Suppose you think a team is going to improve significantly from last year’s performance. You’re not sure exactly how they’re going to do it, but you’re reasonably confident that they will. If you happen to live in the enlightened state of Nevada or are acquainted with a sports accountant, you can bet the team’s over-under. Is there any way to translate that belief into the acquisition of an undervalued player on draft day?

In forecasting team outcomes, we always like to look at a team’s record in 1-run games in the preceding season. The Elias Sports Bureau long ago discovered that (to simplify but not distort their insight) teams that perform poorly in 1-run games in Season 1 and then have a good record in the spring training games of Season 2 significantly outperform almost everyone’s expectations during Season 2 itself. The teams with the worst 1-run records in the majors last season were the Astros (17-28) and the Reds (22-38). Is there any reason—right now, before any spring training games have been played-to think they’ll do better in 2015? Yes, we think. Read the rest of this entry »

I’d like to talk to you about Dellin Betances.

Wait! Wait. No. No, I wouldn’t. I’d like to talk about Mike Podhorzer first. Mike has published a lot of great work covering the fundamentals of the xK% (and xBB%) metric for pitchers (and hitters), so if you are unfamiliar with or falling behind on his work, I recommend you first click here, here or here. But if you’re lazy, the short of it is: xK%, or expected strikeout rate, is an equation birthed from a linear regression that measures how a pitcher’s looking, swinging and foul-ball strike rates as well as overall strike percentage correlates with his strikeout rate. It doesn’t predict future strikeout rates as much as it retrospectively adjusts past strikeout rates; thus, it is a good tool for identifying pitchers who potentially benefited (or suffered) from good (bad) luck in a previous season – say, 2014.

Like many other metrics completely unrelated to xK%, however, there is evidence that certain players consistently out-perform (or under-perform) what their xK% rates predict their actual K% rates should be. (Mike alludes to this trend in his quip about Jeremy Hellickson, a xK% underachiever, in one of the articles linked above.) Similarly to how a power hitter will post consistently higher ratios of home runs to fly balls (HR/FB) than a non-power hitter, or how Mike Trout will probably post some of the highest batting averages on balls in play (babip) in the league for years to come, it appears there is some skill, or perhaps a particular characteristic, inherent to pitchers who consistently best, or fall short of, their xK% rates.

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It’s time for our Depth Chart Discussions to begin. In an effort to suss out every team, we’ve divided them into four parts (infield, outfield, bullpen, and rotation) and will begin breaking them down for you over the next few weeks. You can find them gathered here.

The Brewers lost half of what was a decent bullpen to free agency (Francisco Rodriguez, Zach Duke, and Tom Gorzelanny) and trades (Marco Estrada) this offseason and would enter the 2015 season pretty thin as currently constituted. That makes Milwaukee a relief corps in flux for fantasy. K-Rod recorded 44 of the team’s 45 saves last season, and Duke earned 1.3 WAR in a dominant campaign that featured 11.4 strikeouts per nine, which placed him in the top 25 of relievers with at least 50 innings pitched last season. Duke is now a White Sox and cannot return, but K-Rod remains unsigned. His return would mute much of the intrigue that centers on a likely new old closer.
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I am huge proponent of the pitch type work which has led to the Arsenal Score here are FanGraphs. One possible issue with pitch type data is a reasonable amount of data needs to be collected before any conclusions could be draw. I am going to take it a step further today and look for MLB ready fastballs knowing just the pitcher’s velocity and break. Just knowing how the pitch’s speed and trajectory, some conclusions can be drawn on how the pitcher will perform in the future.

This past summer, I found how to estimate a pitcher’s fastball ground ball (GB%) and swinging strike rate (SwStr%) knowing just the velocity and break. The ground ball rate was the same for all pitches while the swinging strike rate varied a bit. Well, I went into meld/average mode to come up with a method to find a simple way to determine how productive a fastball may be knowing its current break and speed.

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So many things happened. Everyone was traded. Everyone was released. And everyone was signed. It’ll fuel RotoGraphs pieces for weeks to come. You’ll see more in-depth pieces on these guys. But, with the dust settled, it seems like a good time to run all through some of the players that changed addresses, and talk a little bit about how they may have changed their fantasy outlooks for the coming season.

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Last week, I examined rookie pitchers Daniel Norris and Rafael Montero for my weekly Quick Looks piece. With both pitchers, they had several pitches which just got away from them. There was no way a hitter was going even think about swinging at them. These pitches put the pitcher constantly behind in the count. While I could use Zone% to determine the amount pitches in or out of the strike zone, I wanted to look a little further out of the zone to find pitches not even close to the strike zone and I ended up with, Way Out of the Zone Percentage (WOOZ%).

I have wanted to look into this subject for while after hearing Brian Bannister mention something in a Baseball Prospectus podcast. He said some pitchers can have problems with their grips as they transition from the higher seamed minor league baseball to the lower seamed MLB baseball. Specifically, he noted it hurt pitchers who throw four-seam fastballs and curve balls. Since starting Quick Looks, which concentrates on young, new pitchers, I have seen a ton of pitches not near the zone which may be caused by not having a good grip. I needed to find and solution and for now it is WOOZ%.

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There are a sundry of middle/set-up relievers that can succeed in the closer role if given the opportunity. A few obvious: Wade Davis if something happens to Greg Holland… Wil Myers for James Shields and who?!; Ken Giles if the Phillies can somehow find a trade partner for the grundle-grabber; and Brad Boxberger if not Jake McGee. I assume David Robertson signs elsewhere and Dellin Betances steps in.

Lets’ look at potential closers using reliever outcomes. Here are the average contact and balls in play-related outcomes for all relievers that qualified and specifically relievers with 10+ saves:

Our Filters: 

• Contact-related outcomes: For K-BB%, Ct% and SwStr%, I filtered simply by the general relief pitcher averages. Everyone below average in these 3 categories was filtered out.
• Command-related outcomes: For zone% and first-pitch-strike%, I used 1 standard deviation below average and 1.25 SD’s below average in BABIP as filters. 1.25 SD allowed me to omit only the relievers that had career BABIP’s higher than we would like to see for a closer or in general. I didn’t want to screen out Jenrry Mejia (1.24 SD below the mean) or Tim Stauffer (1.19 SD below), because there’s a possibility for BABIP regression.
• Balls In Play-related outcomes: I was lax on the balls in play outcomes. I went with a 40% Grounder rate and 45% Flyball rate as my filters versus the averages that you see above because below average fly-rates don’t mean much in places like Tampa (where Boxberger is elite but below average in fly-rates); and above average grounder-rates don’t mean as much with atrocious defense behind you (hence Corey Kluber’s unlucky BABIP, which should have been closer to .299 per end-of-season xBABIP/Inside Edge data), but I digress.

Using these filters, we’re left with a robust list of above-average relievers beyond just closers (scroll down for the noted filters):

What happens if we use the command (K-BB%)/contact (Ct% and SwStr%) related averages for relievers with more than 10 saves this year? 

…We’re left with some elite closers and then a few interesting names.

Last year, Danny Farquhar (just missed the list this year) had a top 20 swinging-strike rate – about a percent better than Fernando Rodney, but it was masked by his BABIP and left-on-base rate that killed his surface stats (4.20 ERA vs. 2.40 xFIP). This year, he actually outperformed his xFIP with a 2.66 ERA. After an early season MASH Report on Rodney’s velocity, I eyed Farquhar. At least keep him in mind next year if anything does happen to Rodney.

Josh Edgin (Mets for those of you that don’t know) has a top 65 contact-rate sandwiched between Mark Melancon and Jake McGee and even induced grounders 50% of the time. He has a pretty extensive repertoire as well. In order of usage: Fourseamer, Slider, Curve, Change, Cutter and Sinker. According to his Brooks Player Card, he has great swing and miss rates on his Curve (>56%), Cutter (50%), Slider (>42%) and Sinker (33%). Even his Change approaches 30%. This isn’t the case on his Fastball, but at 93+ MPH, it induces a decent amount of grounders (1.8 GB/FB). Keep in mind he had late-season elbow issues which effected his velocity by a MPH or so, but he could be called upon to get Mejia out of a jam. I like him better than an unhealthy Bobby Parnell and Jeurys Familia from a command perspective for another year.

Zach Duke did his best Craig Kimbrel impression prior to the R2M monster hitting him in August. Prior to 8/1, Duke had a 34.9% K-rate and 27.3 K-BB%. Kimbrel ended the year with a 38.9% K-rate and 28.3 K-BB%. I think August and September brought him back to his realistic value (~2.50 ERA, 1.15 WHIP). It will be interesting to see who closes for the Brewers if they let Francisco Rodriguez go. Both Duke and Will Smith have above average (even for RP w/ 10+ saves) swing-and-miss. Will Smith should have additional command next year, but Zack Duke induces grounders better which I like in my closers/in Milwaukee. They also have Jonathan Broxton. The hierarchy seemed to be K-rod-Broxton-Smith late last season. If that’s the case, they should use Duke more (former starter) and in higher leverage situations. He was equally solid against both lefties (.258 wOBA) and righties (.262 wOBA).

Oliver Perez everybody! I thought I could filter him out by his splits being a lefty, but like 2012, he was more effective vs. righties (and faced 44 more of them). The D-backs have Addison Reed, up-and-comer Evan Marshall as well as Daniel Hudson caught touching 97 MPH so if not by outcomes or splits, we can filter Perez out by opportunity.

Darren O’Day and Andrew Miller is part of one dominating Baltimore bullpen – one that gets referenced by anyone who thinks the Orioles can beat the Tigers in the ALDS. Notice that Zach Britton didn’t make either of the above lists! He was filtered out by his below average K-BB% (13.70%). It’s his 75+% grounder rate (hence the 81+% left-on-base rate and .215 BABIP) that keeps him elite in Baltimore. The only concern you can have with O’Day is a fastball velocity almost 2 standard deviations below the mean for relievers, but his arm angle combined with that slider still induces a 30+% whiff-rate on both pitches. Miller’s slider though is a world apart from O’Day’s: only Pedro Strop, Will Smith, Jake Diekman, Greg Holland and Oliver Perez induces more whiffs than Miller’s 55% according to Baseball Prospectus’ Pitchf/x Leaderboards.  I doubt we’ll see a closer-transition next year in Baltimore unless Britton’s GB/FB ratio takes a drastic dive because his HR/FB ratio, which approached 18%, could be an issue.

An xBABIP review

On the last day of the season, @jeffwzimmerman provided me with Pitch xBABIP based on inside edge data. Let’s look at some of the bigger xBABIP differentials to keep in mind:

The last column depicts the z-score for BABIP differential. Francisco Rodriguez was expected to have a BABIP about 120 points above his actual BABIP. I highlighted (red/bad; green/good) the xBABIP z-scores as well so that you know whether or not to actually be concerned meaning sure Aaron Sanchez has the 5th biggest BABIP differential (over 2 standard deviations from the mean), but a .239 xBABIP is still utterly elite (3.34 SD’s from the mean). On the other side of the equation, it’s nice to see Evan Marshall, Carlos Martinez and Adam Ottavino (albeit in Colorado) with large BABIP differentials. Marshall and Martinez even have xBABIP’s over .5SD from the mean.

The last bit of fun

It was a very fun year to be doing bullpen reports for RotoGraphs. Aroldis Chapman broke the single-season strikeout rate of 2012 Craig Kimbrel (50.2%). He struck out 52.5% of the hitters he faced. Andrew Miller (42.6%) and Brad Boxberger (42.1%) also made the top 10 seasons ever. Dellin Betances (39.6%), Wade Davis (39.1%) and Craig Kimbrel (38.9%) made the top 20. Chapman’s swinging-strike% of 20% beat ’12 Kimbrel by .8%, but he couldn’t pass ’04 Lidge, ’03 Gagne, ’04 Gagne, ’02 Gagne or ’05 Lidge. Chapman, Miller, Doolittle, Boxberger, Betances (Wade Davis and Kenley Jansen close behind) all had historical, top 20 K-BB rates. Relievers dominate this list: only ’99 Pedro Martinez (#12), ’00 Pedro Martinez (#21), ’01 Randy Johnson (#27) and ’01 Pedro Martinez (#28) make it into the top 30, but it’s clear that we have a growing list of elite relievers.

From a fantasy perspective, thanks to 45+ saves totals out of Holland and Kimbrel, we had two relievers ranked in the top 20 pitchers. If Chapman didn’t miss time and Betances and Davis consumed the closer role, we would have had 3 others. Last year, Craig Kimbrel and his 4 wins, 50 saves, 98 SO’s, 1.21 ERA and .88 WHIP campaign made him the 3rd most valuable pitcher. This year with Kershaw, Cueto, Felix and Kluber, it would have taken even more.

If we combined the 3 more dominating performances exclusive of saves this year: Aroldis Chapman’s K-rate (52.5%) and saves total (36), Dellin Betances IP (90) – who was dominating in his own right, Wade Davis’ ERA (1.00) and Wins total (9) and Sean Doolittle’s WHIP (.73) – let’s call this guy Aroldellin Dooldavis, we would wind up with a 10.95 z-sum…just above Corey Kluber (10.72), but under Clayton Kershaw (13.74), Johnny Cueto (12.95) and Felix Hernandez (12.50). Even 50 saves wouldn’t have done the trick (12.43 z-sum):

Any day now, Zach Sanders (@zvsanders) will come out with his end-of-season FAVRz/Fantasy Rankings. Look out for them.

For this post, I will provide three sets of rankings using that same approach (summed up z-scores) for our end-of-season “Bullpen Report: Expected Fantasy Rankings”. The Bullpen Report team should follow up with role reports for each division in the coming weeks as well.

The first set of rankings you will find almost anywhere: on the fantasy sites that you use, via player-raters, etc. It’s the standard 5×5 fantasy value (Wins, ERA, WHIP, SO and Saves). The second grid will be for 6×6 leagues (addition of Holds). The last grid will be for 5×5 and 6×6 leagues, but instead of standard ERA and WHIP, we’ll look at rankings if you were to use expected ERA (via SIERA) and adjusted (adj)WHIP through BABIP differential: I will explain below.

1) 5×5 Rankings (Wins, ERA, WHIP, SO and Saves) – actual 5×5 value in column 4; expected 5×5 value in column 5:

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• As a brilliant reader pointed out, Zach Putnam has received the last two save opportunities for the White Sox so he has moved ahead of Jake Petricka on the grid below. So naturally in tonight’s game Petricka recorded the four out save. At this point it could be anyone’s game but with Petricka throwing tonight, Putnam could get the next opportunity, although Petricka only threw 20 pitches. If both are on the wire and you need saves over the season’s final week, I would first make sure other closers aren’t around but if that’s not an option, certainly go for Putnam over Petricka. Skill wise, they are similar with Putnam’s 3.65 xFIP narrowly beating out Petricka’s 3.69 number but the value is mostly tied to who’s pitching the ninth.

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• As Dan mentioned last night, Edward Mujica is sticking at closer for the Red Sox for the remainder of the season, and recorded his eighth save of the year today. Mujica won’t rack up the strikeouts for the Red Sox or your fantasy team but for the season’s final weeks, he’ll be the guy in the ninth in Boston and could net your team a few more saves.

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