Archive for Research

The Effects of Repeating Pitches on Pitcher Success Rate

It’s the bottom of the sixth inning at Minute Maid Park. Down 3-2 in the series and facing elimination, the Yankees are trailing the Astros, 4-2, in Game 6 of the 2019 American League Championship Series. Facing Yordan Alvarez with two out and nobody on, Tommy Kahnle needs to keep the game within reach to give his offense a shot at coming back. After falling behind 2-0 to Alvarez, Kahnle comes back and gets a strike looking on a changeup up in the zone. On 2-1, Kahnle throws a changeup below the zone and gets Alvarez to swing through it for strike two.

After getting a swinging strike and with the count now at 2-2, what does Kahnle do to try to get Alvarez out? Does he attempt to repeat the previous pitch after successfully inducing a swinging strike, or does he throw a different pitch in anticipation that Alvarez is expecting the same pitch? Kahnle repeated the changeup below the zone and got Alvarez swinging on strikes to keep the Yankees within two runs.

Pitch sequences like these are very intriguing because of the variety of factors that affect the at-bat, such as the pitcher and hitter’s game plans, game situations, and recent performance. It is a big reason organizations carefully study pitch sequencing. I wanted to quantify and analyze the effectiveness of situations like Kahnle’s against Alvarez. That is, I wanted to determine the most effective two-strike strategy for the pitcher after the batter swung and missed on the previous pitch. Organizations can then share these findings with their pitchers so that they have better success as a staff. Read the rest of this entry »


Reflecting on the Cubs’ Five-Year Run

Last month at The Athletic, Patrick Mooney and Sahadev Sharma took a deep dive into the Cubs’ successes and failures from 2015 through the conclusion of 2019. This led me to reflect upon this five-year run and try to figure out if, on the whole, it should be considered a success. They famously broke the franchise’s 112-year World Series drought, yet this Cubs team has seemed to leave baseball fans (especially Cubs fans) wanting more. When they took the league by storm in 2015 to the tune of 97 wins (a meteoric 24-win increase from the previous season), the baseball community was not asking if this group would win a World Series, but when. After the 2016 championship, we spent all offseason dreaming about how many World Series this group could claim over the next five years.

Of course, this Cubs group has yet to win another ring. Joe Maddon has left. Trade rumors have been swarming around the likes of Kris Bryant and Kyle Schwarber following reports of the Ricketts wanting to cut payroll. How do the Cubs pivot after this lost season? How much longer does Theo Epstein have to turn this ship around? These are the existential questions being asked in and around Wrigleyville after this season’s second-half collapse.

However, I would argue these questions are not totally fair. This Cubs core of Bryant, Anthony Rizzo, Javier Baez, and Jon Lester has led the team to post one of the best five-year runs in the National League since the inception of the Wild Card in 1994. The Cubs were swept in the NLCS in 2015 at the hands of the Mets, won the World Series in 2016, lost the NLCS in five games to the Dodgers in 2017, lost in the wild card game to the Rockies in 2018, and missed the playoffs this year. Those regular seasons, in order, consisted of 97, 103, 92, 95, and just 84 wins this past season. Simply looking at the playoff and regular season results, this does not feel like a completely dominant stretch of baseball. Read the rest of this entry »


Playoff Execution: A Look at Asdrúbal Cabrera’s Baserunning Error in NLDS Game 2

Each play in the playoffs holds extra weight compared to the regular season. An error can change a game, and a loss can doom a series. In close games and series, it is often the team that executes the small plays that comes out on top.

A particular play in Game 2 of the NLDS between Washington and Los Angeles stood out in this context: Asdrúbal Cabrera singled to right field, driving in Ryan Zimmerman. However, the throw from the outfield held up Kurt Suzuki at third base, and Cabrera was thrown out trying to advance to second base on the throw. Although the Nationals still won the game, the baserunning error was not inconsequential in the series.

Evaluating the Result with WE and RE24

Two statistics – Win Expectancy and RE24 – can be used to show why trying to advance was a bad decision.

Win expectancy (WE) is the probability a team will win given the specific circumstances. Greg Stoll’s Win Expectancy Calculator [1] shows how potential baserunning outcomes by Cabrera change Washington’s win expectancy in Table 1 below.

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The Nationals’ win expectancy before Cabrera’s single was 82.7%. The highest WE is 93.4% and results when Cabrera gets to second base, however, staying at first only decreases Washington’s win expectancy by 0.9%. In comparison, getting thrown out decreases their chances by 6.7% compared to staying at first. A 0.9% increase in WE is probably not worth risking 6.7%, especially in a playoff game where you have the lead. Read the rest of this entry »


Machine Learning Our Way to the Gold Glove Award

I love good defense. Watching a center fielder chase down what should have been a blooped-in single, instead creating a shocked reaction from the baserunner as he turns and realizes he’s out is priceless. That classic, one hand in the dirt, rest of the shortstop’s body flying through the air snag, is truly my favorite. I know what people say about the excitement of a home run and I get it. The rifle-like, cracking sound of bat on ball, closely followed by fans standing and cheering and spilling and spitting! God, I’m going to miss baseball this winter!

As the season comes to a close, we celebrate more than just home runs. We celebrate and award players for all their actions on and off the field. With that, it’s nearly time to award the best defensive players of the year with the Rawlings Gold Glove Award. There’s nothing like having a gold glover on your team and being able to watch them hold it down in the field all season long.

​Like many awards, managers and team coaches get to vote on the Gold Glove. Managers can’t vote for players on their own team and they have to stay in their own league. In addition, they have to vote for players who qualify (mostly needing at least 713 total innings) as laid out by Rawlings. It’s nice to have the men who are closest to the game voting and giving out these awards, but there must also be some quantifiable way to determine who is deserving. According to Rawlings, 25% of the vote is left up to metrics. Using the SABR Defensive Index, advanced analytics are now built into the award. This index includes:

– Defensive runs saved (DRS)
– Ultimate Zone Rating (UZR)
– Runs Effectively Defended
– Defensive Regression Analysis
– Total Zone Rating Read the rest of this entry »


Which Playoff Team Has the Most Former Players from Your Favorite Team?

When your favorite baseball team misses out on the playoffs, October offers a brief free-agency in fandom. Do you temporarily switch to root for a loved one’s team, or choose to root against a rival?

For me personally, I want former Rockies players to do well. It would be fantastic to see DJ LeMahieu lift the Commissioner’s Trophy, even if he’s now wearing the wrong color of pinstripes.

But do I really have to root for the Yankees? I wanted to see if I had any other option — to see if there was any other playoff team with more former Rockies.

I started with FanGraphs data showing player totals from every season going back to 2001 (CC Sabathia is the player on a 2019 playoff roster with the earliest debut, having come on April 8, 2001 for Cleveland). I also defined “playoff roster” as only including players who made an appearance for a playoff-bound team in September of 2019, so if someone hasn’t seen the field in the last month, they’re not counted here.

I didn’t want a player with only 15 ABs to have the same weight as a player with 1,000 at-bats, so I used each player’s “appearances” for their former clubs (plate appearances as a batter plus total batters faced for pitchers, including both regular season and playoffs). Read the rest of this entry »


Six-Trick Ponies: Could the Reds Do More with Michael Lorenzen’s Tools?

Michael Lorenzen is one of a kind. We’ve heard plenty of his multi-faceted accomplishments as of late. As far as pitchers go, the guy can mash. He was a two-way star at Cal State Fullerton, slashing .335/.412/.515 in his draft year, while posting 19 saves and a 1.99 ERA out of the bullpen. In the big leagues, he’s a career .267/.306/.514 hitter with seven home runs in 116 plate appearances. The hits haven’t come cheap, either. His 14 batted balls this season have had a remarkable 98.70-mph average exit velocity, despite him only barreling one of them.

Lorenzen can also move better than most. His top sprint speed of 28.2 mph in 2019 places him in a tie with Bryce Harper, Shohei Ohtani, George Springer, and Jorge Polanco, among others. His general athletic prowess means he’s been serviceable enough to play in the field with some level of frequency, converting all eight of his chances in 34.2 innings split over three outfield positions. On September 5th, the day after his historic performance, he even earned himself a start in center field for the out-of-contention Reds.

His Swiss Army knife capabilities aren’t the only reason he’s unique. Lorenzen is an outlier on the mound as well. Lorenzen has spent the last four years as a solid reliever, working to a cumulative 3.48 ERA over 290 innings out of the Cincinnati bullpen. After a failed run as a starter in his rookie season, he seems to have found his niche working in the later innings.

This is far from out of the ordinary. But while failed starters tend to simplify their pitch mix upon shifting to the bullpen, Lorenzen has doubled down on his six-pitch arsenal, throwing each of his four-seamer, sinker, cutter, changeup, cutter, slider, and curveball between 7% and 28% of the time. It’s rare enough for any pitcher to have a true six-pitch mix, much less a reliever. I ran a Statcast search to find every pitcher since 2017 (when Statcast arsenal data begins) to have thrown at least six pitches between 5% and 40% of the time, minimum 250 pitches per season. Lorenzen still stands out: Read the rest of this entry »


Walks, Strikeouts, and the Playoff Race

There are still a lot of teams that are fighting for a postseason spot while we wind down the season. As I watch a lot of games down the stretch, I hear many different announcers bring up the same thing. If you want to win championships, then you need to follow a key ideology that the Astros and Red Sox have both preached in their title-winning seasons: You need to be able to take your walks, and you need to be able to put the ball in play instead of striking out.

In 2017, the Houston Astros had the lowest strikeout rate as a team. They were able to do that while also having the league’s highest team isolated power. Last season, the Red Sox had the third-lowest strikeout rate in the league while having the ninth-best walk rate and fourth-best ISO. This got me to thinking about teams’ walk and strikeout rates. But I did not want to just look at it from the full season perspective. I wanted to compare teams’ rates from before the All-Star game to post-All-Star game (more specifically August 26th, because that is when I am writing this).

Thanks to FanGraphs, I was able to pull the data for the two date ranges and compare the numbers. Let us take a look: Read the rest of this entry »


Pitch Sequencing Trends in the Statcast Era

As the Statcast era continues to age, we baseball obsessives are collecting more and more pitches to analyze in countless different ways. MLB Advanced Media releases 90 different metrics for every pitch thrown, including a pitch’s classification, where all the defenders are standing upon the pitch being thrown, exit speed, launch and spray angle, etc. Analysts across the web, armed with this exhaustive data set, have been able to unearth previously unknowable mysteries regarding team and player performance and league-wide trends.

One area of pitching analysis that has been largely untouched by the public is pitch sequencing. Baseball Savant has done some work with visualizing how a pitcher sequences his pitches, but to my knowledge there is no way to look at pitch sequencing for the league as a whole and see which sequences are most used and most effective. I was curious how pitchers have attacked hitters since 2015 (the beginning of the aforementioned Statcast era), so I parsed through every pitch thrown in the regular season starting from the beginning of the 2015 campaign up until August 11th of this year. I looked at how pitchers have paired pitches during every plate appearance. I discarded pitches that were not thrown to the same batter or the same inning; a pitch that is thrown to end an inning followed by a pitch to start an inning should not be considered a sequence (same can be said for two different plate appearances). The sequences should be read as the pitch on the right precedes the pitch on the left. Now, let us look at the trends:

This chart includes all sequences that represent at least 2.5% of all sequences used in a given season. Every year, the most-used sequence is a four-seamer preceded by a four-seamer. Sequences involving a slider and a four-seamer have been used more every year in the Statcast era. In response to the league-wide trend of increasing launch angle, two-seamers and sinkers have been going out of style; we can see sequences involving these two fastball variants are also on the decline.

Read the rest of this entry »


A Better Understanding of Pitch Overlays

I make pitching gifs on a regular basis. In fact, there are dozens of other accounts on Twitter that do it as well. We participate in trying to help other fans understand what happens during plate appearances that go beyond what meets the eye. They can be great for seeing pitch shapes and how they contrast each other, but it’s important to know that there are some factors that can make them a bit deceptive (I myself have been guilty of making more out of an overlay that there actually is).

Overlays can be good for viewing how pitches move in relation to each other or noticing how different spin and axis affect the shape of a pitch. The Athletic’s Joe Schwarz is great at writing about and breaking that stuff down with the help of another gif-creating giant, ‘cardinalsgifs‘.

These two use gifs to demonstrate how a pitcher has made adjustments, for better or worse, and compare how it impacted the shape of their respective pitch. Having a good camera angle for that practice matters as we are less concerned about how the hitter sees the pitch and more about how certain tweaks can alter its personality.

Most MLB cameras do not lend themselves to a good visual representation of an event. You’re not getting the actual pitch shape nor are you getting the real trajectories from the hitter’s perspective. Even direct-level views (via the Braves, Marlins, or Orioles, to name a few) aren’t always beneficial, especially if you’re trying to make a point of how “filthy” or “nasty” pitches are to hitters. Read the rest of this entry »


playerElo: Factoring Strength of Schedule into Player Analysis

*Note: All numbers updated to August 12th, 2019*

Introduction

Consider the following comparison between Freddie Freeman (29) and Carlos Santana (33). Both players were starters for the 2019 All-Star teams of their respective leagues, and both are enjoying breakout seasons beyond their usual high production level, with nearly identical statistics across the board.

  PA wOBA xwOBA wRC+
Freeman, 1B 533 0.400 0.398 146
Santana, 1B 503 0.390 0.366 142

However, I argue that there is an underlying statistic that makes Santana’s success less impressive and Freeman’s worth MVP consideration. Recall the quality of competition of pitchers faced. The Atlanta Braves’ division, the NL East, contains the respectable pitching competition of the Mets (13th in league-wide in ERA), Nationals (15th), Marlins (16th), and Phillies (19th). Contrast this with the competition of the Cleveland Indians in the AL Central: The Twins (ninth), White Sox (22nd), Royals (24th), and Tigers (28th). Over 503 plate appearances, Santana has faced a top-15 pitcher (ranked by FIP) just 15 times, compared to 46 times by Freeman over 533 plate appearances. wRC+ controls for park effects and the current run environment, while xwOBA takes into account quality of contact, but all modern sabermetrics fail to address the problem of Freeman and Santana’s near-equal statistics despite widely different qualities of competition. Thus, I present the modeling system of playerElo. Read the rest of this entry »