Archive for Meta Analysis

Zig when others zag. That could be the RotoGraphs motto. It’s certainly a theme I pursue with gusto. Fantasy advice tends to fit in a one-size-fits-all vehicle. Articles haven’t learned to mutate to match their reader. I can tell you my ideas and strategies, but it’s up to you to decide when to apply them or twist them to your own devious needs.

Recently, I wrote about using major league talent to effect a rebuild in a deep dynasty setting. Traditionally, rebuilding is done by rostering as many top prospects as possible. Sometimes, too many teams are rebuilding at once, making fringy major leaguers with breakout potential the much more affordable asset class. In other words, try to find the next Jose Bautista, Ben Zobrist, or J.D. Martinez rather than Mike Trout. Today, I’ll add a useful tactical wrinkle to the plan.

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Is Justin Upton going to bat second for the Tigers? Or sixth? What would happen to Addison Russell‘s value if he moved from ninth in the order to second? Is Corey Dickerson really going to hit cleanup for the Rays? How does Yoenis Cespedes‘ signing affect Michael Conforto’s projections?

When it comes to evaluating our typical fantasy baseball hitting categories, we know that home runs, batting average, and stolen bases are dependent upon some mix of skill and playing time (let’s say “skill-dependent”). Runs and RBI are a bit more difficult to assess. They have playing time and skill elements (getting on base and hitting for power), but they are also largely team-dependent. The projection systems, like Steamer, project R and RBI for us, but what if we want to make an adjustment? Or we want to take a gamble on a player winning a particular battle?

My goal with this post is to provide a simple framework that can be used to quickly answer questions like those above. I’m also not the first to attempt this task. So when I’m done displaying my method, I’ll share the other strong approaches I’ve been able to find. Read the rest of this entry »

What was Wade Davis like as a starting pitcher? Boring, hittable, and barely a major leaguer. As a reliever? Possibly the top player at the position. Davis has an interesting history – he was a mediocre starter, then a good reliever, then a bad starter, and then an elite reliever. Some thought that second stint as a starter would go a lot better.

Compare that to Carlos Carrasco. He scuffled as a starter, pitched well out of the pen, then pitched just as well back in the rotation. Why did it turn out differently for Carrasco? Today we’ll examine that question and talk about the types of players who benefit from stints in the bullpen. But mostly, we’ll look at the more common trend of pitchers getting stuck in relief.

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Three years ago, I conducted an exhaustive study of ESPN Home Run Tracker data. At that time, it was the primary tool I used to validate a batter’s power, before we got into the sexy new batted ball distance, and then combined that with standard deviation of distance and average absolute angle. The short story is I found that hitters with an unusually high percentage of “Just Enough” (JE) homers saw their HR/FB rates decline the following year, significantly more than the rest of the player population. On the other hand, those who hit a high percentage of “No Doubt” (ND) homers maintained their HR/FB rate much better than the rest of the group.

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Finish third in each rotisserie category and you’ll win the league. We’ve all heard or read that at some point. Where does it originate from? Is it founded in fact?

I’ve spent a lot of time acquiring and analyzing standings data of late (AL-only, NL-only, mixed).  And looking at that information got me to thinking that I could easily determine if finishing third across-the-board is really what it’s cracked up to be.

Turns out that it is. Finishing third in all ten categories would have won 67 of the 76 AL-only leagues and 47 of the 51 NL-only leagues I looked at.

But in looking at things a little closer, I found an even better reason you should set your sights on finishing third. One that had never crossed my mind. Read the rest of this entry »

Yesterday, I used Alex Chamberlain’s xBABIP formula to determine which hitters most underperformed in the BABIP department, if you believe in xBABIP, of course. Today, I check in on the other side of the coin — those hitters whose xBABIPs were well below their actual BABIP marks. Since we’re dealing with an equation here that still has much room for improvement (it’s r-squared is the best I’ve seen, but still only in the mid-0.40 range), it’s possible, heck quite likely, that it’s missing things.

So let’s not take what it says as gospel, but it would be foolish to ignore what it suggests. Though you might not agree that these hitters all have the type of downside xBABIP hints at, everyone should agree that there’s far more downside here than upside and elevated risk if fantasy owners are paying for a repeat BABIP, or close to it.

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Seemingly every year, we try to develop a new hitter xBABIP equation. Obviously, the goal is to improve upon the previous iteration because BABIP is really hard to predict. We can be reasonably sure that some hitters own high BABIP, average BABIP or low BABIP skills, but that’s about it. In any given year, the metric could swing wildly. As more granular data continues to be made available to us, we could use it to keep bettering our ability to predict BABIP and understand which underlying skills drive it.

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Last week we took a look at standings data, for 12-team AL-only leagues. Now I’m back to present the same findings using a sample of 51 NL-only leagues. What follows is a look at the points required to win an NL-only roto league, what it takes to finish first in each rotisserie category, standings gain points information, and a downloadable Excel file for you to analyze further. Read the rest of this entry »

How does one project a power breakout? It is difficult, perhaps impossible, to develop a system that more often than not uncovers a player due for a power spike. So rather than sift through an array of underlying metrics searching for clues, there’s an easier way. It’s the same thing we do when we look at a hitter’s BABIP and compare it to his xBABIP or check a pitcher’s BABIP and assume better/worse fortune the following year will lead to improved/decreased performance.

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Yesterday, I laid out the framework for how I go about projecting players entering MLB from a foreign league. This year, two hitters from the KBO League of South Korea will be making their debuts this year, Byung-ho Park of the Twins and Hyun-soo Kim of the Orioles. Refresh your memory of each of their statistics by reviewing yesterday’s post linked to above. Now that you’re back, let’s get to projecting.

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