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MLB DFS Pitching Analysis: April 6, 2021

Our MLB DFS lineups don’t start and end with pitching. My first five-figure tournament score came on a night where Collin McHugh scored negative points, I think–or maybe it was, like, six points. Extremely flukey, as I made the big money because Justin Turner hit three HRs for me at nearly no ownership. I’m not saying to put pitcher every night or even every now and then. I’m just stressing that each and every slate does not rest upon our pitching.

The pitcher position is so vital because it’s the slot where we can get the most accurate projection in an extremely volatile wing of DFS.

Our pitching isn’t just a source of fantasy points. The price tags on pitchers make it so they shape they dictate the freedoms and restrictions of building our lineups. Before reading this article, it’s highly suggested that you read my article, “DFS Pitching Primer,”so the concepts discussed here make more sense.

That we’re not selecting the best players to win tournaments. We’re constructing the lineups which carry the most leverage without sacrificing many projected fantasy points.
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DFS Pitching Primer

Pitching can be the spot of our lineups where we experience the lowest degree of variance in a sport full of volatility. We’re not going to explore how to minimize variance, but how to make the best plays tailored for our lineups to fit the contests we’re playing using projections and leverage.

A lot of things factor into a pitcher scoring fantasy points via strikeouts, innings, and run prevention. A pitcher’s skill is pretty important, but what’s the best way to gauge a pitcher’s skill? The answer is mostly through the predictive analytics of their past performance, which ought to be distinguished from the descriptive analytics.


We get fantasy points for outs, Ks, and Ws (on FD, we also get the points for the QS); it’s that simple. We lose points for ER on both sites, baserunners on DK. Keep it simple. Find a predictive run prevention metric between xERA, FIP, xFIP, or SIERA. Decide between the per-nine rates or per-100 batter-faced stats like K/9 versus K%. I prefer xERA, based on the Statcast numbers, or SIERA, as they isolate that over which the pitcher has the most control. I prefer K/9 because K% can double-count for events already recognized in the run preventers, as we’re really just looking to project Ks with that metric.

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