Archive for Managers

Do Teams that Strike Out a Lot Steal More Bases?

This is a question that intuitively would seem to be answered by: Sure, why not?  The assumption was recently made in the comments section of this article by an FG writer:

Think about it — if you are Rougned Odor and you are on first base and, say, Joey Gallo is at the plate, there’s a good chance he’s going to cool down the stadium with some high-powered fanning.  He’s not exactly known as a high-contact guy.  There’s a roughly one-in-three chance that his at-bat is going to end in a backwards K sign being held up by someone in the stands.  So ‘Ned might decide this is a good time to steal because the ball isn’t likely to be put into play in the air, where, if caught, he would have to double back to tag.  Maybe he’s also thinking that, like Brad Johnson alluded, the break-even point for a steal (famously ~75% success rate as calculated by Bill James in Moneyball, ~66% in this more recent FG article) is lower if the guy at the plate is likely to cause an out, specifically a strikeout which normally doesn’t allow a runner to advance like a bunt, grounder or long fly might.

On the other hand, maybe Odor doesn’t have such a cynical view of Gallo, and doesn’t change his mindset on the basepaths.  Maybe he doesn’t try to assume what Gallo might do, so he doesn’t go for any more risky of a steal than he otherwise might.  So maybe he isn’t stealing at a higher rate than normal if the guy at the plate is a K machine.  Heck, maybe Joey Gallo is a specifically bad example here, because, though he does whiff a lot, he also hits a lot of home runs, which might cause a runner to take fewer risks when waiting on the outcome of his plate appearance.

So, let’s looks at what the numbers have to say.  I ran a simple correlation analysis between team stolen-base totals and team K%.  Here’s what I got:

So, no real correlation to be seen here.  But perhaps that shows that it could be a market inefficiency.  In 2016, the Brew Crew led the league in both K% and stolen bases.  Even without John Villar’s big SB season, they are a top-five SB team.  Below is a chart from last year — in yellow are the top five teams in both total SBs and K%.

Perhaps the Rays should have been trying to steal some more?  Though some of these anomalies could just simply be explained by personnel issues — maybe teams like the Orioles just have no one who can steal on the entire squad?

Here’s the same chart, for 2015, just for sugar and giggles:

For the Astros, this is starting to look like a trend — Orioles too.  I think my final answer to the question posited by this post is — Hmm, not sure exactly.  But maybe?


The Twins Gave Up on Pitching to Contact Before We Did

For many Minnesota Twins fans, the recently vintage dominance of the AL Central that spanned seemingly the entirety of the first decade of the 2000s had been taken for granted. I, for one, am guilty of this, and like many fans, am starting realize that winning is not easy, although the Twins made it seem as easy as Torii Hunter made robbing home runs look effortless. Nostalgia aside, the Twins, and their fall toward mediocrity, are an interesting topic to look into. To some, they seemed a similar team to the Oakland Athletics (perhaps aiding in the creation of a post-season rivalry). The Twins, who were not quite as much of a small-market team as Oakland, seemed to develop from within. They had a deep minor system, so deep that when Johan Santana or Torii Hunter deemed it time to cash in, the Twins were able to find a quick replacement and continue their success. Santana, and Hunter, as well as Joe Mauer and Justin Morneau (who have both had their careers altered due to more recent concussions) and many other corner pieces, all made their debut in a Twins uniform and became cornerstones, yet they could never win the big playoff series.

They did not have the ability to flex the financial muscle that the Red Sox, Yankees, and even division rivals Detroit Tigers were capable of; however, they still managed to win the AL Central six out of the 10 years in the previous decade, including a loss in a playoff game to decide the division winner in 2008. The success carried into the Target Field era, represented by a beautiful ballpark that fans spent what seems like an eternity waiting for. After another disappointing playoff loss to the hated Yankees, the Twins entered 2011 looking to improve, with a similar roster and the intrigue of Japanese second baseman, Tsuyoshi Nishioka. That year was filled with injuries, and despite a post-All-Star Game push, the Twins ended the year with the worst record in the American League. Since then, the Twins have failed to reach the playoffs, and are currently battling with the Atlanta Braves for the worst record in baseball. Not to mention, long-time general manager Terry Ryan, the one credited with building the farm system leading to the team’s prior success, was fired on July 18th. Time to find out where the Twins went wrong.

Those successful Twins teams were always credited for their small-ball and defensive skills. With Joe Mauer behind the plate, Torii Hunter (replaced by Carlos Gomez, who could also flash some leather) and many other solid defenders manning the diamond, a lot of the Twins’ success was credited to this defense.

Yet the Twins were far from a one-dimensional team. The Twins had a solid pitching staff, including, most famously, Johan Santana, who was a two-time Cy Young winner with the club, before being sent off to New York. The Twins also produced one of the most exciting pitching prospects at the time in Francisco Liriano. Liriano’s career was marred by injuries, which led to his inconsistency. Despite Johan’s departure and Liriano’s ineffectiveness, the Twins’ pitching was still an effective unit. The Twins raised their pitchers not on the attractive strikeouts, but on “pitching to contact.” The premise behind this was that pitchers would attack the lower half of the strike zone, induce weak contact, and show excellent control to give up few walks. It seemed to work, as pitchers with low to average strikeout rates were able to be effective pitchers, such as Scott Baker, Nick Blackburn, Kevin Slowey, and Brian Duensing.

Before I delve into my research, I should point to Voros McCracken’s ideas about Defense Independent Pitching for those less sabermetrically inclined (if you are sabermetrically inclined, feel free to skip the next few paragraphs). If I were to give a brief summary of his work, I would say McCracken’s main point is that if a pitcher does not give up a home run or strike out or walk a batter, then he has little control of what happens to the batted ball in play. A lot of what happens can be credited to luck, sequencing, and how good his defense is. For those unaware of sequencing, it is the idea that if a pitcher gave up three singles and a home run in an inning, there are many different possibilities of what could happen. The three singles could come in a row, followed by the dinger, for a total of four runs, or, two singles could come early, the pitcher gets a double play or some other way to get out of the jam, then gives up a home run with the bases empty, followed by another single and an out. In that scenario, only one run was surrendered, despite an equal amount of hits. McCracken suggests there is randomness in this effect, which combined with the quality of defense behind the pitcher and a good deal of luck, can make ERA a poor indicator of a pitchers true skill.

McCracken looked at defense-independent pitching stats (HR, BB, K) and defense-dependent stats (ERA), and noticed that the defense-independent stats correlate much better from year to year, and are a better indicator of how a pitcher will perform, since a pitcher does not have control of what happens to balls in play.

While McCracken did not actually create FIP, his work was a building block for modern pitching analysis. FIP (Fielding Independent Pitching) tracks what a pitcher’s stats would look like if he played behind a league-average defense and experienced league-average luck. It is a much better indicator of future performance than ERA. All the data I used was from 2007-2014. Over that span, for pitchers who pitched more than 100 innings in at least a two-year span, a pitcher’s ERA from one year to the next (tracking how consistent the stat is in tracking performance) had a correlation coefficient of 0.338. FIP, conversely, had a correlation coefficient of 0.476. Clearly, FIP performs better when predicting future performance, as McCracken suggested.

To end my digression on McCracken’s importance, if I had to sum up its importance to this article, it is that pitchers have little or no control over what happens to a ball in play.

When I was talking Twins recently with some recent, justifiably uneasy Twins fans, they attributed the Twins’ recent troubles to injuries and inconsistent pitching. This was when I was reminded of the “pitch to contact” philosophy heralded by the Twins. Since the days of recently past successes, the Twins have changed management, and hopefully have let go of this ideology. Anyways, I thought to myself that McCracken’s work and subsequent furthering of the topic do not go along with the pitch-to-contact philosophy. Sure, if a pitcher can prevent walks and home runs, then it does go along with part of McCracken’s ideas. But, if the goal is to induce weak contact, yet the pitcher does not have control of what happens to a ball when it is contacted, then there is a bit of a discrepancy.

So, like any other statistically-oriented college mind looking for how to spend the rainy days of my summer break, I decided to run some regressions to test if “pitch to contact” actually succeeded and the Twins were able to induce weak contact, or if the relative success of the pitching staff is related to luck and a good defense.

To reiterate, the data I looked at came from the seasons of 2007-2014. To sum up the Twins’ pitching through the period, the period starts with solid pitching from guys who lack the ability to post high strikeout rates, excluding the one season Santana pitched in the study. Guys like Scott Baker and Nick Blackburn had solid seasons early on, but Blackburn and many others faded once things went downhill for the team. From the outside looking in, it may seem like a chicken-or-the-egg scenario, whether it was pitching that caused the downfall or some other factor that caused the pitching to fail.

I gathered data for Twins pitching over this span, and compared it to the rest of the league. The pitch-to-contact philosophy was easily visible, as over this eight-year span, only five Twins pitchers had higher strikeouts per nine innings than league average (Johan Santana, Phil Hughes, Scott Baker, Francsico Liriano, Kevin Slowey). At the same time, only four pitchers had a walks per nine innings above league average (Nick Blackburn, Boof Bonser, Sam Deduno, and Liriano), and most of those seasons came in that pitcher’s last season with the team. The data shows that despite few strikeouts, Twins pitchers found some success in limiting numbers of walks. However, for those pitchers who struggled with control, their combined ERA in those seasons was 4.82, with a FIP of 4.60. Clearly, if a pitcher struggled with control, their success was hindered by the high walk rate.

Much of the Twins’ pitching was inconsistent over this time as well, as pitchers such like Blackburn or Brian Duensing seemingly went from quality starters to below-average pitchers. For the most part, I found this to be a team-wide theme. For pitchers with multiple years with the club, I correlated year-by-year ERA and FIP, to see if any consistent trends arose. Amazingly, there was no correlation from ERA from one year to the next, as the R-squared value was 0.002, stressing no relationship at all (graph). FIP, on the other hand, showed an R-squared value of 0.15; so while not a concrete relationship, a weak relationship exists (graph).

Why this lack of consistent ERA and FIP? This is where I think BABIP comes into play. Since FIP does not take into account BABIP, it did produce more reliable data. A few outliers threw off the data, and since it is not a large sample size, those outliers did affect correlation. By the nature of the relationship, this probably did more to affect the FIP correlation than the ERA, but nonetheless, the small sample size of pitchers from this period did affect the relationship. Interestingly, but perhaps not surprisingly, I performed a regression graphing FIP to ERA, and a solid relationship exists, with an R-squared of 0.36 (graph). This would be even better of a correlation if I took out seasons by Phil Hughes and Liriano, as in those two seasons their FIP was almost a full point lower than their ERA, respectively. This shows the validity of FIP as a metric, as it accurately predicts how a pitcher likely will perform based on independent factors.

Nonetheless, there is a clear difference here in the two pitching metrics. FIP implies a relationship, while ERA does not. How can this be? My theory is that it has to do with the pitch-to-contact philosophy. If pitchers are constantly relying on luck and defense to produce outs, rather than getting batters out themselves, then random variation will play much greater of a role in a pitcher’s effectiveness. Additionally, a team’s defense will play much greater of a role in pitching.

How much can a defense affect pitching? Well, I graphed the total WAR produced by the various Twins defenses against the team ERA from the 2007-2014 seasons. I additionally graphed BABIP against team defense. Amazingly, an ERA to defense regression produces an R-squared of 0.47 (graph), while a Defense to BABIP regression produces a 0.37 R-squared value (graph). Team defense clearly has a relationship with team ERA and team BABIP, as when the Twins defense was in its prime (2007, 2010), pitching performed well. Similarly, in the defense’s worst two seasons, the team also had its highest BABIP (2013, 2014). For those wondering, FIP to team defense produces no correlation (as we expect, since it does not account for a team’s defense) with an R-squared of 0.003.

What does this all mean?

Putting it all together, we notice a few trends. After 2010, the defense took significant steps back, along with pitching (ERA). As we expect, the team’s BABIP was affected by the defense’s regression. FIP, on the other hand, remained fairly constant through the span, showing how the defense must play a role in team ERA. For example, we will look at 2014. This was the defense’s worst year in the span, with a defensive WAR of -46.5. Team ERA was second-worst in this year, at 4.58. FIP, conversely, showed the team had its second-best year in pitching, with a value of 3.97. This shows that if the Twins would have had an average defense, their ERA would have been much lower.

As team ERA ballooned, the quality of the Twins’ defense fell. Since Twins pitchers were taught to rely on their defense through the pitch-to-contact ideology, this relationship was amplified. Pitching to contact, although relying on luck and defense, may have had some merit when the Twins’ defense was in its prime. If the team could get to more balls, produce a few more outs, then as long as the pitchers kept batters from getting on for free via the walk, the team would succeed. The pitcher would not need to strike out as many batters since the defense would make more outs than the normal team. This sounds nice on paper, but as the team defense decayed, the pitching regressed. This is most evident in 2014, as a solid pitching staff was marred by the defense behind them.

If the Twins were to truly focus on pitching to contact, then they should have looked at the defense, not the pitcher. At the same time, pitching to contact is flawed in a way. Why should a pitcher rely on a defense if he can just get the batter out himself? Teaching a pitcher not to use his natural talent to strike out a batter is counter-productive. I am not saying the Twins’ coaching staff directly did this, but when only four pitchers in an eight-year span have above-average strikeout rates, it raises the question. Perhaps the Twins looked for pitchers who were undervalued because of their low strikeout rates, and used these undervalued pitchers in their pitch-to-contact system. Yet, this does not seem to be the case, as the Twins pitchers with the lowest ERAs and FIPs were the pitchers with the highest strikeout rate, excluding Brian Duensing, whose downfall could have been predicted by his 3.82 FIP (to a degree), as it showed is 2.62 ERA would be much closer to 4.00 with an average defense. Even in a pitch-to-contact system, the pitchers with the best ability to get the batter out without putting the ball in play were the best pitchers.

If pitching to contact were to have a textbook year, it would be 2007, where a team with a 4.37 FIP had an ERA of 4.18. Yet, soon after, the defense plummeted, bringing the team pitching down with it. Clearly, through the team’s porous defense, the Twins gave up on pitching to contact, too. They just hadn’t realized it yet.

Hopefully, with the new management in place, pitching to contact is forgotten. While it is also important to keep a viable defense behind the pitcher, I still can’t trust the pitch-to-contact ideology. It had a good run, but seriously, when was the last time the Twins were able to produce a consistent pitcher out of a highly-praised prospect? Liriano wasn’t consistent, Kyle Gibson has yet to dominate, and Jose Berrios has looked shaky is his brief appearances. I think Scott Baker might be the answer to my question, but if not him, then maybe Johan Santana?

Clearly, the Twins need a new philosophy for grooming pitching. It’s a team riddled with questions, and this is not the lone answer, but it can be one step in the right direction for the team currently pegged at the bottom of the AL barrel.


Lineup Construction is Changing

Lineup construction is a topic that comes up far more often in proportion to how important it is. But if you can save a few runs in a year by using the proper lineup, it’s worth it. Put your OBP up top, not your steals. The #2 hitter should be better than your #3.

With 14 going on 15 years of lineup splits available on FanGraphs, are any trends clear? Yes, actually. In regards to the two specific issues above, managers do seem to be getting better. Let’s explore. (Note: All “league averages” are non-pitchers. Pitchers aren’t real hitters, after all.)

The on-base percentage of leadoff hitters vs. the league average has climbed. In 2002, the league average OBP was .336 whereas it was just .332 for leadoff hitters. Ten years later, in 2012, league average was .324 but leadoff hitters hit .344. The gap has begun to decline since then, but the trend is still apparent, and in 2016 leadoff hitters have a .332 OBP vs. the league’s .324. Overall, here’s a simple chart of the league’s leadoff OBP minus the overall average OBP for each year since 2002:

Not everyone has caught on; either Dusty Baker or Ben Revere really need to figure things out soon for the Nationals, for example. But leadoff hitters are getting better at getting on base.

Meanwhile, managers have a longer way to go in their understanding of the fact that a #3 hitter will most often find themselves batting with the bases empty and two outs which, naturally, is not a good situation for scoring runs. However, just by comparing the wRC+ of the league’s #2 and #3 hitters shows that some teams are learning. In the dark days of 2002, #2 hitters had a wRC+ of 92, compared to 128 for #3 hitters. Since then, #2 hitters haven’t been that bad, but they haven’t been great, either. However, the last three years have been #2 hitters’ most productive since 2002: they had a 102 wRC+ in 2014, 107 in 2015, and currently a 105 in 2016. Teams haven’t moved their best hitters out of the three hole (this will be #3 hitters’ seventh straight year with a wRC+ of 120 or better), but they are starting to see the value of a good #2 hitter. This has led to the wRC+ gap between #2 and #3 hitters to exhibit a clear downward trend since 2002:

 

Even if you take out that 2002 season, the trend holds. It is still basically due to a change in the past two years, but the more hitters like Andrew McCutchen or Manny Machado, Corey Seager or George Springer bat in that second spot in the order and have success, the more we can expect out of the two hole. A lot of these #2 hitters, you might note, are young guys with a lot of career ahead of them with their current teams. It’s up to managers to keep them at #2 instead of moving them to #3 as these players continue in their careers. They may not, leaving 2015 and 2016 as anomalies so I can be wrong again. (Actually, I’m never wrong, because where’s the fun in that?)

But next time you lament the general failures of managers to put out the correct lineup, remember, things are getting better. Maybe it’s just your favorite team’s manager.


The Risk and Reward of Attempting to Pick Runners Off

Recently, Dave Cameron examined a planned back-pick by Russell Martin and the Blue Jays in Game 1 of the ALDS.  The play didn’t have a chance to happen because Delino DeShields put a 2-1 change up in play.  Not just in play, but on the ground to directly where the second baseman Ryan Goins would have been had he not been breaking for second in anticipation of the pick.  Dave wrote a great article that covered the play in depth, so feel free to go read it here.  In this article, I analyze the strategy of calling for a set pickoff attempt. What I found not only vindicates Martin and the Jays, but also questions one of my longest-held beliefs about pickoffs.

My strategy for evaluating the set pickoff was to calculate the break-even point (BEP) for a pickoff attempt using Run Expectancy (RE), similar to previous analyses on bunting and stealing. To calculate the BEP for a given pickoff attempt, I calculated the RE benefit (to the defense) of an out and the weighted RE cost of a safe call or an error.  This sounds simple enough, but calculating the RE after an error involved some guesswork.

Although errors can result in multiple outcomes, I chose to pick one outcome for each base to simplify the analysis. Thus, I assumed 2 bases for all runners on an errant throw to first, 1 base for all runners on an error to second, and, after much thought, 2 bases for runners on second and 1 base for runners on the corners on an error to third. If you have data that can replace these assumptions, please let me know.  Otherwise, be cognizant of my assumptions when you attempt to make use of the findings.  For example, if there is a slow runner on second, the BEP for a pickoff attempt to a corner will be overly conservative (inflated).  Additionally, I didn’t differentiate between pickoff attempts from the pitcher and the catcher.  The pitcher has a shorter, unobstructed throw, and favorable balk rules when picking to second or third, but still has to deal with the risk of a balk, especially to first, along with the added difficulty of throwing off the mound.  Finally, while calling for a back-pick from the catcher can put a defender out of position, I chose to ignore this factor because a) I assume it is rare for a hitter to find the vacated hole, and b) the defense can choose to avoid contact.

In order to weight the cost of a failed pickoff attempt appropriately, I had to estimate what the error rate would be on attempts.  While we do have data on pitcher error rates on pickoff attempts (around 0.95%), the data are only from throws to first.  Set pickoff plays are more challenging for the defense, so the error rate should be higher than on typical attempts to first.  My solution, in lieu of empirical data from actual set pickoff attempts, was to estimate catchers’ throwing error rates from the 2015 season.  I chose this strategy for two reasons: First, catchers are one of the primary players who can attempt a set pickoff, so it made sense to sample from their performance.  And second, catchers accumulate a large portion of their assists under similar conditions to the pickoff attempt (for example, in 2015 nearly 40% of all catcher assists came from caught stealing).  Thus, I expected catcher throwing error rates to approximate the error rates we would observe on set pickoff plays.

While not a perfect method, I estimated catcher throwing error rate as Throwing Errors / Assists + Throwing Errors + Stolen Bases.  The mean throwing error rate in a sample of catchers (n = 38) who played at least 500 innings in 2015 was 3.6%.  Do you accept that set pickoff plays will result in 3.8 times more errors than typical pickoff throws to first? If not, adjust your own estimates accordingly.

Using the estimated throwing error rate for catchers, the formula for estimating the BEP on a set pickoff attempt is RE cost / (RE cost – RE benefit). In this equation, RE benefit = RE after a pickoff – RE before a pickoff; RE cost = RE before a failed attempt – RE with a failed attempt, and RE with a failed attempt = (RE of a safe call *.964) + (RE of an error *.036).  Using the RE tables found here, I generated Table 1 below.

 

Runners Outs First Second Third
1 _ _ 0 3.51%
1 3.32%
2 3.24%
1 2 _ 0 3.32% 2.18%
1 4.21% 1.93%
2 9.17% 2.33%
1 _ 3 0 2.37% 0.74%
1 3.47% 1.92%
2 6.72% 5.99%
_ 2 3 0 1.70% 1.41%
1 1.93% 1.73%
2 5.06% 5.06%
1 2 3 0 10.21% 1.97% 1.64%
1 4.85% 2.78% 2.48%
2 7.58% 3.92% 3.92%
_ 2 _ 0 1.54%
1 1.43%
2 1.26%
_ _ 3 0 0.11%
1 1.74%
2 5.61%

Table 1.  Success rate required to attempt a pick at each base.

Table 1 presents the BEP for the defense of (successful pickoffs / attempts) X 100.  In other words, Table 1 provides the minimum expectation of success required for the defense to attempt a set pickoff and it be a break-even strategy. Unfortunately, it is difficult to guess how successful set pickoff attempts typically are.  In Dan Malkiel’s study of pickoffs to first, he found that righties and lefties were successful about 2% and 4% of the time, respectively.  However, Malkiel’s study sampled situations with base-stealers on first, so the stolen-base rate was between 17% and 21%.  It’s impossible to know what percentage of successful pickoffs occurred when the runner intended to steal, but it’s safe to say 2% and 4% success rates are a little high if the runner on first isn’t planning on going. Set pickoffs usually work differently than throws to first, since neither the pickoff nor the steal are always expected. Therefore, the data on picks to first can only serve as a point of reference, helping to calibrate expectations rather than serving as predictions themselves.

One way to assess if teams are over- or under-utilizing set pickoffs is to compare their pickoff to error ratios with the BEPs for that metric. Unfortunately, I could only find data for one special case of the set pickoff: a catcher back-pick to first.  In the Malkiel study, successful back-picks were 96% of back-picks plus errors.  If we assume an error puts the runner on third, the BEP for pickoffs/pickoffs + errors is 50%, suggesting that catchers have room to get much more aggressive in attempting to pick runners off first.  Without more data, it’s difficult to comment further on current MLB behaviour regarding set pickoff plays. Nevertheless, the estimates in Table 1 provide interesting insights into the risks and rewards of pickoff plays. Below, I list six lessons that can be gleaned from Table 1.  At least two of these lessons fly directly in the face of my own long-held beliefs, and maybe yours too!

Lesson 1

If, at any time, the defense notices that it has better than a 15% chance of picking off a runner, they should attempt the pickoff.

Lesson 2

Pickoff attempts require greater confidence with two outs, with three exceptions.  Often, the required success rate is over 5%, requiring a fairly egregious mistake by the runner to warrant a throw. The exceptions to this rule are with a runner on first, a runner on second, or a pick to second with runners on first and second.

Lesson 3

A runner on second with no runner ahead of him should probably be targeted frequently.  The BEPs are consistently low for attempting the pickoff to second, while the runner is motivated to be aggressive by the chance to score a run or steal third. Even failed attempts have the favorable by-product of keeping the runner close, a factor not considered in Table 1.

Lesson 4

Throwing behind the runner on first with runners on first and second or the bases loaded is dangerous.  This doesn’t mean it’s a bad play if the runner on first opens the door, but the defense should be really confident to make the throw.

Now for the lessons that go against everything I thought I knew…

Lesson 5

Pitchers should throw over to third with runners on 1st and 3rd in a steal situation.  Ever since the MLB outlawed the fake-to-third move, pitchers haven’t been allowed to bluff the throw in hopes of catching the runner breaking from first.  Based on Table 1, it seems strange that pitchers ever faked the throw to begin with.  With no one out, the defense would only need to pick the runner off third 8 times per 1000 attempts, or nail the runner stealing second 3 times per 100 attempts, or a combination of the two to break even.  Additionally, if the runner on first breaks for second it’s an easier throw from third than from first, which was often the result with the fake-to-third move.  While many old-school baseball people will object to throwing over to third, the common refrain “he’s not going anywhere!” doesn’t necessarily apply to the 1st and 3rd steal situation.  The runner could be trying to get closer to home so he can steal on the catcher’s throw to second, making it the perfect time to throw over.  Although the third baseman’s positioning will sometimes make a true pickoff attempt at third difficult, the rules do not require the pitcher to throw directly to third.  Thus, teams can make legitimate efforts to get the runner on third when the situation allows it, while other times making throws away from the base solely to catch the runner on first breaking for second.

Lesson 6

The situation that requires the lowest probability of success to attempt a pickoff is when there is a runner on third with no one out.  The defence needs to nab merely 2 runners out of every 1000 attempts to break even. And get this, the BEP on pickoff attempts to third with 0 out is lower than the BEP for typical throws to first, even with the much lower error rate on throws to first (0.95%), and even after adjusting the assumed cost of an error to one base.  Holding probability of success constant, the pickoff attempt to get a runner on third with 0 out is the least risky pickoff attempt possible. The LEAST risky.

Of course, a runner who is on third with no one out should be taking no chances.  But that doesn’t mean a pickoff will never work…

 


7 Reasons Why the A’s Will Win the AL West in 2015

The A’s winning the West after a huge offseason makeover in 2015 might seem like an unlikely achievement, but here are seven reasons why this is not at all unachievable:

 

1. The New-Look Infield

In 2015 the Athletics will be throwing out a fresh face at each of the four starting infield positions. Here’s a quick look:

2014 2015
1B: Brandon Moss 1B: Ike Davis (Mets)
2B: Eric Sogard 2B: Ben Zobrist (Rays)
SS: Jed Lowrie SS: Marcus Semien (White Sox)
3B: Josh Donaldson 3B: Brett Lawrie (Blue Jays)

Especially from an Athletics fan’s perspective, the left side of this chart looks very nice. The names Moss and Donaldson are very important and dear to you; however, the right side of this chart is actually more productive overall. While Moss and Donaldson have the highest wOBA of the eight players at .351 and .339 respectively, Jed Lowrie and Eric Sogard have the two lowest at .300 and .262 respectively. This averages out to be a wOBA of .313. The Average wOBA for 2015’s infield is .320.

You might be thinking that Lawrie does not compare to Donaldson, and you could be right. The fact of the matter is that Lawrie is a downgrade from Donaldson, but not by all that much, meanwhile, Zobrist is a huge upgrade from Sogard at 2B. And even Sogard is an upgrade from Punto as the UTIL infielder.

Other important categories that favor the 2015 infield are BB%, K%, FB%, Contact%, OPS, OBP, etc. Also, the new infield got quite a bit younger and faster.

The 2015 infield also has a higher average wRC+ at 104 in comparison to 2014’s 102.5. These aren’t huge differences, but the A’s are expecting better years from Lawrie, who was injured a lot in 2014, Davis, who hit 32 HR in 2012, and Semien, who hasn’t really had much of a chance in the majors yet. These moves were necessary, not only to save money, but because the 2014 team didn’t actually win the AL West. I’m now going to compare this new INF to a team that did win the West, the 2012 A’s.

The 2012 INF consisted of Josh Donaldson, Stephen Drew, Cliff Pennington and Brandon Moss. There were other guys in the mix earlier on in the season, i.e. Jemile Weeks, Brandon Inge, however, these were the guys that got it done down the home stretch.

2012 A’s INF WAR wOBA wRC+ 2015 A’s INF WAR wOBA wRC+
Brandon Moss 2.3 .402 160 Ike Davis 0.3 .324 108
Cliff Pennington 1.0 .263 65 Ben Zobrist 5.7 .333 119
Stephen Drew 0.0 .310 97 Marcus Semien 0.6 .301 88
Josh Donaldson 1.5 .300 90 Brett Lawrie 1.7 .320 101
2012 AVG 1.2 .319 103   2014 AVG 2.1 .320 104

These numbers are almost identical, however the 2015 team has a slight edge in every category. That is despite the fact that the A’s expect growth from the incoming players this season. Even after the significant losses of Josh Donaldson and Brandon Moss the A’s infield is more than capable of pushing them toward another Western division title.

 

2. The Designated Hitter

The Athletics’ DH numbers from 2014 are not where you want them to be. Yes, Melvin will still use this spot as a “half-rest” day for players like Crisp, Reddick and Lawrie, but the newcomer Billy Butler will most likely fill the spot the majority of the time. Butler is a huge upgrade from the A’s team DH numbers last season in which Callaspo, Moss, Norris, Jaso, Vogt, Dunn, among countless others had at bats. Let’s take a look at the 2014 A’s DH numbers vs. Billy Butler’s 2014 numbers. (he also had a down season):

Player WAR wOBA wRC+
2014 Team DH -1.3 .284 82
Billy Butler -0.3 .311 97

This chart shows that Butler is a significant upgrade at the DH spot, as he will bring a lot more production to the middle of this lineup. I should also bring up his career numbers, which are a wOBA of .351 and wRC+ of 117. If Butler can get back to his career form, the A’s offense is looking at a huge boost, but even if he doesn’t and repeats his 2014 performance, the DH spot is still getting a nice upgrade.

 

3. The Rotation

The starting rotation for the A’s no longer consists of Jon Lester, Jeff Samardzija or Jason Hammel, but it is still a very strong group with huge potential. I’m going to compare the projected 2015 group to the 2012 and 2013 rotations that led the A’s to division titles.

2012

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Tommy Milone 190 6.49 1.71 1.14 3.74 1.28 2.8
Jarrod Parker 181.1 6.95 3.13 0.55 3.47 1.26 3.5
Bartolo Colon 111 5.38 1.36 1.00 3.43 1.21 2.4
Brandon McCarthy 82.1 5.92 1.95 0.81 3.24 1.25 1.8
A.J. Griffin 79.1 7.00 2.08 1.09 3.06 1.13 1.4
Team Average  / 6.35

2.05

0.92 3.39 1.23

2.4

 

2013

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
A.J Griffin 200 7.70 2.43 1.62 3.83 1.13 1.5
Jarrod Parker 197 6.12 2.88 1.14 3.97 1.22 1.3
Bartolo Colon 190.1 5.53 1.37 0.66 2.65 1.17 3.9
Tommy Milone 153.1 7.10 2.29 1.41 4.17 1.29 1.3
Dan Straily 152.1 7.33 3.37 0.95 3.96 1.24 1.4
Team Average  / 6.76 2.47 1.16 3.72 1.21 1.9

 

Projected 2015 (2014 STATS)

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Sonny Gray 219 7.52 3.04 0.62 3.08 1.19 3.3
Scott Kazmir 190.1 7.75 2.36 0.76 3.55 1.16 3.3
Jesse Chavez 125.2 8.52 2.94 0.93 3.44 1.30 1.7
Jesse Hahn 70 8.36 3.73 0.51 2.96 1.13 0.8
Drew Pomeranz 52.1 8.6 3.44 0.86 2.58 1.13 0.7
Team Average  /

8.15

3.10

0.74

3.12

1.18

2.0

As you can see, the 2015 rotation wins four out of the six categories. They won the majority of the categories already, but this 2015 staff has the potential to be better than these numbers show. In past years, the A’s success had a lot to do with their strong pitching staff — this is a big reason why I believe they will win the west in 2015 — however, we need to take a look at the projected rotations of the four other teams in the division to see how the A’s compare to each of them.

Here are the five teams’ projected rotations for 2015:

 

Angels

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Jered Weaver 213.1 7.13 2.74 1.14 3.59 1.21 1.5
C.J. Wilson 175.2 7.74 4.35 0.87 4.51 1.45 0.6
Garrett Richards 168.2 8.75 2.72 0.27 2.61 1.04 4.3
Matt Shoemaker 121.1 8.16 1.56 0.67 2.89 1.07 2.6
Andrew Heaney 24.2 5.84 2.55 2.19 6.93 1.50 -0.4
Team Average  / 7.52 2.78 1.03 4.11 1.25 1.7

 

Mariners

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Felix Hernandez 236 9.46 1.75 0.61 2.14 0.92 6.2
Hisashi Iwakuma 179 7.74 1.06 1.01 3.52 1.05 3.2
Roenis Elias 163.2 7.86 3.52 0.88 3.85 1.31 1.4
J.A. Happ 153 7.53 2.71 1.24 4.12 1.31 1.5
James Paxton 74 7.18 3.53 0.36 3.04 1.2 1.3
Team Average  / 7.95 2.51 0.82 3.33

1.16

2.7

 

Rangers

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Colby Lewis 170.1 7.03 2.54 1.32 5.18 1.52 1.6
Yu Darvish 144.1 11.35 3.06 0.81 3.06 1.26 4.1
Nick Tepesch 125.2 4.01 3.15 1.07 4.30 1.34 0.4
Derek Holland 34.1 6.29 1.05 0 1.31 1.02 1.3
Ross Detwiler   /   /   /   /   /   /   /
Team Average   / 7.17

2.45

.8 3.46 1.29 1.85

 

Astros

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Colin McHugh 154.2 9.14 2.39 0.76 2.73 1.02 3.3
Dallas Keuchel 200 6.57 2.16 0.50 2.93 1.18 3.9
Scott Feldman 180.1 5.34 2.50 0.80 3.74 1.30 1.6
Brett Oberholtzer 143.2 5.89 1.75 0.75 4.39 1.38 2.4
Brad Peacock 122 7.97 4.57 1.48 4.50 1.52 -0.1
Team Average   / 6.98 2.67 0.86 3.59 1.28 2.2

 

Athletics

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Sonny Gray 219 7.52 3.04 0.62 3.08 1.19 3.3
Scott Kazmir 190.1 7.75 2.36 0.76 3.55 1.16 3.3
Jesse Chavez 125.2 8.52 2.94 0.93 3.44 1.30 1.7
Jesse Hahn 70 8.36 3.73 0.51 2.96 1.13 0.8
Drew Pomeranz 52.1 8.6 3.44 0.86 2.58 1.13 0.7
Team Average   /

8.15

3.10

0.74

3.12

1.18 2.0

The Mariners and the Athletics both have really solid pitching staffs. The Mariners have arguably the best pitcher in the American League in Felix Hernandez. The Angels also have a good young ace in Garrett Richards, but he is coming off an injury; it will be interesting to see how he bounces back. Sonny Gray proved that he is a true ace last season, going over 200 innings and pitching extremely well in big games. The numbers do give the A’s a slight edge; they won three of the six categories and the Mariners won two of them. King Felix, Iwakuma and the solid supporting cast are hard to bet against, but 1-5, the A’s have a better staff according to last year’s numbers.

 

4. Speedee Oil Change

Anytime manager Bob Melvin calls on the bullpen, the A’s should be confident. There are so many capable arms out there that it’s really not fair. Honestly, a starter could go four innings with a lead and that would be enough for this bullpen with Otero, Abad, Cook, O’Flaherty, Clippard and Doolittle in the mix. There are plenty of other options as well that might not get a shot because it’s already crowded with talent out there. The starters, however, are very capable of giving you six or seven innings consistently, which makes this bullpen even that much more deadly, allowing Melvin to create left-on-left matchups or vice versa. The fact of the matter is, if you can’t score, you can’t win. While the starting staff is very solid, getting to the bullpen might not be the opponent’s best option when facing the A’s. Another positive for the A’s has been their ability to fight their way back into ballgames the last few years. With a bullpen like this who can keep the deficit where it is, the probability of achieving a comeback is that much greater.

As shown by the Royals on the successful end and the Dodgers on the opposite end, the strength of your bullpen can make or break your season.

Let’s compare the A’s bullpen to the other teams in the division by highlighting the projected top six bullpen arms for each team:

 

Angels

Player IP K/9 BB/9 HR/9 ERA WHIP HLD SV
Joe Smith 74.2 8.20 1.81 0.48 1.81 0.80 18 15
Huston Street 59.1 8.65 2.12 0.61 1.37 0.94 0 41
Mike Morin 59 8.24 2.90 0.46 2.90 1.19 9 0
Fernando Salas 58.2 9.36 2.15 0.77 3.38 1.09 8 0
Cory Rasmus 37.0 9.24 2.92 0.73 2.68 1.16 0 0
Vinnie Pestano 18.2 12.54 2.41 1.45 2.89 1.23 1 0
Team Average  / 9.37 2.39 0.75 2.51 1.07  /  /

 

Mariners

Player IP K/9 BB/9 HR/9 ERA WHIP HLD SV
Tom Wilhelmsen 75.1 8.12 2.7 0.72 2.03 1.00 8 1
Danny Farquhar 71 10.27 2.79 0.63 2.66 1.13 13 1
Dominic Leone 66.1 9.50 3.39 0.54 2.17 1.16 7 0
Fernando Rodney 66.1 10.31 3.80 0.41 2.85 1.34 0 48
Yoervis Medina 57 9.47 4.42 0.47 2.68 1.33 21 0
Charlie Furbush 42.1 10.84 1.91 0.85 3.61 1.16 20 1
Team Average  /

9.75

3.17

0.60

2.67 1.19  /  /

 

Rangers

Player IP K/9 BB/9 HR/9 ERA WHIP HLD SV
Robbie Ross 78.1 5.86 3.45 1.03 6.20 1.70 2 0
Shawn Tolleson 71.2 8.67 3.52 1.26 2.67 1.17 7 0
Roman Mendez 33 6.00 4.64 0.55 2.18 1.12 10 0
Neftali Feliz 31.2 5.97 3.13 1.42 1.99 0.98 0 13
Tanner Scheppers 23.0 6.65 3.91 2.35 9.00 1.78 1 0
Phil Klein 19 10.89 4.74 1.42 2.84 1.11 0 0
Team Average  / 7.34 3.90 1.34 4.15 1.31  /  /

 

Astros

Player IP K/9 BB/9 HR/9 ERA WHIP HLD SV
Luke Gregerson 72.1 7.34 1.87 0.75 2.12 1.01 22 3
Pat Neshek 67.1 9.09 1.2 0.53 1.87 0.79 25 6
Josh Fields 54.2 11.52 2.80 0.33 4.45 1.23 8 4
Chad Qualls 51.1 7.54 0.88 0.88 3.33 1.15 2 19
Tony Sipp 50.2 11.19 3.02 0.89 3.38 0.89 11 4
Jake Buchanan 35.1 5.09 3.06 1.02 4.58 1.50 0 0
Team Average   / 8.63

2.14

0.73 3.29 1.10  /  /

 

Athletics

Player IP K/9 BB/9 HR/9 ERA WHIP HLD SV
Dan Otero 86.2 4.67 1.56 0.42 2.28 1.10 12 1
Tyler Clippard 70.1 10.49 2.94 0.64 2.18 1.00 40 1
Sean Doolittle 62.2 12.78 1.15 0.72 2.73 0.73 5 22
Fernando Abad 57.1 8.01 2.35 0.63 1.57 0.85 9 0
Ryan Cook 50 9.00 3.96 0.54 3.42 1.08 7 1
Eric O’Flaherty 20 6.75 1.80 1.35 2.25 0.95 3 1
Team Average   / 8.62 2.29 0.72

2.41

0.95

 /  /

The Mariners and Athletics each won two out of the five categories. The Athletics also came in second in two other categories. Although this chart shows the Mariners and the A’s as pretty evenly matched, the Mariners have a lot of aging players in their pen, so we cannot be sure if they will keep up the good numbers. The Astros got a lot better by adding Luke Gregerson and Pat Neshek, but that still wasn’t enough to make them the best in the division, especially after the A’s went out and traded for the two time All-Star, Tyler Clippard. All of these teams except Texas have a very strong bullpen, so trying to come back from a deficit is going to be a tough feat in this division.

The A’s also have a lot of other options past these six players, probably more so than the other four teams, making injuries less of a factor for them.

 

5. Coco Crisp

When Coco Crisp is at the top of the lineup, the A’s are a better team. Over the past three seasons there’s no player who has had as much of an overall impact on this team than Coco. Whether it’s at the plate, in the field or in the clubhouse, Crisp’s impact is significant. Despite losing a lot of star players, the A’s will not take a step backward because they still have their most important piece in Crisp. If Crisp would have been traded away this offseason, I don’t believe the A’s would be ready to compete for the AL West title in 2015. There would be too long of an adjustment period, someone else would need to step up big time and fill his shoes. Luckily, the A’s don’t have to worry about that yet. Bottom line: the A’s need Coco Crisp.

 

6. Depth and Versatility

Having a deep roster is always important in a 162 game season. You will have players go on the DL, it is unavoidable. Being able to replace the injured players with capable major leaguers is key to a team’s success in the long run. Billy Beane has constructed a 40-man roster with tremendous depth, especially with pitching. The A’s have eight or nine guys capable of making the starting rotation, not to mention two others (Jarrod Parker and A.J. Griffin) due back this summer. There are upwards of ten players competing for a spot in the bullpen as well. It will be interesting to see who makes it on to the 25-man roster, but I wouldn’t be surprised if Triple-A Nashville has a stacked opening day roster. Having great options in the minor leagues is key for any team, and the A’s will definitely have that this season with Kendall Graveman, Chris Bassitt, Sean Nolin and Brad Mills, four starters likely to be starting in Triple-A. Also, RJ Alvarez, Eury De La Rosa and Evan Scriber, three above-average bullpen arms will likely be starting down there as well.

The A’s lineup is a very versatile group this season. Eric Sogard, A’s second baseman the last few seasons, has moved into a utility INF role; he plays excellent defense, and for a defensive replacement, he can handle the stick pretty well. Ben Zobrist is known for his ability to play all over the diamond with above-average defense, and also for getting the job done from both sides of the plate; his career wOBA is .344. Craig Gentry and Sam Fuld can play all three outfield positions with ease while providing speed off the bench in pinch running situations. Marcus Semien will likely be the everyday SS, but he can play all over the infield as well. Stephen Vogt will mostly catch, but he can play first base and corner outfield if the A’s need him to. The amount of options the A’s have, if injuries do occur, are limitless. It will be entertaining to see how Bob Melvin constructs his lineup card every day.

 

7. The Manager

Bob Melvin is the perfect manager for a team of misfits and players who have never played together previously. He will bring this group to play for each other, as a unit, one day at a time. Melvin is great at creating matchups that benefit the team and give them the best chance to succeed. The roster that has been assembled this season is perfect for just that. It is loaded with skilled, versatile players. Bob Melvin has done it before and he will do it again.


Collins Working the Lineup

Over the course of 162 games, there’s only so much influence a manager of any baseball team could have over their outcome. After 105 games the Mets actual record is 3 wins shy of their projected record of 53-52, making this a .500 team. Several factors contribute to this discrepancy like losing your ace pitcher to injury, scrambling for a closer to begin the season, developing a major league catcher, adapting to a new hitting coaches philosophy, and setting the most productive lineup possible just to name a few. What Terry Collins has done with this team to this point can only be admired, but help has arrived and changes must be made to maximize team production.

The move of Curtis Granderson from the cleanup to leadoff role proved to be successful as the team surged from June’s end through July. Daniel Murphy and Curtis Granderson’s slash line numbers are almost identical, batting average is the only big difference which Daniel Murphy leads Granderson by about.060 AVG points and make him a more ideal leadoff hitter. Curtis Granderson hit 6 home runs from the leadoff spot which minimized his RBI potential which essentially is the reason Sandy Alderson signed him. In moving Daniel Murphy into the leadoff spot, the Mets actually increase their leadoff OBP while putting Curtis Granderson into a role where his RBI opportunities increase dramatically.

Daniel Murphy’s SLG% is nearly that of Curtis Granderson with half as many HRs, meaning that Daniel Murphy is doing a better job of getting into scoring position than our current leadoff hitter. The only 2 reasons the Mets have kept Murphy out of the leadoff spot in the past were lack of speed on the basepaths and low OBP. Now Daniel leads our starting players in SB showing he has some speed and base running ability and his OBP is amongst the team leaders. David Wright being the best hitter on the team (despite struggles in 2014) deserves the 2nd spot in the order. His power has declined this season, however his OBP is still respectable and he should remain in a table-setting role followed by Granderson. Lucas Duda has earned his cleanup role as he’s hit over .280 in the past couple of months with at least 5 HRs per month. He is driving the ball to all fields and should be a key contributor to driving in runs once our table-setters do their jobs.

The top 4 lineup spots should be configured as follows:

1  2B Daniel Murphy        (.293/.340/.412) 28 2B, 7HR, 11SB

2  3B David Wright           (.278/.339/.401) 24 2B, 8HR, 5SB

3  RF Curtis Granderson  (.232/.339/.415) 18 2B, 15HR, 8SB

4  1B Lucas Duda               (.259/.356/.500) 22 2B, 18HR, 3SB

For the next spot in the lineup, this player has had a tale of 2 seasons. Travis d’Arnaud has adjusted quickly since his demotion to AAA on June 6th. Since being recalled on June 24th, d’Arnaud has a slash line of (.302/.337/.646). He has lengthened our lineup and has earned the spot of the 5 hitter.

5  C Travis d’Arnaud

Before June 6th demotion    (.180/.271/.320) 3 2B, 3HR

Since June 24th Promotion (.302/.337/.646) 7 2B, 4HR

Season Stats                            (.232/.298/.379) 10 2B, 7HR

Right after Travis d’Arnaud in the Mets order is when they begin to look thin offensively. Having early success in the season but struggling as of recent is Juan Lagares, the defensive wizard and minor league doubles machine. This kid showed an advanced approach to lead off the year and is capable of making the bottom of our order a productive one. He isn’t seeing the ball well like he was in the first half, but we need to remember he is in his first full season in the bigs and known primarily for his route to catch baseballs and cannon for an arm, any offense is a plus.

6  CF Juan Lagares (.271/.306/.375) 16 2B, 2HR, 2SB

7  RF Chris Young/Eric Young/Kirk Nieuwenhuis/Bobby Abreu/denDekker

Our right field position is a question mark. I’m not saying the Mets haven’t produced anything from the position, but they don’t have an everyday right fielder which is a need to be addressed in the off-season or via trade before Thursday’s deadline. Though not one player has stepped up and taken over this position, I still believe they have produced more than my “ideal” 8 hitter, Ruben Tejada. In every championship team there is that one scrappy player that is on the squad solely for defensive prowess. Through the course of the season I have seen many different Ruben Tejadas. I’ve seen the defensive shortstop, the slap hitter, the kid in way over his head, and the wanna-be slugger with warning track power. This player is undoubtedly our 8 hitter and those who look too dependently on his OBP must take into consideration how many times he has walked for the sole reason that the worst hitting pitching staff is just 4 pitches away.

Ruben has been intentionally walked 10 times, twice as much as any player on the Mets. Ruben Tejada hasn’t defended the way he has in the past which quieted his lack of offense. In a New York setting, he shouldn’t start and the Mets executives know that. Ruben is a bridge to the future, an inexpensive filler until we land in a position of contention where an offensive producer is necessary at the position. Until then we have a shortstop with a strong arm and instincts but lacks the speed to get too many balls up the middle or steal a base when we need him to. He has no power and is offensively irrelevant as his slash line below shows. A shortstop with any tools is an upgrade here.

8  SS Ruben Tejada (.226/.351/.281) 9 2B, 2HR, 1SB


“The Managers’ Favourite Stat”

In the June 26 Nats-Cubs broadcast, Washington announcers Bob Carpenter and F.P. Santangelo had a conversation about how managers use statistics, and in particular how Matt Williams managed a 16-inning epic against Milwaukee.

“He was relying on batting average with runners in scoring position,” Santangelo said, “and to me that’s the best stat going.” They added that Cardinals manager Mike Matheny uses it, and Matheny told them that “it was a lot of managers’ favourite stat.”

Go ahead and freak out a little. But I’m curious. If you were judging hitters based on batting average with RISP, how different would your judgments be than if you judged them based on AVG, wOBA, or wRC+?

I can’t figure out how to insert a table, so here is a handy and also dandy chart for your surveyal, with helpful, pretty colors! You shall behold the 2013 leaders – minimum of 100 plate appearances, to include pinch-hitters who might pop up in a 16-inning game – for average, wOBA, wRC+, and BARISP. Hitters who appear in all 4 columns are colored peach, and hitters who appear in 3 of the 4 columns are colored blue. (Note: Hanley Ramirez should have been in the fourth column, too. Somehow the leaderboard I pulled left his name out.)

What do you notice? Well, yes, there is a lot more overlap between the first three columns than the last one. I might be counting wrong, but it looks like over half of the BARISP leaderboard does not appear in a single other column. (Many of them are Cardinals.) And yet, the truth is, almost all of the top 25 BARISP leaders were, in fact, good hitters in 2013. The three worst hitters on the list, by wRC+, are Michael Brantley (104), Manny Machado (101), and Brandon Phillips (91). That’s not a terrible bench. (On the other hand, Pete Kozma looms.)

The truth is, good hitters are good hitters. A manager relying on BARISP would not suddenly disregard Josh Donaldson, Miguel Cabrera, or Paul Goldschmidt.

Who would lose the most from a reliance on BARISP instead of advanced stats? Arguably, the guys who appear in the wOBA and wRC+ columns, but not the BARISP one. There are 15 of those players, favored by advanced numbers but not by “the managers’ favourite”. Of those 15, 8 still have BARISPs above .280. Here are the bottom five:

5. Khris Davis, .250 (43 PA)
4. Shin-Soo Choo, .240 (144 PA)
3. Joe Mauer, .239 (113 PA)
2. Yasiel Puig, .234 (99 PA)
1. Jeff Baker, .162 (44 PA)

On my custom BARISP Snub Leaderboard, there are only a handful of players a real manager might pass over. (Who would bet against 2013 Joe Mauer?)

In other words, even though a true stathead might yelp in terror at the thought of his team’s manager using BARISP to select a hitter, the process does not actually yield many bad results. Good hitters will be good hitters, even if your measure is slightly faulty. Your coach might bench Yasiel Puig for Brandon Phillips, which obviously would be bad. It’s also unlikely. More likely might be benching Khris Davis for Michael Brantley, and would you truly be that offended?

On the other hand, Pete Kozma looms.


MLB’s New Replay System: A Breakdown of Plays So Far

Well well well, MLB has a new replay system set up for every game of this year. Some people – although I would say most – are not too fond of this new system, myself included. They would say that it slows down an already slow enough game, which is true. The way the system is structured allows managers to be exploitative by confirming with their bench to see whether or not it the call should be challenged. This part of the process is what really gets me. Granted I haven’t seen too many games this year but already I miss the arguments between managers/coaches and the umpires; they were fun and made the game pretty interesting (especially when the manager of the team playing against yours got ejected). Regardless, this post is not intended to analyse the dynamics between managers and umpires but rather look at how successful the replay system has been and to examine the tendencies of the challenges. Using the twitter account @MLBReplays I examined all of the calls challenged so far this season. While the sample size is arguably small it did take quite a long time to examine various angles from the 49 calls made (as of the morning of April 9th 2014). For each replay I collected the following information which I then organized into a spreadsheet: Read the rest of this entry »


The Worst Playoff Bunts from 2002-2012

I’m generally opposed to the sacrifice bunt, except in the rarest of circumstances. This less than optimal strategy is utilized even more in the playoffs. Derek Jeter, the all-time leader in playoff sacrifice bunts with 9, bunts almost twice as frequently in the playoffs as the regular season. That in itself should tell you that managers tend to go bunt-happy in the postseason since Jeter is a career .308/.374/.465 playoff hitter. I used Win Probability Added (WPA) and Run Expectancy (RE) in my calculations. For the record, the sum of Jeter’s sacrifices is -0.13 WPA and -1.88 RE. Anyways, here’s the list of the five worst playoff sacrifice bunts since 2002. Data is provided by Baseball Reference’s Play Index.

5. Daniel Descalso 2012, NLDS, Game 1. The Cardinals were losing to the Nationals 3-2 in the 8th when Descalso came to the plate with Adron Chambers on first and Tyler Clippard on the mound. Descalso laid down a bunt, sending Chambers to second. WPA: -0.04 RE: -0.19. Pete Kozma and Matt Carpenter would be retired, and the Nationals would go on to take Game 1. Descalso would hit two home runs in the series.

4. Eric Bruntlett 2004, NLCS, Game 6. Down 4-3 in the 9th, the Astros pinch-hitter faced Cardinals closer Jason Isringhausen with Morgan Ensberg on first and no outs. Bruntlett had 4 home runs and a 111 wRC+ in 61 regular-season PA, but a go-ahead home run was not on manager Phil Garner’s mind. Bruntlett bunted Ensberg to second. WPA: -0.05 RE: -0.21. After Craig Biggio flew out, Jeff Bagwell would deliver a game-tying single, but the Cardinals would eventually win it in the 12th. Though I’m not a fan of judging decisions based on results rather than process, you could say that this decision “worked.”

3. Brad Ausmus 2005, WS, Game 4. The Astros were trailing 1-0 when Jason Lane led off the bottom of the 9th with a single off White Sox closer Bobby Jenks. The 36 year-old catcher had posted a .351 OBP in 2005, one of the best marks of his career. Nevertheless, he sacrificed on the first pitch he saw, moving Lane to second and decreasing the Astros’ chance of scoring. WPA: -0.05 RE: -0.21. Pinch hitters Chris Burke and Orlando Palmeiro would be retired, and the White Sox took game 4 on their way to winning the series.

2. Elvis Andrus, 2010 ALCS, Game 1. The Rangers shortstop came to the plate against Mariano Rivera in the bottom of the 9th inning, with the Rangers trailing 6-5 and Mitch Moreland on first with no outs. With the count at 1-2, Andrus got down a bunt, sending Moreland to second. WPA: -0.06 RE: -0.22. Rivera would strike out Michael Young and get Josh Hamilton to ground out, ending the game. This bunt is even worse than the numbers because of the 1-2 count on Andrus and the fact that there was little to no risk of grounding into a double play, as the speedy Andrus had just 6 GDP in almost 700 PA. I should add that noted lover of bunting Ron Washington was managing the Rangers, who have had the most sacrifice bunts in the AL during his tenure.

1. Danny Espinosa, 2012 NLDS, Game 1. The Nationals were trailing the Cardinals 2-1 in the top of the 8th. With Ian Desmond on first and Michael Morse on third and no outs, Espinosa came to the plate, facing Cardinals reliever Mitchell Boggs. Espinosa was 0-3 on the day with 3 strikeouts. He still had some pop though, as he had 17 home runs on the season. For whatever reason, on an 0-1 count, Espinosa tapped a bunt to Boggs, advancing Desmond to second. WPA: -0.09 RE: -0.44. The next hitter, Kurt Suzuki, would strike out. Fortunately for Espinosa and the Nationals, pinch hitter Tyler Moore would come through with a two-run single, and the Nationals would win the game 3-2.

The sacrifice bunt by a position player is almost universally a negative play, but even in the age when statistical information is readily available and most teams are employing an army of nerds, the tactic refuses to die. Perhaps it’s because “that’s the way the game was played” when many of these managers were players. Or maybe it’s the conservative nature of managers. The players usually get saddled with the blame if an opportunity with runners in scoring position is squandered after a sacrifice bunt. But if a player grounds into a double play when he could have bunted, the manager might be taking the heat. Whatever the case, expect managers to keep ordering the bunt come October.


Three More Albert Pujols Bunts

Mea culpa. After posting an in-depth look at Albert Pujolslone sacrifice bunt, readers both friendly and unfriendly pointed out to me that there is record of three more major-league Pujols bunt attempts, two for hits and one a squeeze (but no other known sacrifice attempts). The only satisfactory way to own up to my mistake is to follow up with a new essay asking: why did Pujols bunt those other times? Any errors in this new post are the responsibility of Session Lager the author.

Bunt No. 2: May 23, 2003

What was the bunt? Albert Pujols had a good day. He struck out in the first inning and then racked up five hits (two doubles), including one in the top of the tenth inning. It’s the 10th inning we’re looking at here.

With two outs and a runner on second base, J.D. Drew hit a triple to deep center field; the runner scored, giving the Cardinals a 9-8 lead. Next, Albert Pujols singled on a bunt to third base, scoring Drew and making the lead 10-8. The Pirates couldn’t recover in the bottom of the inning.

Was it a good idea? This was a squeeze play with two outs. In the tenth inning. Using a batter who had only bunted once before. On the other hand, the Cardinals already had the lead they needed. It was a daring mad-scientist gamble. The bunt had to be perfect.

Did it work? The bunt was perfect.

Bunt No. 3: July 27, 2003

What was the bunt? Only two months later and against the same Pirates, Pujols attempted to bunt for a hit and failed in the 8th inning. His Cardinals were losing 3-1, and there was one out and no runner on base.

Was it a good idea? Albert Pujols was facing Brian Boehringer (5.41 FIP, 4.33 BB/9, -0.7 WAR that season). He may have been emboldened by the memory of his recent success, but given how good Pujols was at not-bunting, and how bad Boehringer was at pitching, this attempt is only understandable if it was an attempt to take the enemy by surprise. Pujols bunted on 0-1; whether he showed bunt on the first pitch (a called strike) is lost to the sands of time.

Did it work? No, but in the next (9th) inning, with two outs, Pujols had a walk-off single to win the game.

Bunt No. 4: August 25, 2004

What was the bunt? It came on another good day: Pujols singled, doubled, and homered. And the single was a bunt to third base on a 1-0 count in the 8th.

Was it a good idea? See, this is the thing with bunt-for-hit attempts; without seeing the defense at work, and without understanding the state of play, all we have to go on is hindsight. John Riedling was another troubled pitcher, almost identical to Boehringer (5.24 FIP, 4.64 BB/9, -0.7 WAR that year); both also suffered from inflated home run rates. They were, presumably, easy pickings. And, indeed, Jim Edmonds brought Pujols home on a game-tying line drive over the fence.

Did it work? Yes.

Conclusions (Again)

What can we learn, aside from that the author needs to be a little more diligent? That Albert Pujols has done okay as a bunt artist. His first try, as a rookie, remains incomprehensible, but he then executed a flawless two-out squeeze play and went 1-for-2 in tries for a hit. I’m inclined to believe that the tries for hits represent opportunism, and that the lone sacrifice and the squeeze play represent Tony La Russa’s management philosophy at work. On my last post, reader Tim A wondered if that first bunt was La Russa simply testing Pujols’ ability to lay the ball down.

It’s still kind of weird that the then-best (or best non-Bonds) hitter in baseball tried a squeeze bunt on two outs. It’s definitely weird that a rookie with 20 homers would be called upon to bunt from the cleanup spot. But hey, we discovered a new wrinkle: Pujols is pretty good at yet another part of baseball. And in games in which Albert Pujols bunts, his team is 4-0.

Possible Teasers if I Decide to Write More of These at Some Point

According to the batted ball data (except where this data is incomplete, starred*), here are some more career bunt attempt totals: Adam Dunn 3, Manny Ramirez 2*, David Ortiz 11. In 2009 Jack Cust went 3-for-3 on bunt hit attempts. That same year, 3 successful bunt singles were laid down by Pablo Sandoval.