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

Tags: , , , , , , , , , , , , , , , , , , ,

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.

Comments (2)

Down On The Farm – Updating Prospects You Were Expecting in 2013

Tags: , , , , , , , , , , , , , , , , , , , , ,

Down On The Farm – Updating Prospects You Were Expecting in 2013

Posted on 19 September 2012 by Blake Murphy

When it comes to prospects, timelines change all the time. Injury, exceptional performance, poor performance, or the needs of the parent club can all slow or expedite a prospect’s path to the Major Leagues.

In an attempt to give fans and fantasy players a gauge of when to expect prospects in the Majors, Baseball America attached an ETA (Estimated Time of Arrival) with their write-ups for this year’s Top 100 Prospects list. Today, Down On The Farm looks at those players ranked inside the top 50 at the beginning of the year who had an ETA of 2013. I chose 2013 because this group is essentially players who were expected to be adding the final seasoning to their repertoires or profiles.

Players with a 2012 ETA are either no longer prospects, having reached the Majors, or probably have a well-publicized reason for not making it yet, whether it be injury or performance. For your reference, the players Baseball America listed as Top 50 prospects with a 2012 ETA who have not had significant time in the Major Leagues this year are: Julio Teheran (struggles), Trevor Bauer (organizational decision), Danny Hultzen (struggles, ETA too aggressive), Mike Montgomery (struggles), Manny Banuelos (lack of team need), Brad Peacock (struggles), and Arodys Vizcaino (injury). Players with a 2014 ETA were not expected to be on our radar quite yet, so if their projection is now 2013, they are likely a special case, and we’ll highlight them in the future. That is all to say…I couldn’t update on everyone, and those with a 2013 ETA seemed the most logical.

Top 50 Prospects, Pre-Season 2012, 2013 MLB ETA
Per Baseball America rankings.

#7. Jurickson Profar – The Rangers shortstop prospect is still just 19 but getting a taste of Major League life already as a September call-up. Profar’s glove profiles as extraordinary, and his bat held up at Double-A with a 129 wRC+ (.281/.368/.452 AVG/OBP/SLG triple-slash line). Profar has some power and will steal bases in the Majors, making him a potentially hot commodity in 2013 drafts due to positional scarcity. While he hasn’t played at Triple-A yet, the Rangers will likely try to make room for him next year, potentially moving Elvis Andrus and/or Ian Kinsler to new positions.

#8. Shelby Miller – The Cardinals’ top pitching prospect is getting a September taste of the Majors out of the bullpen even though his 2012 was not as successful as the Cardinals had hoped. His 4.74 ERA and 4.48 FIP were disappointing, but he still grades out well in the “stuff” department, striking out 10.54 per 9. The Cardinals have their entire rotation locked up beyond this year, so Miller will probably be forced to compete for a spot in the spring.

#10. Dylan Bundy – Bundy pitched at three different levels this year, dominating at each stop and closing with a 3.24 ERA and 3.86 FIP in three Double-A starts. If it were me, I’d probably send the 19-year old back for more seasoning next year, but the Orioles’ sudden rise to contender may make them more aggressive with his timeline. As I was editing this, in fact, news broke that Bundy will be joining the Orioles for the stretch run later today.

#11. Manny Machado – Machado got the nod in early August to help out the contending Orioles at the hot corner, and he hasn’t disappointed with a 95 wRC+ and solid defense thus far. He was aggressively promoted right from Double-A, where he showed 20-20 potential, making him a potential 2013 draft target and a solid keeper option. He can probably stick at shortstop, too, and may be one of those valuable dual-position fantasy players while J.J. Hardy is still around.

#12. Gerrit Cole – Cole shot through the system this year, starting in High-A and finishing with a single start at Triple-A. He strikes out over a batter an inning and had FIPs around 3.00 at High-A and Double-A, making him a likely candidate for many prospect top-10 lists next year. His ETA is probably more in the mid-season range, though.

#17. Travis d’Arnaud – d’Arnaud had a solid season derailed by injuries, and the Blue Jays have J.P. Arencibia and a recently-extended Jeff Mathis in house to hold the fort down if d’Arnaud needs extra time at Triple-A next year. When he did play, though, d’Arnaud sure looked ready, with a 147 wRC+ showing that his .333/.380/.595 line was not just fuelled by the Las Vegas air.

#19. Anthony Rendon – The Nationals’ third base prospect is blocked by Ryan Zimmerman but it may not matter in the short-term since his first year in the minors was cut short by an early ankle injury. As it is, Rendon played just 43 games across four levels, finishing up in Double-A where he was a slightly below-average bat. Rendon will almost certainly be back in Double-A to start the season but will probably be among the first call-ups should the Nationals run into injuries.

#28. Wil Myers – I believe my love and excitement for Myers has been well publicized at this point, and I don’t see how the Royals could justify him not being on the 2013 Opening Day roster. Jeff Francoeur, Alex Gordon, Lorenzo Cain, and Billy Butler are holding down the OF and DH spots, but I don’t see a way Myers doesn’t force one of them to the bench or the trade block come March.

#31. Martin Perez – Perez has a brief stint with the Rangers in the summer and is now back working out of the bullpen down the stretch. The 21-year old lefty hasn’t missed many bats in Texas (5.52 K/9), a fact that isn’t surprising given his downward trending K-rates in the Minors (bottoming at 4.89 K/9 in Triple-A this year). I know some are still high on Perez, but if he can’t miss bats at a greater rate he won’t be ownable for fantasy purposes.

#34. Jonathan Singleton – Singleton is not really blocked in Houston given Brett Wallace’s relative lack of pop for a first baseman, but he didn’t get pushed past Double-A despite a pretty successful Minor League season. The Astros may be taking it slow with the 21-year old and waiting for his power to further develop before tapping him for a call-up.

#35. Zack Wheeler – The news that the Mets will now be affiliated with Las Vegas at the Triple-A level is bad news for Wheeler, as he’ll now essentially need to break camp with the Mets or head to a pitcher’s graveyard. Wheeler had great success at Double-A and performed well in a 6-start stint at Triple-A, so making the Opening Day roster is certainly not out of the question, though fantasy owners would want to express caution at first.

#38. Gary Brown – Brown was merely league-average at Double-A this year, but he’ll likely be challenged with Triple-A at age 24 next season. I can’t see Brown making the club out of Spring given that his only Major League-ready tool is his speed right now, but he could be in line for a call-up if he starts off hot, especially if the Giants don’t improve their outfield in the offseason.

#39. Anthony Gose – Gose was forced into action with Toronto well ahead of schedule but despite struggling so far, he seems relatively assured an Opening Day spot unless the Jays make an addition in left. Gose is ownable right away for the speed, but he’ll probably bat ninth and he’s only ever had a strong average at Triple-A Las Vegas, so tread carefully.

#41. Christian Yelich – Yelich’s timeline is definitely not 2013 anymore, if it ever was. He spent the year at High-A and outclassed the league with a 164 wRC+, but the fact that he didn’t get a Double-A promotion means it’d be far too big a jump to expect him to have an impact next year.

#42. Nolan Arenado – Many were calling for Arenado’s promotion early in the year but his Double-A performance hasn’t really warranted it. He’s been a shade above average with a 109 wRC+ but hit just 12 home runs and adds little on the bases. With the Coors Field effect he could be rosterable for fantasy, but there’s no reason for the Rockies to think he’s a better 2013 option than Chris Nelson or Jordan Pacheco.

#43. Mike Olt – Olt has struggled to an atrocious 14 wRC+ over 39 at bats with the Rangers, but this is obviously far too small a sample in which to judge him. Instead, expectations for 2013 should be tempered but his long-term outlook should stay the same. A half-season or more at Triple-A could be beneficial given that he jumped from Double-A to Texas, albeit after a dominant 95 games (28 HR, 170 wRC+).

#44. Hak-Ju Lee – Lee got his first full-season crack at Double-A and was league-average with the stick, chipping in 37 stolen bases as well. He also improved in the second half and maintained his reputation as a stud defender, so a strong start at Triple-A could put him on the radar for Tampa, where I’m sure Joe Maddon would find a way to get the most out of him.

#50. Jarred Cosart – Cosart followed the path this year, performing well enough at Double-A to get the promotion to Triple-A, where he looked great across six appearances. He’ll need to work on an out pitch to improve his strikeout rates, but there’s no reason to think he won’t join the Astros at some point in 2013.

Hopefully this article was able to update you on some players you were expecting to be fantasy-relevant in 2013. It should also serve as an early reminder to take ETAs from prospect sites with a grain of salt, as a whole lot can happen between March and September. Next week, I’ll take a look at prospects in general who could have a 2013 fantasy impact.

Follow me on Twitter @BlakeMurphyODC.

Comments (1)

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

Tags: , , , , , , , , , , , , , , , ,

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.

Comments (1)

Down On The Farm: Fun With Major League Equivalencies

Tags: , , , , , , , , , , , , , , , , , , ,

Down On The Farm: Fun With Major League Equivalencies

Posted on 12 September 2012 by Blake Murphy

Last week’s Down on The Farm began what was to be a multi-part series on the Arizona Fall League rosters. After it was brought to my attention just how much AFL-related content had already been produced, I decided it would be best to switch gears. So rather than looking forward to October and November, this week Down On The Farm will go in the opposite direction and look back at the year that was 2012 in Minor League Baseball.

My primary interest in looking through the leaderboards at Triple-A and Double-A was, of course, prospects on the rise. But when doing some digging and trying to interpret Minor League numbers within the scope of future Major League impact, I found myself doing a lot of Major League Equivalency conversions.

For some background, the statistically-inclined have been trying for years to effectively translate Minor League statistics into “Major League Equivalents,” that is, a translation of what a player with numbers X-Y-Z would have looked like at the Major League level. To quote Dan Szymborski from a Baseball Think Factory piece, “One thing to remember is that MLEs are not a prediction of what the player will do, just a translation of what the major league equivalence of what the player actually did is. This is useful for predictions however, because like, major league statistics, MLEs have strong predictive value.” Thus, for the purposes of identifying 2013 fantasy assets, or simply keeping our expectations in check, MLEs can have value.

While there is no standard, widely-accepted MLE calculator, most of the ones available will give you roughly the same outcome. I chose to use this one because I found it easy to use and straight forward. There are more available, I believe, beyond paywalls, but this one is free (though it stopped being updated recently – not a large concern since changes over a small amount of time would not significantly alter our results). Basically, what follows is a look at some of the Minor League leaders at varying levels, with a focus on the higher levels for 2013 fantasy impact, and how their Minor League numbers stack up in terms of potential Major League production. Just a small note that I used context-neutral “Major League Team” as a means of comparing apples to apples for this exercise.

AAA – Pacific Coast League
Long known as a hitter’s haven, the PCL is home to many parks pitcher’s dread. As such, it tends to be the long-term home of many Quad-A players. Still, even with the inflated numbers we see some strong performances.

Adam Eaton – The 2012 PCL Rookie of the Year and Most Valuable Player was absolutely Trout-ian at AAA Reno (without the homers), earning himself a September audition with the Diamondbacks. His .381/.456/.539 triple-slash line equates to a .318/.368/.440 MLE, while his 38 SBs and 119 runs convert to 31 and 89, respectively. Eaton is just 23 and could get a shot, at least as a 4th outfielder, in 2013.

Alex Castellanos – A part of the 2011 Rafael Furcal trade, Castellanos led the PCL with a 1.010 OPS while also chipping in 17 HR and 16 SB, making him and enticing prospect for fantasy owners. Unfortunately, his line translates to just a .250/.316/.435 mark, though MLEs see him as a potential 20-20 man if given full playing time. Castellanos earned a September call-up from the Dodgers, his third stint in the Majors this year.

Mike Hessman – Your 2012 PCL home-run champion is the 34-year old Astros minor leaguer, the proud owner of 35 HR…and a .231 AVG, .301 OBP, and 136 K. The profile says Quad-A all the way and MLEs agree, pegging him to hit below the Mendoza line and strikeout in 33% of his at bats, albeit with 26 bombs.

Jedd Gyorko – The Padres’ third-base prospect got the bump to AAA-Tucson early in the season and cruised to an impressive .968 OPS. At just 23-years old, Gyorko is headed for the Majors in the near future, especially if Chase Headley is moved in the offseason. MLEs like him to perform at a .272/.318/.463 level with 20+ home-run power now, and that’s not taking into account his development curve as a young player.

PCL/TEX Experiment – Wil Myers
Wil Myers – Myers gets a category of his own, having dominated at two levels this year. Plugging in his numbers separately for each league (combined he hit 37 homers with a .987 OPS), the MLEs spit out a .260 AVG with 27 homers and 80 RBI. Once again, these calculations don’t take into account the fact that Myers, at just 21-years old, is still very early on his development curve.

AAA – International League
So how does the International League, a notoriously friendlier league for pitchers, hold up in comparison to the PCL when it comes to MLEs for its top players? Let’s start with their MVP.

Mauro Gomez – At 28, Gomez is beyond prospect status and probably won’t crack the Red Sox as much more than a bench bat in 2013. With that said, his .960 OPS and 24 HR earned him an August call up, so at least he’s on the right track. MLEs think he could stick as a bench bat or lower-tier first baseman as well, projecting him for a .266/.314/.491 slash line and 19 homers in just 400 at bats.

Dan Johnson – The former Rays’ hero lead the IL in homers with 28, earning him a shot with the White Sox down the stretch. MLEs see Johnson’s power and keen eye (94 BB to 94 K in 476 AB) and think they could carry over (22 HR, 74BB). Unfortunately, they don’t see much else, pegging him for just a .221 AVG and a .715 OPS, below replacement level for a first baseman.

Matt LaPorta – The bane of fantasy writers everywhere, LaPorta has once again enticed with his Minor League numbers. Believe it or not, he’s now 27, so the clock is ticking. Unfortunately, his .822 OPS and 19 homers don’t translate, showing a .234/.298/.403 equivalency. Out of curiosity, I looked up his MLEs from after his first season in AAA-Columbus, back in 2009 if you can believe it, and let’s just say they were a lot higher on him then (.772 OPS, but at that time he was just 24).

AA – Eastern League
Darin Ruf – The 2012 Eastern League MVP and ROY is a bit old for the level at 26, a former 20th round pick and likely 1B/DH eventually. Still, an MVP deserves some attention, and his 38 HR and 1.028 OPS are cause for a double-take. The MLEs see him as a .258/.320/.476, 27HR player right now, though given his age and player type that might be his eventual upside.

Gary Brown – I chose Brown, the 23-year old CF prospect for the Giants, out of curiosity for how MLEs would treat his 33SB and 18CS (he also had a .279/.347/.385 slash line). They weren’t kind, showing an equivalent of 26 SB and 20 CS, marks that would give him a permanent red light (and also an awful .575 OPS). In all likelihood, ESPN’s 68th ranked prospect will repeat Double-A for at least part of 2013.

AA – Southern League
Hunter Morris – Sticking with our MVP analysis, I took a look at the MLEs for 23-year old Brewers first base prospect and 2012 Southern League MVP Hunter Morris. With a .920 OPS, 28 HR, and 113 RBI (though with a 40:117 BB:K ratio), the MLEs see Morris as needing more seasoning, pegging him for a .454 SLG and 21 HR but just a .295 OBP.

Matt Davidson – Davidson was ESPN’s #82 prospect before the season, and his success at AA at just age 21 is somewhat encouraging. With the caveat once again that MLEs are backwards-looking and not predictive using a development curve, MLEs like Davidson’s .836 OPS and 23HR to translate to a .215/.287/.367 line and 17HR right now. While that’s not enticing on the surface, it’s not a bad sign for the 2009 1st round selection.

AA – Texas League
Oscar Taveras – Rounding out our MVPs is 20-year old Cardinals’ OF prospect Taveras, owner of a batting title and Texas League MVP. His .321/.380/.572 slash line came with 23HR and 10SB, and MLEs think he could almost be an adequate regular already with a .254/.295/.425 slash line with 16HR and 8SB. It seems likely Taveras will play at AAA next year, but his double-digit potential in HR and SB, along with improving contact skills, make him an intriguing dynasty league watch.

Mike Olt – The Ranger’s heavy hitting third base prospect got the call to The Show in early August, stunting his AA numbers a bit, and disappointing those expecting instant MLB production. Had fans looked to MLEs, they would have known his .288/.398/.579 slash line equates to just a .224/.303/.421 Major League line. That’s nothing to scoff at, especially for a 24-year old, but it doesn’t scream savior.

Major League Equivalent stats aren’t perfect, and the fact that we have to wait until the offseason for projection systems to merge them with development curves to give us a predictive tool can be frustrating. Even still, MLE converters allow us to put the numbers of prospects, Quad-A mashers, and potential call-ups into the proper frame of reference, and can also aid as a fantasy tool by providing a check and balance for overzealous prospect hoarders.

Follow me on Twitter @BlakeMurphyODC.

Comments (2)

Sabermetric Mining – Leverage Index

Tags: , , , , , , , , , , , , , , , , , , , ,

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.

Comments (5)

Advertise Here
Advertise Here