MLB DFS Primer – Pt 1

Happy Holidays, everyone! It’s my favorite time of the year – baseball season, and more importantly, MLB DFS! The flowers are blooming, the pitchers are fresh, and hope springs eternal for all 30 teams.


Before we get into any stats or analysis that is MLB-specific, I want to reiterate this – DFS lineups, projections, predictions, and everything about them are subject to the inherent variability of sports. Unfortunately, baseball arguably has the most variance of any of the major sports, DFS-wise or just plain old actual game-wise. Fortunately, however, we can learn to embrace and overcome that variance by focusing on the right stats, using better information, and, of course, luckboxing into a Scott Schebler 4 home run game. Much like in NHL, success in MLB DFS is strongly dependent on matchups. The key is finding which pitchers and which hitters are in the best matchups to succeed. Of course, elite players can sometimes overcome poor matchups, just as a player can crap the bed in an elite matchup. Now, let’s dive into what stats are important to look at to find those good matchups for both pitchers and hitters when making your lineups.


Enough preamble, it’s time to get down to brass tacks. Hopefully you’ve reviewed the scoring systems for pitchers and hitters on all the DFS sites – if not, go do that now. As is the case for most daily fantasy sports, there’s different roster construction for both cash and GPP contests. We want safety and high floors in cash, and we want upside and ownership edges for GPPs. Cash games – target pitchers in line for the win, value guys near top of the order, maximize floor, spread exposure. GPPs – target high-K pitchers and stack it up in your lineups. See the upcoming GPP strategy article for more in-depth analysis and discussion on GPP lineup construction.



As noted above, even the best hitters in the world get out more often than not; as such, that means that your Starting Pitchers are going to be the most consistent source of points in your lineups on a daily basis, if not necessarily always the highest scoring source. So let’s start with how your pitchers get you points and what we want to focus on. For me, this is simple – STRIKEOUTS. Take a look at the scoring systems for pitching in DFS, and you’ll see that strikeouts are king. The quickest way to rack up pitching points is to have your pitchers rack up the strikeout. If the only thing you cared about when rostering a SP is whether or not they are in a good spot to rack up the Ks, you’d be ahead of the curve. Additionally, many of the highest priced SPs on a given night might not actually be high-K guys or have a poor matchup for strikeouts. There’s often an advantage to be found here, as the pricing algorithms typically weigh Vegas info more than K potential, meaning that you’re paying up for safety, but not necessarily upside. Think about this – a pitcher that goes 6 innings and gives up 2 runs on 5 hits but also has 9 Ks would score more DKpts (24.5) than one who goes 8 innings, allows 1 run on 5 hits, gets the W but only has 3 Ks (23pts). So while win bonuses are great, think of them as icing on the cake, not THE cake.


So now that we know that we want to target high-K pitchers, let’s briefly review the important stats to look at when selecting pitchers. The first thing you need to take note of is a pitcher’s splits. This means how he performs vs RHBs and LHBs. Typically BUT NOT ALWAYS pitchers fare better vs hitters of the same handedness (i.e RHP vs RHB) and worse vs opposite handed batters. Some pitchers don’t have much splits, whereas some pitchers have HUGE splits, meaning they are really matchup-dependant. For now, let’s just look at key stats for pitchers in general. All stats below can be divided further by the SP’s splits vs each handedness of a batter. Look for an upcoming pitching primer to expound upon each of these stats and metrics as well as include some more advanced stats.


K% – simply put, how often a pitcher strikes opposing hitters out. Since K’s are king, ideally you want to target pitchers with a high K%. (we want to stay over 20%, and anything above 25% is elite).


wOBA & xwOBA – the weighted on-base percentage allowed and expected weighted on-base percentage allowed. A good overall indicator of how a pitcher has performed and the contact he has given up. The lower this is, the better the pitcher has performed.


xFIP & SIERA – xFIP and SIERA are much more indicative of how well a pitcher has actually pitched. A pitcher with a large difference between his ERA and these 2 metrics should expect to see regression to the mean.


Exit Velocity – STATCAST! Since a pitcher can’t strike everyone out (although Chris Archer tries), we want the contact that is made to be soft, not hard. The softer the contact, the less distance the ball can travel. It’s science.


Opponent’s Vegas Implied Run Total, Vegas Lines, Etc – This is pretty self-explanatory, but again it’s important to note that Vegas data can sometimes skew pricing and an advantage can be found upon digging further into the matchup.


There’s so much more that goes into each of those stats, influencing them one way or the other, and also a lot of noise that you don’t need to focus on as much. The pitching-specific article will go into more detail on how to apply these as well. Feel free to always ask questions in chat if there’s a stat you may be confused about, or how it applies to a matchup, etc.


Upcoming in part 2 – Hitting

Evan Burgmeier

27 year old sports junkie. I graduated from Indiana University in 2011 with a degree in Sports Marketing and Management. I've been a DFS degenerate since April '15 specializing in GPPs, squeezing in a career in radio and digital marketing and advertising in between daily lineup lock. Marketing Director and Podcast Host for DFS Datalytics. Get at me on Twitter @BurgsTheWord1

evburgme has 16 posts and counting.See all posts by evburgme

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