Archive for Meta Analysis

Earlier in the week, I went over how I set up a for a draft (Part 1 and Part 2). Last night I participated in the draft set up by Howard Bender and his Mock Draft Army.  The pre-draft work I normally go through may seem a little tedious, but after the dust settled, I look to start the season in good shape.

The draft was fairly standard 5×5 league with 23 rounds and 15 teams. The draft participants consisted of nine industry experts and six non-experts. I will give some thoughts on my draft results (full results), my final roster and the projected finish.

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Well, it is finally time to move on and show how I finish setting up for a draft (Part 1). I was hoping the following article was going to be in two parts and have a little more detail and polish. Instead I am have been dealing with a fan error on my computer and a pile of oil under my car. Neither is really resolved. So with a borrowed computer, I will step through the rest of my procedure. Let me know if you have any questions.

Note: For reference, here is my spreadsheet I used to get the initial SGP values and has the rest of the sheets referenced in this article.

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Two years ago, I conducted an exhaustive study of ESPN Home Run Tracker data. At that time, it was the primary tool I used to validate a batter’s power, before we got into the sexy new batted ball distance, and then combined that with SDD and angle. The short story is I found that hitters with an unusually high percentage of “Just Enough” (JE) homers saw their HR/FB rates decline the following year, significantly more than the rest of the player population. On the other hand, those who hit a high percentage of “No Doubt” (ND) homers maintained their HR/FB rate much better than the rest of the group.

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On Monday, I unveiled the xHR/FB rate equation I devised a year ago. Yesterday, I began my look back at 2014, discussing a selection of hitters whose xHR/FB rates suggest serious HR/FB rate upside this year. Today, I finish my look back, this time at the overachievers. These are the bust candidates, at least when it comes to home runs, assuming of course they sustain similar batted ball distances, average absolute angles and standard deviation of distances (SDD). Since my formula ignores home ballpark which absolutely plays a major role, I will mention it as a possible explanation for such overperformance as warranted.

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Yesterday, I unveiled the xHR/FB rate equation I devised a year ago. So today I’ll begin my look back at 2014 and discuss a selection of hitters whose xHR/FB rates suggest serious HR/FB rate upside this year, assuming of course they sustain similar batted ball distances, average absolute angles and standard deviation of distances (SDD). Since my formula ignores home ballpark which absolutely plays a major role, I will mention it as a possible explanation for such underperformance as warranted.

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Over the last couple of weeks a few of my fellow writers here are FanGraphs have been going over some draft strategies. This week, I am going to step through my exact process for a draft. On Thursday night, I will be joining a few fantasy writers for a 15 team mock draft. Over the next few days, I will go through the exact process I normally use to set up for a draft. If one of the other drafters steals borrows my info, fine. This is actually more a way to show you my process for drafts.

First off, I will be following the procedure I set out last season in this three part series. There is quite a few good links in those articles for the procedures I use.

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A year ago, I took our batted ball distance and HR/FB rate analysis to its final step. Armed with additional metrics from our in-house math ninja Jeff Zimmerman, I published an equation to predict a hitter’s home run per fly ball rate, which I cleverly dubbed xHR/FB. Unfortunately, the actual equation derived has been hidden behind the FG+ pay wall since. No longer.

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Last week, I tried to puzzle through something that has been bothering me – how depth affects replacement level. Consider outfield and shortstop. Last season, I might be looking at Josh Reddick and Adeiny Hechavarria as my first picks off the wire. I can do something with Reddick. In fact, I actually want to own Reddick. I like his offensive capabilities, even if he’s underwhelmed in recent performances. With Hechavarria, I would desperately seek a replacement.

The post wasn’t my best – I failed to communicate my message. My first gaffe was to create a pre-draft scenario. I probably should have gone with “in a vacuum” since my goal was to explore the possibility of trading $20 for $17. Despite my lack of clarity, the post did generate a lot of useful comments. Today, I’ll try to summarize and incorporate the dialogue.

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Yesterday, I dove into the three xHR/FB rate components to explain what fueled Todd Frazier’s home run surge. Today, I’ll dissect Miguel Cabrera, who set a new career low HR/FB rate, as it dipped from a career high of 25.4% in 2013 to just 14.0% in 2014.

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I am huge proponent of the pitch type work which has led to the Arsenal Score here are FanGraphs. One possible issue with pitch type data is a reasonable amount of data needs to be collected before any conclusions could be draw. I am going to take it a step further today and look for MLB ready fastballs knowing just the pitcher’s velocity and break. Just knowing how the pitch’s speed and trajectory, some conclusions can be drawn on how the pitcher will perform in the future.

This past summer, I found how to estimate a pitcher’s fastball ground ball (GB%) and swinging strike rate (SwStr%) knowing just the velocity and break. The ground ball rate was the same for all pitches while the swinging strike rate varied a bit. Well, I went into meld/average mode to come up with a method to find a simple way to determine how productive a fastball may be knowing its current break and speed.

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