Archive for Hitters

With our auction calculator update, I going to look at three top-rated batters.

Ozzie Albies

In my recent Launch Angle podcast, I had a tough time deciding if I’d draft Ozzie Albies or Jose Altuve in a new 2018 league. I decided I’d go with Altuve but being that I had to think about it, I needed to see if I buy Albies’s ascension into a possible first or second round talent.

Others and myself had an idea Albies was going to be good but just not hit for so much power. If someone would have told me to predict six and 13 for Albies’s home runs and stolen base totals, I would have swapped them. Last year, he had 15 HR and 29 SB. The season before, it was 6 HR and 30 SB. I figured the speed was safe (and it is) but his power may come later. I was right, he isn’t hitting for more power, he’s utilizing his it better.

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Yesterday, I used the Statcast expected metrics to calculate player xISO marks, then comparing them to actuals. I began by looking at those hitters who have posted ISO marks most below their xISO marks, suggesting the potential for significant upside in the near future. Today, let’s discuss hitters whose actual ISO marks have most exceeded their xISO marks, suggesting possible downside.

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This year, the Baseball Savant pages at MLB.com have shared even more data, as new metrics are being calculated and published for our analytic pleasure. The Expected Statistics page calculates a hitter’s expected batting average, slugging percentage, and wOBA. Sweet! Though I can’t recall any exhaustive research studies on the predictive power of their expected metrics, I know the Statcast guys are top notch and trust they have done a good job.

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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.

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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.

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It’s an all Mets edition of the deep league waiver wire! I could hear the excitement through my computer screen.

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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.

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Yesterday, I used my xHR/FB rate equation to identify and discuss ten hitters who have enjoyed a surprise HR/FB rate surge that is actually justified. Today, let’s dive into the disappointing HR/FB guys who xHR/FB rate confirms the power outage. An appearance here doesn’t guarantee doom and gloom the rest of the way, but that these hitters haven’t been unlucky so far, but actually powerless.

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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.

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Last week, I calculated every hitter’s xHR/FB rate, and then listed and discussed those whose marks were well above their actual HR/FB rates (suggesting imminent improvement) and those whose marks were most below their actual HR/FB rates (suggesting imminent decline). Those posts provided some actionable advice, in the form of buy low targets and guys you might want to consider selling high. But what about the hitters whose underlying skills actually justify their HR/FB rate spikes? These are the guys you might consider buying high, or if you already own the player, shouldn’t rush to sell high.

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