Archive for Meta Analysis

Early this offseason, I determined that Mike Matheny doesn’t hate stolen bases, but could be a boost to the Royals stolen bases. At the time, I noted to check on Joe Maddon’s tendencies but never got to it. Then, my podcast mate, Rob Silver, basically begged me to run the same analysis for Dusty Baker. After that, one of my other team owners brought up Joe Girardi and Gabe Kapler. I was done talking to people before every manager needs to be analyzed on their stolen base tendencies. The following are the numbers on the four and the results were a little surprising.

To examine the managers’ tendencies, I compared how the baserunner’s tendencies changed with or without the manager in question. There were three groups of hitters to examine

1. The hitters who were on the manager’s team and then on a different one that same season.
2. Hitters who were on a different team the season before or after a season on the manager’s team.
3. The hitters who were on the manager’s team the season before or after his tenure started and ended.

Also, I combined all the values for an overall rate.

Read the rest of this entry »

Finally, it’s time for the main event! After weeks discussing the various xHR/FB rate components and reviewing 2019 performance, we now set our sights toward 2020. Today, I will identify and discuss a handful of fantasy relevant names that underperformed their xHR/FB rates most significantly. Remember that this doesn’t automatically mean we should be projecting a higher HR/FB rate this season. But perhaps rather than take the hitter’s actual HR/FB rate at face value, we should substitute our xHR/FB rate mark when reviewing his historical marks and making a 2020 projection.

Read the rest of this entry »

Last week, I identified and discussed a slew of hitters whose xHR/FB rates validated their surprisingly strong HR/FB rates. Today, let’s look at some of the disappointing hitters in HR/FB rate that were validated by a dramatic decline in xHR/FB rate. Remember, an appearance here doesn’t mean these hitters should be projected to repeat their disappointing 2019 marks, as this is merely a backward-looking analysis. We need to account for additional factors when making a projection. But all else equal, the guy with an xHR/FB rate validating his disappointing HR/FB rate should be expected to have a worse chance of rebounding than the guy who suffered a disappointing actual mark, but underperformed his xHR/FB rate.

Read the rest of this entry »

Last spring, an industry consensus emerged – if you wanted to win, you needed to invest early and often in starting pitchers. When a consensus becomes too commonplace, a profitable contrarian approach is often available. And so I explored the possibility of finding an advantage by investing in cheaper pitchers. I dubbed it nuLIMA. To be certain, it was meant as theorycrafting, not gospel. Check it out if you’d like to revisit an old idea that might still have legs.

The setup to that article still applies.

Read the rest of this entry »

Fantasy baseball season is heating up. Dynasty drafts are escaping the early rounds, NFBC ADPs are starting to get a serious sample size behind them, and the interminable football season finally ended when Tom Brady didn’t see his shadow. If you’re waiting to get started on your 2020 baseball prep, well… stop waiting! Get crackin’.

Read the rest of this entry »

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.

Read the rest of this entry »

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.

Read the rest of this entry »

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.

Read the rest of this entry »

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.

Read the rest of this entry »

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.

Read the rest of this entry »