First, a few words about my offseason writing at Rotographs. Besides reporting any possible relevant fantasy news, I plan on systematically going through two groups of players and work on their 2017 values. I will start at the top of the 2017 rankings and also somewhere in the middle and work my way down each list. I may be able to do a handful of players each article or I might by limited to just the two players. Either way, I will start putting together a 2017 draft ranking.

Additionally, I will try to follow Eno’s schedule for the other writers (e.g. players on playoff teams for the next couple of weeks). If they are looking at outfielders for that specific week, I will also look at outfielders.

The other project I will work through is being able to put a better evaluation on prospects for fantasy purposes. I will use the evaluations of various prospect writers and publications and put their evaluations into something which can be used in fantasy circles. I have some ideas of what I want to accomplish, but I am sure there will be some roadblocks and detours on the way. I will start this series Friday.

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A.J. Cole Breakdown

The National’s righty made a spot start for the Nationals on August 22 and allowed four runs over seven innings of work. To start getting a profile of Cole, here are his pitching comps using his 2016 MLB.com grade. Additionally, I included his previous grades which I will discuss in a bit.

The pitcher list isn’t that exciting with most pitchers falling into the #4 to #5 starter range.

The big note I took away from the grades, was his declining fastball grade from MLB.com. It was 70 in 2014, 65 in 2015, and 55 this year. Additionally, here are the velocity speeds stated in the Baseball America Handbook for each year.

Season: Fastball Speed Description
2014: “… sits at 94-95 mph and regularly hits 97.”
2015: “… pitched comfortably at 91-93 … and bumped 96.”
2016: “… sits comfortably in the low 90s, pushing as high as 96 mph …”

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Valuing Danny Duffy

Putting a future value on Danny Duffy is fairly difficult because he has never performed like he is right now. Here are some of his stats after his one run complete game last night.

• 9 wins
• 2.82 ERA, 3.14 FIP
• 10.0 K/9
• 1.8 BB/9
• 1.0 HR/9

Let’s start with the biggest changes and the one value which sticks out, the 10.0 K/9. Duffy is getting more strikeouts by getting hitters to swing at his pitches at career high rate (52%) and they make contact at a career-low rate (72%). This dual combination has led to a career high swinging strike rate (14%). By using the simple rule K% = 2 * SwStr%, Duffy is projected to have a 28% K% and it is 29% this season. With these matching up, I expect little regression with his strikeout rate.

A search for why the transformation as occurred begins with a 1.5 mph increase in his fastball velocity. The velocity increase raised his swinging strike rate on the pitch to a 13.6%. With the fastball velocity increase, his other pitches are playing up with his slider (15%) and change (21%) being at career highs in swinging strike percentage. I am not sure why these two pitches are performing better this season. They made be playing off his fastball as hitters are concentrating on his fastball. The pitches have a bit more movement and this could be the cause. Also, he may have changed his deception some. Or it could be a combination of several factors. All I know it is working.

Besides the increase strikeouts, Duffy continues to bring is walk rate under control which hurt his value early in his career. In 2012, his BB/9 was at 5.9 and this season it is a third of that value (1.8). The drop in walks along with the increase in strikeouts put his season’s K%-BB% at 23% for 6th best in the league.

The final aspect to understand with Duffy is he lives up in the strike zone and will give up a ton of fly balls which hurts and helps him. More fly balls mean more home runs and he will likely always have an HR/9 at or above 1.0. One the good side, the additional fly balls lead to more easy outs and he will normally have a suppressed BABIP (.286 on career) and an ERA less than his FIP (3.58 ERA vs 3.98 FIP in his career).

The big question is if Duffy can keep the high strikeout and low walk values going forward. It’s tough to tell, but we still have a month and a half of starts to monitor. Additionally, Duffy pitches at a spring training park with a Pitchf/x system installed so we will have an idea where his velocity sits next spring. I see his 2017 value being a major discussion point this offseason.

What’s left of Carlos Gomez?

Gomez was designated for assignment by the Astros and may end up a free agent soon. He was a top hitter from 2012 to 2014 when he averaged over 20 HR, 35 SB, and .270 AVG per season.

Examining his stats, the two big keys to his decline are his declining power and complete inability to make contact this season. With his power, it climbed steadily until he was 27 and has been falling fast as seen here.

Additionally, his Contact% (66%) abruptly fell to a career low. Gomez has always been a free swinger, so with his ability to make contact deteriorating, he has seen his strikeout rate jump 10 percentage points to 31% and batting average drop to .210.

Looking forward, overcoming both factors is going to be tough for him. He has not been able to stop the three-year decline in power. Even if he does, how much will it jump up? I don’t see him turning this issue around much. I could see him make quite a bit more contact since the drop was all at once. Even if the contact rate increase, will the lack of power just make him a bottom of the order player which produces some stolen bases?

With him, I am watching two items. Where he ends up this year and does that team try to make an improvement in his contact rate. The second is what his spring training and early season contact rate are next year. Right now, I don’t see him being productive and should only be taken as a late-round flier.

Michael Pineda’s Sky High ERA

Michael Pineda has seen a nice steady rise in his velocity to go with a 10.4 K/9 and a reasonable 2.6 BB/9.

With these great numbers, Pineda has a 5.07 ERA because he is getting hit hard as seen by his 1.4 HR/9 and .338 BABIP. The biggest issue I see with Pineda is he is basically just a fastball-slider pitcher (rarely throws a bad change). Hitters are holding off swinging at the slider (which he rarely throws for strikes) and looking for his fastball. Here are the triple slash lines when he is ahead, even, or behind in the count.

Behind: .345/.510/.628
Even: .312/.319/.563
Ahead: .186/.192/.314

He becomes dominant once ahead, but hitters are sitting on his fastball in all other counts because it is the only pitch he can throw for strikes. Unless he can start throwing the slider for strikes or develop a third league average pitch, he is going to continue to get hit hard.

Leaders in Home Runs Plus Stolen Bases

In this series, I often talk about player covariance — or the effect that a player’s performance has on his teammates and opponents — and its importance in building DFS lineups. This week, I’d like to expand on some nuances within that topic by looking at a visualization of this phenomenon.

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Maximizing the upside of your lineups is crucial to having success in DFS tournaments. Oft-discussed strategies like stacking and targeting home run hitters are important for upside, but another strategy is to target bad bullpens.

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This week over at SaberSim, I released a tool that allows users to view more detailed projected performance for their lineups. Rather than just adding up projected points for each player, this new Lineup Analysis tool allows us to view mean, median, standard deviation, and percentile projections for the lineup as a whole. In other words, rather than combining each player’s distribution separately, SaberSim analyzes the performance of the entire lineup across each simulated game.

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So far, this weekly column has largely focused on various general aspects of DFS strategy for the first half of the post, and specific projections for the day in the second half. Today, I’d like to switch gears a bit and discuss my process for building lineups in small (2-5 game) slates, using today’s early 3-game slate as an example.

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Last week, I discussed the importance of randomness in DFS, and some strategies one can use to take advantage of the large amount of random variation that occurs in daily fantasy. I’d like to expand further on that topic today by delving deeper into the process of focusing on specific portions of player projection distributions.

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The summer is heating up which means the standings are starting to solidify in your league. Big trades are going down and you find yourself uncharacteristically indecisive – do I make this deal or not? Will it actually improve my team enough to matter?

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The past few months have brought a lot of debate about whether daily fantasy sports are a game of luck or skill. It’s a complex question from a legal standpoint, but from a purely logical and statistical standpoint, it’s fairly clear that both are involved. In fact, much of the skill portion of DFS relates to understanding the luck portion, and utilizing strategies to take full advantage of it.

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