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MLB DFS Analysis: Mixing and Matching with Pitches

MLB DFS Analysis: Mixing and Matching with Pitches
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Pitchers are pretty incredible. I think that within all the realms of sport they are the most impressive with their art and do some very underrated things. They use physics to make a baseball dance in midair. They can throw a ball over 100 mph and with relative accuracy to boot. Have you ever tried doing either of those things? It’s insanity.

Hitters in MLB can do some equally incredible things. They can physically see a ball coming to the plate at over 100 mph. They can track that baseball doing the tango as it travels through the air and make contact within a razor thin margin. I have said how a home run is a miraculous feat and some hitters can do it 50+ times a season. Just the pure idea of what baseball is and what it takes to be successful as either a pitcher or a hitter gets lost while people talk about the Warriors and the Cavaliers heading to yet another NBA finals matchup.

The common theme here for me is the pitches that are thrown and tracked within a game of baseball. Can we extract anything for DFS purposes by understanding what might be thrown and the opposing team’s strengths against those pitches?

Let’s talk about outcome probability

I’ve gone on rants about this kind of thing in the past. Last year, hitters and their strengths against certain pitch types became a hot subject, and the new predictive stat that everyone should know. Anthony Rendon has a .550 ISO against a Slider and the opposing pitcher throws it 11% of the time so he’s a lock for a home run!


If a pitcher throws a specific pitch 11% of the time, and they throw 100 pitches, that means he will throw that specific pitch 11 times in a game. Of those 11 times, Anthony Rendon will probably see the pitch between zero and three times, depending on luck or what the pitcher sees. Of those zero to three times Rendon sees that pitch, it will be a hittable ball about half of the time.

What this is telling me is that in this specific (hypothetical) scenario Rendon will have about one hittable chance against that slider, and you want me to believe that he is a good play because of that single instance? I’m not so keen on that chance.

The other side of this is of course when a hitter is a bad play because they are bad against a specific pitch that the opposing pitcher throws some amount of the time, but unless that pitch is a fastball, it’s generally something noisy.

Stronger in numbers

An individual hitter trying to take advantage of less than five pitches is probably not something worth focusing on, but what about stacking a team to take advantage of the entire pitching performance? In that scenario, I think we can find something worth utilizing. After all we know that a larger sample size is more indicative of true performance, so trying to attack a larger amount of the pitching portfolio would make more sense as something worth strategizing with.

Below, we have all the pitchers on the slate and their pitching mix:

If you don’t know what the acronyms stand for at the top of the matrix, you can find them at

There are eight pitch types tracked, and really seven that are truly applicable since hardly anyone throws a knuckleball anymore. If each pitch was used an equal amount in that mix, a pitcher would throw about 14% of each pitch, so we will use that as the cutoff for a “significant” amount. Doing it this way, we can eliminate pitches from a future sample and their effect on this exercise if they only throw something around five percent of the time.

Now that we know what pitches these guys are throwing on the year, we need to find how well the opposing team hits the significant pitches in order to look for stacking opportunities against them. To do that I went back to Fangraphs and pulled the pitch value for each team in 2018, and then took on the ones that fell into the insignificant amount thrown by the opposing pitcher. I then took the averages of each value to find the strength against pitch types and what teams had the highest value on the slate:

Now regression assumptions aside, we have a pretty good snapshot of the teams on the slate and their strength against the significant pitches they will see in their upcoming matchups. Some obvious things to note here are that good teams are good, and that it’s equally important to understand the strength of the pitcher before blindly making plays based on data like this. Yes, the Yankees are murderers of all things fastball and curveball, but Charlie Morton is especially good with what he throws (2.84 xFIP and 31% K% on the year, that boy good). On the other side of this, The Astros are great against two of the main pitches CC Sabathia throws and could be a stack that goes overlooked even though they are in a spot to succeed based on pitch mixes and strengths.

From the bump

You can use this kind of data to look for viable pitchers on the slate as well, as we can see that certain teams will struggle a lot against significant pitchers that may look undesirable on the surface. Dan Straily, for example, is not the best pitcher on the slate by any means (5.53 xFIP, 1.38 WHIP, 49% hard contact rate) but he faces a Padres team that struggles mightily against his two significant pitch types and has a strikeout rate north of 25% overall. Kyle Gibson isn’t a great pitcher, but the Royals have a lot of issues against fastballs and sliders, and he may be in a position to reach his strikeout ceiling (24% K% on the season) at a very fair price tag.

As always there will never be a single stat that can lead you to victory. But if you can dig around and find little pieces and exercises that make you a couple ticks better than the field, you can build yourself a solid process that will pay off in the end.

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