Last Thursday, I discussed the 10 hitters I identified in the preseason as potential 2017 BABIP surgers, due to xBABIP marks well above actual marks. Eight of the nine hitters that actually recorded an at-bat enjoyed a BABIP increase. Let’s see how the nine hitters I identified in the potential BABIP decliners list fared.
Let’s stick with my xBABIP equation and discuss the group of 10 hitters I identified as potential 2017 BABIP surgers. These were the guys whose xBABIP marks were significantly above their actual BABIP marks.
Most fantasy owners expect drafted players to under and over perform some amount. When a player overperforms, the owner looks and feels great because they “knew” a breakout was coming. Owners hope they didn’t pick too many players on the other end of the spectrum. The underperformers are the ones who drag down a team and owners find as escape goats for a bad season. I’m going to start laying the groundwork to determine a hitter’s disappointment chances.
The first major step in finding a disappointing hitter is to define what is a disappointment. After owning too many fantasy teams over the years, I’ve had my share of disappointments (e.g. Brandon Webb in a 2009 first round) and feel they are just part of the game. This ambivalence doesn’t mean I shouldn’t know the breakout chances. Even small changes in the odds can make a major difference after rostering 20 or more players.
Last offseason, I created an individual pitch metric, pERA, which gives each pitch an ERA and prospect grade based on its ground ball nature and swing-and-miss capability. With the 2017 season over, I’ve compiled the final 2017 values. This year, I’ve added in exit velocity (EV) grades for each pitch.
The process I used for creating pERA is in the article linked above but here is a quick rundown.
Most people in fantasy baseball have tiers of players. You likely believe that certain players are roughly interchangeable, and others are definitely superior or inferior to one another. Sometimes the differences between players appears so vast that it is obvious where you would draw the distinction, and other times it feels more fluid and dynamic. Intuitively you might feel there is a strong distinction between the two tiers, but it may be difficult to find a precise line in the sand.
In the past few days I have been going through my xStats, looking at where certain players fell on certain metrics. In doing so, I have noticed several pairs of players. Players who have performed very similarly on several different metrics. Some of these pairs, arguably, cross skill tiers. And boy do I love when players cross tiers. Read the rest of this entry »
After squeaking out the top finish in the inaugural 2016 Tout Wars Head-to-Head league, I moved to the 15-team mixed auction league. It was an interesting season and here are some of my “highlights”.
Aaron Judge, my thank you card is in the mail.
This was my fourth year in the American League only Tout Wars league after winning the inaugural Tout Wars mixed draft league back in 2013. I’ve had two bottom tier finishes, a second place in 2015, and now…a Yoo-Hoo shower! And since this post is partially meant to give me a pedestal to brag from, I will giddily share that I not only won, but set multiple league records along the way as well. My team scored 109 of a possible 120 points, the most in AL Tout history, while my 26 point victory margin was also the most. In addition, I set a couple of category records, and actually won eight of the 10 categories. It was quite the season.
On Wednesday Jeff Sullivan wrote a piece about the ‘flyball revolution’, which I am sure many of you have already read. Today I want to expand upon what he wrote, and tie in a theory for what may be going on. My theory is probably wrong, but it ties together a few of the articles I’ve written recently (1, 2, 3, 4,) and expounds on my current thinking on the Statcast data.
I know I’ve been touching on the topics of exit velocity and launch angle a lot recently, but I think it is important to understand and begin integrating into everyday analysis. Before anyone can use that analysis, we as a community need to build up an intuition for how the stats relate to one another. This is new for everyone.
We’re running out of time to improve our teams, so let’s use my xHR/FB rate equation (unadjusted for home park) to identify a handful of hitters potentially due for a home run spike over the remaining month-plus of the season.
A few days back, I start the process of trying to find breakout hitters. I found some possible traits which point to hitters breaking out but didn’t get into the stickiness of the stats over different time frames. I’m back to see how the “breakout” stats main their values over time.
For a quick review, here are the claims I made in the previous article.
Overall, here are the rules.
• K%-BB% (plate discipline) changes by +/- 4.5%.
• Flyball rate (FB%) changes by +/- 3%.
If the above two items can’t explain the change move onto the following three points.
• Pull% change (only) by +/- 5% this value can good or bad depending on the hitter’s other traits.
• Raw power can start decline once a player reaches 30-years-old.
• BABIP changed by +/- 30 points. (A change in plate discipline can cause this change)
I will just start walking through the points comparing the results for the year after the breakout. Also, I will look for hitters breaking out in the season’s first month and how those stats carried forward.
