Category Archives: Sabermetric Stuff

One stat that explains why Eric Thames is for real

Eric Thames is in the MVP conversation. He may not be leading the charge, but he’s definitely among consideration. In the National League, the strongest man alive (probably) ranks sixth in WAR (1.8), fourth in wRC+ (183) and is tied for second with 13 home runs. He’s also a big reason as to why the Brewers are over .500 and in first place of the NL Central — which is just as astounding as Kim Kardashian’s level of fame. So yes, he’s right up there with Bryce Harper for MVP votes.

The critics, pundits and idiots — hi, John Lackey — choose to believe Thames is on performance-enhancing drugs, because, of course, that’s the only explanation for Lackey not being good. But that’s a silly statement, and one that’s been denied time and time again by the numerous negative samples Thames has produced for Major League Baseball thus far. We won’t spend anymore time on these lunatics.

The truth is that Thames is refusing to swing at bad pitches and is crushing the good ones. It’s really as simple as that. He’s always had power. But now that his eye at the plate is superior, so is his performance. And there’s a new statistic that leads us to believe his performance is the real deal.

A few weeks ago, Statcast rolled out  new stat called xwOBA or expected weighted on-base average. I’ll let the folks at Statcast give you the definition.

Expected weighted on-base average (xwOBA) is formulated using exit velocity and launch angle, two metrics measured by Statcast.

In the same way that each batted ball is assigned a Hit Probability, every batted ball has been given a single, double, triple and home run probability based on the results of comparable batted balls — in terms of exit velocity and launch angle — since Statcast was implemented Major League wide in 2015

To sum up,  xwOBA is what a player’s wOBA is expected to be based on exit velocity and launch angle. Simple enough. For example, Freddie Freeman has the highest xwOBA in MLB with a mark of .463 (minimum of 100 plate appearances), while his actual wOBA is .491, which is behind only Harper and Ryan Zimmerman. Freeman has been absolutely demolishing the ball and both his xwOBA and wOBA portray that.

This is where Thames comes in.

Thames has a .396 xwOBA — the 23rd-highest mark in baseball. That’s better than Justin Upton, Robinson Cano and Manny Machado. His actual wOBA, however, is .464. That’s a pretty large difference, and at this point you may be confused about the title of this post. If he’s outperforming expectations, why do I believe he’s still for real?

Well, because Freddie Freeman isn’t going to finish with a .491 wOBA. In fact, I wouldn’t be surprised if his final wOBA fell beneath .400. Last season, only six players finished with a wOBA of .400 or higher, and one of them was the greatest player on earth. In 2015, only five players completed that feat. In other words, it’s not easy to do. If Thames finishes with his xwOBA of .396, that’ll probably be among the top 10. Kris Bryant had a .396 wOBA in 2016, and he won MVP.

RW23 projected Thames for 31 home runs and a .360 wOBA. Odds are he exceeds both. I don’t know if he’ll still be in the MVP conversation in August or September, but it’s May, and he’s still smack dab in the middle of it, and according to his xwOBA, he’s for real. Thames will come down to earth, and he won’t hit 11 home runs every month. But his performance isn’t Chris Shelton-esque (if you don’t remember him, look him up). He made real changes in Korea and has translated those changes to MLB.

But it must be the steroids, right?


Are the Milwaukee Brewers a sabermetric team?

Ben Baumer recently wrote a terrific piece for in which he ranked Major League Baseball teams based on their openness (or reluctance) to using advanced analysis and statistics. According to Baumer, the Boston Red Sox are on top of the sabermetric food chain while the Philadelphia Phillies, as expected from a caveman-esque team, writhe on the bottom.

The Milwaukee Brewers, meanwhile, fell under the “One Foot In” category. Baumer writes:

Despite GM Doug Melvin’s background in scouting and old-school reputation, the Brewers are definitely not in the dark on analytics. Melvin calls himself “a big believer in ballpark effects,” challenges his analytics staffers to bring him useful information, and cites their work when they’ve helped him make a move.

Still, the Brewers aren’t all the way in the sabermetric movement.

All of this does not mean the Brewers live on the cutting edge. Melvin and manager Ron Roenicke could hardly be described as true believers. While the Brewers have a relatively large analytics staff, including two analysts and three programmers, the overall approach in Milwaukee appears to be less sophisticated than that of the top sabermetric teams.

Baumer also talks about how the team values Jonathan Lucroy‘s pitching-framing abilities and their knack for infield shifting. But their lack of analysts and programmers troubles me. As a big believer of sabermetrics, I want my team to believe as well, and it frustrates me to see them tripping over their shoes when they completely ignore the stats (i.e. signing Francisco Rodgriguez). Maybe it’s time to hire more stat nerds, Doug. Yet, as Baumer mentions, the Brewers do somewhat utilize sabermetrics, just on a much lesser scale than their competitors.

So, based on the information I have available to me — which is the same information you have — I’m going to see in what instances the Brewers have used sabermetrics and what instances they’ve ignored it. I’m sure I’m missing a ton, but here are few I can think of.

The Brewers used sabermetrics when…

  • When they shift, and they shift a lot. In fact, as of Sep. 9, 2014, Milwaukee had shifted 634 times, which was ninth-most in Major League Baseball and second among National League teams.
  • When they signed Lucroy to a five-year, $11 million contract extension in 2012. They locked up a phenomenal pitch framer and OBP-guy for way less than what he’s worth.
  • When they platooned Scooter Gennett and Rickie Weeks last season. The two combined for 3.0 WAR.
  • When they reeled back on steal attempts in ’14. However, Ron Roenicke has recently said he intends to implement aggressive baserunning once again.
  • When they refused to match Zach Duke‘s $33 million offer from the White Sox. He had a career year and is sure to regress at least a little.

The Brewers didn’t use sabermetrics when…

  • When they signed K-Rod to a two-year, $13 million deal. His WAR has declined in four consecutive seasons, not to mention the Brewers already have at least two capable closers.
  • When Doug Melvin said he’s not smart enough to figure out WAR. He went on to say he doesn’t really believe in it.
  • When they bunt their non-pitchers. Since 2011, Milwaukee’s position players have bunted 380 times (second-most among MLB).
  • When they brought back Yuniesky Betancourt after he posted a 0.0 WAR with them in 2011 and a -1.0 WAR with the Mariners in 2012. After the ’11 season, Melvin said he thought Betancourt played “better than what the critics said.” Betancourt accumulated a -1.9 WAR in 409 PA over the course of his return.
  • When they preach a swing-first approach. Yes, this helps Carlos Gomez, but taking pitches and working counts is Sabermetrics 101.
  • When they batted Gennett leadoff (23 times) and in the two-spot (43 times) during the ’14 season. A team’s leadoff hitter and two-hole hitter are supposed to be either the first- or second-best hitters on the team, something Gennett is nowhere near.

For the most part, as Baumer stated, the Brewers don’t seem to be a team that relies too heavily on sabermetrics. Melvin believes in certain aspects of it, but clearly isn’t all-in. Roenicke is an old-school guy who likes bunting far too much, particularly suicide squeezes. But at least he shifts his players quite often.

I’d like to hear your thoughts on if the Brewers are a sabermetric team. Am I missing anything from my list?

A few quick thoughts on Wily Peralta

There’s no one out there that wants to see Wily Peralta succeed more than me, unless someone somewhere has money invested in his success. But that’s the only scenario.

When Peralta was in the minors, I was excited about him, as was everyone. He was always considered one of Milwaukee’s top prospects, and while I realize that’s not saying much when looking at the team’s past farm systems, he was still exciting nonetheless. His fastball exceeded 95 mph and he boasted admirable minor league stats; of course we were going to be waiting on the edge of our seats for him. During Peralta’s first full season as a major league starter (2013), he pitched like a bottom-of-the-rotation pitcher, but followed that up with a “breakout” performance last season. I put the word “breakout” in quotes because yes, he broke out in terms of wins (17) and ERA (3.53), but he didn’t do enough to prove his success was sustainable. And that’s why I caution people to expect big, unreasonable things from him in 2015.

The statistic that sticks out to me the most is Peralta’s Fielding Independent Pitching. FIP is my favorite pitching statistic because it eliminates luck and is the best indicator of a pitcher’s performance. Peralta posted a 4.11 FIP last year, meaning when we look at all the things a pitcher can control (walks, hit batters, strikeouts and home runs), he was a below average pitcher (league average FIP was 3.74). A big reason for this is due to his HR/FB ratio being the second-highest among starting pitchers. Now, if he can find some way to keep the ball in the park (he can start by keeping his fourseam fastball away from the middle of the plate), he should be okay, but in his two big league seasons, he hasn’t figured how to do that yet. His HR/FB has actually gotten worse each season.

Another thing that worries me about Peralta is his strikeout rate. For a guy who averages 95.6 mph on his fastball, 95.8 mph on his sinker and 85.6 on his slider, his strikeout numbers should be a lot higher than they are. In 2014 there were nine qualified pitchers who averaged velocities of 94 mph or more on their fastball. Of those nine, Peralta ranked seventh in K%. He needs to start getting more hitters out via the strikeout, because not only with that limit the number of home runs he allows, but it should decrease his BABIP as well.

However, while those are two reasons to be cautious about Peralta, there are plenty of stats that bode well for him, and it would be unwise of me to leave them out just to strengthen my argument. Despite his K% being low, it has still increased over the last two seasons, so hopefully that’s a trend that will continue. Meanwhile, he’s started to walk less as he saw his walk rate drop considerably in 2014. His ground ball rate is also in very good shape — 51.0% in ’13 and 53.6% in ’14.

But, in order to have a real “breakout” season, the two things Peralta needs to do in 2015 season is limit the home runs and raise his strikeout rate.

Carlos Gomez and capitalizing on opportunities

It’s been a long road to baseball prominence for Carlos Gomez. After bumming through two years with the New York Mets and one with the Minnesota Twins, the speedy outfielder finally found a home in Milwaukee. Even as a part-time player, Gomez showed improvement at the plate almost as soon as he put on a Brewers uniform. His wOBA has risen every season since, and he has gone from a 76 wRC+ player to creating 32% more runs than league average. We’re all aware that he’s become somewhat of a power hitter and has been able to draw more walks and get on base at a higher clip in recent years. Anyone who watches the Brewers can tell you that. But, one of the main reasons he’s a dominant threat at the plate is because he’s capitalizing in opportune moments.

When it comes to hitting, RE24, or run expectancy based on the 24 base-out states, attempts to quantify how well hitters capitalize on their opportunities. As you might have guessed, RE24 gives more credit for hits with runners on base than with the bases empty. Baserunners can also improve or diminish their RE24 by advancing on a wild pitch or stealing a base. This is one of my favorite statistics because it’s simple to understand and it’s a good way of measuring the context of  a player’s performance.

Because FanGraphs can explain this much more thoroughly than I am capable of, here’s an excerpt from its library:

Calculating RE24 for a specific play or game is extremely easy as long as you are working with the appropriate run expectancy matrix. A run expectancy matrix presents the expected number of runs scored between a given point and the end of an inning based on the overall run environment, the number of outs, and the placement of the baserunners. For example, in the RE matrix below (run environment set at 4.15 runs per game), the expected number of runs given a runner on first and no outs is 0.831 runs.

Runners 0 Outs 1 Out 2 Outs
Empty 0.461 0.243 0.095
1 _ _ 0.831 0.489 0.214
_ 2 _ 1.068 0.644 0.305
1 2 _ 1.373 0.908 0.343
_ _ 3 1.426 0.865 0.413
1 _ 3 1.798 1.140 0.471
_ 2 3 1.920 1.352 0.570
1 2 3 2.282 1.520 0.736

Unlike most sabermetric statistics, RE24 isn’t hard to calculate. Here’s more from FanGraphs:

To calculate the RE24 of a given plate appearance, simply take the run expectancy of the result of the play, subtract the run expectancy of the the starting state, and add in any runs scored during the play. For example, if the play started with a man on first and no outs there was an original run expectancy of 0.831. If the batter hits a single that results in the runner getting to third and the batter ending on first, the resulting run expectancy would be 1.798. Since no runs were scored on the play, you would simply do the following:

1.798 – 0.831 + 0 = 0.967 RE24

So, if Gomez was the hitter in the above scenario, he would be credited with 0.967 RE24. If he had failed to move the runner over, he would be docked -.342 RE24. A player with a 15.5 RE24 means he was about 15 runs better than the average player with the same amount of opportunities. Pretty simple, right?

Let’s get some perspective on this now. Mike Trout led MLB with a 64.54 RE24 in 2014, while Matt Dominguez‘s -34.96 was the league’s worst. Gomez, meanwhile, had a career high and baseball’s 33rd-best RE24 (25.43). His 34 stolen bases and baserunning skills surely helped, but he also hit considerably better with men on base (.313) than he did with no ducks on the pond (.268). And this may mean he’s not suitable for the leadoff position, but that’s something to look at at a different time.

Gomez’s year-by-year RE24 paints a pretty clear picture on how he’s improved as a hitter and how he’s been able to take advantage of the opportunities he’s faced.

Year Team RE24
2007 Mets -7.59
2008 Twins -17.26
2009 Twins -15.70
2010 Brewers -13.85
2011 Brewers -3.03
2012 Brewers 5.38
2013 Brewers 24.13
2014 Brewers 25.43

He went from being the runt of the litter to one of the strongest and healthiest. All he needed was the freedom to swing away and reliable playing time. Credit Ron Roenicke for giving him the green light and credit Gomez for earning a spot in the lineup.

Look at the table again and remind yourself that Gomez strikes out. A lot. And remember, a strikeout decreases run expectancy. So, despite the fact that Gomez struck out 141 times last season, he still managed to have one of the game’s best RE24 by not striking out with runners on base. When Gomez batted with the bases empty, his strikeout rate was 24.9%. With runners on, he was set down on strikes at a 16.8% rate. Basically, Gomez struck out at the perfect moments.

Gomez is just entering his prime, and even though RE24 is not a predictive stat, it’s still fair to assume his will continue to rise as it has since 2008.

A plea to Adam McCalvy: Start using sabermetrics

I hope most of you already know who Adam McCalvy is, but if not, here’s a little background. He covers the Milwaukee Brewers for and is a fantastic follow on Twitter. And that’s about it. Well, there’s actually probably a lot more to him, but I don’t want to bore you. No offense, Adam.

But on a more serious note, he’s a top-notch writer. However, he does have one glaring flaw; he fails to use advanced statistics in his articles. Now, we can’t place full blame on McCalvy for this. Sabermetrics is still a relatively new theory and people question the audience for it, so maybe McCalvy’s bosses want him to shy away from metrics and use well-known stats like pitcher wins and batting average to get his point across. On the other hand, maybe McCalvy is weary of the sabermetric movement, and chooses to ignore it. To be honest, I really don’t know, but he did tweet this earlier in the year:

Before I go any further, I must reiterate that I’m not trying to criticize McCalvy’s work or call his credibility into question. Brewers fans should feel lucky to read his stuff every day, as he is great at educating and entertaining.

Back to the tweet. McCalvy acknowledges that Quality Starts are flawed, but then uses the statistic anyway to make a point. A start  is considered quality when a pitcher throws at least six innings and gives up no more than three runs. That translates to a 4.50 ERA, which never should be considered quality. Therefore, Quality Starts, or at least how they’re defined, are completely useless. He also uses wins when it comes to talking about a pitcher’s performance, but then again, almost every other beat writer does so as well.

McCalvy, along with the rest of baseball media, should consider using sabermetrics in his blogs. I can just see it now; Adam McCalvy: A Sabermatician. Sabermetrics is growing more and more popular by the day and site’s dedicated to advanced metrics, like FanGraphs and Beyond the Box Score, are thriving. We don’t want to see McCalvy get lost in the shuffle from his inability to adapt. We are already too accustomed to that as Brewers fans.

The three statistics I would like McCalvy to start incorporating are wOBA, wRC+ and FIP. None of those are difficult to explain and even the old school guys would be able to wrap their head around it. By doing this, McCalvy would not only give his readers a better understanding of how a player is performing, but he’d sound smarter. And everyone wants to sound smarter.

I’m pleading to McCalvy because I don’t think there’s a chance in a unicorn’s horn that Tom Haudricourt would take up arms in the sabermetrics movement. I’m pretty sure that ship has sailed. At least I have a small chance of convincing McCalvy (I think).

So please, Adam, give more insight to your readers and jump into the world of awesome statistics. The people will love you for it. I know I will.



Looking at FIP and SIERA

Starting pitchers for the Milwaukee Brewers ranked 22rd in FIP and 17th in SIERA last season. In order to understand these numbers, however, we need to go over what FIP and SIERA mean. After all, one of our goals is to inform the public on advanced statistics, so we shouldn’t automatically assume you know what all these weird stats are.

We’ll start with Fielding Independent Pitching or FIP.

Most of you are probably at least somewhat familiar with FIP as it is extremely popular in the sabermetric community. FIP attempts to estimate a pitcher’s ERA in the future by measuring only what a pitcher can control; walks, hit batters, strikeouts and home runs. A pitcher has little control over balls in play, so FIP completely ignores that aspect. I like FIP more than SIERA, but that’s only because it’s more well known and I understand it better. For a much better and in-depth explanation of FIP, check out the FanGraphs library.

Skill-Interactive ERA or SIERA is another ERA estimator. SIERA places a higher emphasis on strikeouts, and while walks are bad, they’re not as bad as FIP suggests. SIERA also doesn’t ignore balls in play. SIERA says that pitchers who allow less contact will force weaker contact from hitters. In other words, ground balls are good. Additionally, pitchers who have higher fly ball rates allow fewer home runs per fly ball. Again, go to FanGraphs to learn more.

Now that we have some understanding of these statistics, let’s compare FIP and SIERA among Milwaukee’s starting pitchers.

FIP SIERA Difference
Yovani Gallardo 3.94 3.78 0.16
Kyle Lohse 3.95 4.04 -0.09
Wily Peralta 4.11 3.73 0.38
Matt Garza 3.54 4.02 -0.48
Marco Estrada 5.73 4.04 1.69
Mike Fiers 2.79 2.76 0.03
Jimmy Nelson 3.82 3.76 0.06

These seven starters combined for a 3.98 FIP, 3.73 SIERA and 3.72 ERA. In terms of ERA, they outperformed their FIP and were basically identical with SIERA. Individually, SIERA and FIP were not far from each other, with the exceptions of Peralta, Garza and Estrada. However, SIERA was more favorable to every pitcher but Lohse and Garza. But why?

Well, Lohse and Garza owned a 17.3 K% and a 18.5 K%, respectively, but still held opposing hitters to a lower batting average on balls in play than other pitchers with similar strikeout rates. Remember, SIERA assumes that pitchers who strikeout more batters also give up weaker contact. Neither Lohse or Garza were big strikeout pitchers in 2014, but still managed to limit their BABIPs.

If you’re wondering why Estrada’s FIP is so high, I have two words for you : home runs. Luckily, Estrada’s back where he belongs in the bullpen, so we won’t have to worry about him killing the curve again next year.  I wrote about Estrada’s struggles here.

Milwaukee’s starting rotation was dependable in 2014, and we should see some sort of improvement from their young guys next year. It’ll be interesting to see whether or not Fiers is the real deal and if he can sustain his fantastic numbers.

Once again, if you want a primer on statistics like FIP and SIERA, head on over to That site is fantastic and they’re the best at what they do.