The A’s may not boast one of the more prolific rotations in baseball but entering the season they certainly featured one of the deepest. Now with Felix Doubront lost to Tommy John Surgery and one of my favorite sleepers, Chris Bassitt, likely facing a similar fate, the rotation suddenly looks a tad shallower. So this week we look at two pitchers available in a vast majority of leagues who’ve either made it back to the East Bay or who we can expect to arrive there shortly.
Two years ago, I modified the first equation I developed to yield an improved expected strikeout rate formula. The formula uses a trio of strike type rates found at Baseball-Reference.com, including a pitcher’s looking, swinging, and foul strike percentages, as well as his overall rate of strikes thrown. The beauty of the equation is that it uses components that stabilize quickly, as the rates as per pitch, rather than per inning or per batter.
Yesterday, I discussed the starting pitchers whose xK% most exceed their actual strikeout rates. Today, I’ll look at the other side of the list — those starting pitchers whose actual strikeout rates most exceed their xK% marks. These pitchers are at significant risk for regression.
Two years ago, I modified the first equation I developed to yield an improved expected strikeout rate formula. The formula uses a trio of strike type rates found at Baseball-Reference.com, including a pitcher’s looking, swinging, and foul strike percentages, as well as his overall rate of strikes thrown. The beauty of the equation is that it uses components that stabilize quickly, as the rates as per pitch, rather than per inning or per batter. I calculated the xK% marks for all qualified starting pitchers, compared it to their xK% marks, and sorted. Let’s discuss those with the most significant potential upside, as suggested by xK%.
Can you believe we’re almost done with April? It seems like the season just started. I swear baseball weeks move 10x faster than winter weeks. Anyway, it’s time to check in on the National League starting pitchers. I didn’t do an official first run of tiers last month, instead letting my final rankings update serve as my baseline, but from here on out I will be updating monthly per usual.
The release of Drake’s fourth studio album today gave me an easy theme for the May tiers, though I obviously need more than four tiers so I’m including some mixtapes and collaborative albums to get us to eight. They’re just in order of how I like them so “Views” comes in last just because I’ve only listened to it three times so I don’t really have a feel for it yet. I think first impressions with albums are kind of worthless.
I can’t tell you how many times I hated something on first listen only to love it two weeks later after another 10 spins. With due respect, I don’t care what you think about Drake in this particular forum (btw, that probably reads as more aggressive than I’m intending… I’m just saying, it’s tangential to the piece so let’s not get too hung up on it). I know some people don’t like him. I don’t like some music that others love. That’s just how it works. Comedy and music are two subjective arts that I finally stopped telling people how they should feel about once I realized that not everyone had to like what I like.
In Ottoneu, like any fantasy format, small sample sizes to begin the season drastically impact the standings. The team with the worst pitching in your league has probably allowed more homers than expected. While the team in last place has likely has pitched the fewest innings. It’s easy to blow off these types of starts due the unsustainable performances that aren’t likely to continue (or to front-loading innings). I thought it would be fun to take a different approach today. So let’s play a game…
The rules: Pick 5 SP, total salaries for this rotation of $30 or less based on Ottoneu average values (round up $1 dollar). No picks with an average salary over $12. Arbitrary limitations, I know.
The goal: Build a 5 man rotation assuming you can bank all points that have occurred thus far with the goal of accumulating the most Fangraphs points by seasons end. Let’s make some picks.
A Better Way to Create Optimal Combinations of Players
Many DFS players utilize a fairly unscientific approach to creating stacks (combinations of batters from one particular team) when building lineups. Rather than making educated guesses at optimal combinations though, it’s more effective to approach the strategy from an objective standpoint that accounts for the interdependence between players within the same game. Batters in different spots in the lineup will be affected differently by performances from other batters within the lineup depending on how many slots they are away from one another. Furthermore, one batter’s specific skillsets and projected rates of outcomes like home runs, steals, and strikeouts will affect others with different specific skillsets and projected rates.
I practice extreme patience in fantasy baseball because to me there’s nothing worse than overreacting on a guy, cutting him, and then watching him get back on track for one of your competitors. However, I also realize that sometimes the patience is exercised to a fault, especially in shallower leagues (10-13 team mixers where the waiver wire is going to be more plentiful). I’m trying to strike a better balance this year and be willing to take chances on available guys, even if it means cutting someone who might get back on track, but just isn’t performing right now.
Of course, to pick someone up, someone has to go. And that decision is often the more agonizing of the two so today I’ve got three arms drafted in the top 60 starters of NFBC leagues that I’m ready to move on from in favor of the latest hot prospect being called up or fast starters with some bankable skills changes behind their run. We’ve already seen Blake Snell, Henry Owens, Aaron Blair, and Jose Berrios get the call. And sure, they could flop and have you back on the wire picking one of these guys back up, but for now I’m comfortable cutting them to invest elsewhere in the hopes of a big payday.
Going into the season, we had two young fireballers with straight fastballs and meh results in their rear view mirror. Kevin Gausman and Nate Eovaldi both have good walk rate totals but bouts with homeritis and bad balls in play results that hint at bad command, or perhaps hanging secondary pitches. They’ve had incomplete arsenals, but they’ve recently added a pitch that threatens to make them whole. They’re in the same division, in ballparks that are better for hitters! They even had good starts last night! So… which one you got?
This kind of post is right in Mike Podhorzer’s wheelhouse. We have a lot of common interests as far as baseball research topics are concerned — namely, xK%, xBB% and xBABIP — but he’s typically the one who periodically updates RotoGraphs with x-leaders and x-laggards.
So, again, this would be the kind of post Pod would tackle: an update on which starting pitchers will likely regress in their strikeout rates (xK%). But instead of using the xK% equation, to which the above paragraph is hyperlinked, I want to focus on a particular metric: zone contact rate, or Z-Contact%.
I’ll be up front about this: I haven’t done much research regarding pitcher zone contact rates and how it sticks from year to year. That’s primarily what this post will entail, and my evidence is largely anecdotal. But it’s important to note that zone contact rate plays a profound role in determining a pitcher’s strikeout rate; the Pearson correlation coefficient between K% and Z-Contact% is -0.72. In other words, K% and Z-Contact% are strongly negatively correlated.
