In my last article, I did a Quick Look at Freddy Peralta and found his fastball fascinating. He manipulated the pitch to provide the look of different pitches by changing his grip. What I wanted to know is if he could get by with just a good, varying fastball.

First, everyone needs to take a trip over to Peralta’s game page at BrooksBaseball.net and examine the pitch groupings. Usually, different pitches form clusters when examining variations in break, velocity, and spin. Ignoring the possible changeup, he has two groups, fastball and curve. His fastball has an estimated spin which varies from 1300 rpm to 2500 rpm. Its velocity differs from 88 mph to 97 mph. The spin values on Brooks are interpreted based on the ball’s break. The spin rates may be off because Peralta releases really close to home as Jeff Sullivan documented.

Read the rest of this entry »

Freddy Peralta

• I got swamped last night during my chat on Peralta questions. I just didn’t know much about him so I went and watched his debut.

• It looks weird in that he is nearly falling down on every pitch and just averaging 90 mph on his fastball. It reminds me of high pitchers trying to reach 85-mph.

• Fastball: It sat at 87-95 mph with some nasty glove-side run. It could be considered a cutter at times. He used it like Mariano Rivera did by changing the spin. Partway through the game, MLB Gameday started labeling some of his pitches as cutters. It was not two separate pitches since there was no unique spin-velocity grouping

Read the rest of this entry »

A week ago, I examined how prospect rankings could add more context to hitter projections. It’s time to take the research a step further by dividing up the prospect list to see if projections can be refined. And they can be.

Initially, I shied away from dividing up the prospect lists because the sample size quickly gets into single digits. I started dissecting the data hoping to keep reasonable sample sizes. I sort of achieved my goal.

I used the same parameters in the last article. I compared a hitter’s Steamer projected OPS (on-base percentage plus slugging percentage) from 2010 to 2017 to the actual results in their debut season. To designate prospects, I used Baseball America’s top-100 which has been compiled since 1990. I collected the average and median change in OPS. The median value helps to smooth out any major outliers.

Read the rest of this entry »

Rarely is OPS (on-base percentage plus slugging percentage) a fantasy stat. It’s off many people’s radar but it’s widely available and closely mimics a position player’s overall hitting talent. While other stats (e.g. wOBA and wRC+) also give a hitter an overall value, these stats aren’t available at every website. Most sites have their own unique blend but OPS is commonly available. Because of this availability, I’ve been using it as a baseline in recent articles on adjusting projections based on prospect pedigree and when hitters get platooned ($$). Now, it’s time to use OPS to help predict the individual categories.

The process I used for this study was to simply see how much various stats changed when OPS changed a certain amount. For rate stats (e.g. batting average) the conversion is straightforward. For counting categories, I put the stats on a per 600 plate appearance scale. Additionally, I only compared data from 2015 to 2017 during the current “juiced” ball era. I know the process is not close to being 100% precise and that is fine. I’m just trying to create general adjustments and can look to hone the process later. I’m putting in 20% effort to get 80% of the answer.

Read the rest of this entry »

My focus is on lineup position and the amount of regular play each hitter get. I’m not concentrating on positions played. Also, if a team isn’t listed, I didn’t find any new information.

Note: I highlighted what I consider to be the seven most important findings.

Angels

• Zack Cozart’s value is taking a major hit as he’s been drawing the short straw when Shohei Ohtani DH’s.

Astros

• Evan Gattis owners may need to start looking for a replacement as his struggles with the bat (.187/.260/.275) has led to him starting three times in the last 10 games.

Read the rest of this entry »

As a Royals fan, I try my hardest to not roster any to make sure I limit my hometown bias. Two bats, Alex Gordon and Jorge Soler, are heating up to the point they are being rostered in 15-team leagues and even some shallower ones with Soler. It’s time to perform an unbiased examination of the pair.

Alex Gordon

All I’ve been able to hear when Alex Gordon’s name is brought up is:

“F’ it, I guess I’ll take Alex Gordon.”

One of my league mates blurted this statement after struggling to locate an available outfielder in my home AL-only league auction.

Read the rest of this entry »

My current aim in fantasy baseball is to find instances where player evaluations can be improved. With several prospects recently getting called up, I am trying to answer the simple question: is there any projection information to be gained from being a highly touted prospect. The short answer is yes, but it took me a while to get good results.

I wanted to keep the analysis simple so I used all available Steamer projections which to back to 2010. Additionally, I used Baseball America’s top 100 ranked prospects for that time frame. From these two data sets, I compared the hitter’s projected results to the actual results for their first few seasons.

Read the rest of this entry »

Last week, I wrote about the expected power from JaCoby Jones and Lewis Brinson. In the article, I mentioned the hitter’s rank compared to other hitters with no context resides just the rank. Today, I correct this flaw in my analysis by finding the league averages and putting the data on the 20-80 scouting scale.

While overall ranks do provide some information, it’s tough to put the rankings into context. Nelson Cruz is first in average exit velocity (EV) at 97 mph. Dropping down 2 mph in exit velocity is Luke Maile at #10. Two more is Jacoby Jones at #26. And another two is Francisco Lindor at #75. The first 4 mph in drop was just 26 players while the next 2 mph was 49 players. The batted ball decline rate is not linear and just a few tenths of a mile-per-hour can jump a player 20 spots in the rankings.

I need a way to label hitters and had to invoke some math. I took the hitters with 100 batted balls per season from 2015 to 2017 and found the overall average value. Using the 20-80 scouting scale, I gave the average values a 50-grade.

Read the rest of this entry »