Sir Isaac Newton’s second law of gravity tells us exactly how much an object will accelerate based on the given net force.

For baseball hitters, this is directly applicable considering the goal to hit baseballs as hard and far as possible. And when it comes to generating net force against baseballs, Mike Trout is an expert. He has been crushing baseballs with the league’s elite since he became a full-time regular at age 20 in 2012. Trout’s offensive production, in particular, has gone to another level over the course of his career. The following table breaks up his career into two distinct parts. The numbers show Trout’s production compared to league average, with a mark of 100 denoting exactly average.

Trout has always produced elite offensive numbers, but he’s at an entirely different level now. He has transformed into baseball’s best hitter by walking more, striking out less, and pulling more hard-hit baseballs in the air. Trout is both barreling up more baseballs and raising the launch angle of his batted balls. Unsurprisingly, he had baseball’s second-best sweet-spot percentage in 2019. Trout has talked about a gap-to-gap approach in the past but recent trends show him moving away from hitting balls the other way. Read the rest of this entry »

With the 2020 Major League Baseball season on hiatus due to the Coronavirus, one can’t help but wonder of a season that could have been. Do the Nationals, after losing slugger Anthony Rendon to the Angels, have what it takes to repeat as World Series champs? Can Pete Alonso be this season’s home run champion again? Will Trout win another MVP?

Hopefully we will know sooner rather than later. In the meantime, I took the liberty of looking at players who will become first-time arbitration eligible following the 2020 season, focusing on Lucas Giolito of the Chicago White Sox.

Rather than conduct an analysis based off of career numbers (excluding the vacant 2020 season), I utilized The BAT Projection System by Derek Carty, which is part of FanGraphs, to fill in the gap for 2020 season statistics.

The BAT is a standard projection system that predicts outcomes in accordance with basic factors such as hitter and pitcher, park quality, umpires, weather factors, and more. Read the rest of this entry »

It’s a cool and breezy April afternoon down by Baltimore’s Inner Harbor, and the mid-rebuild Orioles are taking on the division-winning and record-breaking Minnesota Twins. Trying to salvage the final contest of a three-game series, the O’s — to no one’s surprise — find themselves trailing in the bottom of the ninth. But not all hope is lost. The Twins’ lead is small — two runs — and the Orioles have some of their best players due up. Out of the gate, Twins pitcher Taylor Rogers hits the first Orioles batter, Joey Rickard, in the foot. Then, after a Chris Davis lineout, Jesús Sucre resurrects the inning with a single to left that advances Rickard to third. The comeback is on.

Hanser Alberto then plunges the Orioles hopes back down to earth with a swinging strikeout that gives his team just one more out with which to work. But then comes Jonathan Villar, who rips a double to deep left, scoring Rickard and advancing Sucre to third. The Twins lead is cut in half. After an intentional walk to Trey Mancini that loads the bases, the game now rests in Pedro Severino’s hands. With two outs and the bases loaded, still down by one, Severino manages to work the count to 3-0. His team is one pitch away. The crowd is on its feet. Rogers winds and delivers his pitch. It’s outside! “Ball 4!” the commentator exclaims. The fans cheer, Severino begins to walk towards first, and the tying run starts his trot towards home. But suddenly, the umpire punches his arm through the air. He called it a strike. Severino walks back towards home plate, distraught. He pops up the very next pitch, and just like that, the game is over.

***

Using data from Baseball Savant’s pitch-by-pitch library, we can begin to understand the role that these incorrect calls play in baseball. By matching up the database’s pitch locations to the calls associated with those pitches, we can see which calls were supposedly correct, and more importantly, which were not. The results are pretty astounding. Last year, by this data, MLB umpires made a total of 33,277 incorrect calls. That’s good for 13.8 per game, or just over 1.5 per inning. While not every bad call is a comeback-killer, these mistakes have the ability to greatly alter an at-bat, a game, and maybe even a season. Read the rest of this entry »

Baseball as a sport, like most activities of daily life, is one which we consume primarily through our eyes. While I’m certain some people still enjoy it by listening to the radio (a mode I’m still partial to), I think you would be hard-pressed to that argue baseball is not visual. That’s not to say we don’t listen to the sounds (personally I find baseball on mute to be close to a kind of torture). However, our judgments of the game, and more importantly our judgments of the players in it, are based on what we see visually. We don’t know Mike Trout is good just because the announcer tells us he is good, we know he is good because we can see how good he is. We can see the balls he snatches away as they clear the fence, as well as the balls he smashes over them.

There are other methods we can use to see that Trout is good as well. Sabermetrics and Trout have seemingly been tied together in their emergence into the public baseball consciousness. As he blossomed into a star, so did Sabermetrics as it rose to the forefront and into the view of the average fan. Like Trout, the way we digest sabermetrics is in a sense almost purely visual. We come to FanGraphs, and we read a stat line off the screen. When we look at exit velocity or launch angle, we’re looking at metrics we’re aware of because a computer system visualized them for us.

To a large extent, what I’ve said above is simply a result of us privileging sight more than our other senses. Baseball utilizes the other senses as well. We all likely have memories tied to the smell of the stadium or a leather glove. Maybe every time you go to a game you get a hot dog, and that taste is as connected to baseball as the sound of a cheering crowd. Baseball at its best is a palimpsest of all of these senses working together to create our experience. Read the rest of this entry »

Excitement. Disappointment. Tradition. They make baseball great! Following your favorite team for six months a year will cause any halfway-devoted fan to learn more about the 25th man on the roster than will ever be necessary. It also might mean that fans could always recite the name of the prospect that never was, even years after the fact. And if those fans continue to follow that same team for many seasons, the list of players that they remember will continue to grow. Not all the memories are pleasant, however. That’s not how life works. In fact, the not-so-happy moments from the playing field tend to be what most fans remember the most. It’s those memories of a certain type of player type that live on in the collective mind of fans everywhere, and it’s those types of memories that I will be delving into in this piece.

A recent Grant Brisbee piece at The Athletic set out to create an all time team of “lightning rod players” he loved from the San Francisco Giants, and I felt it was a delightful read. It seemed like a lot of fun to dig in on all those players, and as a Cardinals fan, it made me think about what a similar team of St. Louis players would look like. What follows is my detour down the cul-de-sac of memory lane that many would rather soon forget, an imaginary lineup dubbed the Tino Martinez All-Stars.

Let me be clear — this will not be a scientific process. There will be a statistical element, but it will not be a “who was the worst player at every position” contest. Maybe the players were overrated, maybe they are overpaid, or maybe they were just overplayed. The bottom line is, apathy is the enemy! Also, I was born in 1983, so the team I’ll be picking will undoubtedly be influenced by the Cardinals clubs I have seen the most. You have been warned. Read the rest of this entry »

[Editor’s note: I mentioned to my father that I run the Community Blog and that anyone can write in, and he decided to submit a piece. This is a reminder that you or anyone you know may send us words.]

In 1960, I was a 10-year-old baseball fanatic. For me, live baseball consisted of the Triple-A Seattle Rainiers, and access to major league baseball meant getting your chores done in time to get to watch Pee Wee Reese and Dizzy Dean preach the baseball gospel on the game of the week every Saturday (and if you were living right, sometimes on Sunday). That summer I was lucky enough to get to fly back to Kalamazoo, Michigan, and spend a week with my grandparents. I was transported to a world where if the Tigers had a baseball game on Tuesday, then major league baseball was on the television on Tuesday! Long live Ernie Harwell!

The big buzz that summer was who got the short end of the stick in the big trade when the Tigers sent Harvey Kuenn to Cleveland for Rocky Colavito. Free agency was still a long ways in the future so trades were infrequent, and dealing away the league-leader in average, hits, and doubles (Kuenn) for the leader in homers (Colavito) was a big deal.

Being a young Tigers fan, it was easy to become a life-long Yankee-hater. The Tigers would finish the year 12 games under .500 and 26 games behind New York. From the year of my birth until the year of my 14th birthday, the Yankees were the American League champs every season but two. Back then there were no playoffs, which meant the AL and NL regular-season champs met in the World Series, and the only question at the start of the year was who was going to face the Yankees. Read the rest of this entry »

With the advent of Statcast, statistics like exit velocity, spin rate, and launch angle have become easily accessible to baseball fans. Catcher pop time data has also become available. However, unlike some of the other Statcast metrics, catcher pop time data has existed for much longer, with scouts measuring pop times in the minor leagues years before Statcast entered the mix.

This sounds all well and dandy, right? Well, it would be, if the Statcast numbers were consistent with scouting pop time tool grades. Baseball Prospectus, for example, calls a pop time from 1.7-1.8 a 70 pop time, which sounds reasonable enough without any context. However, considering the best average Statcast pop time to second base from 2015 to 2019 was JT Realmuto’s 1.88 (minimum 10 throws to second), something seems amiss here. I decided to take a deeper look into Statcast’s pop time data to get a better idea of what’s going on.

Read the rest of this entry »

Back in February, Justin Toscano wrote that when the Mets acquired reliever Brad Brach last August, the team asked Brach to do the one thing he couldn’t do with the Cubs in the first half of the season: throw his cutter.

The 6-foot-6, 33-year-old right-hander was designated for assignment by Chicago after signing a $1.65 million deal with the team during the 2018–19 offseason. Brach posted a 6.13 ERA in just 39.2 innings across 42 games for the Cubs in 2019.

After having spent most of the second half of 2019 with the Mets, Brach re-signed with the team on a $850,000 deal, with a player option for 2021, that can increase to $1.25 million with incentives.

From March 27 through August 10 of 2019, among 197 relief pitchers with at least 30 innings pitched during that time frame, Brach ranked 123rd in the league in GB% (41.1%), 70th in K/9 (10.21), 193rd in BB/9 (6.35), and 97th in FIP (4.12). Suffice it to say, Brach was not the most productive pitcher for the Cubs, thus justifying his being DFA’d from the team in the middle of the year.

When analyzing Brach’s career numbers, however, it is clear that his time with the Cubs is not indicative of his overall arc. From 2011–18 with the San Diego Padres and Baltimore Orioles (and half a season with the Braves), Brach pitched to a 3.08 ERA (132 ERA+), a 3.68 FIP, and a 9.6 K/9 in 456 IP.

Prior to 2019, Brach only recorded an ERA over 4.00 once (5.14 in seven innings in 2011 — his first year in the league) and has never allowed more than 28 earned runs in a season. Moreover, since 2013, Brach has posted an ERA+ over 100 in every year but 2019, including a 210 ERA+ in his All-Star 2016 campaign for Baltimore. Read the rest of this entry »

Why don’t teams ever sign former quarterbacks to try and turn them into pitchers?

This thought stems from watching Patrick Mahomes and his pre-draft NFL tape and discovering that his father was a former major league pitcher. Can a quarterback’s arm strength transfer to pitching? What can be learned from football velocity to uncover a future successful pitcher?

ESPN was ramping up their coverage in the weeks leading up to the 2017 NFL Draft, and Mahomes was gaining momentum. A SportsCenter interview with the future MVP explored his multi-sport background, which caught my attention.

I was vaguely familiar with the story about Mahomes’ father reaching MLB as a pitcher. Apparently there was a time when Mahomes considered following in his father’s baseball footsteps. The interview spilled over into the prospect’s appearance in the Gruden QB Camp. He mentioned then that he was drafted by the Detroit Tigers in high school, but due to a strong desire to play quarterback at Texas Tech, he went in the 37th round. If his football passion wasn’t as strong, scouts told him that the top three rounds were a likely landing spot.

As the video continued, it featured highlights of in-game play and practices where Mahomes showed a dynamic skill set. He had special throwing abilities, and his baseball background and natural talent was obvious in just a few of his tosses. There were impressive clips of him throwing a football from his knees about 50 or so yards, and another highlighting a final pregame warmup toss and ritual: throwing the ball about 75-80 yards in the air. Read the rest of this entry »

The other day I began pulling together Savant data to determine whether there was an ideal zone percentage for different types of curveballs (CUs) and sliders (SLs). I haven’t found much on that front yet. However, I did realize that I don’t really know what makes curveballs effective, both from a results standpoint (extra whiffs, weaker contact, etc.) or a trait standpoint (vertical break, horizontal break, velo). I took a look at all of these factors for the curveballs in the 2019 baseball season to see if anything stuck out.

I analyzed a sample of 214 pitchers, representing everyone from 2019 who threw at least 20 innings, a curveball at least 10% of the time, and qualified for Savant’s pitch movement leaderboard. From this sample I pulled info on every pitcher’s spin profile, wOBA, xwOBACON, zone percentage, SwStr %, and RHB/LHB splits. I even noted all that same info for the rest of their arsenal as well as just to have a full view. Then they were bucketed in every way imaginable with averages and standard deviations to see which ones stood out. I do want to preface all my findings by saying that the sample size is not ideal, as the buckets were mostly of decent size (roughly 100-plus players), but I did get granular at times (the smallest group was 48).

I am most focused on the following metrics: CU wOBA, CU xwOBACON, CU SwStr %, CU Drop & Tail (as a % difference vs. the average pitcher at similar velocity). Here are the averages across the entire sample: Read the rest of this entry »