Archive for Meta Analysis

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.

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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.

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Yesterday, I listed and discussed the hitters whose xHR/FB rates most exceeded their actual HR/FB rates, suggesting the potential for dramatic upside in the near term. Today, let’s check in on those hitters on the opposite side of the ledger — those whose actual HR/FB rates most exceed their xHR/FB marks. This is the group at major risk of regression.

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With about a month of the season in the bag, it’s finally time to analyze my newest version of xHR/FB rate unveiled last year. This is the best method publicly available for calculating an expected HR/FB rate, so ignore any analysis solely revolving around Brls/BBE or exit velocity. So let’s find out which hitters have posted xHR/FB rate marks at least 10% above their actual marks. These hitters are due for imminent improvement.

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A week ago, I discussed the American League starting pitchers who have improved their Z-Contact% (batter contact rate on pitches inside the strike zone) most versus 2017. Today, let’s take a look at the pitchers on the other side — those who have induced fewer in-zone whiffs, meaning their Z-Contact% marks are well above what they posted last season.

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Yesterday, I ran my newest xBABIP calculations, comparing it to actual BABIP. I discussed a slew of hitters who are due for an imminent BABIP surge given xBABIP marks that greatly exceed their actuals. Today, let’s check in on those hitters whose xBABIP marks are significantly below their actual marks. These batters are at serious risk of dramatic regression over the rest of the season.

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Last year, I unveiled the latest version of my xBABIP equation. It utilizes our Splits Leaderboard in order to account for defensive shifts, something that has never been reflected in xBABIP formulas previously. As a result, it does the best job we’ve seen so far in estimating BABIP. So let’s check in on early xBABIP marks and discuss those who have underperformed the most. These guys are due for an imminent surge as balls fall in for hits on a far more frequent basis.

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As it remains far too early to actually analyze results (like ERA and WHIP), let’s continue discussing the underlying skills driving those results. Those skills are significantly more important when projecting for the future. So let’s find out which American League starting pitchers have improved their in-zone whiff rate the most. In FG metric parlance, that means calculating who has reduce their Z-Contact% by the greatest rate.

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Yesterday, I discussed the early fly ball & line drive exit velocity surgers. Today, let’s dive into the decliners. Explanations for such a dramatic drop in exit velocity ranges from a small sample cold streak, playing through injury, or good old deterioration of power skills. If only we knew which of those explanations applied to each player!

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On Friday, I examined hitters with new plate approaches focusing on launch angle and plate discipline. I notice Brad Miller, Javier Baez, and Leonys Martin had each dropped their groundball rate but I didn’t have an idea of what is the ideal value. After spending way too much going down way too many paths this past weekend, the simple answer is average 20 degrees (32% GB%) with weak and/or fast hitters needing to push the value down to 15 degrees (42% GB%).

There is no need for readers to hang around any longer if they have more pressing matters like setting their daily or weekly lineups. For those looking for a little more explanation, thanks for staying around. I went through several methods I’m not going to discuss. I like simple useable answers and that’s what I’ll provide today.

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