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The Curious Case of Starling Marte

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The Curious Case of Starling Marte

Posted on 14 May 2013 by Patrick Hayes

Sabermetric Spotlight: The Curious Case of Starling Marte, OF, Pittsburgh Pirates

The Reason -

How many times have you taken a look to check Starling Marte’s stats the past few games, waiting for his downfall to start? Shoot, the past two weeks or so I can count at least a dozen for myself. Which is why I decided it’s finally time to return to baseball writing and to dig into Marte’s season thus far.

First of all, before I get to the good stuff, how awesome is his name? I’m automatically including it in my 2013 MLB All-Names team, which I now just decided to create. Be on the look out for that soon, lucky you. Now let’s continue.

Starling Marte

Basic Numbers -

Starling busted into the Majors late last year for the Pittsburgh Pirates and cranked a homer in his first at-bat (Only July 26). In 47 games and 167 ABs, he hit .257 and did his fare share of striking out and not taking many pitches. Because of his less than stellar OBP, he found himself in the later half of the Pirates lineup for the majority of his first go in the bigs.

Heading into the 2013 season, projections seemed to think his first full year would play out much like 2012 did. Frustrating fantasy baseball owners by teasing them of stealing 20+ bases but lacking a high average to make him truly worth an early gamble.

Flash forward to May 13th. Starling is hitting .329 in 36 games with just as many HR (5) RBI (17) and two less steals (10) than he had in 18 more at-bats in all of 2012. The biggest change? His BABIP has skyrocketed from .333 last year to .413 in 2013. Before digging into his stats tonight, I was under the impression that he was/is due for a slump eventually and that this number will recede closer to .350-.375 and his AVG would likely end up around .275. However, looking at it a little more, I believe this isn’t the case. Every year of the his professional baseball career (starting in 2009), Marte has had a BABIP of .389 or higher, except in 2012.

Last year was his first time in both AAA and MLB, was it just part of the expected learning curve? Has he figured it out in 2013? What’s changed?

Sabermetrics -

Looking at Batted Ball data through almost the same amount of at bats in 2012 to 2013, surprisingly, not much has changed. Ground Ball Percent has risen to 57.5 from 57, Line Drive Percent up to 19.8 from 18.4 and Fly Ball Percents down a hair to 22.6 from 24.6. If none of these ratios have changed, his Plate Discipline must be the answer, right?

Bingo. Starling is now swinging is almost half of the pitches he sees (49% from 46.1% in 2012) and is making contact 79.2% of the time, up from 72.3% last year. The biggest jump comes is pitches contacted that are thrown outside of the strike zone as balls. A whooping 63.9% rate from 51.5% last year.

Why are more pitches being connected with you ask? Looking at Pitch Type, Marte is now experiencing an increased dose of Fastballs (56.8% from 52.1%) as well as change-ups (9.4% from 6.8%). The pitch he is seeing less of? Sliders. Now at only 14.2%, down from 18.7%. It seems that batting exclusively in the lead-off spot has led to a more appetizing array of pitches for Starling to hit, and he has taken advantage of the opportunity.

Forward Looking -

It’s only normal to expect his BABIP to take some sort of a dip (especially if pitchers start throwing him more sliders), but not to the depths that experts have predicted. It will stay north of .380 and average will hover just north of .300 to finish the year. Tack on a potential 30 stole base campaign, along with a resurgence of Andrew McCutchen and you have all the makings for one valuable and exciting player.

Fantasy Analysis -

If you are fortunate enough to have Marte on your squad, you most likely picked him up via Free Agency. His ESPN Average Drafted Position saw him being taken around 224. Do you sell high? Well if your team is in trouble, go for it. Starling will easily end up a 20/20 OF and could easily eclipse 100 runs scored. He will go in the top 100 next year.

Did You Know? -

His middle name is Javier and he was born outside of Santo Domingo in the Dominican Republic.

Milwaukee Brewers v Pittsburgh Pirates

Reactions and opinions are always welcomed. Find me on twitter: @pf_hayes

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Sabermetric Mining: K-BB Metrics – The Simpler The Better?

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Sabermetric Mining: K-BB Metrics – The Simpler The Better?

Posted on 21 September 2012 by Blake Murphy

Over the past few weeks, I’ve done a weekly Sabermtric Mining piece attempting to provide utility for fantasy owners using more advanced statistics. But is it possible that when it comes to predicting in-season pitching performance that it’s one of the simplest “advanced” metrics that you should be using?

For my first Sabermetric Mining piece, I had looked at FIP, xFIP, and SIERA as ERA predictors, highlighting SIERA as the favourite but identifying the benefits of each. Earlier this week, though, Glenn DuPaul of The Hardball Times put the estimators to the test in terms of their ability to predict in-season performance.

His conclusion?

At the same time, I think these results should be taken as both a lesson and a cautionary tale. The ERA estimators that were tested (xFIP, FIP, SIERA and tERA) all did a better job of predicting future ERA than actual ERA; which was to be expected and is the normal assumption in the sabermetric community. But although they did better than ERA, simply subtracting walks from strikeouts did a better job of predicting ERAs for the second half than any of the advanced statistics.

In other words, for all the advancing ability of ERA estimators to predict future ERA, it is still this simple formula that does the best as it pertains to in-season ERA prediction:


Tom Tango of Inside The Book reflected on Glenn’s work, suggesting:

I also seem to remember that in terms of forecasting 2, 3, 4 years down the line that kwERA did better than anything else out there.  Basically, for all our sabermetric advances, simply relying on K and BB (differential, not ratio) is just about the best we’ve been able to come up with.

He also noted that (K-BB)/PA (plate appearances) is preferable to using innings pitched as a denominator, but that the results would be more or less the same. Further to that notion, he indicated he uses FIP and kwERA but not really xFIP. He goes into detail on why, but basically it’s because we know for certain what these two are measuring.

For the record, kwERA is an ERA estimator with K and BB as its sole inputs. I didn’t identify it in my original piece, but it is another tool you can utilize when it comes to predicting pitcher performance, and it seems it may be both the simplest and the easiest. Again, though, using (K-BB)/IP or (K-BB)/PA would tell you the same story, just not on an ERA scale (rather, it would be a ratio).

Pursuant to that, I found a 2011 post from Tango that summed up some research as follows:

Overall, we see that while the ratio may have some additional information for us, a simple and straight strikeout minus walk differential per PA is a great indicator of performance.

Not to over-link, but I thought Eno Sarris’ piece at Getting Blanked did a nice job summing up this week’s saber-community discussion on this topic:

If you make a simple sauce, it’s easy to evaluate the ingredients. The more complicated the sauce, the more likely you’re left wondering which input was the spoiled one. Everything we needed to know about pitching we learned in the kitchen, it seems.

Here, of course, K and BB are the simple ingredients he is referring to.

None of this is to say that FIP, xFIP, SIERA and others don’t have a place or value, because they definitely do, especially for offseason analysis. Anything that improves your understanding of the components of pitcher success has value, this new research simply reinforces that scanning the xFIP leaderboard is not sufficient.

In addition, further research could be done on how the components of strikeouts and walks, for example swinging strike percentage or first pitch strike percentage, do in predicting future ERA, perhaps letting us improve on K-BB metrics.

Beyond just this K-BB analysis, you can expand your research to include components of strikeouts, as I outlined in August, and perhaps look for pitchers due to improve or decline in the strikeout category, and thus, K-BB metrics.

On the odd chance you’re still streaming pitchers to try and win a fantasy title at this point, the chart below shows pitchers available in more than 60% of Yahoo leagues and their ERA, FIP, (K-BB)/IP and (K-BB)/PA.

The higher the (K-BB)/PA, the better, obviously, as it indicates a greater ability to generate outs and a decreased propensity to allow free baserunners and thus scoring opportunities. Since those two things are the core components of ERA, it makes sense that a ratio that indicates increased outs and decreased runners (and therefore scoring opportunities) is a strong predictor of ERA. What’s even more appealing is that strikeouts and walks are generally considered the elements most within a pitcher’s control, so there are less situational mitigating factors at play than with some other metrics.

It will certainly be an interesting offseason in the statistical community, as I’m sure Glenn’s findings will encourage further research on ERA estimators, their efficacy, and how the components of K and BB work to predict ERA as well.

Follow me on Twitter @BlakeMurphyODC.

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Sabermetric Mining: Relief Pitchers Part 2 (LI & WPA)

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Sabermetric Mining: Relief Pitchers Part 2 (LI & WPA)

Posted on 14 September 2012 by Blake Murphy

In last week’s Leverage Index piece, I discussed potentially using Leverage Index and its derivative stats (gmLI, inLI, pLI, and exLI) to identify potential future closers. It seems, based on responses from Gary in the comments and from Tom Tango himself that I failed to fully explain and made some incomplete assumptions. With that said, I thought it was deserving of an encore article explaining the stats and their potential use further, especially since at this point in the season there are not many Sabermetric Mining topics that could help you make a last-minute push for a title anyway.

It was great to get a response from Tango, who happens to be the man responsible for this beautiful creation, a series of charts showing the Leverage Index of every game, inning, score, and baserunner situation imaginable. I referenced this chart in response to Gary’s question about how LI evaluates leverage, so allow me to explain in greater detail now.

Review Of Leverage Index
Leverage Index is an index of how important a game situation is. Based on the game situation (inning, score, outs, etc), it assigns a grade to how important the situation is when a pitcher pitches, across the totality of their appearances. The higher the Leverage Index, the more the game is “on the line,” when that pitcher is on the mound. A pLI of 1 is a neutral situation, while roughly 10% of situations have an LI of more than 2 and 60% have an LI of less than 1, per Fangraphs.

How Leverage Index Is Calculated
Leverage Index takes into account the inning, score, number of outs, and baserunners to give a score to each and every baseball situation.

Inning – The later in the game, the more leveraged the situation in general, especially in close games. This makes intuitive sense, as the bottom of the 9th is essentially the “last” opportunity (ignoring extra innings) for the outcome of the game to change, whereas in the 8th inning a team that fails to score still has the 9th inning to give it another shot. In blow outs, the leverage actually decreases as the game gets later, since a comeback is less likely for the trailing team. As an example, a tie game with nobody out and the bases empty has a Leverage Index of 1.2 in the bottom of the 5th but a Leverage Index of 2.3 in the bottom of the 9th.

Outs – The logic here is the same as with the inning, as an out essentially advances the “inning” situation by a fractional inning. However, it also decreases the chances of any runs being scored in that inning, since the batting team has fewer opportunities to score a runner who reaches base. Therefore, the overall impact of an out on Leverage Index is multifactorial and can’t just be assumed. As an example, a tie game in the bottom of the 4th with the bases empty has a Leverage Index of 1.1 with nobody out and a Leverage Index of 0.5 with two out. However, a tie game in the bottom of the 4th with runners on the corners has a Leverage Index of 1.6 with nobody out and a Leverage Index of 2.1 with 1 out.

Score – Again following logic and intuition, a closer game has higher leveraged situations than a blow-out. Since Leverage Index measures situational importance, a 1-run game is more leveraged than a 5-run game, since any single at bat could have a large impact on the expected outcome. As an example, a 1-run game in favor of the away team with the bases empty and nobody out in the top of the 7th has a Leverage Index of 1.7, but if that team’s lead were instead 3 runs, the Leverage Index would be just 1.0.

Baserunners – Obviously, baserunners increase the leverage of a situation by increasing the potential for runs to score. As an extreme example, a tie game in the bottom of the 9th with nobody on and two outs has a Leverage Index of 1.5, but if a runner reaches first it increases to 2.4, and if that runner instead had reached third it would balloon to 4.7.

Leverage Index and Relievers
Tango’s main concern with my original article was the assumptions I made with Leverage Index and relievers, as well as the way I described how to use the four different measures for reliever analysis. His piece said the following:

If a reliever enters a game with an LI of 2.00, but his PA-by-PA LI for the game is 2.50, it usually means that he got himself into more jams. It could ALSO mean that he came into a high LI situation in the 8th, then his team made it an even closer game when they came to bat so that when he re-enters the game in the 9th, he was faced with a very high LI scenario. Which is why inLI helps (LI when he enters each inning). If he exits with a low LI it could mean that he put out the fire, or it could be that the fire burned down the whole house that there was no leverage left.

To paraphrase, I incorrectly suggested that you could assume things about a pitcher’s performance based on his gmLI versus his exLI, or just his pLI overall. It’s far more useful and relevant to look at a pitcher’s game by game performance, though this can be tedious and time-consuming. Instead, we can still use Leverage Index in reliever analysis, but we have to utilize it in a slightly different manner than I suggested last week.

Manager Trust – I had outlined how Leverage Index stats could be an indicator of manager trust in a reliever, and I stand by that. With that said, we should not care about pLI (total Leverage Index), as poor performance can inflate this, as can a team’s batting performance. Instead, if we just look at gmLI, which is the average Leverage Index of the game when a reliever is first deployed, we can get a sense of how much the manager trusts a reliever in big situations. Your league leaders in gmLI tend to be closers because of the late-inning leverage increase mentioned earlier, but you also see set-up men like Vinnie Pestano and Antonio Bastardo among the leaders as well.

Win Probability Added (WPA)
As Tom mentioned in his response, Win Probability Added (as WPA, or WPA/LI to control for leverage) is a better method of analyzing reliever performance. Win Probability Added is the difference between a team’s Win Expectancy before and after a play. LI and WPA are closely related, as LI is a measure of the potential for change in Win Expectancy in a situation. That is, a higher LI indicates a greater potential for Win Expectancy to change.

Based on that relationship, there is more opportunity for Win Probability Added to be accumulated or lost in high leverage situations. We can thus use WPA to analyze the totality of contributions by a relief pitcher to a team’s win expectancy. This is a better measure of reliever success than the difference between gmLI and exLI (a method I had mentioned last week), as it accounts for pitchers increasing the leverage of a situation by getting into trouble and ignores the impact of his team’s hitting on LI.

As expected given that WPA values high-leverage success, we see a lot of closers among the league leaders. However, we again see Pestano, as well as a few other successful high-leverage relievers like Darren O’Day and Mike Adams, among others. One strong indicator that this method is better is that you see very few high ERAs when you sort by WPA, indicating that this is probably a better measure of actual performance.

We can also utilize WPA/LI, which is just WPA brought to a leverage-neutral context. I should note that it is not simply WPA divided by LI, but the sum of WPA divided by LI in each situation. This makes it difficult to calculate as a back-of-the-envelope calculation, but luckily Fangraphs provides it on its leaderboards. WPA/LI can be utilized to identify relievers who may be successful if given more high-leverage situations. After all, since a reliever doesn’t control when they enter the game, their total WPA is somewhat dependant on how they’re deployed.

Here we see many closers among the leaders, but we also see relievers who have had strong success but have not necessarily been given high-leverage situations to work with. An example is Darren Oliver of the Blue Jays, who has just a 1.70 WPA despite a 1.62 ERA, mostly because his pLI is just 1.32. Thus, it seems Oliver could be trusted in more leveraged situations to add more win probability to the team.

Utilizing LI and WPA Stats
Again referencing Tango’s responsorial piece, I want to emphasize that using any one of these statistics in isolation is ill-advised. While gmLI can tell you how leveraged the situations have been when a reliever has been deployed, which may be a proxy for manager trust, it tells us nothing of his success in those situations. WPA alone can be effected by manager usage, while WPA/LI is not sufficient alone either as it doesn’t tell us much about usage patterns. Thus, we have to look at our entire menu of LI and WPA related stats when trying to identify high-performing relievers and potential future closers.

I realize it might be frustrating to fantasy owners looking to Sabermetric Mining for fantasy tools, to have read back-to-back pieces that essentially summate to “you have to look at a lot of stats and contextualize,” but that’s the nature of the saves chase and reliever performance. As Gary had pointed out in the comments, too, it is sometimes as simple as going off of what managers say, whether or not we trust their word or agree with their choice. Even still, these stats provide us with a means of evaluating relievers and let us speculate on manager trust and potential future closers, while also letting us appreciate reliever performance outside of the fantasy context (Vinnie Pestano!).

Predictive Ability
At Tom’s suggestion, I pulled 2011 LI and WPA stats and compared them to save totals from this year. If there is predictive ability in leverage stats and win probability stats when it comes to the save chase, we should see some of 2011′s top non-closers in LI and WPA get save opportunities in 2012. While a regression analysis showed that the most predictive of the stats (WPA alone) only accounted for 17% of the variance in 2012 saves, regression is a somewhat flawed tools since save opportunities are finite. Instead, we’re more interested in individual closers who earned jobs in 2012. The chart below shows relievers who had less than 10 saves in 2011 but more than 10 saves in 2012, as well as their LI and WPA stats (heat-scaled for all relievers to give context to each mark, with darker green being a higher rank). Here we see that while not all relievers with strong LI and/or WPA got saves in 2012, most who did get saves previously had strong WPA and/or LI numbers. Casilla (injury-related), Frieri (unexpected improvement), and Cishek (Ozzie Guillen bullpen nightmares) are the exceptions, but with reason.

Buy/Sell Candidates
Because I want to provide some sort of fantasy utility here anyway, at least for those in dynasty formats looking for potential 2013 closer candidates, I have provided the chart below. I have shown the top few in WPA and WPA/LI while highlighting current non-closers that show the potential to close, while also providing the laggards in WPA and WPA/LI, highlighting current closers who seem to have floundered in the role.

Follow me @BlakeMurphyODC.

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Sabermetric Mining – Leverage Index

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Sabermetric Mining – Leverage Index

Posted on 07 September 2012 by Blake Murphy

With the baseball regular season winding down, fantasy owners have very little time left to make appreciable gains in the standings. With rosters expanded, out-of-contention teams experimenting, and injuries shutting players down early, September baseball does not always resemble what we see from April through August. Thus, it can be difficult, albeit valuable, to mine for advantages this late in the fantasy season, especially with most trade deadlines having long since passed.

With that in mind, today’s Sabermetric Mining piece will look toward next season a bit more than usual, although there are still practical rest-of-season implications for save chasers. Today, we will examine pLI, or Leverage Index, a statistic that can be used to identify how relief pitchers are deployed and hopefully give us insight into their future saves potential.

The Stat
pLI – Leverage Index is an index of how important a game situation is. Based on the game situation (inning, score, outs, etc), it assigns a grade to how important the situation is when a pitcher pitches, across the totality of their appearances. The higher the Leverage Index, the more the game is “on the line,” when that pitcher is on the mound. A pLI of 1 is a neutral situation, while roughly 10% of situations have an LI of 2 and 60% have an LI of less than 1, per Fangraphs.

inLI – This is Leverage Index broken down to more specific situations, in this case only the LI when a pitcher starts an inning. This is generally a good indicator for closer usage, since few managers will still deploy their closer at any time except the start of a new inning.

gmLI – This is Leverage Index broken down to just when a pitcher enters a game, and thus more often includes runners on base. This is generally a good indicator for identifying relievers that managers trust a great deal as their “firefighters” so to speak, brought in to handle tough situations.

How To Use
Unfortunately with Leverage Index, we are doing an analysis that involves intuition, logic, and attempting to predict the actions of sometimes irrational managers. I won’t get on a tangent about managing to the save rule, but you will soon notice that if a closer is the best reliever on a team, they are sometimes deployed sub-optimally based on game situations.

With that said, we can use LI to attempt to predict future closers. The logic here is that if a manager trusts a reliever in high-leverage situations, they should, in theory, be in line for the closing gig should it open up. It can also help us to identify closers that appear to be closers in name only, those whom managers do not trust a great deal. These are closers that are likely to be replaced with a string of poor performances or with a manager change.

Again, we cannot take pLI as a clear ordering of the bullpen roles. Managers are a funny breed when it comes to bullpen usage, plus we can introduce unintended bias when using a catch-all like pLI due to the deployment of handedness specialists, ground ball specialists, and more, whereby a pitcher can be deployed for a particular skill rather than his overall effectiveness.

Given that we are taking pLI as only a rough indicator of bullpen hierarchy, it suffices as a general means of trying to predict future save opportunities. Our assumption will be that, for the most part, a high pLI or gmLI is indicative of a manager’s trust and thus, a manager’s likelihood of promoting that player to the closer’s role if the opportunity opens up.

Vinnie Pestano – Pestano has long been thought to be a potential closer-in-waiting thanks to strong success, a high strikeout rate, and great peripheral numbers. For the second year in a row, Pestano has a large pLI (1.80) indicating he pitches in situations that are 80% more influential than a neutral situation, on average. He has a gmLI of 1.82, further indicating that he usually enters the game in these high-leverage situations, rather than creating them due to poor performance. His exLI, which I did not explain but is the average LI when a pitcher exits the game, is 1.52, highlighting Pestano’s success in lowering the leverage of situations, on average from 1.82 to 1.52. Pestano is the ultimate firefighter and would make a great closer should Chris Perez stumble in the role.

Addison Reed – Reed is currently the closer, so this is not necessarily in the spirit of the analysis, but it is worth pointing out that Reed leads all relievers with a 1.99 gmLI and is second to Jonathan Broxton with a 2.09 pLI. Basically, Reed is being deployed more optimally than any other closer in baseball. His 4.28 ERA is inflated by some early-season struggles, and it’s clear from these numbers that he has the complete trust of manager Robin Ventura.

Josh Roenicke – There are multiple pitchers that fit this same narrative, but I chose Roenicke because as a prospect he was identified as a potential future closer. This year, Roenicke has a 2.67 ERA over 81 innings, which would lead some to tap him as the potential heir apparent behind Rafael Betancourt. Alas, beyond his 4.43 FIP we also see that Roenicke simply does not have the trust of his manager, checking in with a miniscule 0.67 pLI and a 0.64 gmLI. Even worse, his exLI is 0.89, meaning that he has increased the leverage of situations while pitching. While this could be skewed by things such as the Rockies catching up in games where they’re behind, it could also be indicative that he is getting into trouble, a narrative backed up by him being among the league leaders in Pulls, or times removed in the middle of an inning. Add it all up and the 2.67 ERA is a mirage, not backed up by peripheral pitching stats or his usage pattern.

Carlos Marmol – Marmol has been in and out of the closer role this season, but our leverage stats allow us to examine how he has been deployed overall. Basically, Marmol is our best example of a “closer in name only,” someone who is deployed based on the save rule but not used in important situations. Despite the 17 saves, Marmol is around the league median for reliever pLI with a mark of 1.28, while his gmLI is just 1.04, by far the lowest mark of anyone with at least 10 saves.

Potential Buy Low – These pitchers have less than 10 saves on the season but have strong pLI, inLI, and gmLI marks. These are relievers that are trusted a great deal by their managers and may see closing opportunities down the stretch, or, for those of you in keeper leagues, next season.

Potential Sell High – You probably can’t sell off closers in most leagues at this point, but for those of you in keeper formats, these are pitchers with 10 or more saves but poor pLI, inLI, and gmLI marks, indicating they have yet to earn the full trust of their managers.

The saves chase is a difficult but necessary evil in most leagues. The axiom “don’t pay for saves” is a good one, but only if you can effectively identify those players who will be losing or acquiring closer gigs. Leverage Index stats are a good means of evaluating a manager’s trust in a pitcher, as well as a pitcher’s success relative to the game situation. Identifying pitchers deployed in high leverage situations can be a key asset for identifying future closers and thus, future sources of saves.

Follow me on Twitter @BlakeMurphyODC.
All stats courtesy of Fangraphs, for games through September 5.

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Sabermetric Mining: FB%, HR/FB, “Lucky” Homers

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Sabermetric Mining: FB%, HR/FB, “Lucky” Homers

Posted on 31 August 2012 by Blake Murphy

Finding ways to leverage sabermetric statistics for the purposes of finding Home Run value can be a tricky game. Where batting average allows us to delve into BABIP, batted ball type, and more, and there are plenty of peripheral indicators for pitching stats, Home Runs tend to be a stat that most people look at as having been earned, with less luck involved than others. However, that view can be detrimental to our analysis, as we can look to three indicators to aid us in mining for power over- and under-performers: Fly Ball Rate (FB%), Home Runs Per Fly Ball (HR/FB), and the Hit Tracker tool.

My apologies for no DOTF or Sabermetric Mining piece last week. I was driving from Kitchener, ON to Vancouver, BC and then settling in to a new place.

The Stats
FB% – Fly Ball Rate is the percentage of batted balls that a player hits in the air. When we analyzed hitter BABIP, FB% was thought to be a negative as fewer fly balls drop in for hits than ground balls or line drives. For power hitters, however, fly balls are of grave importance. After all, ground balls cannot clear the fence. FB% can help us to determine whether a player has the right batted ball profile to succeed in hitting home runs, but it is the rate at which those fly balls leave the park that is key.

HR/FB – this is the percentage of fly balls that clear the fence. HR/FB is the key item we will examine when trying to determine over- or under-performers, as HR/FB stabilizes at about 300 plate appearances. This means it can help to both identify lucky and unlucky players and players demonstrating a legitimate change in skill. It is important to compare a player’s HR/FB to his career norms, as we must judge if a drop in HR/FB is a blip or a trend, and vice versa. As a reference, an average HR/FB rate is about 10% in recent history. I should note that there is a lot more research done on pitcher HR/FB, if you are interested in further reading, as it is generally thought that a hitter has more control over his rate than a pitcher.

Hit Tracker – Thanks to the great Hit Tracker Online tool, we now have a resource for determining lucky homeruns. That is, a ball that clears Petco would likely clear any park, while a ball leaving Coors may not leave most stadiums. The key areas to view on this site are “No Doubts,” or balls that would cleared the fence by 20 vertical feet and 50 horizontal feet, “Just Enoughs,” or balls that cleared the fence by less than 10 vertical feet or just past the fence horizontally, and “Lucky Homers,” or balls that would not have been home runs on a neutral weather day. Obviously, Lucky and Just Enough homers are less indicative of a power skill than No Doubt homers or other, unclassified home runs somewhere between those end points. It is a lot to take in at once, but I highly recommend exploring the site as it has a ton of interesting information that extends beyond fantasy use.

Park Factors Affecting HR/FB
Park Factors should always be kept in mind, as HR/FB does not control for parks. Again referencing Petco and Coors as our polar examples, a HR/FB of 15% is far more impressive at spacious Petco than it is at the bandbox in Colorado. If you are interested in further and much more specific information on the topic, Jeffrey Gross of The Hardball Times tackled park factors extensively in June of 2011.

How To Use
It is difficult to just provide a link or a chart to help utilize these stats, as they do not all indicate over- or under-performance. The best means of approaching the analysis may be to scan the home run leaders for names that do not intuitively make sense or look out of place, both at the high and low end, and then use these tools to confirm or reject your initial thoughts. Additionally, using the Hit Tracker tool and subtracting “lucky” homers from totals, or simply looking for extreme performances at either end of the HR/FB spectrum, can provide a good starting place.

Edwin Encarnacion – Let’s begin with the league’s leader in FB% and one of the more surprising home run providers of the season, the hitter formerly known as E5. With a 49.7% FB%, you could employ hyperbole to say “he hits everything in the air” and you would hardly be wrong. Because his FB% is so large, even a modest HR/FB rate would lead to a large number of long balls, but Edwin also sports a 17.9% HR/FB rate, a near-elite rate. Edwin has 34 home runs, 8 more than his previous career high, and in less at bats (thus far) to boot. Looking at prior seasons, Edwin displayed an above-average HR/FB every year but 2007 and 2011, with an above-average 12.8% career mark. He also has a 45.2% career FB%. Add it all up, and Edwin has made a modest improvement to his HR/FB, increased his FB% to make the impact exponential, and received consistent playing time, making his home run surge only a moderate, and likely sustainable, surprise.

Billy Butler – People have been waiting for Butler to turn his 240lbs+ into home runs for some time now, and his previous career high of 21 bombs was nearly maddening. Butler had essentially been a monster who hits like a lead-off man. So what’s changed? In terms of batted ball profile, Butler has actually gone in the opposite direction of what you would expect given his homer surge, as his ground ball rate is at a stand-still and he’s traded fly balls for line drives. His home run total of 25 has been fueled entirely by a 22.5% HR/FB rate that is nearly double his previous career high. While Butler’s body type might lead one to expect an elite HR/FB rate, this kind of an extreme jump has to be cause for concern. I would expect Butler to slow down on the long-balls down the stretch, and his 2013 first half rate will be worth watching.

Asdrubal Cabrera – Cabrera was in the Butler/Encarnacion break-out class last year with 25 home runs, but he has fallen back to just 14 this year. When we consult his batted ball data, we see that he has hit slightly fewer fly balls (35.3% compared to 38.7%) and had a fewer percentage clear the fence (10.1% compared to 13.3%), neither of which is surprising given the magnitude of his 2011 breakout compared to his established norms to that point. Even still, we find that he might be over-performing in the category, as he is tied for second in baseball with 4 “lucky” home runs, while just 3 of his 14 have been of the “no doubt” variety. Last year, he came second in the league with 15 “just enough” home runs, indicating that he was getting lucky last year as well, which I’m sure many assumed. It seems likely Cabrera is not even an above-average power hitter, though at shortstop he obviously still holds fantasy value.

Ryan Ludwick – Ludwick technically does not have enough plate appearances to qualify for the leaderboards in FB% and HR/FB, but he sure has enough power to qualify as a leader in the counting stats. In just under 400 plate appearances, Ludwick has smashed 25 homers, a total he hasn’t touched since 2008 when he hit 37. So what happened to Ludwick between then and now, and how did he get back here? Well, Ludwick has always had good HR/FB rates, except last season, but this year he’s setting a career-high mark of 21.2%, a mark that would be top-15 in baseball if it qualified. Ludwick has always hit a lot of fly balls, and though his rate has declined to 43.1%, he’s still in the Edwin mold of ‘hit everything in the air and hope it flies.’ What is even more encouraging is that Ludwick leads the NL in “no doubt” shots with 9, and while he gets some benefit from playing in Great American Ballpark, 20 of his homers (80%) would have left at least half the parks in baseball.

I should note here that the ‘candidates’ section this week might be more useful for those in keeper or dynasty leagues, as the month of September may not be a large enough sample to see appreciable correction for any of these players.

Potential Sell Highs and Buy Lows – Instead of identifying both separately like most weeks, this week I will instead show the home run leaders with their relevant statistics heat-mapped, as discussed. Some may have unsustainable HR/FB rates, be getting lucky on home runs, or be legitimate sluggers.

Home Runs are not always spread nice and evenly throughout the year, and power hitters tend to be streakier than contact hitters, it seems. Thus, we must be careful when looking at short-term blips in home run-related statistics, using all three of these tools together to identify the Edwin-like breakouts and the Asdrubal-like over-performers. While September may not be long enough to see full correction to the totals, those lucky enough to be in tight races will want to leverage any potential advantage available to them.

Come get to know me on Twitter @BlakeMurphyODC.
All stats courtesy of FanGraphs and Hit Tracker, for games through August 29.

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