In the winter between the 1980 and 1981 baseball seasons, one of the best catchers of all time informed his club, the Cincinnati Reds, that he would no longer catch more than two days each week.

But what if Reds general manager Dick Wagner, the man who dismantled the Big Red Machine, took exception to the demand, and dealt with Bench like he did Tony Perez, Pete Rose, Joe Morgan, and Sparky Anderson?

“If Johnny wants to come to the Phillies, I’ll be happy to find another position.”

The words could have been considered tampering. The speaker could not have cared less.

The speaker was Pete Rose, doing what Pete always did, having fun with the sportswriters. Why not? His Phillies were world champs, and there was no reason to think they couldn’t repeat, just like his Reds teams did in the mid-70s. Back then, he had one of the greatest players at his position alongside him in Johnny Bench, just like he did now in third baseman Mike Schmidt.

The Phillies didn’t really have room for Bench, what with the solid Bob Boone behind the plate, Schmidt at third, Bake McBride in left (with young Lonnie Smith ready to take over), and the newly arrived Gary “Sarge” Matthews in right field. Sarge had averaged over 20 home runs and 70 RBIs across the four years before for the dreadful Atlanta Braves as one of the few bright spots for that woeful franchise.

Pete was about to turn 40, but he felt strong. His knees were still good, and as long as he had those, he felt like he could not only play, but play at the high level to which he’d grown accustomed.

He didn’t really think much of his comment — but when it made it to the papers in Tampa, Reds GM Dick Wagner thought about it. A lot. Read the rest of this entry »

There are a few articles, including one by Travis Sawchik, arguing that tie games might not be as bad for baseball as you think. The truth is that not only would ties have had no impact on who reached the postseason in 2018, but they would have shaved off four minutes from the average time time.

Using regular expression to parse box score data from RetroSheet, I’ve looked at how the 2018 season would’ve been different without extra innings. Here’s a look at the postseason standings as they were compared to how they would’ve looked with ties (scored 3 points for a W, 1 point for a T, and 0 for a L):

With ties, the 2018 postseason still has the same cast of characters, although the Dodgers and the Rockies would have swapped places in the NL West, causing the Dodgers to go to the Wild Card game.

That’s only looking at 2018. When examining the past five seasons, I found that the postseason implications of tie games would be pretty minimal.

In the plot below, each point represents one team’s season. The X-axis is the number of games that would end in ties and the Y-axis is the number of places a team would’ve moved in their division.

For simplicity, I’m defining postseason implications (PS Implications) as a team missing or making a Division No. 1 or Wild Card No. 1 or No. 2 with the scoring system described above.

Read the rest of this entry »

The infield shift is a much-maligned defensive strategy, hounded as one of the worst analytics-based changes to baseball. Multiple times each season there will be some conversation about banning the shift, and each time pros, ex-players/managers, commentators, and analysts will chip in with their two cents. But for now, the shift is here, and it is as popular (with the fielding teams) as it has ever been. Just under 26% of all pitches were thrown with some form of infield shift in place in 2018, 22% of at-bats had a shift for the entirety of it, and 30% had at least one pitch shifted.

As you can see, left-handed hitters are far more likely to be shifted than their right-handed counterparts, with 46% of left-handed ABs seeing a shifted pitch versus 19% for righties. This makes rudimentary sense as the shifted players for a left-hander are closer to first base, so they have a greater chance of impacting the play to first and therefore stopping a potential single.

I have taken players who have 100-plus at-bats in 2018 both against a shifted and non-shifted infield, then I compared the outcomes. There were 132 such players, and their combined number of at-bats was 72,389 (39% of the seasons total). I have split these up into four categories based on the handedness of the batter and the pitcher.

Read the rest of this entry »

The Braves have a slew of young stars, including the electric Ozzie Albies. Albies is a speed demon on the bases, a gold-glove caliber second baseman, and a force to be reckoned with at the plate. He got off to a blazing start in 2018, hitting .281 with an impressive 20 homers in the first half, earning him a trip to the All-Star Game. Albies’ luck was not so good in the second half, as his average dropped 55 points to a lackluster .226 to go with four homers. This day-and-night difference makes it hard to evaluate what exactly Albies will be, but his second-half struggles have people looking for answers everywhere. One of the most common questions raised is related to how his switch-hitting impacts his consistency at the plate. Let’s look closer at his splits.

In 2018, Albies looked like two different players when you compare his production from the left side of the plate to the right side.

From the left, where Albies gets most of his at-bats, he struggled tremendously in 2018. From the right side, his numbers looks like those of a perennial All-Star. His splits averaged out to a respectable-but-not-amazing .261 average with 24 homers and a perfectly league-average 100 wRC+ over the course of the full season. Read the rest of this entry »

Andrew Miller has an undeniably nasty slider. As a Red Sox fan, I remember it far too well from the 2016 postseason. Big Papi’s farewell tour didn’t seem all that fair when you consider the way the Red Sox ran into the buzz-saw that was Miller and the Cleveland Indians. Sure, I’m grateful for Miller helping the 2013 version of the Red Sox win a third world title since 2004, but come on Andrew, you had to ruin Papi’s goodbye?

With Miller’s recent signing with the St. Louis Cardinals, I found myself exploring his FanGraphs page. I stumbled upon this article, Andrew Miller on the Evolution of his Slider, and I instantly began to wonder if pitchers had similar experiences developing their sliders in the 2018 season. The first step in this analysis was to evaluate the evolution of Miller’s slider.

What jumps off the page is the change in velocity. Miller saw a 4.6 mph increase in his slider from 2011 to 2012, then another 3 mph added from 2012 to 2013. This in large part had to do with Miller moving from a starting role to a relief role during his time with the Red Sox. Given that information, however, an increase in velocity that drastic not only shows a pitcher’s willingness to adapt, but also a pitcher’s ability to adapt. By observing Miller’s slider splits, we see that ability to adapt almost immediately.

Read the rest of this entry »

Heat. Smoke. Velocity. Stuff. Gas. Cheese.

I’m sure there are other words to describe our beloved “fireballers” (see, there’s another one). Pitchers who throw at high speeds are treated like fine china — see Stephen Strasburg in the 2012 postseason. I’m guilty of falling victim to the allure of a 98-mph fastball, regardless of its location. We love it, and, frankly, we’d like to see more of it. Major League Baseball has created a setting in which if a pitcher doesn’t break 90 mph with his fastball, he’s considered a “finesse” pitcher, or even a “soft-tosser” if left-handed. We love strikeouts, especially when a power pitcher blows a fastball by a hitter. But why?

Matt Harvey was stellar in 2015. He’s not so good anymore. Why do teams keep giving him second chances? Mostly because he throws hard.

However, it’s not entirely our fault. After reading Thinking, Fast and Slow by Nobel Prize-winning psychologist Daniel Kahneman, I began to understand why this happens. It shows how you can overcome cognitive bias, but in order to do so, you have to understand which one of your “thought systems” is making that decision for you. He explains that each human being has essentially two modes of thought.

System 1 – fast, instinctive, and emotional (gut feeling)

System 2 – slower, more logical (critical thinking) Read the rest of this entry »

When Statcast was launched, we were graced with incredible new stats such as Exit Velocity and Launch Angle, which revolutionized how we evaluate hitting. This new information confirmed obvious things like that Giancarlo Stanton hits missiles, but it also gave us a new breed of hitter. Daniel Murphy, Justin Turner, J.D. Martinez, and others looked at the data and made adjustments that started maximizing their power outputs. The standard evaluation method has become to look at EVs mixed with LAs to determine who is one tweak away from stardom. Hitting is a complex beast, with pitchers throwing 95-plus with nasty hooks to go with shifting defenses. Ultimately, a hitter is looking to produce solid contact regardless of where the ball goes. The goal of this analysis is to identify hitters who have an inefficient spray chart and see how they could optimize their profile by hitting more balls in a different direction to maximize production. Luckily with Statcast, we can now try to find these answers.

To do this analysis, I used Baseball Savant to gather 2018 Exit Velocity and xwOBA to Pull Side, Straight Away, and Oppo Side for all hitters with at least 50 plate appearances. I then used FanGraphs to pull the 2018 data for Pull%, Mid%, and Oppo% to discern how often a hitter attacks that field. I used 50 PAs as a filter since this is about where exit velocities become stable and helps weed out pitchers and other noise. This does create gaps in the data because some players didn’t register 50 PAs of a batted-ball direction. This dataset gives us the ability to look at how hard a hitter hits the ball to a field, what was their expected damage (xwOBA) to that field, and how often they went that way.

The first category I looked at was players who could use the opposite field more often. To do this, I looked at players who had an above average Oppo Side xwOBA and a below-average Oppo%. I used exit velocities to each field as a proxy to justify the directional swing change. Read the rest of this entry »

Far and away the most expensive current player on the Padres is Eric Hosmer, who has proven to be quite disappointing so far in his short time with the team. He’s due $21 million next season, which is three times the second-highest salary on the club. For a team that will almost certainly never have a payroll that compares with larger market teams, that’s a significant price to be paying.

Although he was signed in February of 2018 to be a cornerstone for the team, he promptly put up a mere -0.1 fWAR last season. He’s always been an inconsistent performer year-to-year, and this campaign was no exception. How could a player who put up 4.1 fWAR in value in 2017 see his level of play drop off so quickly in the following season? Can he bounce back in 2019 as he often does?

To begin, we need to know what held him back last season as a hitter. Hosmer’s a first baseman, so the majority of his value is going to be derived from his bat. Here’s a table showing the differences in his offensive production between the 2017-18 seasons:

Read the rest of this entry »

This article was originally published on my blog, cargocultsabermetrics.com. 

Developing a new pitch can be a great way for a pitcher to have a breakout season. In 2018, we saw big improvements from Trevor Bauer adding a slider, Adam Ottavino adding a cutter, and Patrick Corbin adding a curveball. A new pitch can sometimes be the missing puzzle piece when trying to figure out why a player is good and not great. For Jose Berrios, a cutter might be that missing piece.

Examining Berrios’ arsenal

Jose Berrios has one of the nastiest curveballs in baseball. Instead of having the typical downward break associated with curveballs, Berrios’ curve averages 15.5 inches of glove-side break. This results from Berrios imparting gyro spin (think bullet spin) on his curveball rather than 12-6 top spin. Because of this, Berrios generates close to no vertical break caused by Magnus force, which is just a fancy way of saying the only drop we see on Berrios’ curve is due to gravity rather than top spin.

To pair with his curve, Berrios has a four-seam fastball which generates 9.5 inches of arm side run and 16.5 inches of upward vertical break due to Magnus force (causing the pitch to drop less), a two-seam fastball which generates 16.5 inches of arm-side run and 11.0 inches of upward vertical break, and a changeup with 14.5 inches of arm-side run and 5.5 inches of upward vertical break (the changeup will drop even more than the fastball since it is thrown slower and gravity will have more time to bring the pitch down). Read the rest of this entry »

With so many complex statistics out there, I wondered if there was an easier way to project winning percentage or runs, a way that is simple yet more complex than Bill James’ classic Pythagorean Win Expectancy. To create a statistic like that, I would have to create one comprehensive stat for offense and one for pitching. Ultimately, I came up with the following and named them “Run Value” and “Pitching Run Value,” respectively.

RVAL = ( ( TB + BB – SO )/4) + RBI + HR  

PRVAL = ( ( ( H + BB – SO )/4 ) + HR) x FIP

These two metrics are used for teams. In the batting RVal formula, the higher the better. I tried to get down to the pure number of runs that a player or team produces by using the very relaxed definition of a run being four bases. In the pitching PRVal formula, the lower the better. I did something very similar to the batting stat by trying to get the pure run total. I then put the two stats into the win expectancy formula:

RVALWinExp = RVal^1.83 / ( RVal^1.83 + PRVal^1.83)

I then ran a program in R to see how closely this stat correlates to actual team win percentage for all teams from the 1998 season through the 2018 season. In addition, I tested to see how Bill James’ win expectancy formula correlates to team win percentage over the same period of time. The results are below. Read the rest of this entry »