I imagine most people reading this have a favorite team. And over time, you’ve likely had numerous players on that team whom you particularly enjoyed watching play. But when push comes to shove, who receives your greatest loyalty, the team or the players?

I’m a Cardinals fan, and I greatly enjoyed Albert Pujols‘ contributions to the Redbirds’ success during his 11 years wearing the birds on the bat. Since he’s left St. Louis? Sure, I’ve been happy for him when he’s done well — getting his 3,000th hit as well as his 500th and 600th home runs — but it’s not the same. He’s an Angel now, not a Cardinal, so I’m simply not as invested in his accomplishments.

This stance is probably understandably similar for most of you. Teams are (mostly) eternal, while players are ephemeral. Can I name the starting eight position players for the 2011 Cardinals? Probably not, but I still know they won the World Series that year.

When it gets flipped, however, is when we go off the playing field and into the negotiating room. When the owners and players are battling over matters of the game — particularly the divvying up of the loot — I largely stand behind the players. The owners become the faceless, monolithic corporations that extort billion-dollar ballparks from their communities and work extremely hard to give the players as small a portion of the pot as possible, while the players have short careers and are positioning themselves to take care of their families as much as possible before their careers end.

Of course, it’s not that cut-and-dried. Both sides have their virtuous and unseemly characteristics. Each group is willing to put their interests before others.

But regardless of who sticks it to whom for their own benefit, it’s largely the players who suffer the vitriol of the fans and media when the two sides clash. The question is, why is that? The answers actually make a lot of sense — even if they really don’t. Read the rest of this entry »

Fielding percentage is often criticized for the selection bias introduced by a player’s range (good defenders attempt more difficult plays, leading to more errors). A similar issue of selection bias is present in stolen bases. On any given pitch, it is at the sole discretion of the runner if he will steal a base or not. Naturally, the runner will only attempt a stolen base when he believes he has an advantage over the pitcher and catcher.

Ivan Rodriguez caught 46% of base-stealers throughout his career, topping out at a 60% caught stealing rate in his prime and leading the league in CS% in nine seasons. Knowing that stealing against Pudge is little more than a pipe dream for most, only the best baserunners would dare to attempt a steal. If this assumption holds, Rodriguez’s CS% would in fact be far more impressive than initially reported due to the level of competition he faces relative to a typical catcher.

To adjust for selection bias in stolen-base attempts, I developed an ELO model. For those unfamiliar, ELO ratings are a method of calculating the relative skill levels of players in zero-sum games. You might recognize ELO from chess rankings or FiveThirtyEight’s sports prediction models. These ratings can be used to directly estimate the probability of winning a match between two individuals or teams. The ratings change after each match, rewarding a win by an underdog more than a win by the favorite.

On a stolen-base attempt, the runner, pitcher, and catcher all play a major role in the outcome of the play. An argument could also be made for the importance of the fielder receiving the throw, especially when considering the select few who can make tags like this: Read the rest of this entry »

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 »