Yesterday, I used Jeff Zimmerman’s recently published xBABIP table to identify five trade targets, with the underlying assumption that they have suffered from poor BABIP fortune, which is likely to reverse over the final two months of the season. Today, I will check in on some hitters whose BABIPs are far above their xBABIP marks and could be in for quite the decline over the rest of the season. As such, they are perhaps strong sell candidates.
On Friday, Jeff Zimmerman published the calculated xBABIP marks using his new equation incorporating Inside Edge data. While we are well aware that we remain far away from a perfect xBABIP equation as some commenters pointed out what’s missing, it’s still a useful metric to analyze. So I have curated a list of hitters whose xBABIP marks are significantly higher than their actuals and could represent good acquisition targets for the final two months of the season.
This past offseason, I found an xBABIP equation which correlated better than just BABIP when looking at season 1 to season 2 values . By using the the new Inside Edge data and player speed score, I kept the process simple yet accurate. I have tweeted out the results a few times during the season, but it is time for another full updated list.
The Inside Edge data tracks the normal bunts, grounders, fly balls and line drives. In addition to the four groups, they classify the batted ball into weak, medium and hard contract. For the xBABIP equation, I looked at all the line drives and all the hard hit fly balls and grounders. Additionally, speed is a component of getting on base so Bill James’s Speed Score is also added into the calculation.
I bought Mike Trout for $10 in 2012. He brought me two top-three finishes in the ottoneu experts league. But I was not up to the challenge, and now he’s $49 and my team is 8th. I see no other option but to sell Mike Trout.
It’s utterly depressing.
It’s also utterly exhilarating.
The packages I’m seeing! They are delightful. I thought I’d break down the two best packages before I hit the accept button. Even if you aren’t in ottoneu, you may find my process interesting. Or abhorrent.
Yesterday, I identified five hitters whose batted ball distance suggests a HR/FB rate surge could be in their futures. Today, I check in on those hitters who are at risk of experiencing a decline in HR/FB rate over the rest of the season. As a reminder, batted ball angle, standard deviation of all fly balls and fence distances all play a role, but are ignored during this exercise due to lack of data.
As we continue into the second half of the season, it’s time to check back into the batted ball distance leaderboard. As usual, I am identifying potential HR/FB rate improvers by comparing a hitter’s mark to his distance. However, distance alone doesn’t tell the complete story, so this isn’t a perfect analysis. I found that batted ball angle and the standard deviation of fly balls were also quite important. But since this data isn’t publicly available yet, we have to do the best with what we have. So without further ado, here are your power surgers.
After talking a bit with Eno Sarris about his pitch type benchmarks, we decided to change up the process a bit. Instead of taking the full average of all of the pitches thrown in a category, we limited the pool to only pitchers who had thrown 20 innings from 2011 to 2013. And we limited the number of pitches we counted to ones that had been thrown 50 times. Hopefully this takes out gimmick pitches and small sample anomalies, for the most part. We’re focusing now on regularly-thrown pitches from somewhat-established pitchers.
We also decided to take the median value within each pitch type. This is a better representation of what’s out there — the old way could have allowed the very excellent pitches to pull the benchmarks north of what could actually be considered an average pitch. We also decided to show you where the 40% benchmark was — the ‘good’ but not ‘above average’ pitches. There are a lot of pitches thrown here that are neither ‘show-me’ pitches nor are they ‘strong’ — think ‘useful.’
Eno will take a look at Matt Cain to bring this all into focus after the new table of benchmarks.
The conventional wisdom says that your starting pitcher needs three pitches. Fastball, breaking ball, change-up is best, but three pitches by hook or crook. If they don’t, they’ll have platoon splits and a tough time getting through the lineup. That’s largely true, of course, but there are always exceptions.
Let’s see what we can learn from the exceptions.
I am often asked what I use a hitter’s average fly ball and home run distance for. There are various ways the distance number could be used, such as identifying potential HR/FB rate surgers and decliners. This is a good method to uncover short-term breakouts and busts, but batted ball distance isn’t exactly a predictive statistic. It merely tells us what has already happened.
We have come a long way when it comes to evaluating a pitcher’s performance. No longer do we look at W/L record, hits allowed and other metrics that are greatly influenced by factors outside the pitcher’s control. We have learned to focus on the pitcher’s underlying skills. However, it remains very difficult to look past ERA for the majority of fantasy owners. This is somewhat understandable when looking at full year results, but when we’re still just a month and a half into the season with pitchers generally in the 50-60 innings range, ERA should be almost completely ignored. ERA estimators such as SIERA are much more predictive of rest of season performance. So with that in mind, here are your SIERA underperformers and acquisition target list. I have only included pitchers whose SIERA marks are below 4.00. If a pitcher is sporting a 7.00 ERA with a 4.70 SIERA, then sure he may be a bit unlucky, but he still stinks!
