Shortly before the season began, I wrote a post in which I identified minor leaguer hitters who saw stark decreases in their Zone% in the final month of the 2014 season. This research was meant to emulate work done by Rob Arthur, who found that hitters who saw fewer pitches down the heart of the plate late in the year often outperformed their projections the following season. The player who jumped out most from my analysis was Richard Shaffer — a former first round pick in the Rays organization who had fallen on hard times since his draft year.

I wrote a follow-up piece on Shaffer a few days later, noting that his plummeting Zone% wasn’t the only reason to think he might have turned a corner. He had also reportedly made some adjustments, which were acknowledged by people in the Rays organization and by Shaffer himself. And perhaps most importantly, he raked over the season’s final month. He hit .357/.486/.768 with six homers over his final 17 games. To wit.

A little more than a month later, those adjustments seem to be working. Through 25 games, Shaffer’s belted 6 homers, and holds a 147 wRC+ in Double-A. His .260/.363/.490 triple slash is a far cry from the .222/.318/.440 he posted at the same level last season.

Lead prospect analyst Kiley McDaniel had the chance to see Shaffer in action multiple times this year, and was able to confirm that Shaffer indeed looks like a different player. Kiley noted that Shaffer seemed much more relaxed and comfortable at the plate than he was last season and didn’t seem quite as worried about his mechanics. He noted that the bat speed looked the same, but that Shaffer’s doing a better job of crushing mistakes and not guessing on pitches.

Kiley said he’d now put the 24-year-old ahead of fellow Rays third base prospect Patrick Leonard, who managed to crack the ranked portion of his list. He now sees Shaffer as a 40 FV player, who could wind up providing value as a part-time corner infielder. That’s not super exciting, but it’s a useful player. As recently as last summer, Richie Shaffer looked like an extreme long shot to ever become a productive big leaguer. Now, it seems like a strong possibility.

Before his start on Thursday in Milwaukee, Dodgers right-hander Carlos Frias — celebrated on multiple occasions by the present author in these same pages — Dodgers right-hander Carlos Frias had thrown 110 pitches over three appearances, including a 70-pitch start on May 1st against Arizona.

Over the course of those three appearances, Frias’s fastball sat at about 96 mph — unsurprising, that figure, given both his scouting reports and also his previous work in the majors. More notable with regard to those first 110 pitches was this, though: the absolute slowest among them was a changeup he’d thrown at 88.9 mph to David Peralta on May 1st.

This changeup:

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Scapegoat is an often-used word when a manager or coach is fired. As the saying goes, “You can’t fire the players,” and the person responsible for putting together the roster is rarely let go during the season. That puts a select few in the cross hairs when things aren’t going as expected. Or, in some cases, what many predicted comes to fruition.

The Red Sox fired their pitching coach today, 28 games into a season where their beleaguered staff is living down to its expectations. Unable to magically turn the likes of Clay Buchholz, Joe Kelly and Justin Masterson into more than what they are, Juan Nieves is now unemployed.

Somewhat surprisingly, Nieves’ replacement is not yet known. According to Ben Cherington, at least one internal candidate and one external candidate is being considered. The Boston GM did say “Nothing happened specifically, recently, that precipitated this.” Cherington said once the decision was reached, the right thing to do was make the move, even without a successor in place. Read the rest of this entry »

Early 19th century French diplomat Charles Maurice de Talleyrand-Périgord argued — according to late Romanian aphorist Emil Cioran, at least — argued that it’s preferable “to follow one’s inclination rather than to make one’s way.”

Having pointed his internet browser to this site, the reader has already exhibited a willingness — in part, at least — a willingness to follow his inclination. What follows, however, represents an opportunity for that same reader to indulge his inclination to an even larger and more substantial degree.

As of press time, talented Cleveland starter Carlos Carrasco had struck out talented Kansas City outfielder Alex Gordon in each of the latter’s four plate appearances tonight (box). What the footage below documents are two pitches from the second of those plate appearances — both swinging strikes and both a result of Carrasco’s changeup.

The reader will find that his or her inclination is to meditate upon these animated GIFs for an extended period of time, committing them to what may or may not be called “spiritual memory.” Is that a real term or one just invented by the author right now? It neither matters nor does anyone care.

Moving on, here’s the first of those changeups to Gordon, in real time:

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With our new probable starters leaderboard, you no longer need to manually enter in all of the day’s starting pitchers yourself.

This particular leaderboard will also auto-populate all of the batters from the day’s lineups as they are announced.

You’ll always be able to access the current day’s probables from the link on our live scoreboard.

Last week we made some changes to the custom dashboard on the player pages, which allow you to choose exactly which stats you would like displayed in the order of your choosing.

The first update is that all the stats on the site are now available to you. For a long while there was only a subset of all our stats, but this is no longer the case.

The second change we made is that the custom dashboard now scrolls within the page so it doesn’t bleed over the boundaries of the site indefinitely. There are a non-trivial number of custom dashboards that basically include all the stats on the entire site, which makes for an extremely wide custom dashboard.

As I’ve monitored feedback on the change, some people like the scrolling, but others are very opposed to it. So, we’re going to have a poll to decide how to set the default behavior. Unless the results are overwhelmingly in favor of one particular choice, we will have an option in the custom dashboard to toggle the scrolling on/off.

We’ve quietly added a few new stats to our batted ball section.

First we have percentage of balls a batter pulls, hits to center field, and hits to the opposite field. This will make it much easier to figure out the general direction a player hits the ball without going to the splits pages and pulling out a calculator.

Pull% – Percentage of balls in play that were pulled by the batter
Cent% – Percentage of balls in play that were hit to center field by the batter
Oppo% – Percentage of balls in play that were hit to opposite field by the batter

Then we have how hard each ball is hit as provided by Baseball Info Solutions. It’s important to know that these are all relative to the batted ball type. For instance, a fly ball might be classified as hard, but if that ball were a line drive, it could potentially be classified as medium. If you are interested in seeing how line drives/fly balls/groundballs are classified into soft/medium/hard, we have that information available on the splits pages.

It’s also important to know that prior to 2010, these were all graded visually. From 2010 onward, the batted ball type, hangtime, and distance hit are all used to calculate the soft/medium/hard classifications.

Soft% – Percentage of balls in play that were classified as hit with soft speed.
Med% – Percentage of balls in play that were classified as hit with medium speed.
Hard% – Percentage of balls in play that were classified as hit with hard speed.

First, Pedro Martinez pointed out something really fun about hitters at the plate, and how they basically tell the pitcher where they want the ball as they step into the box. Watch the waggle.

So I asked Tim Hudson and Madison Bumgarner if they’d ever heard this before. Neither had, but they did shed some light on why this Bat Waggle Finding may not be super useful to all pitchers.

Bumgarner said he wouldn’t use it much, but that’s because he doesn’t really plan for hitters that way. Scouting reports? “We can get em, I don’t really,” he said. “I got everything in my memory bank.” He’d tried planning for each hitter before, but then he said he got too rigid and had a hard time adjusting. “I don’t have a gameplan when I go out there — I mean I have my gameplan going out there, but my game plan is to get outs and adjust as I need to, and try to read the situation. I’m not dead set on what I’m going to do before the game starts.”

Hudson laughed when he heard the youngster’s prep work. “Don’t ask any young stud about their prep work,” the veteran Giants starter said. “They haven’t had to figure it out. They just throw the glove out there and get shutouts.”

But when Hudson goes to prepare for the game, he gets fairly granular. “I look at the scouting reports and tendencies, what they can hit, what they can’t hit,” Hudson said. “Pitches in certain counts and certain zones. Breaking balls, where in the zone. If there are some red flags with a hitter, you want to know those.”

This isn’t all pitchers all the time, but Hudson and Bumgarner prepare for the game with drastically different styles. And, in both cases, as cool as this Bat Waggle thing is, it’s not super useful. Either the pitcher isn’t looking to scout the hitter that way, or the pitcher has already seen the heat maps that tell you that Albert Pujols likes high pitches.

Danny Salazar threw three curves in the major leagues before this year. Today, he threw two to Sal Perez. Here’s the second one, which froze Perez and allowed Salazar to steal a strike.

Considering that Salazar’s problems have mostly been in the command department, not the stuff section, a pitch that batters won’t swing at but can thrown into the strike zone could be a tremendous asset. At 82, with nice drop, it’s also going to be useful for whiffs and grounders — that’s one of the harder curves out there (would have been top 20 in velocity if it qualified last year), and that’s above-average drop, both good signs for a curveball.

You might not have realized it, but last night’s slate of games had seven teams reach double-digit run totals. Throughout the night I began to notice there were quite a few games with really high run totals. Then last night while watching the Astros pile runs on the Padres into the 9th inning, armed with a laptop and some SQL code, I set out to research how often this many teams score 10 or more runs on a given day.

MLB Game Days with Teams Scoring 10+ Runs

It turns out this hasn’t happened since 2012, and it has only occurred on nine days over the last decade of baseball. One of those days in 2007 had eight teams break double digits, and all of these days occurred on days with 15 games being played. Yesterday, there were only 14 games played due to the postponement of the White Sox – Orioles game. Most of the occurrences happened earlier in the 2000s when the run environment in Major League Baseball was higher. Today’s low run environment makes days like yesterday less and less likely.

This also gives us a chance to look at how a team’s runs per games are distributed to find out how likely it is that one team scores 10 or more runs in a game. They don’t follow a normal, bell-curve distribution, but instead it’s more skewed (and roughly follows a negative binomial distribution). I’ve illustrated the distribution below using actual results. This represents the percentage of games that a single team scores a certain number of runs.

The run scoring is skewed heavily to the right. I’ve called out the percentage (6.9%) of team-games that have had 10 or more runs scored, so they don’t happen very often. I have the box going all the way out to 30 runs, because that’s the record the Rangers set in Baltimore in 2007. Seven teams having 10 or more runs on one day is obviously less frequent than having just one team do it especially when there are only 28 teams playing instead of 30.

For those of you interested in reading more about run distributions, I have already laid out the math behind modeling run and hit distributions with the negative binomial distribution, and Tom Tango has produced a more accurate but slightly more complicated distribution as well.