Early NFBC ADP Analysis

A look at some of the early NFBC ADP data.

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Hitter Analysis, Players to Target

Players to Target: Tim Anderson

I have to start this post off with a caveat: Tim Anderson’s value takes a major hit in OBP leagues (his BB rate last year was 3%) so take that into account if you play in one of those leagues. Having gotten that out of the way, I love Tim Anderson for 2017. In 431 PA in 2016 Anderson logged a solid fantasy line of 9 HR, 57 R, 30 RBI, 10 SB and a .283 AVG. Tim Anderson will be an up and down player in 2017 but he is virtually guaranteed to be the full-time SS and may end up in a very favorable lineup situation. Overall Anderson has a solid shot at being a top 15 or even top 10 SS and can be had at a late round price.

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Hitter Analysis, Players to Target

Players to Target: Maikel Franco

In 2016, Maikel Franco finished as the 19th 3B (according to ESPN’s player rater) with a fantasy line of 25 HR, 67 R, 88 RBI, 1 SB and a .255 AVG. Not bad but certainly not cracking the top 10 at 3B with that line. Franco is a near-zero in steals but has upside in every other category. Franco definitely needs to make some adjustments but the foundation is there for a 2017 breakout.

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Players to Target, SP Analysis

SPs to Target for 2017

These three pitchers aren’t exactly sleepers but they are three guys I expect to own in a lot of leagues next year. All three have an elite K rate, an average to poor BB rate and an HR problem. Sounds like three guys to really get on board with right? However, if we look at year to year correlations for these stats it might start to make sense.

Metric Y2Y Correlation
K Rate 0.803
BB Rate 0.692
HR Rate 0.39
HR/FB -0.029

(via Fangraphs)

What this chart shows is that in terms of year to year correlation, K rate is strong, BB rate and HR rate are moderate and HR/FB has no correlation.  Based on that we can reasonably assume that these players will maintain their elite K rates and will potentially see fluctuations in their BB and HR rates (with HR rate having the highest chance at fluctuation). FIP and xFIP are the best metrics we have that combine these three stats. Given that these are the three stats that the pitcher controls the most, FIP and xFIP should always take precedence over ERA. The main difference between FIP and xFIP is that xFIP takes the league average for HR rate because it assumes (as mentioned above) that HR rate tends to fluctuate and typically regresses back to the mean. What we have are three pitchers who should continue to strike out batters, and might see some positive regression in the HR and BB department. The three pitchers I’m talking about are Jon Gray, Robbie Ray and the SP I can’t quit Michael Pineda.

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Hitter Analysis, Statcast

Statcast Analysis: Identifying HR Underachievers

Statcast is pretty new to the larger sabermetric scene but it’s uses are already becoming relevant, in particular to the fantasy baseball world. The vast amount of data can be intimidating to parse and draw conclusions on. Luckily, there are others much smarter than myself who have already done the grunt work. Drawing from their work, can we identify potential HR sleepers for the 2017 season?

From Alan Nathan’s excellent analysis, we know that the ideal launch angle is between 25 and 30 degrees. Angles lower typically don’t have the loft to get out of the park and angles higher are typically flyouts. Alan also found that 95 mph seems to be the starting point for when batted balls in this launch angle range start turning into home runs. Using the fantastic search tool at BaseballSavant, we can now return some data within these ranges and run some numbers.

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