Predicting bat speed using the publicly available Statcast data is easier said than done. For much of the past few years there was a section on Baseball Savant which displayed a bat speed number of each player, but without much explanation for how it may be calculated. I haven’t inquired for an explanation, but I feel rather comfortable saying it was probably a derived stat using a formula published by Alan Nathan.

This formula takes the pitch speed and batted ball speed, and manipulates them using laboratory tested values for the various relevant coefficients–basically the bounciness of the ball and the bounciness of the bat. If you assume values for those coefficients, you can get a rough estimate for bat speed by plugging in the pitch speed and batted ball speed.

I don’t have proof that this is how bat speed was being estimated by Baseball Savant, but I feel it is the most likely explanation for the numbers.

Two weeks ago I proposed a formula for estimating future exit velocity using past exit velocity and launch angles. This method is far from perfect, and there is a whole lot more research that can be conducted into this area.

Over the past week I have been thinking about what performance changes may or may not be predictable from one season to another. Part of the variance that we see from season to season are large dips or climbs in offensive production, which often in retrospect we might be able to explain. Maybe there were signs that pointed towards decline, but we overlooked them for one reason or another. Maybe we didn’t know what the signs meant until further research had been conducted.

No doubt, these mistakes are often due to a lack of information. In some cases it may be bat speed. We don’t really know how much of a role bat speed plays between seasons or during the course of a career. We don’t know how injury plays a role with bat speed, nor do we understand the aging curve. Read the rest of this entry »

A few days back, I start the process of trying to find breakout hitters. I found some possible traits which point to hitters breaking out but didn’t get into the stickiness of the stats over different time frames. I’m back to see how the “breakout” stats main their values over time.

For a quick review, here are the claims I made in the previous article.

Overall, here are the rules.
• K%-BB% (plate discipline) changes by +/- 4.5%.
• Flyball rate (FB%) changes by +/- 3%.
If the above two items can’t explain the change move onto the following three points.
• Pull% change (only) by +/- 5% this value can good or bad depending on the hitter’s other traits.
• Raw power can start decline once a player reaches 30-years-old.
• BABIP changed by +/- 30 points. (A change in plate discipline can cause this change)

I will just start walking through the points comparing the results for the year after the breakout. Also, I will look for hitters breaking out in the season’s first month and how those stats carried forward.

Read the rest of this entry »

Yesterday, I shared with you a trade I made in the keeper league in which I took over for an inactive team a couple of months ago. It was a classic dump deal, with me acquiring who I considered one of the top two keepers in the league — super prospect Yoan Moncada. We’re still in the middle of August, so we’ll have another month and a half worth of Major League stats with which to evaluate Moncada and put together a 2018 projection. But I don’t want to wait a month and a half and you probably don’t either. So let’s take a stab at an early 2018 projection, along with a dollar value for that stat line.

Read the rest of this entry »

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%

Read the rest of this entry »

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.

Read the rest of this entry »

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 »

Over the past three years I’ve had a lot of fun studying quality of contact stats, and I’ve tried to put everything I have learned into xStats.  This uses exit velocity, launch angle, running speed, park factors, temperature and more to measure a player’s offensive performance and displays this information in the form of basic slash line items to make it easy to understand for any baseball fan.  You’ll see stats like expected batting average, slugging, BABIP, and wOBA. (xAVG, xSLG, xBABIP, and xOBA, respectively).

Recently I’ve run 7 day, 30 day, and 60 day stats, giving an insight into players who have been out or under performing their game results in recent weeks. Today I want to touch on a few of the batters I find particularly interesting, and I’ll include a little table showing a few other notable players as well. Read the rest of this entry »

I’m not sure of the exact Sleeper and the Bust podcast but Paul and Eno were discussing Jeff Samardzija and how his ERA would look better without a Colorado start. Right now Samardzija’s ERA is 4.31 but if the seven run, five inning start in Colorado is removed his ERA drops to 3.81. I’m not a fan of removing starts from a pitcher’s stats. The good and bad will happen. The deal is that Samardzija owners probably sat him that week like I did in Tout Wars. If owners aren’t going to use pitchers in their Colorado starts maybe owners should start ignoring the stats generated there.

The increase scoring environment in Colorado is about impossible to overcome. In an article at Rotowire ($) last year, I stated the following:

What we do know is that the road pitcher should expect between a 1.00 and 2.00 increase in ERA. I might put the ERA drop near 1.50, but I could understand if an owner wants to use a smaller or larger number. Additionally, I will put the K/9 drop near 0.9 and the WHIP increase at .25.

Those increases are just too much to absorb over an entire season. Sure an owner can get away with a start every now and then but eventually, the odds will even out. Once the hammer drops, an owner will spend a couple weeks trying to recover their overall rate stats.

Read the rest of this entry »

As a Royals fan, I had my doubts Jorge Bonifacio would be a major league contributor. Over the past couple of seasons, my opinion has changed as he showed some power in AA in 2015 (17 HR) and AAA in 2016 (19 HR). I fostered some reservations on the AAA power because he played in the offensive happy Pacific Coast League.

He’s started 2017 off great with 3 HR and a 10% HR/FB ratio in AAA before getting his major league call-up. Since the promotion, he’s hit six bombs with a 29% HR/FB rate. Owners may be expecting some heavy regression from Bonifacio but they shouldn’t. The “juiced” ball era has reversed a trend of position players hitting for less power once getting a major league promotion.

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Back in early April, I wrote a Five Under 50% post in which I implored fantasy owners to invest in the likes of Nick Franklin and Dan Altavilla. I urged readers to steer clear of Marwin Gonzalez, and promised that Edwin Diaz’s job was as safe as they come.

Naturally, Franklin has a 38 wRC+, Altavilla has a 6.60 ERA, Gonzalez has a .419 wOBA, and Diaz is no longer the Mariners closer. Sorry about that.

Before you quit reading this post because, really, why should you trust me, just know that my predictions weren’t all terrible. I also forecasted success for Scott Schebler (.365 wOBA), Mark Reynolds (.419 wOBA), and Ryan Zimmerman (.469 wOBA), who have been among the best hitters in baseball this year.

Not every gamble will pay off. But when it does, it can transform a fantasy team. It’s been about a month and a half since my last Five Under 50% post, and with renewed confidence, I’m prepared to do it again now.

Below are five (actually seven; I cheated) players owned in less than 50% of Ottoneu leagues whom I think may be worth an add in most leagues. There’s no need to jealously yearn for these players later when you can win them now for pennies on the dollar.

1. Chris Taylor (2B/SS/3B; 39%) Read the rest of this entry »