Archive for Meta Analysis

Quickly identifying legitimate breakout hitters is tough. Is the hot streak just that, a streak or is something more? With pitchers, it is easier to find the breakout’s cause. New pitch. Added velocity. Improved control. These traits can be seen in a single start after facing 25+ batters. A hitter has only about five plate appearances a game to display a new skill. It’s a different world with them. Today, I am going to try to find a simple process with a few key stats to focus on.

With hitters, their data contains so much noise, especially once the ball is put into play. To get rid of some of this noise, I started to find with the following stats:

• Power: Hard%, HR/FB
• Plate Discipline: BB%, K%, O-Swing%, Contact%
• Batted Ball Distribution: Pull%, Flyball%

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Publicly available Statcast data is just over two years old. Eric Logenhagen posted his first set of prospect grades before this season started. I have decided to say screw it to small samples and see how well Eric’s power grades match up with exit velocity number. Even with the limited sample, the results ended up fairly consistent.

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This past weekend I spent two days in Boston at the Saber Seminar. As always, it is a great event and here is some the fantasy relevant information I extracted from it (full list of presenters and abstracts).

Note: My notes got a jumbled so there may be a chance the information I attribute to a speaker is incorrect. I apologize to the speakers in advance if I made this mistake.

Rich Hahn: Q and A

He broke some big news at the conference which he stated that Reynaldo Lopez will be starting for the White Sox this weekend against the Royals.

Will Carroll: Saving the Pitcher, 2017: A Data Driven Approach

He was promoting the use of the Motus Sleeve to help measure short and long term elbow stress. He discussed that each pitch has different levels of elbow stress depending velocity and pitch type. As for predicting injuries, I wonder if some general injury guidelines can be created, especially incorporating Motus sleeve data. I’m going to investigate this idea further. If anyone else is interested, let me know and I may be able to share some ideas and/or acquired needed data.

Mike Reinold: An Update on the Effect of Weighted Ball Training on Arm Stress, Range of Motion, and Injury Rates Read the rest of this entry »

I’m going to start by picking on Jon today. In a recent article on Deep League Stolen Base Options, he wrote the following in the comments:

Nice thoughts, but are any of these guys actually available in any “normal” (e.g. AL or NL-only) deep leagues? I don’t think there’s any useful way to use ESPN ownership rate as a proxy for deep league availability. I’m sure there are many players with a sub-1% ownership rate that are taken in nearly every “only” league.

I get his point that in 12-team ‘Only Leagues, there are no players on the waiver wire at 1% ownership, yet alone 5%. In Jon’s case, his best option to get steals is probably from making trades. There is a transition depending on league size where team rebalancing transitions from waiver wire pickups to trades.

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I was wrong a few weeks back when I wrote:

With the new juiced baseball, home runs rates are up, and they are the main driver for the scoring increase. If a pitcher can limit home runs, they will be affected less and the easiest way to do this is to generate ground balls.

By using OPS for and against, the production flyball pitchers have gotten worse over the past 2.5 seasons. By examining the problem using the difference between both FIP and xFIP to ERA, I found the opposite answer and can’t explain why.

I expected this article to go a different route than it did. When I wrote that owners should focus on groundball pitchers, I knew I wanted to eventually determine how much the difference between ERA and both FIP and xFIP shrunk for extreme flyball pitchers.

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Yesterday, I took the first dive into the intriguing waters of Statcast’s Sprint Speed metric. After comparing correlations with other speed-related stats to Spd Score, I now want to see which players are actually faster and slower than Spd would have use believe. Because Spd uses some context-dependent metrics, it could sometimes give us a false impression of a batter’s true raw speed.

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This is a long one. I appreciate your patience in advance.

Mike Podhorzer, I and sporadic others have — but primarily Mike has — carried the torch on developing ‘expected’ metrics, such as xBABIP (expected batting average on balls in play), xHR/FB (expected home run-to-fly ball ratio) and xK% (expected strikeout rate), all and the rest of which can be found here. For the uninitiated, these xMetrics help describe how a hitter or pitcher should have performed based on various measurements of the events that unfolded and typically are more predictive of future performance than the original metric. They’re not perfect, but, like other advanced metrics, they give us a better understanding of player performance and ability.

Each metric — xHR/FB, xK%, etc. — has formulas for both hitters and pitchers, with the hitter metrics typically having stronger correlations than those for pitchers. Unfortunately, pitcher xBABIP has always eluded us. It’s inappropriate to repurpose hitter xBABIP for pitchers, but it’s because the model coefficients (weights) would be different, not because the theory underpinning the model is flawed.

That’s the problem, though: hard hits, line drives, infield fly balls — these all should affect a pitcher’s BABIP allowed. Our intuition begs it to be true. Yet there’s a resounding lack of evidence that suggest otherwise. The correlation between BABIP and hard-hit rate (Hard%), line drive rate (LD%) and infield fly ball rate (IFFB%), among others, borders on nonexistent:

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In April, Statcast quietly introduced a new speed metric dubbed Sprint Speed. It wasn’t until late June that Baseball Savant made the leaderboards publicly available and we now have data going back to 2015. I have been meaning to dive into the data to find any incremental value, and finally the day has come. From the leaderboard page, the metric is described as thus:

Sprint Speed is Statcast’s foot speed metric, defined as “feet per second in a player’s fastest one-second window.” The Major League average on a “max effort” play is 27 ft/sec, and the max effort range is roughly from 23 ft/sec (poor) to 30 ft/sec (elite). A player must have at least 10 max effort runs to qualify for this leaderboard.

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Since 2013, I have polled you dashingly attractive readers on which group of pitchers you think will post the better aggregate ERA post all-star break. The two groups were determined based on ERA-SIERA disparity, pitting the underperformers versus the overperformers during the pre-all-star break period.

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Yesterday, I identified a group of hitters worth buying for their home run upside, given the discrepancy between their actual HR/FB rate and xHR/FB rate. Today, I move over to batting average, as I identify the hitters whose xBABIP marks most exceed their actual BABIP marks. These are the guys to target for batting average that you may be able to get at a discount.

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