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Rick Lucks breaks down how to use Pitch Info data to identify pitching sleepers and busts as his series on making sabermetrics more accessible to fantasy baseball managers continues.
One of the most fundamental questions in fantasy sports is if a player's current performance is sustainable. More than any other sport, baseball has a slew of statistical measures that can be dissected in numerous ways to analyze player performance.
Pitch Info is a publicly-available pitch tracking system that provides a lot of data to help fantasy managers make this determination for mound breakouts and busts alike. Unlike many of the other metrics we've looked at in this series, Pitch Info data stabilizes (or becomes predictive) very quickly so you can use it early in the season. Let's look at how to effectively use this data to give you an edge in your fantasy baseball league throughout the season.
The first step is finding it. Basic Pitch Info information such as pitch selection and velocity can be found toward the bottom of a pitcher's page on FanGraphs, but you'll have to use the Pitch Info tab for more detailed information. Hit the Splits tab located on the banner toward the top of the page and then choose the option on the far right, Pitch Info. That will bring up three tables that are loaded with information, the third of which is most important for fantasy purposes.
Be sure to check all of our fantasy baseball lineup tools and resources:- BvP matchups data (Batter vs. Pitcher)
- PvB matchups data (Pitcher vs. Batter)
- Who should I start? Fantasy baseball comparisons
- Who should I pickup? Fantasy baseball comparisons
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- MLB Props finder tool
- Fantasy baseball closer depth charts
Interpreting Pitch Info Data: Velocity
The first data point to understand is velocity. Generally speaking, a pitcher that loses fastball velocity is losing something to either an undisclosed injury or the aging process. Pitchers that gain velocity can expect to increase their production. For example, Shohei Ohtani posted the best pitching numbers of his MLB career in 2022 with a 15-9 record, 2.33 ERA, and 2.65 xFIP in 166 IP, all backed by career-best 33.2 K% and 6.7 BB% marks.
One reason why is that his fastball velocity spiked from an average of 95.7 mph in 2021 to 97.3 mph last year. The pitch generated more whiffs (9.1 SwStr% vs. 8.3% in 2021) as a result. Furthermore, Ohtani felt more comfortable throwing it for strikes (62.4 Zone% vs. 56.7% in 2021). Perhaps most importantly, the increased fastball velo helped the rest of Ohtani's arsenal play up. His slider (14.4 to 18.2), split (24.9 to 25.4), curve (8.1 to 12.1), and cutter (9.8 to 11.7) all saw SwStr% improvements last year.
When evaluating a pitcher's velocity, you should always look at his baseline velocity as opposed to an arbitrary league average. Ohtani has always thrown a "good" fastball, but his velocity spike allowed him to take his game to a new level. Other variables like movement and location matter, but velocity is a good introduction to using Pitch Info data.
Interpreting Pitch Info Data: Pitch Mix
Slightly more advanced is pitch mix, or what pitches a pitcher throws and how often he throws them. A pitcher may improve his production by abandoning a poor pitch or developing a new one. This is a good stat to consult if a pitcher sees a sharp change in his K%, as a change in pitch mix could represent the change in approach that supports the new number. If the change does not have a corresponding pitch mix shift, it may prove less sustainable.
It wasn't just a velocity spike that propelled Ohtani's stellar season. He also made a substantial pitch mix change, throwing more sliders (21.9% in 2021 to 39.3% last year) and curves (3.7% to 8.8%). Unlike most sliders, Ohtani's offers a great Zone% of 57.4, meaning that it can both set up other pitches and put a batter away with its 18.2 SwStr%. Ohtani leaned into this and made his slider his most thrown pitch, forcing opposing batters to deal with an elite offering more often. Is it any surprise his K% increased?
That meant Ohtani threw fewer fastballs (44% to 27.7%), cutters (12.1% to 8.8%), and splitters (18.3% to 11.5%). He still featured them all though, and throwing the hard stuff less may have allowed Ohtani to put more zip into each heater.
The same type of analysis may be performed for several other stats, including BABIP, FB%, LD%, GB%, and HR/FB. All of these variables may be considered over a pitcher's complete pitch mix to determine how good he could be without relying on any conventional metrics. This can be good for identifying sleepers, as pitchers that have one or two standout pitches could break out by simply using them more often.
Interpreting Pitch Info Data: Pitch Results
What is the baseline for this type of analysis? As strikeouts have increased throughout the league, the benchmark for what constitutes a good SwStr% for each pitch type has shifted. The following data from Eno Sarris provides the 2022 average for most pitch types:
Fastball: 10.4 SwStr%
Sinker: 7.2 SwStr%
Cutter: 12.3 SwStr%
Slider: 16.4 SwStr%
Curve: 12.2 SwStr%
Change: 15.9 SwStr%
The fastball will generally be inferior in results to pitches that do not need to live in the strike zone, as hitters typically fare better on pitches in the hitting zone. However, getting ahead in the count is necessary to make those put-away pitches work as intended, making (sometimes) mediocre fastball results a necessity.
It is dangerous to generalize, but 2-seam fastballs and sinkers tend to stink for fantasy purposes. They're usually hit harder than fastballs. They may post strong GB% rates, but also have high BABIPs and scary triple-slash lines. Any sinker hit in the air was probably a mistake, so the HR/FB rate is usually high for the limited number of fly balls hit against them. Their SwStr% rates also tend to be poor. Overall, fantasy managers usually prefer a straight four-seamer or a cutter to be the "zone pitch" in a pitcher's repertoire.
Unicorns like Ohtani's slider are the exception, not the rule.
Conclusion
To conclude, Pitch Info tracks a lot of data of interest to fantasy managers, including average velocity, pitch mix, and individual pitch results. All of this data may be used to predict who will break out or which breakouts can sustain their current performance. If you would like more analytical tools to help you dominate your leagues in 2023, stay tuned!
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