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.

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Let’s begin our discussion of a foursome of American League starting pitchers saddled with inflated ERAs by presenting two tables first:

Pitching Metric YoY Correlations

Metric	YoY Correlation 2002-2012
WHIP	0.430
ERA	0.373
LOB%	0.238
BABIP	0.235
HR/FB	-0.029

SOURCE: http://www.fangraphs.com/blogs/basic-pitching-metric-correlation-1955-2012-2002-2012/

Pitching Metric Stabilization Points

Metric	Stabilization Point
HR/FB	400 fly balls
BABIP	2,000 balls in play

SOURCE: http://www.fangraphs.com/library/principles/sample-size/

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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.

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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?

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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.

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Before you know it, we’ll be turning the page on the first month of the season even though it feels like Opening Day just happened. The baseball season seems to move at about 300x the speed of winter so I feel like the All-Star break will be smacking me in the face way too soon. You’ve probably heard it a million times – even from me – that early season analysis is hard because of the scant sample sizes. Yeah, it is hard, but if it were easy then there probably wouldn’t be jobs for twerps like me so I’d best stop complaining and get to analysis’ing (some say analyzing, but I mean, it’s obviously analysis’ing, right?).

Let’s take a quick look at two arms on the rise and see if we’re buying them the rest of the way:

Vincent Velasquez – If you were inclined to get super-excited by his 16-strikeout masterpiece about the Padres, he promptly laid a bit of an egg five days late (though only two of the five runs were earned) and brought everyone back to earth. Though I don’t think a modest start erases what many believed after the gem: he’s good, really good. In fact, the only thing holding Velasquez back this year is an innings cap that he will face this year. Additionally, if you were ready to discount the big outing because of the Padres, consider that he drew 27 swings-and-misses – a rarity even in this strikeout area. Look at what this joker tweeted out:

Pitchers w/27+ swinging strikes in a gm since '14: Kershaw 35, Kershaw, Carrasco, Stras 30; Sale 29; Max 2x, Kluber, Hamels, & VV today w/27

— Paul Sporer (@sporer) April 14, 2016

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Hector Santiago has historically been the type of pitcher I won’t roster in fantasy. The lefty has a track record of smashing his ERA estimators (3.51 ERA for his career compared to a 4.51 FIP, 4.67 xFIP and 4.26 SIERA) with underwhelming strikeout rates, mediocre stuff and below average control. No, I don’t blindly regress a pitcher like Santiago to his ERA estimators. It appears some skill or skills he possesses allows him to post an ERA that’s better than them. That said, I’m usually leery of the gap between the ERA and the ERA estimators closing, and with the bulk of the lefty’s fantasy relevance tied to posting a helpful ERA, any slippage would cripple his value. This isn’t the same Santiago anymore, though, and I now roster him in all three fantasy leagues I’m in this year. Read the rest of this entry »

After a breakout 2015 campaign (2014 wasn’t a legit breakout, as it required a 6.9% HR/FB rate), this was not the start Chris Archer owners were expecting. He has posted an ugly 7.32 ERA and a rather hilarious 2.08 WHIP. That’s not a typo — his WHIP is above 2.00! It has only been four starts, but since he has already allowed six runs in half of them, it’s logical that there would be some concern. So let’s try to figure out what, if anything, is wrong.

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Towards the end of last season, FanGraphs partnered up with SaberSim, a site that provides daily baseball projections by running thousands of simulations of each game every day, utilizing Steamer handedness-split rest-of-season projections as a base. The sim matches up the specific lineup of each team against the pitchers of each opposing team. The simulated games then operate as actual baseball games operate, and each individual plate appearance is simulated until the game is complete, at which point the process is repeated. While the raw projections available on FanGraphs and the SaberSim site itself are helpful in their own right, digging into some of the player and game projections, both for the current day and next, can help illuminate players to target for your season-long and DFS lineups.

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The early season is the trickiest part of the year for fantasy players. How much do you react to any given start for a pitcher? Is Vincent Velasquez really awesome? Is Adam Wainwright all of a sudden trash? While we can’t know definitively on any of it this early, we can start looking for signs that will help us make our decisions going forward. Today, we’re going to look at two really intriguing arms off to solid starts and see how they’re attacking their biggest flaw: walks.

Matt Moore

Moore entered 2016 with an 11% career walk rate, just too many walks to find consistent success. As is often the case when someone is struggling with walks, it is fastball command for Moore. He has never had lower than a 10% walk rate with the pitch. So when I see the 4% walk rate for Moore through three starts, it instantly jumps off the page for me. Once you see a stark difference in a skill like that, you want to investigate it further and not just accept it at face value.

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