Welcome! My name is mutt18 and I’ll be your guru up the mountain that is MLB DFS. I’m super excited that MLB will be back this year, despite only a 60 game season. I’m grateful you are choosing to read this and hope it takes your DFS game to the next level. In this first of 2 parts, I’ll be diving into how to find and select pitchers that you want to target in your DFS contests. I’ll first dive into some definitions of certain stats/numbers I look at and what they mean, and then I’ll tell you how to put them all together to select your pitching targets. Alright…enough chit-chat, let’s start to dig in here! Be sure to look out for my HITTING PRIMER which will be available over the next few days.
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Pitching Metrics – Types and What They Mean
Here I’m going to dig into some common items that I look at when selecting a guy I’m going to target as my SP (or SPs) on any given night, and give you some background information on what those mean and how we can apply them. For each section I’m going to give you: the definition, the why (as in why it is important) and how to apply it when selecting pitchers to target for your daily fantasy contests. I’m going to start with a few that should be familiar, and we’ll work our way down to the more complex ones, now let’s get into it!
Definition: This one is pretty self-explanatory, as it measures the amount of strikeouts a pitcher averages for every 9 innings they pitch. In an ideal situation, we want a pitcher who averages over 9K’s per 9 innings, as it means they average at least one strikeout per inning. From last year, some of the guys that have the highest K/9 aren’t too surprising. Gerrit Cole (13.62K/9), Max Scherzer (12.69K/9), and Robbie Ray (12.13K/9).
Why: We want to target pitchers that have a high K/9 simply because more K’s = more fantasy points! On FanDuel, each K is worth 3 fantasy points, and on DraftKings each K is worth 2 fantasy points. If a pitcher can rack up 10+ K’s that can make a huge difference as that is worth 20-30 additional fantasy points depending on which site you are on.
How To Apply It: We can apply this when breaking down an MLB slate, is we want try to target guys that have a high K/9 (which increases they’re overall fantasy point ceiling). The higher ceiling your SP has, the better chance you have at taking down those hard to win GPPs. Here’s an example of how picking a guy with a higher ceiling can lead you to better outcomes in GPPs. Which guy would you prefer here? We’ll use FanDuel scoring for this example:
Pitcher A: 8 IPs, 3 Ks, 1 ERs, 0 BB, and a W
Pitcher B: 6 IPs, 9 Ks, 3 ERs, 2 BB, and a W
Pitcher A looks pretty enticing right? He allowed 2 less ERs, pitched deeper into the game, and got the Win and Quality Start. On FanDuel this pitcher would have gotten you 40 points, which is pretty solid! However, the value of those strikeouts really hurt him, and believe it or not, Pitcher B scored 46 fantasy points as he also qualified for a QS and Win. Noticing little things, that are as easy as this to find, can make a huge difference, especially in MLB!
K% (Strikeout Percentage)
Definition: Strikeout percentage represents the frequency with which a pitcher strikes out hitters, determined by total strikeouts divided by total batters faced.
Why: K/9 and K% go hand in hand when we are trying to find a pitcher to target. We want to target guys with a high K% for the same reason we want to target guys with a high K/9…more strikeouts = more fantasy points! The higher the pitchers K% the more often he strikes guys out, so even if a guy doesn’t pitch deep into a game, if he K’s hitters at a high percentage then he’s going to essentially score more fantasy points in a smaller amount of innings (assuming he doesn’t give up a ton of HRs and ERs).
How To Apply It: We can apply K% essentially the same way that we apply K/9, pitchers with higher K%’s tend to score more fantasy points and have higher ceilings! And when it comes to taking down GPPs on either FanDuel or DraftKings, a pitcher with a higher ceiling can really separate you from rest of the field.
Definition: The statistic shows how well a pitcher has kept runners off the base-paths. The formula is the sum of a pitcher’s walks and hits, divided by his total innings pitched.
Why: We’re going to want to target pitchers that have a lower WHIP for several reasons. The lower a pitcher’s WHIP the less baserunners they are allowing, which means they are likely throwing less pitches, and giving up less ERs since they are not allowing guys to get on base! It is an often very overlooked stat that can pay big dividends when targeting pitchers for your lineups.
How To Apply It: Here’s what we want to look for when it comes to WHIP:
Elite = 1.00 or lower
Great = 1.10
Above Average = 1.20
Average = 1.30
Anything above 1.30 is generally not something that we want to target. The lower a pitcher’s WHIP the less baserunners he is allowing, thus it reduces their risk of losing fantasy points (especially where walks and hits allowed LOSE fantasy points on DraftKings) as WHIP likely correlates with a pitchers ERA to a certain extent. However, the downside of WHIP is that is doesn’t take HRs allowed into account.
Definition: Stands for Batting Averages of Balls In Play. It is a calculation of a hitter’s batting average, or pitcher’s batting average allowed, on batted balls put into the field of play. That means walks and strikeouts don’t count, neither do home runs since they don’t land in the field of play.
Why: BABIP takes into account essentially two main things, defense and luck. Because essentially a pitcher does not have control over what happens to a ball after it is put in play aside from his team’s defense and luck (such as weak ground ball hits or bloop hits that fall in) We want to look at BABIP as it may show a trend where a pitcher is having a bad stretch of being “unlucky” if they’re BABIP is higher than normal or above league average. If a pitcher’s BABIP is higher than the league average, or their career average, we can determine that they may be going through a bad stretch and are due for some positive regression in the near future. This can help us identify targets that many other DFS players will overlook.
How To Apply It: If a pitcher’s BABIP is higher than their career average and the league average, it can show us that they are having an “unlucky” stretch that and those numbers will normalize (or regress) over the course of a season to near their career or league average. If we are able to identify this, it can helps us find pitching targets that will likely be lower owned as most average DFS players will not dig this deep.
Hard Hit Rate (Hard%)
Definition: A ‘hard-hit ball’ as one hit with an exit velocity of 95 mph or higher, and a player’s “hard-hit rate” is simply showing the percentage of batted balls that were hit at 95 mph or more. It is broken down into 3 categories, Soft%, Med%, and Hard%, it will equal up to 100%.
Why: Hard Hit Rate is actually one of my underlying favorite statistics as many people overlook it, but it can call our attention to stats that on the surface, look pretty good, but the underlying numbers tell us that regression or a pitcher imploding is likely coming in the near future. For example, a pitcher has an ERA of 2.50 and a low BABIP, but has a hard hit rate of over 40%. These numbers can show us that this pitcher has been a bit lucky, because generally, hard contact is going to result in extra base hits and home runs. Generally for a Hard % we want to look for a pitcher who is allowing a hard hit rate of 35% or less. This actually surprised me as well, but the MLB leader with the least Hard% last year was Eduardo Rodriguez (28.7%). Also, we can also target guys that have a high Soft% (above 15%), which means that hitters are not squaring them up when they make contact.
How To Apply It: Given the example above, if a pitcher has a low ERA and low BABIP (usually below league average) but is allowing a lot of hard contact, it tells us that this certain pitcher has been getting quite lucky and that some regression is coming. If we can identify this before it happens, we can avoid picking a SP that is due for a blow-up game, thus likely saving our lineup, especially in cash games where your starting pitcher is extremely important.
ERA (Earned Run Average)
Definition: Represents the number of runs a pitcher allows per 9 innings.
Why: We want to look at a pitcher’s ERA because it directly correlates to what we’re trying to target, pitchers who will score the most fantasy points! The lower a pitcher’s ERA, the less fantasy points they LOSE because giving up earned runs causes a pitcher to lose fantasy points. Each earned run is -3 on FanDuel and -2 points on DraftKings. When it comes to ERA here’s what we want to look for:
Elite = 2.50 or below
Very Good = 3.00 or below
Average = 3.75 or below
Below Average = 4.30 or above
Bad = 4.60 or above.
How To Apply It: Generally we want to try and target guys that have a higher tendency to strike guys out, while allowing the least amount of earned runs at the same time, this is the ideal scenario. The key thing to know is scoring on each site you are playing, for example (we’ll again use FanDuel scoring):
Pitcher A: 7 IPs, 5 Ks, 1 ER, QS and a W = 43 FanDuel points
Pitcher B: 6 IPs 9 Ks, 4 ERs = 33 FanDuel points
Pitcher A doesn’t have as many strikeouts a Pitcher B, but the amount of ERs that Pitcher B gives up takes a few more factors out of play that put Pitcher A over the top. On FanDuel, a pitcher that allows 3 ERs or fewer over 6 innings, which is an additional 4 fantasy points. Giving up 4 ERs likely also away the ability to get a Win for Pitcher B, giving Pitcher A and additional 6 points. By giving up too many ERs that takes away an additional 10 fantasy points from Pitcher B! Giving up a lot of ERs likely also means that a pitcher is throwing more pitches, which means they won’t pitch as deep into games.
Definition: Stands for Fielding Independent Pitching, measures what a player’s ERA would look like over a given period of time if the pitcher were to have experienced league average results on balls in play and league average timing. Essentially it removes the effects of fielding from the equation and only look at the aspects of the game that the pitcher can control.
Why: FIP essentially only takes into account, strikeouts, walks, hit by pitches, and home runs allowed. It essentially gives us a more accurate ERA of how a pitcher is actually performing, rather where as ERA also involves the defense behind him and some luck as well. The lower the FIP, the better that pitcher is performing on the things that they can control. Here’s what to look for when it comes to FIP:
Elite = 3.20 or below
Very Good = 3.50 or below
Average = 4.20 or below
Below Average = 4.40 or above
Bad = 5.00 or above
How to Apply It: FIP allows us to isolate the performance of the pitcher by using only those outcomes we know do not involve luck on balls in play or defense, these are the things I listed above which are strikeouts, walks, hit batters, and home runs allowed. It essentially allows us to see how well a guy is actually throwing the ball based on the factors that he can control out there on the mound and is very useful at helping us predict how they will perform. We can also compare it with xFIP (which I will explain below) to get even more accurate results.
Definition: xFIP finds a pitcher’s FIP, but it uses projected home-run rate instead of actual home runs allowed. The home run rate is determined by that season’s league average HR/FB rate, this rate is usually between 9 and 10%. This helps take out more of the randomness and things that are out of a pitcher’s control.
Why: xFIP can be used in conjunction with FIP as a way of predicting if a pitcher has been giving up HRs at above a league average rate by comparing his FIP to his xFIP since the HR rate is actually built into xFIP where FIP it is that pitcher’s actual home run rate. By comparing the two, we can get a sense if a pitcher is either giving up too many home runs, (unlucky) or not enough based upon the league average (lucky). For example is a pitcher’s FIP is 4.20 but his xFIP is 3.70 it can tell us that they may be getting a bit unlucky in the HR department, and knowing their HR/FB ratio can also help as well. If a pitcher has a HR/FB rate of 13+% we know that it is above league average (9-10%) which may mean they be getting a bit unlucky, or if the rest of their numbers (ERA, Hard%, etc.) fall in line with that, it shows us that they’re perhaps just not very good.
How To Apply It: You can compare all of these together when selecting which pitcher that you want to target, if a player’s FIP is higher than his xFIP it can tell us that this pitcher may be due for some positive regression, (i.e. should be giving up less HRs for a stretch of time). xFIP is a great stat, essentially because it gives us more data to compare with some of the stuff I’ve mentioned above.
Definition: Stands for Skill-Interactive Earned Run Average. A SIERA takes ERA into account, but also takes a pitchers skill into account, so it eliminates factors that the pitcher can’t control, such as the type of ball in play (such as weak ground balls or popups that fall for hits), and is said to take a pitcher’s true skill into account. It can be similar to FIP and xFIP in that regard, but SIERA does incorporate balls in play so it is even a more predictive stat than FIP and xFIP.
Why: SIERA builds on both FIP and xFIP and includes balls in play as well, such as ground ball and fly ball rates. It helps us view how a pitcher is truly performing and in my opinion is even better at predicting pitcher success or failure than FIP or xFIP. Here’s what we want to look for:
Elite = 2.90 or below
Very Good = 3.25 or below
Average = 3.90 or below
Below Average = 4.20 or above
Bad = 5.00 or above
How To Apply It: Generally, we’re going to want to target guys that have lower SIERA’s because a pitcher’s SIERA gives us a representation of their true skill set and allows us to gauge how well they’ve been throwing the ball. For example, the top 3 in SIERA last year across the MLB was:
- Gerrit Cole
- Max Scherzer
- Justin Verlander
This isn’t very surprising as all of these guys are some of the most elite pitchers in the MLB.
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Putting It All Together
All of these statistics are available in our MLB Research Station and now I’ll talk about putting all of this information together into choosing your pitching targets when it comes to MLB DFS. I’ll break it down into how I select targets for GPPs and then for CASH games. I tend to weigh some of the stuff I mentioned above in different ways depending on which type of lineup I am building.
This should come as no surprise here, but when it comes to picking pitchers for GPPs, I tend to go with guys that are a bit more volatile (or have lower floors) but have the ability to break a slate when they are on their A game. When it comes to GPP targets, I am going to focus more on guys with a higher K/9 and K% on the slate as they tend to have the highest ceilings. In my opinion, these are the most important things to look for when selecting a GPP target, as K’s are king when it comes to scoring fantasy points. I’m also going to focus less on guys with lower ERA’s as that is a stat that everyone and their mother looks at, along with Vegas lines. Guys that are heavier favorites (typically -200 or more) are going to be chalky, as people will chase that Win, as it factors heavily into a pitchers fantasy point score, especially on FanDuel where a QS and W equals 10 additional points. For GPPs I tend to look go guys that may still be favorites, but have lines of less than -150 as it’ll tend to keep their ownership down. However, you will be taking on more risk here as guys with higher K/9 and K% sometimes tend to give up more HRs. Risk is a good thing in GPPs, but it can sink your lineups if your pitcher does blow up.
However, you can also use things like FIP and xFIP along with Hard% to help identify some guys that can be due for some positive regression. For example, you can compare a pitcher’s FIP and xFIP to see if they are giving up a higher % of fly balls as home runs compared to either they’re career average or league average. If that number is higher than it should be, it can show us that they should be due for some positive regression in the HR department. I’ll also look at Hard Contact Rate to and BABIP to see if a guy has been getting a bit unlucky. Identifying these stats ahead of time can help keep us ahead of the curve and have a serious advantage on the rest of the field.
Lastly, when it comes to GPPs, I also like to look at Ownership Percentage, another feature we have here at DFS Army that we have Ownership Projections in both our Research Station and our Domination Station. What I like to do is find those diamonds in the rough, typically that will have less than 12% ownership that fit in nicely with the rest of the process I outlined above. Over my years of DFS experience I have found that if a pitcher has 12% ownership or less, it can really vault you ahead in GPPs as most people will be chasing the heavy favorites.
When it comes to cash games, the process is very different. Here you’re going to want to target guys that check every box that I have listed above, especially if they have a heavy Vegas line in their favor (such as -200) or above. In cash games, you typically want to go with the safer guys that come at higher ownership, simply because, if a pitcher busts, but is highly owned by everyone else in cash games, you can still hit that cash line if your hitters are on point because most people will have the same pitcher. This has been a very standard procedure that has worked for many of us for years and at times will still be applicable.
However, with how many people now have access to the same data now, I have a feeling we will see MLB cash games change the way that we saw NBA cash games change this year, as it is getting easier to identify value. So I’m going to be building on my cash game strategy this year and I’m sharing it with you here first! Another strategy I will be using for cash games this year (fi we see immense chalk like we did with NBA early in the year) is also going to want to look at those underlying numbers I’ve outlined above, such as FIP, xFIP, SIERA, and Hard Contact Rate as those numbers can show us if a pitcher is due for some positive regression, but they can also tell us if a certain pitcher is due for negative regression (A.K.A. a Blow-Up game). If we can identify if a pitcher has been a bit “lucky” ahead of the rest of the field you can avoid those land-mines that can sink both your cash and GPP lineups. You can find alternative cash game “pivots” that can come at lower ownership and really get you above that cash line. Daily Fantasy Sports is an ever evolving process and I still take the opportunity to learn every chance I get, and you should to! So if you haven’t already, please come join us here at DFS Army for one low price! If you use my code MUTT18, it gets you 20% off of your membership, for as long as you stay subscribed!
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