This is the second installment of my Unlikely Pairs series. Last week I looked at Mike Trout and Freddie Freeman and their elite offensive production. This week we’ll be aiming a little lower in the draft, and maybe it will be more controversial.

Domingo Santana v Shin-Soo Choo.

These two players are on opposite ends of the career spectrum. Santana, 25 years old, just put up great numbers in his first full season in the majors.  Domingo Santana is a bit of a late bloomer, having spent full seasons in both AA and AAA, both with the Astros, prior to advancing. His AA season was particularly mediocre, as he suffered deep drops in both walk rate and batting average. The following season, age 22 in AAA, his walk rate and batting average both bounced back, but he was only given a total of 18 major league plate appearances. In mid 2015, still in AAA, he was traded to the Brewers as part of the Carlos Gomez/Mike Fiers trade. In 2016, Santana injured his elbow and missed most of the season. So, 2017 was his first real chance in the majors, and he certainly ran with it, hitting 30 homers, stealing 15 bases, and achieving 126 wRC+.

Meanwhile, Shin Soo Choo, 35 years old, enjoyed a bounce back year after an injury plagued 2016 campaign.  Choo established himself as a solid and reliable player in 2008, and put up consistent 20 HR, 80 R, 60 RBI seasons from 2008 through 2015 with two exceptions. Read the rest of this entry »

Most people in fantasy baseball have tiers of players. You likely believe that certain players are roughly interchangeable, and others are definitely superior or inferior to one another. Sometimes the differences between players appears so vast that it is obvious where you would draw the distinction, and other times it feels more fluid and dynamic. Intuitively you might feel there is a strong distinction between the two tiers, but it may be difficult to find a precise line in the sand.

In the past few days I have been going through my xStats, looking at where certain players fell on certain metrics. In doing so, I have noticed several pairs of players. Players who have performed very similarly on several different metrics. Some of these pairs, arguably, cross skill tiers. And boy do I love when players cross tiers. Read the rest of this entry »

Well, the season is over, and it is time to review Bold Predictions. In my midseason check in I wrote the following to describe my position on Bold Predictions:

I think these should be fun and spark conversation. As a result, they should probably be controversial, at least in part. There should be a reason for each pick, and the reason shouldn’t necessarily be built on a strong hypothesis. If you have a solid reason to believe something then you aren’t talking about a bold prediction, it is just a normal prediction. Bold predictions should be built on a questionable foundation, that’s the fun part.

Today I will not get into the deeper reasons for each prediction, you can read the mid season check in to find those. Instead, let’s get down to brass tacks. How’d I do? Read the rest of this entry »

Earlier this year I developed two closely related stats which I called OUTs and bbFIP. I’m reasonably proud of these two stats, as I feel they do a pretty good job capturing the skill of each player. They account for the numbers of weakly and strongly hit balls, balls that have high home run rates, strikeouts and walks.

In other words, it accounts for every aspect of bat generated offense, ignoring base running ability. However running speed is used to judge whether batted balls are weakly or strongly hit for each individual batter. For example, a batted balls by Billy Hamilton may be near automatic singles, whereas they would be almost guaranteed outs if hit by Albert Pujols.

The formula is constructed as follows:

$\mathrm{OUTs}=\frac{\left(.77\times \mathrm{W}\right)+\left(.17\times \mathrm{K}\right)-\left(.98\times BB\right)-\left(.69\times \mathrm{HBP}\right)-\left(1.52\times \mathrm{S}\right)-\left(2.52\times s\mathrm{HR}\right)}{\mathrm{PA}}$

Where W = weak contact (xOBA ≤ .245), S = strong contact (xOBA ≥ .634), and sHR = strong home runs (xHR% ≥ .55).

You can convert this OUTs score to an ERA scalar by multiplying by -11 and adding a constant (~5.4). This will give you what I call bbFIP, a version of FIP that is superior to standard FIP both in season and between seasons. You can also find an offense’s average OUTs score by weighting each batter by their number of plate appearances, and then translate that number to the ERA scalar to figure out how many runs you might expect them to score through the course of a season.

There are a few things to keep in mind:

1. Lower numbers are better. I tried to build this concept into the name, so it is easier to remember. It is called OUTs, outs are bad, whoever has the least of them is the best.
2. The average score is about 0.1. This season it is closer to .09.
3. The standard deviation is about 0.1.

As the 2017 regular season is coming to a close and we begin to gear up towards the 2018 season, I have a few preliminary OUTs and xOBA projections. These projections haven’t yet baked in the aging curve, so maybe ‘projection’ is the wrong word to use here. Either way, I have selected what I refer to as the ‘significantly above average’ projections in terms of OUTs. In other words, anyone who has a score less than 0. I’ve also supplied Z-Scores, xOBA, and xOBA Z-Scores. Read the rest of this entry »

Earlier this week Alex Gordon hit a home run. Okay, sorry, that’s a mean joke. I am referring to the MLB single season home run record, of course. There have been more home runs hit this season than in any season in Major League Baseball history. Whether this is a good or bad thing is a matter of debate. I’ve heard from many people who are unhappy with this turn of events, others who are quite pleased, and a small minority who have no strong opinion on the matter. That third group seems to be rather small, but that’s the way of things these days.

It appears that this home run record has been spurred on by artificial means, namely an apparent change to the ball. The exact change is difficult to pin down, unfortunately. On some level it deals with the flight characteristics of the ball, though. What some call the coefficient of drag. Essentially, the air isn’t slowing down the ball as much as before. There are conflicting reports as to why this might be the case. Some say the radius of the ball is smaller. Others say the seams are smaller. Maybe both are true, maybe neither. Maybe there are other variables. It is all very much up in the air at this point. But, we do know that the ball is flying ever so slightly further now, in 2017, than it was in, say, 2015. Or 2014. Or 2013.

Perhaps counter-intuitively, very small changes in flight distance can make rather large differences in home run rate. Perhaps more intuitively, differences in flight distance can be represented as changes in effective velocity. Read the rest of this entry »

On Wednesday Jeff Sullivan wrote a piece about the ‘flyball revolution’, which I am sure many of you have already read.  Today I want to expand upon what he wrote, and tie in a theory for what may be going on. My theory is probably wrong, but it ties together a few of the articles I’ve written recently (1, 2, 3, 4,) and expounds on my current thinking on the Statcast data.

I know I’ve been touching on the topics of exit velocity and launch angle a lot recently, but I think it is important to understand and begin integrating into everyday analysis. Before anyone can use that analysis, we as a community need to build up an intuition for how the stats relate to one another. This is new for everyone.

Read the rest of this entry »

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 »

Giancarlo Stanton has gone crazy recently, hitting 32 home runs in 48 days. Last I checked, anyhow. It could be up to 36 by now, you never know with that guy. There have been many talks about his MVP consideration, as well there should be. However, Stanton is not the only guy in that Marlins lineup who is hitting the cover off the ball. Oh no, you have Christian Yelich raking behind Stanton, and then Ozuna behind Yelich.

In the second half, Yelich is batting .295/.387/.530 with a .389 wOBA. Marcell Ozuna is batting .292/.378/.522 with a .379 wOBA. Both of these players are sitting high among the second half offensive leaderboards, Yelich 25th and Ozuna 34th. Together with Stanton, the oft forgotten Derek Dietrich, and JT Realmuto the Marlins have 5 of the top 112 batters in the second half, including 3 of the top 34 and, of course, numero uno.

This is the offensive production the Marlins expected to see throughout the course of the entire season. Unfortunately for them, it took a few months for this to gel, in large part due to the relative poor performance of Christian Yelich in the first half.

I am not saying Yelich was terrible, because he wasn’t. He was average in April and May. But Yelich isn’t on the team to be average, he is a core piece, and his performance over the past month and a half shows just how dynamic he can be for a ball club. In April and May, though, he was not hitting nearly as well as he is right now. So let’s see if we can find a reason. Read the rest of this entry »

Over the past two years we have had many conversations about batters increasing or decreasing launch angle. Obviously, these conversations have been brought about by our brand new launch angle stats, which only recently became publicly available. However, launch angle remains a difficult concept to fully grasp. What properties determine launch angle? How do batters control it? Can the batter control it?  These are all very valid questions which I think everyone has struggled to answer, including coaching staffs, players, analysts, and fans.

Yesterday Eno Sarris put up an article touching on this topic, in which he explains his journey towards wrapping his head around the phrase “go get the ball”. In doing so he posted two images that were generated using HITfx data, and he briefly mentioned that one may use a bit of math to make a few deductions from this data. Well, that is the topic of this article. I aim to take these images, compare them to the Statcast information we have for launch angle, and reverse engineer the contact point for batters. Read the rest of this entry »

The relationship between batter exit velocity and league exit velocity is not fully understood. There are many factors to exit velocity that a batter controls. Some of these are physical, such as bat speed and swing path. Others are more psychological, such as pitch selection. However, the batter certainly does not control everything. Pitch speed, for example, is a big factor. There are also environmental effects, like temperature and humidity.

I have been working on this problem with Eno Saris for some time now, bouncing ideas, building small projects, and examining the results. Some of it has been fruitful, others have fallen flat, but each time I feel like I’m getting closer to an answer, and along the way I have accidentally bumped into useful nuggets. Today I want to share one of those nuggets with you. I call it adjusted Exit Velocity, and it is the result of combining and comparing batter exit velocity, league exit velocity, pitch location, and pitch speed.

Yesterday, Eno Saris wrote a bit about our findings, which I suggest reading. Today I wish to explain the methodology, delve a bit into the findings, and conclude with how it may be useful to you going forward. Read the rest of this entry »