Sprint Speed values have been publicly available for a few seasons. While I see it mentioned for this or that, I don’t know how predictive it is or if should I care about it at all. After analyzing the data, Sprint Speed might need to be ignored in favor of Time-to-First. The stopwatch still rules.

The key, in my opinion, is if the ability to run fast can be predictive in any way. No one that I know of is playing in a Sprint Score league, so the speed with have a secondary effect. If a player is running slower, do their stolen bases drop? How about how many infield hits they can leg out? Generally, how will the players change in speed affect their stolen bases and batting average.

One factor to keep in mind is that the aging curve for stolen bases is just a drop with all humans reaching their peak sprinting speed in their early 20’s.  There are going to be a lot of negative speed values coming up but that’s just aging pulling players down.

A second factor to remember is that teams are not allowing hitters to run as much. In 2015, there were over 2500 stolen bases league-wide. Last season, the value was under 2300 for a 9% decline. Again, more negative numbers.

Sprint Speed was first introduced in 2015 at Baseball Savant (links to Time-to-First values) and it is widely cited. Sprint Speed is not the only measured speed metric available. For one fewer season, Baseball Savant has each hitter’s run times to first base which have been the traditional measure of a player’s speed and it’s still used in scouting players. With the two metrics, it’s table time to what conclusions can be drawn.

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Yesterday, I identified and discussed a smattering of hitters who made surprising appearances near the top of the barrels per true fly ball (Brls/TFB) leaderboard. Today, we flip to the opposite end of the list, moving to the laggards. These are going to be fantasy relevant guys you never expected to appear closer to the bottom of the leaderboard than the top.

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New year, new deserved barrels metric. In October, I took a crack at devising a “deserved barrels” metric in which I took the basic components of a barrel — a hitter’s exit velocity (EV) and launch angle (LA) — and determined the capacity in which the components relate to Statcast’s barrel rate metric (barrels per batted ball event, or “Brls/BBE %” on Baseball Savant). I included squared terms (EV², LA²) assuming the relationship is not linear. (A launch angle that’s too steep is detrimental, for example.)

Further offseason research led me to additional insights:

• Launch angle consistency (“tightness”) is important, correlating with higher BABIPs and more repeatable EVs.
• Launch angle grade in general is important, especially for batted ball quality, not just batted ball frequency, as indicated by batting average on balls in play (BABIP).

There exist many measures of contact quality; barrel rate captures how often a hitter produces high-quality contact. (Hard-hit rate functions similarly but ignores launch angle, to my knowledge, making barrel rate arguably superior.) It only made sense, then, that the latter finding above — that launch angle tightness matters to batted ball quality — should be incorporated into my deserved barrels work somehow.

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Yesterday, I discussed surgers in the final important component of my xHR/FB rate equation, FB Pull%. Today, I’ll move on to the decliners. What follows is a list of the hitters whose FB Pull% declined by at least 10 percentage points from 2018.

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The last major component of my xHR/FB rate equation is fly ball pull percentage (FB Pull%). Since hitters generally can generate more power to their pull side and distance along the lines are always shorter than toward center, a higher pulled fly ball rate is almost always better for HR/FB rate. Pulled fly ball rate is a skill, as I calculated soon after revealing my xHR/FB equation, so a change is worth noting. That said, as usual, regression toward individual averages are always inevitable, so typically the batter enjoying a spike or enduring a decline reverses courses and moves back toward their average the following year. Remember that when reading this list and commentary.

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I was wrong. About seven years ago, I wrote on how hitters may overperform their projections since they played through an injury. The injury hampered their production in the season in question, lowered the future projection, and created a buying opportunity. For years, I believed this steadily until last season when I re-ran the numbers and found “jack squat”.

Earlier this week, I examined some of this past season’s hitters who fought through the pain and felt a deeper analysis was needed. I dove in and the results were backwards. I found no bounceback should be expected from hitters who played through injuries, but there is more. For those hitters who play through the discomfort, their future production will take a major hit.

The key to uncovering the following results was getting a usable dataset which is easier said than done. Many of the injuries I’m using for the analysis aren’t well documented, if at all. Real men play baseball and they play hurt because that is what real men do and most importantly, they don’t complain about. Besides the machismo, a player has every right to keep his medical data to himself so vagueness thrives. Simply, there is no good available data. Even with the hurdles, I dug into each of the hitters who were reported to have played through an injury the past three seasons (2017, 2018, 2019).

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On Monday, I laid out my thought process for selecting Mike Trout first overall in 2020. To summarize, we have several legitimate choices. Absent secret information, they’re all a shrug away from the same, albeit in slightly different shapes. Trout stands out because he’s been so consistently good for so long. The alternatives all have shorter track records or a chance to fall out of the top 30 players – even without an injury. By comparison, we know a healthy Trout is a monster fish.

Today we’ll take a peek at all the players you could reasonably select over Trout on draft day. Let’s toss them into a few buckets. ADPs are from NFBC.

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I’ve had a semi-fixation on hitters playing through injuries and how the diminished production could hamper the next season’s projection. At first, I found some correlation. Then, I didn’t. One possible answer to there being no bounceback is that the injury becomes chronic and the hitter never improves. Or the dataset could be too small.

I want to dive further into the subject, but the information around injuries is sketchy at best. Most of the time, there are no usable details. The lack of an answer means that I should stop coming back to the subject but I’m stubborn.

Very.

I’m going to go through this past season’s hitters. The dive has a couple of goals. One is to create a better dataset for future reference. The second is to understand why some hitters may have struggled when creating a profile. And just maybe, I’ll find out if I can put to rest the notion that hitters who played through injuries are under projected.
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There’s a debate going around Twitter. If you want some cheap likes and retweets, try dropping this poll:

Who would you pick first overall?

Mike Trout

Christian Yelich

Ronald Acuna

Other

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Last year, I took a stab at developing what might be loosely defined as park factors using Statcast data. (I called them park “impacts” because they lacked the requisite rigor to be true factors, although it’s all semantics, truly.) I sought to use Statcast’s expected wOBA (xwOBA) metric, specifically on batted ball events (BBEs), such that we would have a measure of xwOBA on contact (or xwOBAcon). This metric accounts for exit velocity (EV), launch angle (LA), and little else — which makes it perfect for this purpose.

The difference between actual and expected wOBA on contact indicates the amount of luck, whether good or bad, a hitter might have incurred on a particular batted ball event. In other words, given ‘X’ exit velocity and ‘Y’ launch angle, what is the most common wOBA outcome, and how much did the actual wOBA outcome differ from it?

The beautiful part about xwOBAcon is it strips away all other context. It removes elements that confound other park factor calculations, such as hitter and pitcher quality or even sequencing (vis-à-vis run-scoring). Except for fielding. Can’t control for fielding, unfortunately.

With this approach, we have the exit velocity. We have the launch angle. We have historical results for that particular combination of EV and LA to use as a benchmark. And then we compare.

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