle ways, such as model selection based on forecasts that others have put forward.  Second, FanGraphs Fans are always fascinating to me, and how they can be so biased, but yet contain some of the best unique and relevant information for forecasting player variation. The takeaway from the Fans forecast set is that crowdsourced-averaging works, as long as you can remove the bias in some way, or ignore it by instead focusing on ordinal ranks.

Some additional notes: it would be interesting to decompose these aggregate stats in to rates multiplied by playing time, but it’s difficult to gather all of this for each projection system. Therefore, I focus on top-line output metrics.  Also, absolute rankings are presented, but many of these are likely statistically indistinguishable from each other.  If someone wants to run Diebold-Mariano tests, you can download the data used in this comparison from bbprojectionproject.com

Thanks for reading, and please submit your projections for next year! Also, as always, I welcome any comments, and I’ll do my best to respond.

R^2 Detailed Tables

system

r

rank

hr

rank

rbi

rank

avg

rank

sb

rank

AVG

AggPro

0.250

6

0.42

9

0.308

8

0.32

1

0.538

8

6.4

Dan Rosenheck

0.296

3

0.45

1

0.340

3

0.3

3

0.568

4

2.8

Steamer

0.376

1

0.45

2

0.393

1

0.31

2

0.572

2

1.6

Will Larson

0.336

2

0.43

6

0.345

2

0.21

13

0.509

10

6.6

Marcel

0.146

12

0.36

12

0.236

12

0.27

8

0.477

12

11.2

ZIPS

0.118

13

0.42

8

0.230

13

0.3

4

0.504

11

9.8

CBS Sportsline

0.278

4

0.44

3

0.320

4

0.25

10

0.542

6

5.4

ESPN

0.241

7

0.43

5

0.317

5

0.29

7

0.532

9

6.6

Razzball

0.239

8

0.43

4

0.314

6

0.24

11

0.553

5

6.8

Rotochamp

0.234

9

0.41

10

0.287

9

0.23

12

0.569

3

8.6

Fangraphs Fans

0.268

5

0.42

7

0.272

10

0.3

6

0.574

1

5.8

Guru

0.186

11

0.33

13

0.263

11

0.3

5

0.476

13

10.6

Sports Illustrated

0.221

10

0.4

11

0.314

7

0.27

9

0.541

7

8.8

system

W

rank

ERA

rank

WHIP

rank

SO

rank

AVG rank

AggPro

0.13

3

0.15

4

0.25

4

0.402

6

4.25

Dan Rosenheck

0.17

1

0.19

2

0.27

2

0.406

5

2.5

Steamer

0.09

6

0.15

3

0.26

3

0.341

12

6

Will Larson

0.16

2

0.19

1

0.24

5

0.413

4

3

Marcel

0.05

14

0.02

13

0.17

9

0.293

14

12.5

ZIPS

0.09

7

0.07

9

0.21

6

0.375

7

7.25

CBS Sportsline

0.1

5

0.08

7

0.15

10

0.359

10

8

ESPN

0.08

10

0.05

11

0.2

7

0.43

2

7.5

Razzball

0.06

13

0.07

8

0.14

12

0.374

8

10.3

Rotochamp

0.08

9

0.06

10

0.17

8

0.359

9

9

Fangraphs Fans

0.11

4

0.08

5

0.28

1

0.435

1

2.75

Guru

0.07

11

0.05

12

0.11

14

0.343

11

12

Sports Illustrated

0.09

8

0.02

14

0.14

13

0.338

13

12

RMSE Detailed Tables

system

r

rank

hr

rank

rbi

rank

avg

rank

sb

rank

AVG

AggPro

22.495

4

7.34

4

23.217

4

0.03

4

7.096

4

4

Dan Rosenheck

20.792

3

6.91

1

21.867

2

0.03

5

6.467

2

2.6

Steamer

20.355

2

7.02

2

21.817

1

0.03

3

6.258

1

1.8

Will Larson

20.091

1

7.2

3

22.234

3

0.03

8

6.864

3

3.6

Marcel

23.473

6

7.51

6

23.831

6

0.03

7

7.334

6

6.2

ZIPS

25.380

7

7.43

5

25.662

7

0.03

1

8.048

10

6

CBS Sportsline

25.866

10

8.63

13

26.837

10

0.03

12

8.527

13

11.6

ESPN

25.698

8

8.37

12

26.418

9

0.03

6

8.120

11

9.2

Razzball

25.831

9

8.01

9

27.842

12

0.03

9

7.920

8

9.4

Rotochamp

26.199

11

8

8

25.995

8

0.04

13

7.686

7

9.4

Fangraphs Fans

26.854

13

8.12

10

30.804

13

0.03

11

8.289

12

11.8

Guru

23.187

5

7.58

7

23.608

5

0.03

2

7.198

5

4.8

Sports Illustrated

26.609

12

8.24

11

27.173

11

0.03

10

8.009

9

10.6

system

W

rank

ERA

rank

WHIP

rank

SO

rank

AVG rank

AggPro

4.4

3

1.031

4

0.17

4

47.01

1

3

Dan Rosenheck

4.25

1

1.014

1

0.17

1

47.9

5

2

Steamer

5.02

8

1.030

3

0.17

2

49.45

7

5

Will Larson

4.34

2

1.017

2

0.17

3

47.44

3

2.5

Marcel

4.62

5

1.158

13

0.18

8

50.84

8

8.5

ZIPS

4.78

7

1.101

7

0.17

5

47.85

4

5.75

CBS Sportsline

5.56

13

1.134

11

0.19

11

57.14

14

12.3

ESPN

5.81

14

1.126

10

0.18

7

53.54

11

10.5

Razzball

5.39

12

1.115

8

0.19

12

55.55

13

11.3

Rotochamp

4.71

6

1.138

12

0.18

9

51.81

9

9

Fangraphs Fans

5.29

10

1.123

9

0.17

6

52.57

10

8.75

Guru

4.51

4

1.093

6

0.19

13

48.79

6

7.25

Sports Illustrated

5.33

11

1.176

14

0.18

10

55.32

12

11.8

John Grenci

5.14

9

1.080

5

0.19

14

47.26

2

7.5