Archive for Player Analysis

Now that Chris Sale’s historic strikeout streak has ended, it seems an appropriate time to marvel at the dominance Sale has shown. Over an eight-game stretch from May 23 through June 30, Sale had the following line:

I’ve included BABIP to show that Sale was not on some incredibly low BABIP streak. League BABIP this season currently sits at .297. Sale’s career BABIP is .286. Without showing the normal indications for extreme luck, Chris Sale turned opposing lineups into a bunch of light-hitting middle infielders. For comparison, below is the season-to-date line for Indians SS Jose Ramirez.

The fact that the White Sox were only 4-4 in those eight games speaks to how badly the White Sox have played during 2015. Of those eight starts, six occurred during the month of June. It is these starts I’d like to focus on through the lens of pitch values relative to the rest of the league. I’ve posted some previous work on pitch values. The framework for the calculations can be found here. I’ve made some tweaks to the calculations, mainly to allow for player specific Balls/BB and Strikes/K to be calculated and league specific adjustments rather than MLB-wide constants.

According to Brooks Baseball, Sale threw 694 pitches during the month of June with a pitch mix of 45% Four-seam Fastball, 25% Changeup, 22% Slider, and 8% Sinker. The sinker was clearly Sale’s worst pitch in June. With the 55 sinkers thrown, Sale managed to give up six hits (11%). With the other 639 pitches, he gave up 21 hits (3%). So just how good were his other pitches? Chris Sale accumulated 2.9 WAR by my calculations over the month of June. He accumulated 0.0 of that from his sinker. For reference, here is the top ten in WAR from the month of June.

Sale lapped the field no matter which calculation you look at. Now, let’s take a look at a slightly adjusted version of that top-ten list.

We can split Sale’s June into two separate pitchers, and both “Sales” were top-ten in Pitch Value WAR accumulated. The Chris Sale that threw nothing but fastballs (Four-seam Fastballs and Sinkers) was the best pitcher in baseball in June. The Chris Sale without a fastball (Sliders and Changeups) was the ninth-best pitcher in baseball. With that said, we can dig a little deeper into the value of each of his pitches. First, let’s look at Sale compared to other four-seam fastballs. The table below gives the top five most valuable four-seam fastballs as well as a pitch rating based on June data set to a 20-80 scale.

Since WAR is a counting stat, there are two components to accumulating a high total. First, you have to throw a lot of the specified pitch type. Pitchers that threw 300 four-seam fastballs almost always accumulate more PV-WAR than those who only threw 30. Secondly, the pitch has to be of certain quality. Throwing 1000 of the world’s worst four-seam fastballs isn’t nearly as valuable as throwing 100 of the world’s best four-seam fastballs. In June, nine pitchers threw more four-seam fastballs than Chris Sale. No one that threw at least 90 total four-seam fastballs threw a better four-seam fastball than Sale. In fact, Sale’s four-seam fastball was the third highest rated qualifying pitch in June. To qualify, the pitch had to be thrown more than average for that pitch type. For example, if there were 4,000 curveballs thrown in June by 40 total pitchers, anyone who threw more than 100 curveballs would qualify. Moving on, Sale’s second most used pitch was his changeup. Here’s the table for changeups.

Once again, Sale tops the PV-WAR rankings. He was “slacking” on his quality on his changeup though. His changeup only ranked 14^th out of 126 qualifying changeups, so I guess there’s always room for improvement. The last pitch of interest for Sale is the slider. It’s the pitch I most associate with Sale, but it’s only his third most used pitch. Below is the corresponding table for sliders.

I think a trend is developing. Chris Sale threw the most valuable slider in June as well. By rating, Sale finished fourth out of 129 qualifying sliders. The three pitchers above him in rating (Andrew Miller, Mark Lowe, Darren O’Day) are all relievers. To sum up, in June, Chris Sale had three pitches make up over 90% of his pitch mix. All three of those pitches were the most valuable pitch in their respective pitch types in June. He had the overall best rated four-seam fastball and the best slider thrown by a starter. His changeup was the “worst” of his three pitches and was still a top 15 rated changeup. Chris Sale was completely dominant in June. His nearest competitor for most valuable pitcher in June only accumulated a little more than half of Sale’s value. Who knows when we may see a pitcher in this much of a groove again? For curiosity’s sake, I’ve included a table with the most valuable and highest rated of each pitch type for your perusal. The overall highest rated pitch was Andrew Miller’s slider.

Orioles fans have been pining for Kevin Gausman ever since 2015 Opening Day at Camden Yards when Bud Norris got shelled by the Blue Jays. The now-24-year-old started in 20 games last season as he was yanked back and forth between the majors and minors and posted a 107 ERA+ in 2014. A good number — not excellent, but good. He stabilized the rotation after Ubaldo Jimenez struggled mightily in his first year as an Oriole. In 2015, he will be asked to do the same as Norris has scuffled in the rotation so far. Two starts in, the results are promising, even better than 2014. One key change so far for Gausman is his willingness and ability to elevate his plus fastball.

The community’s own Jeff Sullivan has written about this as well. Both, before the season and after Gausman’s first couple of relief appearances in 2015. The idea being that some teams have emphasized throwing high fastballs and that not every pitcher is equipped to do so. But, Gausman has a heavy fastball that could stand to work more up in the zone; in fact in 2014 he was at the bottom of the league only throwing that pitch in the upper half 33% of the time. Jeff Sullivan also noted astutely that Gausman and the Orioles have talked about him throwing up more and that he had been doing so coming out of the bullpen.

Well, after two starts, Gausman continues to work up in the zone with the fastball and he is getting some great results. In 2015 his strikeout rate is up from 18.5 percent to 21.7 percent while his walk rate is around the same. His hard contact rate is down from 28.7 percent to 26.9 percent and his soft contact rate is up from 17.2 percent to 25.4 percent. Meanwhile, his fly-ball and groundball rates are up as well while his line-drive rate is at only 10.4 percent. This helps to explain how he has only given up eight hits in his last 11.1 innings pitched.

First, below is a graph of the average vertical location of Gausman’s fastballs over the past three seasons.  

As you can see, Gausman has worked more up in the zone. Not by a lot, but enough to call it a trend so far this year. Working mostly down is always going to be more friendly for a pitcher, but the ability to throw high strikes and for the batter to know that you can, is a very effective weapon. For instance, below is a chart of the whiffs Gausman has gotten from batters so far in 2015.

Notice the upper left portion of the zone. Gausman so far has gotten 11 swings and misses in this area in 2015; he had 29 whiffs all of last year in the same area. Every one of those 11 whiffs in 2015 has come on a four-seam fastball. Again, the bread and butter for Gausman will be dotting his fastball on the outside corner and working his splitter away, but the added weapon of a high fastball has produced some great in game results thus far in 2015.

I paid close attention to the game against the Rangers on July 2nd, in which Gausman pitched 6.1 innings striking out 7 walking 2 and allowing only 4 hits. Gausman utilized his fastball in this upper area of the zone to great effect during this start. Below is the strike-zone plot for Gausman from this game. 

Here you can see Gausman was able to work mostly arm-side low, but he also worked up in the zone and got five swinging strikes on balls at or above three feet off the ground. I also wanted to point out some specific at bats from that game. Below is the strike-zone plot against Robinson Chirinos in the third inning. 

Gausman starts him out with a pretty bad fastball that Chirinos simply swings through, although to be fair it was 97 mph. He then throws two more low fastballs and get Chirinos to foul off the second one. Now with the count 2-1 Gausman throws his splitter which Chirinos is again able to foul off. Now, the key pitch in the at bat, Gausman climbs the ladder for the fifth and final pitch with another 97 mph fastball to get the swinging strikeout. Here is another at-bat where Gausman used the elevated fastball, this one is to Adrian Beltre in the fourth inning. 

This at-bat is again five pitches. Gausman first throws a fastball low and away for a ball. Then, Beltre fouls off an inside and low fastball. Gausman then throws the splitter inside for a swinging strike and follows that up with a low and inside fastball for a ball. Now with the count 2-2 and every pitch in the lower half or below the strike zone so far in the at bat, Gausman elevates a 97 mph fastball high and tight to Beltre who swings through to get another strikeout for Gausman. All right, last one, I swear.

This at-bat is against Shin-Soo Choo in the second inning. Gausman starts him off with one called strike fastball up and away at 96 mph and follows that up with another elevated fastball at 97 mph that Choo swings and misses on. So now Choo has seen two high 90s fastballs three feet off the ground. Next, Gausman drops an 88 mph splitter a foot lower and Choo rolled over on it to second base. Not only can the elevated fastball get strikeouts, it can also setup his other pitches.

Here are the video highlights for that Rangers game where you can see the end of the two strikeouts above. Also, here are the video highlights of his start in Toronto wherein he gets two infield flies and a strikeout on elevated fastballs. Watch both for some more context.

The Orioles are now looking for Gausman to become a rotation stabilizer. Gausman has struggled somewhat in his career thus far because of a lack of true third pitch; he has gone back to his curveball this year which has shown some early promise. However, he has also struggled because for the most part he worked everything down in the zone. With the added focus of throwing elevated fastballs in 2015 it changes the hitter’s eye level and lets them know that Gausman can throw to all parts of the zone, so all parts of the zone are in play. So far, in 2015 that pitch has achieved great results. Now, we’ll have to see if Kevin Gausman can keep replicating those results.

The original version of this article was posted on www.camdenchat.com on 7/6/2015

Amidst a disappointing first half for the Boston Red Sox, one of the few bright spots has been the steady offensive improvement of Xander Bogaerts. The 22-year-old shortstop is beginning to live up to hype that has seemingly plagued the former 6^th overall prospect during his first full season in Boston. Bogaerts maintains a .302/.339/.414 clip through July 6, which equates to a 2.2 WAR, second to only Brandon Crawford’s 2.9 for shortstops in the MLB.

First off, its important to point out to all who thought Bogaerts was a bust after his performance a year ago, that he is still only 22 years old. To put it into perspective, consider this: Francisco Lindor was the #3 overall prospect coming into this year. The Indians called him up from AAA on June 14^th to, like Bogaerts, begin his career as an every-day shortstop at age 21. And similar to Bogaerts, Lindor is enduring his share of rookie struggles, batting .215 through his first 79 at-bats. It’s not fair to write off Lindor, or Bogaerts, as busts after their 21-year old seasons. Most players, especially those that young, need time to adjust to major-league pitching.

Bogaerts is walking about the same as a year ago, but has significantly improved his strikeout percentage, which has fallen from around 23% to 14%. His BABIP has risen almost 50 percentage points from a year ago (up to .347) which would help explain the improvements in batting average.

Another explanation for improvement has been Bogaerts new-found use of the ground and opposite field in 2015. Two-thirds of his balls in play are traveling to center and right fields this year, compared to around 40% last year. And his percentage of balls hit to the opposite field has increased from 19% to 31%. While the Monster may bait right-handed hitters into becoming pull-happy, Xander has found better success driving the ball the other way.

Bogaerts has also been putting the ball on the ground more this season. His GB% has risen 12% (to 50%) and FB% has dropped the same amount (his line-drive percentage has stayed roughly the same). Xander isn’t a burner on the base paths (only four steals) but he can put his athleticism to good use when he hits on the ground.

Xander’s improvement may even result in his first All-Star appearance. Alcides Escobar and former Red Sox, Jose Iglesias, are the two American League shortstop representatives for now. Even if Bogaerts is left off the team, his first-half play has been refreshing enough in an otherwise frustrating year for many Red Sox players. The young shortstop is taking some nice steps towards proving he is the player the Boston media, and fans alike, thought he was going to be.

(This is Part 4 of a four-part series answering common questions regarding starting pitchers by use of discrete probability models. In Part 1 we explored perfect game and no-hitter probabilities, in Part 2 we further investigated other hit probabilities in a complete game, and in Part 3 we predicted the winner of pitchers’ duels. Here we project the probability of scoring at least one run in various base runner and out scenarios.)

V.  I Don’t Know’s on Third!

Still far from a distant memory, the final out of the 2014 World Series was preceded by an unexpected single and a nerve-racking error that brought Alex Gordon to 3^rd base with two outs. Closer Madison Bumgarner, who was on fire throughout the playoffs as a starter, allowed the hit but would be left in the game to finish the job. There is some debate as to whether Gordon should have been sent home rather than stopped at 3^rd base , but it would have taken another error overshadowing Bill Buckner’s to get him home; also, next up to bat was Salvador Perez, the only player to ever ding a run off Bumgarner in three World Series. So even though the Royals’ 3^rd Base Coach Mike Jirschele had to make a spur of the moment critical decision to stop Gordon as he approached 3^rd base, it was a decision validated by both statistics and common sense. We will show our own evidence, by use of negative multinomial probabilities, of how unlikely the Royals would have scored the tying run off of Bumgarner with a runner on 3^rd with two outs and we will also consider other potential game-tying or winning situations.

Runs are generally strung together from sequences of hits, walks, and outs; in the situations we will consider, we will only focus on those sequences that lead to at least one run scoring and those that do not. Events not controlled by the batter in the box, such as steals and errors, could also potentially reshape the situation and lead to runs, but we’ll take a very conservative approach and assume a cautious situation where steals are discouraged and errors are extremely unlikely.

Let A and B be random variables for hits and walks and let P(H) and P(BB) be their respective probabilities for a specific pitcher, such that OBP = P(H) + P(BB) + P(HBP) and (1-OBP) is the probability of an out; we combine the hit-by-pitch probability into the walk probability, such that P(BB) is really P(BB) + P(HBP) because we excluded hit-by-pitches from our models, P(HBP) > 0 against Bumgarner in the 2014 World Series, and the result on the base paths is the same as a walk. The first negative multinomial probability formula we’ll introduce considers the sequences of hits, walks, and an out that can occur after two outs have been accumulated, setting the hypothetical stage for the last play in Game 7 of the 2014 World Series.

In the 2014 World Series, Bumgarner’s dominantly low P(H) and P(BB) were respectively 0.123 and 0.027 and his (1-OBP) was 0.849; by applying these values to the formula above we can generate the probabilities of various hit and walk combinations shown in Table 5.1. The yellow highlighted cells in the table represent the combination of hits and walks that would let Bumgarner escape the inning without allowing the tying run (given a runner on 3^rd with two outs and a one run lead). By combining these yellow cells, we see that the odds were overwhelmingly in in Bumgarner’s favor (0.873); all he had to do was get Perez out, walk Perez and get the next batter out, or walk two batters and get the third out.

Table 5.1: Probability of Hit and Walk Combinations after 2 Outs

The Royals could have contrarily tied the game with a simple hit from Perez given the runner on 3^rd and two outs, yet this wasn’t the only sequence that would have kept the Royals hopes alive. Three consecutive walks, one walk and one hit, or any combination of walks and one hit could have also done the job; examples of these sequences are shown in the graphics below:

Generally, any combination of walks and hits not highlighted yellow in Table 5.1 would have tied or won the World Series for the Royals. This glimmer of hope was a quantifiable 0.127 probability for Kansas City, so it was justified that Gordon was kept at 3^rd rather than sent home after shortstop Brandon Crawford just received the ball. It would have taken an error from Crawford or Buster Posey, with respective 0.033 and 0.006 2014 error rates, to get Gordon home safely. The probability 0.127 of winning the game from the batter’s box is noticeably three times greater than the probability of winning it from the base paths (where Crawford and Posey’s joint error probability was 0.039).

We should note that the layout in Table 5.1 is a simplification of what could occur with a runner on 3^rd, two outs, and a one run lead, because it only applies to innings where a walk off is not possible. In innings where a walkoff can occur, such as the bottom of the 9th, the combinations of walks and hits captured in the red highlighted cells are not possible because they would occur after the winning run has scored and the game has ended. However, Bumgarner was so dominant in the World Series that these probabilities are almost non-existent, thereby making our model is still applicable; we would otherwise exclude these red-celled probabilities for less successful pitchers.

The next probability formula considers the sequences of walks, hits, and outs that can occur after one out has been accumulated, which is situation definitely worth examining if there is a lone runner on 2^nd base.

Once again we’ll use Bumgarner’s 2014 World Series statistics to evaluate this formula and insert the probabilities into Table 5.2. According to the sum of the yellow cells, Bumgarner would be able to prevent the tying run from scoring (from 2^nd base with one out) with a probability of 0.762 and would otherwise allow the tying run with a probability of 0.238.

Table 5.2: Probability of Hit and Walk Combinations after 1 Out

To get out of the inning unscathed, Bumgarner would need to prevent any further hits or allow fewer than 3 walks given a runner on 2^nd with 1 out; it would be possible to advance the runner to on 3^rd with 2 walks and then sacrifice him home in this situation (with no hits), but this probability is insignificantly tiny especially for a dominant pitcher like Bumgarner. Once again we depict these sequences that could get the tying run home from 2^nd with 1 out, with the second out inserted randomly.

A runner on 2^nd base with one out is a scenario commonly manufactured in an attempt to tie the game from a runner on 1^st with no outs situation. The logic is that if the hitting team is down by one run and the first batter leads off the inning with a single or walk, the next batter can control getting him into scoring position and hope that either of the next two batters knocks the run in with a hit. However, this method of control, a bunt, sacrifices an out to move the runner from 1^st to 2^nd. The defense will usually allow the hitting team to move the runner into scoring position for an out, but the out wasn’t the only sacrifice made. The inning is truncated for the hitting team with one less batter and the potential to have more hitters bat and drive in runs is reduced. Indeed, against a pitcher like Bumgarner, the out is likely not worth the meager 0.238 probability of getting that runner home.  We’ll see in the next section what exactly gets sacrificed for this chance at tying the game.

We should note that in this “runner on 2^nd with 1 out” model we added few more assumptions to those we made in the prior “runner on 3^rd with 2 outs” model, neither of which should be farfetched. The first assumption is that with the game close and the manager intent on tying the game rather than piling on runs, he should have a runner on 2^nd base fast enough to score on a single. Another assumption is that the base runners will be precautious enough not to cause an out on the base paths, yet aggressive enough not to get doubled up or have the lead runner sacrificed in a fielder’s choice play. Lastly, we assume that the combinations of hits, walks, and outs are random, even though we know the current state of base runners and outs can have a predictive effect on the next outcome and the defensive strategy used. By using these assumptions we simplify the factors and outcomes accounted for in these models and reduce the variability between each model.

The final probability formula considers the sequences of walks, hits, and outs that can occur when we start with no outs accumulated; this allows to forge situation will allow us to forge the outcomes from a runner on 1^st with no outs scenario and compare them to a runner on 2^nd with 1 out scenario.

Table 5.3 below uses Bumgarner’s 2014 World Series statistics, the same as before, although in this model we deal with more uncertainty because the sequences captured in each box are not as clear cut between run scoring or not given a runner on 1^st with no outs. The yellow and non-highlighted cells are still the respective probabilities of not allowing and allowing the tying run to score, however, we now introduce the green probabilities to represent the hit and walk combinations that could potentially score a run but are dependent on the hit types, sequences of events, and the use of productive outs. These factors were unnecessary in the prior two models because in those models any hit would have scored the run, the sequence of events was inconsequential, and the use of productive outs was unnecessary with the runner is already on 2^nd or 3^rd base (except when there is a runner on 3^rd and a sacrifice fly or fielder’s choice could bring him home).

Table 5.3: Probability of Hit and Walk Combinations after 0 Outs

We must break down each green probability into subsets of yellow probabilities representing the specific sequences that would not score the tying run from 1^st base with no outs; we depict these sequences below, but for simplicity, not all are depicted.

Now that we know the conditions when a run would not score, we take the probabilities from the green cells in Table 5.3, narrow them down according to the proportion of sequences and the proportion of hit types that would not score the run, and separate them based on the usage of productive and unproductive outs; the results are displayed in Table 5.4. For example, there are 6 possible combinations for 1 hit, 1 walk, and 3 outs and 3 of these 6 combinations would not score the tying run on a single, where P(1B | H) = 0.755, with unproductive outs; yet, the run would score with productive outs, with unproductive outs on a double or better, or with unproductive outs and the other 3 combinations. When we finally sum these yellow cells, they tell us that an aggressive manager would score the tying run against Bumgarner with a 0.370 probability and Bumgarner would escape the inning with a 0.630 probability. Otherwise, a less aggressive manager would score the tying run with a mere 0.154 probability and Bumgarner would leave unscathed with a significant 0.846 probability.

Table 5.4: Probability of No Runs Scoring after 0 Outs

We summarize the results from Tables 5.1-5.4 into Table 5.5 from the perspective of the hitting team.  We compare their chances of success not only against Madison Bumgarner from the 2014 World Series but also against Tim Lincecum, Matt Cain, and Jonathan Sanchez from the 2010 World Series.

Table 5.5: Probability of Allowing at least One Run to Score

Let’s return to the scenario that is the launching point for this study… The hitting team is down by one run and there is a runner on 1^st base with no outs. If the game is in its early innings, where it is not mandatory that this runner at 1^st gets home, the manager will likely decide against being aggressive and avoid sacrificing outs in order to increase his chances of extending the inning to score more runs; there are several studies supporting this logic. Yet, if the game is in the latter innings and base runners are hard to come by, the manager should lean towards utilizing productive outs and intentionally sacrifice the runner from 1^st to 2^nd base. His shortsighted goal should only be to tie the game.  By forcing productive outs rather than being conservative on the base paths, his chances of tying the game increase significantly (between 0.216 and 0.271) against our four pitchers given a runner on 1^st and no outs scenario.

However, the if the manager does successfully orchestrate the runner from 1^st to 2^nd base with a productive out, he does still lose a little bit of probability of tying the game; between 0.132 and 0.215 of probability is lost against our pitchers. And if he decides to sacrifice the runner further from 2^nd to 3^rd base with another out, his team’s chances would decrease again by a comparable amount; this decision is ill-advised because a hit is likely going to be needed to tie the game and the hitting team would be sacrificing one of two guaranteed chances to hit in this situation. In general, the probability of scoring at least one run decreases as more outs are accumulated, regardless of the base runners advancing with each out. The manager could contrarily decide against sacrificing his batter if he has confidence that his batter can hit the pitcher or draw a walk, yet the imperative goal is still to tie the game. The odds of tying the game actually favor an aggressive hitting team that is able to get the runner to 2^nd base with one out, by an improvement ranging from 0.012 to 0.084, over a less aggressive team with a runner at 1^st with no outs. Thus, even though sacrificing the runner from 1^st to 2^nd base does decrease the chances of tying the game, it would be worse to approach the game lifelessly when the situation demands otherwise.

The scouting report on Cameron Maybin hasn’t changed much since the Detroit Tigers selected him 10^th overall in the 2005 amateur draft. “A prototypical five-tool player trying to learn how to turn his potential into performance.” Well here we are ten years later, and the once glorified minor-league prospect appears to finally be tapping into the potential that scouts used to rave about. At age 28, Maybin is blossoming into one of the biggest surprises of the 2015 season and is showing no indications of slowing down. Do we have another Jose Bautista case on our hands? Perhaps to a much lesser extent, but let’s not get carried away just yet.

Will there ever be a way to validate the Tiger’s decision to take him over Andrew McCutchen back in 2005? Of course not, but nine other teams passed on McCutchen as well so that’s not the point. The point is that in 2015, Maybin is proving that he can be a useful, everyday player in the big leagues with the ability to perform slightly better than your league-average center fielder. Through 81 games, Maybin ranks 7^th in OBP and 10^th in wRC+ among center fielders with at least 200 plate appearances. He is sporting career highs across the board and has given the Braves an unexpected and extremely valuable bargaining chip as the trade deadline approaches.

So what exactly has changed? Maybin has been asked this same question on multiple occasions throughout the season and each time he responds, praise is thrown in the direction of new Braves hitting coach, Kevin Seitzer, who’s hitting philosophy revolves around driving the ball up the middle. Now, I am sure that many other hitting coaches have this same philosophy, but for whatever reason, Seitzer has been able to get through to Maybin and successfully revamp his entire thought process when it comes to hitting. Let’s take a closer look at how.

Seitzer immediately recognized that at 6-foot-3, Maybin had a naturally long swing that was slow through the strike zone. To correct this, he recommended that Maybin start with his hands closer to his body, which would provide a shorter and more compact swing.  This slight mechanical adjustment is allowing him to hit the ball with more authority than ever before. Check out Maybin’s line-drive percentage on a yearly basis since 2010 in the table below.

Maybin’s whole approach to hitting has changed under Seitzer as well. He has never owned a strikeout percentage to write home about and his 2015 mark of 17.4% is no different. However, it’s worth noting that this is down 3.2% from last year, so signs of improvement are not exactly absent in that regard. What’s even more worth noting is that Maybin is walking more than he ever has which is a direct product of his astronomical improvement in situational hitting. Check out Maybin’s walk to strikeout ratio on a yearly basis since 2010 in the table below.

Maybin is thriving with a .400/.452/.898 slash line with runners in scoring position this year (all career highs), showing that he really is taking Seitzer’s philosophy to heart. For the first time in his career, he seems to have a plan every time he steps into the batters box. He is putting the ball in play to the right side 30.9% of the time in 2015 which is 7.1% better than his previous career high in 2012. Nothing illustrates the change in Maybin’s approach better than the spray charts below. In 2011 with San Diego, which is his best completed statistical season to date, you can see that he was almost exclusively pull happy. The shift in the distribution of hits in 2015 shows that Maybin is succeeding in his effort to shorten his swing and drive the ball the other way.

Can he keep this up? That remains to be seen, however, this does not have the makings of a complete fluke. Surely his BABIP is due to come down eventually, but it’s hard to think that he will revert completely back to his old habits. Maybin said earlier this season that he had always felt like he was missing something when it came to hitting. With the help of Kevin Seitzer, the former North Carolina high school standout has finally found it, and it’s a joy to watch.

We see it every night. A weak groundball to a defensively incapable player, a broken-bat roller behind the mound into no-man’s-land, a slap hit into the vacated area caused by the shift, a tomahawk chop resulting in a dirt-bounce that goes 20 feet upward. Not good enough to be a true hit, not bad enough to be an error. Infield hits are awkward.

“It’ll look like a line drive in the box score,” the broadcasters chirp happily. And while that’s very true, I would argue that infield hits are ESPECIALLY demoralizing for pitchers. Usually, the pitcher made a quality pitch, got the groundball he was looking for, and had little control over the infield defensive positioning or assignments. But because the official scorer ruled the play too difficult for a fielder to make, any runs driven in by the infield hit or resulting later in the inning will be earned.

Infield hits are the result of bad defensive skill, poor defensive positioning, poor use of the shift, sloppy weather conditions, speedy runners, jittery infielders, and/or good old fashioned bad luck. So which pitchers have been victimized the most by infield hits? Let’s look at the numbers for each league.

American League pitchers have allowed 9,650 hits, including 1,166 infield hits (as of June 29). The infield hits/hits rate in the American League, therefore, is 12.1%.

The Athletics’ defense ranks worst in the American League with a -23.9 UZR, and the team’s two best starters suffer a plethora of infield hits allowed. Take out Gray’s 17 infield hits allowed, and his already pristine 0.99 WHIP falls to 0.84 WHIP. Without the infield hits, Chris Sale of Chicago would also see his WHIP drop to a crazy 0.82 WHIP. (The ChiSox need to figure out how to shift.) Keuchel is the king of groundballs (64.5% GB), so infield hits are only natural to him. Same goes for Madson and his 56.4% GB rate. The Yankees’ middle infield has been miserable this year, and the team doesn’t know how to shift properly. Warren, Rogers, and Betances have been the poor-luck “beneficiaries.”

Nate Karns (45.4% GB) and Brad Boxberger (36.6% GB) are the real enigmas here, as the Rays have the second-best defense in the AL. Bad luck? Infielders hate them? Poor use of the shift by Tampa Bay coaches? According to Inside Edge, Rays defenders make only 4% of very difficult plays, labelled “remote.” Since these plays are too difficult to be ruled an error if the defender miffs, these balls in-play are often ruled infield hits (if, of course, they occur on the infield). For the curious, the Yankees are dead last (1.2%), and the Blue Jays are first (19%).

Zach Britton’s rate really jumps out, but it is most likely a result of very few hits allowed overall and, as with all the relievers, a small sample size. Britton has only allowed 28 hits on the season, and only 17 have left the infield. Dominant.

National League pitchers have allowed 9,892 hits, including 1,174 infield hits (as of June 29). The infield hits/hits rate in the National League, therefore, is 11.8%.

Noah Syndergaard (16.6% infield hits/hits) just missed this list, so that’s three Mets starters who have allowed way more infield hits than the average NL starter. The Mets have already taken Wilmer Flores off shortstop, but Eric Campbell (-1.1 UZR) and Daniel Murphy (-2 UZR) aren’t helping either. Brett Anderson (68.7% GB rate) is the most predictable pitcher on this chart, but Alex Wood and Shelby Miller are not, especially since 2B Jace Peterson and SS Andrelton Simmons flash the leather on a nightly basis. (Do the Braves  suffer from the Dee Gordon effect or just from poor use of the shift?)

The Cardinals infield has been below average defensively (Matt Carpenter -1.6 UZR; Mark Reynolds -1.6 UZR; Jhonny Peralta -1.1 UZR), which partially explains Lynn and Rosenthal. Starlin Castro (-3.4 UZR) and Arismendy Alcantara (-2.0 UZR) have not helped out Hendricks or Strop defensively either. Benoit is on the wrong team defensively to have a career-high ground ball rate (43.6%).

Finally, who has been stingy with infield hits? For the American League:

And for the National League:

Just something else Max Scherzer has been amazing at in 2015.

Coming into this season, the major league career of Mike Moustakas had been underwhelming. Underwhelming is probably kind, really. After being the second overall pick in the 2007 draft (behind David Price), Moustakas progressed through the minor leagues with consistently above average seasons. Before reaching the major leagues in the middle of the 2011 season, Moustakas had a minor league batting line of .282/.327/.503. From 2008 to 2011, Moustakas was ranked 18^th, 13^th, 80^th, and 9^th on the Baseball America Top 100 Prospects list.

Then he reached the Major Leagues. In his first three-and-a-half seasons with the Royals, Moustakas proved to be a below-average major league hitter, starting with an 84 wRC+ in 89 games during his rookie year, followed by 90, 77, and 76 wRC+ seasons from 2012 to 2014. Over this stretch, Moustakas hit .236/.290/.379, good for a .293 wOBA and 82 wRC+. His wRC+ placed him 177^th out of 186 hitters with 1500 or more plate appearances. From 2011 to 2014, Moustakas had been a worse hitter than Ruben Tejada, Jeff Francouer, and Kurt Suzuki, among others.

This season has been a whole new world for Moustakas. He is currently hitting .328/.379/.478 and has a 141 wRC+. After being below average for the first four years of his career, he is now hitting at a well above average rate.

So what has changed?

The first glaring thing to notice is Moustakas’ .350 BABIP. In his first four seasons, Moustakas had a BABIP of .260. This sky-high BABIP for Moustakas explains much of his success this season. He isn’t hitting for any more power than he did before. His rate of doubles, triples, and home runs in 2015 are right in line with his rate of extra base hits previously. He’s also walking less often than he had in the past (and striking out less often). The difference in Mike Moustakas from 2011-2014 and Mike Moustakas in 2015 is a big increase in the number of singles and a decrease in strikeouts. The chart below shows Moustakas’ 2011-2014 numbers pro-rated to his current 284 plate appearances (as of June 25). As you can see by the columns highlighted in yellow, the “new” Mike Moustakas has 21 more singles and 15 fewer strikeouts than the “old” Mike Moustakas would have.

I took his current stats and adjusted his .350 BABIP down to his pre-2015 career mark of .260, with all of his “eliminated” hits being singles, and his batting line drops to .251/.306/.401. This shows just how much BABIP is influencing Mike Moustakas’ breakout season. That .251/.306/.401 line is better than his pre-2015 career mark of .236/.290/.379, but it’s not that much better. It would only be a slight improvement and nothing like what he is actually doing this year.

So, Moustakas’ BABIP is the main reason for his success this season. Looking at his walk and strikeout rates, we find that he’s walking slightly less often than he had previously (5.4% this year compared to 6.4% from 2011-14). His strikeout rate is also down, from 16.7% coming into this season to 11.1% so far this year. Perhaps he is focusing more on making contact than he had in the past. This would fit in with what looks to be a philosophy of the franchise. The Royals have struck out less often this year than any team in baseball. Since 2010, the Royals have been the hardest team to strike out in four seasons (including 2015) and near the top of the list in strikeout avoidance in the other two seasons. For his part, Moustakas has steadily dropped his strikeout rate from 20.2% in 2012 down to this year’s 11.1%.

Early in the year, Jeff Sullivan wrote about Moustakas’ first opposite field home run . . . ever. He pointed out that Moustakas was hitting the ball to the opposite field during spring training much more often than he ever had. The Kansas City Star had an article in February about Moustakas working on hitting to the opposite field to combat the infield shift, which he had seen more of in 2014 than in previous seasons. In May, an article at Grantland continued with this theme, pointing out that Moustakas was leading the league in opposite field hits at the time and had dropped down a couple bunts against the shift in April. With this in mind, perhaps Moustakas’ .350 BABIP in 2015 is due to his newfound ability to hit to the opposite field?

With almost three months of the season in the books, we can look at some batted ball data. When it comes to hitting the ball to the opposite field, Moustakas is going the other way much more often than he had in the past.

Moustakas has hit the ball the other way 31.4% of the time in 2015, compared to 21.7% of the time in the four previous seasons. He’s hit the ball to center just about as often as ever, so all of that opposite field contact has meant fewer balls hit to his pull field. As for soft, medium, and hard hit percentage, his profile hasn’t changed much; just a slight increase in hard hit percentage.

Along with more balls being hit the other way, Moustakas has hit more balls on the ground. You’d expect a big guy like Moustakas to hit the ball in the air with power, which he did better than he ever had during the post-season last year when he hit five home runs in 55 plate appearances. Instead, this season Moustakas is hitting the ball on the ground more often. So far, it’s working.

Considering that Moustakas is hitting better than he ever has, perhaps he’s figured something out and this is the new Mike Moustakas, a guy who hits the ball on the ground and to the opposite field more often than he ever did and the result is a shiny .350 BABIP.

Except it doesn’t look like this is the case when you take a closer look. Moustakas may have been a new man during spring training and in the first part of the year but he’s looking more like his old self recently.

Using monthly splits means slicing up data into arbitrary points, I know, but it’s not hard to see a reverting to old form when looking at Moustakas’ monthly opposite field percentage numbers. He hit the ball the other way 39% of the time in April, around 31% of the time in May and 23% of the time in June so far. His directional hitting numbers in June are all fairly close to his pre-2015 numbers. It looks like Moustakas was doing something different early in the year but that is no longer the case.

The interesting thing is that he’s continued to be productive and continued to post a high BABIP (.377 in April, .301 in May, .373 in June). He’s also hit the ball hard more frequently in June (36.2%) than he had in April (26.8%) and May (24.0%) and he’s continued to hit more ground balls than he had in previous seasons.

I don’t know how to find data on how often teams are shifting against Moustakas and if that information is broken down by month. The Grantland article referenced above pointed out that he had been shifted against 70.7% of the time in 2014 and just under 60% of the time in 2015 (the article was posted on May 5) and suggested that his ability to hit to the opposite field in April may have resulted in teams shifting less often. If this is true, then his reversion back to pulling the ball in June could be Moustakas adjusting to the adjustments of the opposing defense. It could be that Moustakas began the year facing a shifted defense a high percentage of the time and responded by hitting to the opposite field (39% of the time in April). Then teams may have gone away from the shift against Moustakas sometime in May and Moustakas has responded by pulling the ball more often (going opposite field just 23% of the time in June). This is speculation, of course, since I don’t know the shifting patterns of defenses facing Moustakas.

On the other hand, it could be that the BABIP-Gods are smiling down upon him so far this year and all of this batted ball talk is meaningless. Personally, I’m tempted to lean this direction. Moustakas is currently 18^th out of 161 qualified hitters in BABIP. Of this group of hitters, Moustakas is 81^st in line drive percentage, 88^th in infield hit percentage, and 100^th in hard-hit percentage. He’s also hit infield flies at a very high rate (16.5% IFFB%), with just 10 hitters popping out more often than Moustakas. There are just too many indicators suggesting he won’t continue to have a .350 BABIP, especially if he’s no longer taking advantage of shifted defenses by hitting the ball to the opposite field like he did early in the season.

For their part, ZiPS and Steamer see improvement but not earth-shattering improvement. For the rest of the season, they project a .289 and .280 BABIP, respectively, with wOBAs of .322 and .324, making him a slightly above-average hitter going forward (104 wRC+ and 106 wRC+). That’s not as bad as the old Mike Moustakas but it’s not all that new-and-improved either.

Wilson Ramos is currently third on the Nationals with 7 home runs. He has done that on 41 fly balls, for a HR/FB rate of 17.1%. But Ramos has also hit the ball on the ground in play 103 times. Throw in his 37 line drives and that’s a ground ball rate of 56.9%. For a guy as slow as Ramos, how can that be good?

Ramos has always hit the ball on the ground, however. His career ground ball rate is 54.3%. He has a career average of .206 on those grounders. That is below average, as you’d expect.

Meanwhile, he hits fly balls 27.5% of the time for his career, and homers on 16.6% of his flies. That’s better than the career rates of Jose Bautista (16.5%), Todd Frazier (15.8%), Adam Jones (15.2%), Adam LaRoche (15.0%). It’s better than a lot of players who are known for their power.

The difference, of course, is the in the fly-ball rates. Bautista has a career 45.5% FB% mark. Frazier’s is 40.8% for his career. Jones, 33.4%. LaRoche, 41.2%. And there’s Ramos with a paltry 27.5%.

But here’s possibly the most startling statistic: of 703 qualifiers since 2002, Wilson Ramos ranks 57th in HR/FB and 648th in FB%.

Ramos clearly has power just from watching him, and when he gets the ball in the air, he can hit it out. When he hits it on the ground, his speed makes him less likely than most to reach base.

So why is he hitting the ball on the ground more than half the time?

There might be a good answer to that question, but if he starts to hit more fly balls, there might be a rather less good answer to this question:

Why didn’t he start doing this sooner?

(Cross-posted at federalbaseball.com)

 2^nd Base hasn’t been a particularly stacked position in the major leagues in the past five years. Entering the 2015 season, the 2^nd base position was headlined by Jose Altuve and Robinson Cano. The second tier arguably consisted of Ben Zobrist, Neil Walker, Dustin Pedroia, and Ian Kinsler, and maybe Brian Dozier. Then the next level housed names like Jason Kipnis, Daniel Murphy, and maybe DJ LeMahieu. I’m here to analyze what has possibly happened to this group of baseball players in the past few months.

According to the Depth Charts pre-season projections, the top eight second basemen ranked by wOBA were Robinson Cano, Neil Walker, Ben Zobrist, Jose Altuve, Dustin Pedroia, Ian Kinsler, Howie Kendrick, and Chase Utley. The projections are usually somewhat accurate, but if you’ve been following baseball at all this season, just by looking at those names, you know that we’ve found an exception to that.

These are the top 10 second baseman thus far in the 2015 season ranked by wOBA:

If I told you in April that Logan Forsythe would be the 3^rd best second baseman in the league, you would think I’m ridiculous. He came absolutely out of nowhere to raise his BABIP nearly 60 points and raise his ISO 55 points! Joe Panik’s beautiful swing has moved him up to be the 4^th best-hitting 2^nd baseman. Jason Kipnis has shut up all the critics. He took his .310 2014 OBP as confidence going into this year, and now has a wOBA over .400. Danny Espinosa, who has been previously known as a ‘defensive’ second baseman, has skyrocketed his offensive production into a player who Matt Williams is comfortable having run onto the field every day. Cardinals 2B Kolten Wong is pulling the ball more and more every season. He’s also upped his LD% from 19% to 25%. Braves utility-infielder Jace Peterson is doing a bit of hitting in his rookie year, after being traded from San Diego (who, it turns out, could really use him) to the Braves in December. Think back to when I mentioned the tiers up top. Where is Robby Cano on the list above? Where’s Altuve? I don’t see Zobrist, Walker, or Kinsler on this list either. It is not an error.

So we talked about the breakouts at 2nd; now lets talk about the guys who haven’t or haven’t yet lived up to expectations.

Lets start with the guy who all of his fantasy owners hate this year. Robinson Cano. Yeah, the six-time All-Star Robinson Cano. The 32-year-old — the guy who has a wRC+ of 76. This is easily, by far, his worst season of his 11-year career. Why? Lets talk about it.

Cano has raised his Hard% and his Pull% over 4% each! What does jump out at you is that he’s making a ton less contact than he did last year. Actually, the least of his whole career. His Contact% has plummeted down almost 5%. Along with a raised K%, his BABIP has jumped down nearly 50 points.

The next guy is Altuve. Altuve hasn’t been that bad this year, but compared to his 2014 campaign, he’s not playing like Jose Altuve. He’s even fighting with a mild hamstring injury, but in his 287 PA’s, every single one of his numbers are down. His wOBA has decreased .363 to .304. BB% is down, strikeouts are up, OBP and SLG are both way down. He’s swinging more, and making less contact which isn’t a combination that pulls you in a positive direction.

Same can be said for Neil Walker. Almost all his numbers are down. One of the positives that I found, though, is that he’s hitting the ball harder. My prediction is that the .303 wOBA will start to show positive regression. Ian Kinsler isn’t having a horrible season. He’s raised his OBP a bit, but he’s becoming more of a singles hitter, dropping his SLG from .420 to .338.

Almost every starting second baseman in the big leagues has totally changed their style of hitting this year. Guys like Forsythe and Panik, who were projected to be replacement level or below, have made their names rise to the top of many leaderboards. Cano and Altuve’s value have fallen. Here’s your homework: Think of all the 2nd baseman in the major leagues. How many of them have close to similar stats from their projections? Comment down below.

The Cleveland Indians can be a frustrating team to follow.  As a small to mid-market ball club, the Indians do not have the luxury to spend big in free agency, and when they do, they usually have to spend extra to bring in overrated players (see Kerry Wood, Nick Swisher, and Michael Bourn).  As a result, the Indians hedge their success on taking risks.  This process started in the early ’90s when they signed their young players with big potential to long-term contracts.  As a result, the Indians turned into an offensive juggernaut with stars such as Jim Thome, Sandy Alomar Jr., Kenny Lofton, Omar Vizquel, Carlos Baerga, and many others.  However, many people do not realize that the Indians also lost on some of the risks that they took in that era with cases such as Eric Plunk, Herb Perry, and so on.

This strategy has continued to this day, and has resulted in a very promising, yet frustrating team.  Carlos Santana has been productive with his high OBP, but never the player the team envisioned.  Jason Kipnis has had up and down years.  Lonnie Chisenhall has shown moments of brilliance, but has been unable to sustain those moments, resulting in his demotion to Triple-A Columbus last week.

While the offense has been widely inconsistent, the rotation has, for the most part, been brilliant.  Corey Kluber is second in the league in strikeouts with 111.  Danny Salazar, Carlos Carrasco, and Trevor Bauer have been inducing a lot of long walks back to the dugout as well, ranking tied for sixth, seventh, and ninth, respectively.  These four have a combined 3.65 ERA, which is quite remarkable considering that the most respected starting rotation in the Majors (the Nationals) has logged a 3.60 ERA.

However, this is not where the story ends.  As everyone (hopefully) knows, major-league rotations consist of five starting pitchers (unless you’re the Mets for a few weeks).  With the fifth starter added in, the Cleveland Indians rotation has an ERA of 4.37, almost three-quarters of a run higher.  This is absolutely startling.  By themselves, the fifth starters in the rotation have gone 3-8 with an 8.33 ERA.  I’ll repeat that, an 8.33 ERA.  The only fifth starter to win at all has been Shaun Marcum, the man who had not pitched in the major leagues since halfway through 2013.  In the games that a fifth starter has pitched in, the Indians have been outscored 88-48.  Opponents have nearly doubled the Indians run output when a fifth starter has taken the hill.

Are there games that the Indians should have won when a fifth starter has pitched? Absolutely.  But in order to have any shot of making the playoffs, someone has to step up in the back of the rotation and close up the black hole.  A .375 winning percentage does not a contender make.