Last year, I wrote a post which outlined the application of a K Nearest Neighbors algorithm to make pitch classifications. This post will be, in some ways, an extension of that as pitches will yet again be classified using a machine learning model. However, as one might have presumed given this post’s title, the learner of choice here will be a decision tree. Additionally, this time around, instead of classifying pitches thrown over the course of a single game I will aim to classify pitches thrown by a single pitcher over the course of an entire season.
What follows will be divided into three sections: a brief conceptual explanation of decision tree learners, a description of the data and steps taken to train the decision tree model of choice here, and finally a run-through of the model’s results. I am not an expert on machine learning, but I believe that this is an interesting exercise that (very, very basically) highlights a powerful model using interesting baseball data. The work to support this post was conducted in scripting language R and with the direction of the book Machine Learning with R by Brett Lantz. Read the rest of this entry »
Our beloved pastime has a long history of over-the-hill veteran players serving important mentor roles around the game, but the primacy of the Competitive Balance Tax and the perpetual crush of roster spot competition and “efficiency” has rendered these players largely moot. Players like the 40-something version of Jason Giambi, a bench bat for years on the strength of his contributions to his team as a leader beyond just his metric value, have grown frightfully rare. It is sad to see that sort of quasi-player/coach fade to memory.
As I look over the U.S. Olympic Roster, I see an awful lot of well-loved veterans who have lost a step over the years and, with that lost step, any serious hope of a consistent job under the new normal of roster construction. But I am convinced there remains value to the game of baseball to have players like Todd Frazier, Edwin Jackson, Scott Kazmir, and David Robertson around the sport beyond what they contribute to the back of the baseball card. A glance at the current free agent list reveals a small glut of other interesting, memorable players, such as Matt Kemp, Ryan Braun, Matt Wieters, and Neil Walker, to name a few. Read the rest of this entry »
When trying to determine a batter’s overall offensive value using a single statistic, one of the most popular metrics to use is weighted on-base average (wOBA). wOBA is calculated as a ratio of a linear combination of “outcome” statistics (unintentional walks, hit-by-pitches, singles, doubles, triples, and home runs) divided by, essentially, the number of plate appearances.
With that being said, could one predict whether a given player’s wOBA will be above a certain threshold using “process” statistics such as plate discipline and batted ball parameters? In particular, if we know a player’s zone contact rate, chase rate, and average exit velocity, could we predict with any confidence whether that particular player’s wOBA will be above, say, .320?
Using Statcast data and a bit of machine learning, I have decided to train a shallow neural network to try to do just that. I’ll be using snapshots of the Jupyter Notebook throughout the analysis to try and make it a little easier to follow. Read the rest of this entry »
As an ardent follower of the Baltimore Orioles, I’ve experienced a lot of bad baseball over the past few years, and one specific bit of bad baseball caught my eye recently. On April 5th, Shawn Armstrong returned from the paternity list after his wife gave birth just a few days before. He was continually demolished over the next week, giving up six earned runs in two innings of relief. He wasn’t getting too unlucky either, even if his FIP (20.12) was below his ERA (27.00).
As the parent of a three-year-old, I thought back to my first week after work following a month-long paternity leave. I was distracted, tired, and couldn’t wait to get home at the end of the day. Of course Armstrong got lit up, he just became a dad a few days before! Maybe professional athletes aren’t staying up all night changing diapers, but it stills seems like they would perform worse after a trip to the paternity list as they reorient their life. Is that true though? Do athletes perform worse after returning from the paternity list?
Fortunately, Baseball Prospectus tracks all paternity leave going back to MLB’s implementation of the policy in 2011. Instead of parsing through every season of data, I just focused on the most recent ones: 2017-2020. This still provided 115 different trips to the paternity leave list, enough to give an idea of trends and differences. I separated these individuals into pitchers (62) and hitters (53) to make for easier comparison. For hitters, I used wRC+ as my key metric and tracked it across 7-day, 14-day, and full season time frames. For pitchers, I used ERA and FIP and tracked those across the same time frames.
There are a few quick caveats. Occasionally players will make an appearance before promptly being demoted or ending up on the injured list. I’ve kept the same number for the 7- and 14-day span even if the player didn’t make an appearance during that time frame. This only accounts for six players (five pitchers and a hitter), a fairly small amount of the sample.
The impact on hitters isn’t quite as noticeable, without any clear trend. Hitters’ wRC+ is actually higher within seven days of a paternity list visit compared to their full season performance. There is an 8-point gap between performance 14 days after a paternity list visit when compared to the full season numbers, but it’s hard to see how this squares with the 7-day performance. None of the differences are statistically significant at the 95% confidence interval.
In summary, a trip to the paternity list doesn’t seem to have much of an impact for players. Maybe Shawn Armstrong was pitching badly just because he’s a bad pitcher; There’s a reason he’s since been DFA’d and passed through waivers. The performance for pitchers does still pique my interest, as there is a consistent trend when looking at 7-day, 14-day, and full-season performance across ERA and FIP. Despite this, the only statistically significant difference is between 7-day ERA and full-season ERA, far from anything conclusive.
The small sample (263.2 innings) and other confounding variables leave it far from conclusive in any direction for pitchers, especially given the other t-test results. It does appear, however, interesting enough to look at in a larger sample. A future quantitative analysis incorporating additional years of data may be able to provide more comprehensive answers. It may also be an area where qualitative research can provide answers on the impact of pitcher preparation, stamina, and overall performance.
Major League Baseball is awash in advanced statistics that more reliably describe key aspects of players’ offensive and defensive performance. It has been reported that through the use of Statcast, the MLB Advanced Media group can supply teams with 70 fields x 1.5 billion rows of data per season [i]. Yes, billion with a b. This flood of information has supercharged MLB teams’ and the sabermetric community’s development of ever-more useful statistics for describing player performance.
However, this amount of data brings significant challenges. Perhaps chief among them is that while certain individuals may be comfortable with reams of tables and ever-increasing numbers of descriptive statistics, many others prefer or require analyses and visualization tools that convert disparate metrics into informative and readily interpretable graphics.
MLB’s situation has certain similarities to the discipline of safety toxicology, where the use of high-information content assays for characterizing chemicals’ toxicological profiles has exploded [ii]. Drawing conclusions from multiple biomarkers and test systems is challenging, as it requires synthesis of large amounts of dissimilar data sets. One tool that toxicologists have found useful is the Toxicological Prioritization Index, or ToxPi for short [iii]. ToxPi is an analytical software package that was developed to combine multiple sources of evidence by transforming data into integrated, visual profiles. Read the rest of this entry »
Moving the mound back is a proposed solution to the ever-increasing rate of strikeouts in the modern game of baseball. The effect of moving the mound back one foot will be tested in the Atlantic League from August this year. Without the results of this test, we don’t know much about how this rule change could affect the delicate balance between pitchers and hitters. There are many unknowns such as:
In this article I aim to use my model of predicted pitch outcomes to investigate how moving the mound back may change the game. I’ve written previously about modeling the deadened baseball and I shall take a similar approach here. Read the rest of this entry »
An increasingly popular strategy for drafting pitchers is taking ones with plus control and underwhelming fastballs, with the idea being that the club’s player development team can coax out a velocity jump. Intuitively, this makes sense since it is relatively easy to develop velocity and relatively difficult to improve control. Being able to get a pitcher from control-only to one with above-average stuff and plus control, and suddenly you have a solid rotation arm out of an org-depth pitcher.
However, skill improvement does not happen in a vacuum, and there are potential side effects to this strategy, namely that control might end up getting worse as velocity increases. In general, these end up being fine tradeoffs since we are in a power-oriented offense, but investigating these effects is important in evaluating how valuable this strategy is, which is what I do in this article. Read the rest of this entry »
Rebuilding has become the popular way for MLB franchises to construct a World Series contender. Considering the league’s structure of compensating the worst teams with the best draft picks, it seems like a viable strategy to maximize your losses in order to obtain the services of the best amateur talent available. The Astros and Cubs are two of the more recent franchises to successfully cap their extensive rebuilding process with a World Series victory, and both franchises acquired top-10 draft picks for several years before they turned the corner and became champions, but how often does this strategy work and how long does a rebuild take?
If an organization’s strategy is to not win games right away, when do the fans and ownership realize that the rebuilding process has failed and that their team is in the middle of a downward spiral of ineptitude? I am sure there are fans of the Pittsburgh Pirates and Kansas City Royals from the 1990s and 2000s that know how difficult it is to build a contender and cringe whenever they hear the term rebuild. Hopefully this article can provide a reasonable timeline for contention and an objective overview on how a franchise’s rebuilding effort should be progressing.
For my dataset, I gathered the GM or President of Baseball Operations for each organization since 1998. I chose 1998 because it was the first year the league consisted of 30 teams and it also happened to be the first full season for the current longest-tenured executives, Billy Beane and Brian Cashman. If an executive’s tenure with the team started before the 1998 season, their entire tenure was included in the dataset. This means Braves GM John Schuerholz’s regime is measured in its entirety from 1991-2007 and not just from 1998-2007. Read the rest of this entry »
After starting to look at some inning-by-inning data from my baseball win expectancy finder for another project, I stumbled across something weird that I can’t explain. Here’s a graph of expected runs scored per inning:
Check out how high the bottom of the first inning is. On average, 0.6 runs are scored then compared to 0.5 runs in the top of the first. That’s a huge difference! Let’s look closer:
Holy outlier, Batman! So what’s going on? Here are some ideas:
Read the rest of this entry »
“This player is having a good year, but his xwOBA is slightly lower than his wOBA, therefore he’s going to get worse.”
This is a common concept you’ll hear within the baseball analysis community. With the data made available to us, it’s easy to come to conclusions like this. However, it’s not always about the data made available to us, but the analysis that comes from it.
To better grasp how this “problem” of data analysis came to fruition, let us go back in time.
Starting in 2015, the public was provided with Statcast metrics for MLB players via Baseball Savant. Among those stats were exit velocity, launch angle, hard-hit rate, pitch velocity, sprint speed, and, to be honest, practically anything that can be measured! It’s a fabulous website that provides very useful information we should be exceptionally grateful for.
The most popular metrics on the website, however, are their expected stats: expected batting average (xBA), expected weighted on-base average (xwOBA), expected on-base percentage (xOBP), expected slugging percentage (xSLG), and expected isolated power (xISO). Essentially, these statistics are what you’d expect based on the name; they indicate what a player’s “true talent level is” based on the quality of their contact, frequency of contact, and, depending on the batted ball, sprint speed.
This would appear to be a gold mine on the surface. With the ability to know what numbers a player deserves to have, we should be able to separate their talent level from outside circumstances, and thus better predict future performance. Yet that actually isn’t the case. Read the rest of this entry »