Archive for Meta Analysis

Steamer and I: Mike Trout – A Review

Today, I start my reviews of my Steamer and I series that happened to cover mainly outfielders (not by design). The series compared my Pod Projection to the Steamer projection, as I discussed the players I was either significantly more bullish or bearish on compared to the system. We start with Mike Trout, who I was far more bearish on than Steamer. It was an interesting exercise for me since I had no idea I was in that position to begin with. Diving it to determine why was an enlightening activity.

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Determining Hitter Profile Changes

Right now I am in the middle of writing preseason player profiles for several publications. Like most people, I examine a player’s stat page for most of the information. Additionally, I have access to our database and have created some cheat sheets to help with looking for possible changes in a hitter’s profile to help explain stat changes like a significant bump in home runs. Today, I’ll start with making my hitter sheet available and go over a few players with the information.

I find hitters to be the tougher than pitchers to determine if changes in production are from a new approach or from just plain luck. To help find such changes in approach, I determine changes in several metrics which would point to an overall talent adjustment. The stats I examine are:

Swing based
Pull%
GB%
Hard%

Eye base
BB%
K%

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Reviewing 2016 Pod Projections: Xander Bogaerts

It’s shortstop week, so let’s get the ball rolling on a player I was notoriously bearish on heading into the season. I wasn’t technically pessimistic about Xander Bogaerts, I just felt that he was being massively overvalued by fantasy owners. Oops, I was wrong. Though I did get some things right. He was a particularly difficult player to project as his batted ball profile completely changed from 2014 to 2015. So much so that he appeared to be a totally different player. Which version of Bogaerts would show up in 2016? It was anyone’s guess.

Let’s see how my 2016 Pod Projection compared to his actual results.

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Minor to the Majors: More on Pitcher Grades & Control

A couple of weeks ago I made an initial stab at projecting a pitching prospect’s MLB value knowing their prospect grades. I failed miserably, but got some ideas in which to move forward on. Today, I will test some of those theories and see if I can tease out any information.

From the previous study, I found the following on pitcher grades.

1. Pitchers who are a few years from the majors are likely to change quite a bit before for they reach the majors. They could have arm surgery, find command, or add a pitch. Early grades have too much noise to be used for future results.

2. Fastballs are almost exclusively graded only on velocity. For a soft-tossing prospect to get a major call up, their fastball is likely better than just the velocity shows. They likely have plus movement and/or command of the pitch.

3. Almost all pitching prospects will get stuck with a future 50 control grade because getting graded less than 50 is a death sentence for their progress. Not all major league pitchers have average control. Exactly half don’t, so the control grade needs to be adjusted.

4. From some discussions with people in the game, I found that having one or two plus breaking balls can help carry a pitcher with an average fastball.

With these limitations, I decided to relook at pitchers by implementing the following guidelines for this study.

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Projecting Byung-ho Park – A Review

Heading into the 2016 season, two of the biggest questions we asked related to expectations for the newest arrivals from the KBO League of South Korea — Byung-ho Park and Hyun Soo Kim. Though obtaining historical statistics was easy, translating them from KBO to MLB is a challenge. At the beginning of February, I laid out my process in creating a projection for each, by first obtaining their career statistics, turning them into ratios, and then translating them as best I could into a 2016 MLB projection. Since this is first base week here at RotoGraphs, this article will focus on Park, with Kim’s review coming when we move on to outfielders. So let’s review Park’s performance and how that compared to my forecast.

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Reviewing 2016 Pod’s Picks & Pans: First Base

Yesterday, we published the final 2016 first base rankings and dollar values. So let’s review my preseason Pod’s Picks and Pans at the position, where I identified those hitters I was most bullish and bearish on compared to the rest of my fellow RotoGraphs rankers.

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The 2015 Hitter xBABIP Overachievers – A Review

Yesterday, I reviewed my 2015 hitter xBABIP underachievers and the results were fantastic. Today, I’m going to review the flip side, the 2015 hitter xBABIP overachievers. Let’s see how this group performed.

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The 2015 Hitter xBABIP Underachievers – A Review

**To avoid confusion, yes this is supposed to read 2015. It’s a recap of my preseason article that discussed the 2015 xBABIP underachievers.

Today I continue to work my way through my preseason article recaps, this time focusing on xBABIP, the equation that the intelligent and very handsome Alex Chamberlain brewed up. We’ll start with the biggest underachievers from 2015. Let’s see how they performed this season.

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Minors to the Majors: Calculating Individual Pitch Grades

When I started this series which attempts to determine the projected fantasy value for prospects, I knew today’s step would be the hardest. The issue was converting various pitch grades (and control) into a workable framework for a pitcher’s overall production value. I thought I may not end up with a workable answer, but the following results have promise beyond just grading pitches.

I was able to piece together work from various articles and gave each pitch a grade based on the ERA scale. Combining per-pitch-ERA’s with a control value, it looks like we can estimate a pitcher’s overall value.

A pitching prospect is usually given a value on each of their pitches and a command and/or control grade. For example, the MLB.com’s grades for their top rated pitcher, Lucas Giolito, are:

Pitch: Grade
Fastball: 80
Curveball: 70
Changeup: 55
Control: 55

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ESPN Home Run Tracker Analysis: The 2016 HR/FB Upsiders – A Review

Yesterday, I reviewed the list of players my ESPN Home Run tracker analysis suggested had significant HR/FB rate downside this season. So today I’m going to check in on the group of players I identified as possessing serious upside. I would imagine this group would have done well simply due to the league-wide power surge. Let’s find out if that was indeed the case.

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