Archive for Meta Analysis

Over the years, I have written a lot about the concept of position scarcity. In fact, a quick Google search yields four such articles just on the front page (I wonder how many, if any, are hiding on page 2 and beyond!):

The Position Scarcity Post To End All Position Scarcity Posts, March 2011 (oops, guess this article didn’t actually end all position scarcity posts!)
What is Position Scarcity, Really?, January 2012
What is Position Scarcity, Continued, January 2012
On Best Player Available vs. Position Scarcity, January 2015

This time I’ll take a different avenue to discuss the concept. My inspiration was an article posted last week by our friend Rudy Gamble over at Razzball.com. It was titled Debunking Position Scarcity In Mixed League Fantasy Baseball, and in it, Rudy shared his reasons why he believed that position scarcity was essentially a myth in mixed leagues.

Rudy and I have discussed position scarcity a lot over the years and the wonderful thing is that although it appears that we disagree, we both use math and data to support our positions. So it’s a constructive back and forth that allows both us and our readers to learn. After all, we still don’t have a 100% perfect valuation system, so we don’t know for sure who’s even right!

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A couple weeks back I provided some (hopefully) useful tables demonstrating how R and RBI are affected by the team’s overall run scoring projection and where the hitter is positioned in the batting order.

At the time I assumed that a similar analysis for stolen bases would be unnecessary. I knew that runs and RBI are largely affected by team context and batting order, but had a feeling that stolen bases were simply a function of player skill. Maybe with a small hint of lineup effect.

Thankfully, our very own Birchwood Brothers questioned that assumption and asked if I had come across any similar research for stolen bases. So here I am to present those findings. Does a player’s position in the batting order affect stolen base frequency? Read the rest of this entry »

Today, I continue this year’s Steamer and I series, but switch it up to the other side. That is, the first two players I compared my Pod Projection to Steamer were those I was significantly more bullish on. Now I’ll take a look at a player I am far more bearish on than Steamer.

It should not surprise you that the fantasy relevant player I’m most pessimistic on versus Steamer is actually Xander Bogaerts, who I discussed last week. Obviously, I’m not going to talk about him a second time. So instead, I move down the list and find a big surprise – 2014 MVP Mike Trout. I debated whether it was even worth writing a potentially negative article on him because my opinion isn’t going to matter much. But I’m actually quite curious about what is driving my pessimism versus Steamer since this is a complete surprise to me. So let’s find out, shall we?

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The Pod Projections are back! My projections are based on the methodology shared in my eBook Projecting X 2.0, and the process continues to evolve and improve.

It was fairly easy to decide who should get the third Pod Projection treatment of the 2016 preseason. The second overall pick in the 2013 June Amateur Draft, Kris Bryant shot through the minor leagues after destroying minor league pitching and finally made his highly anticipated debut in 2015. He didn’t disappoint, as he posted a .371 wOBA and performed exactly as we expected – lots of swings and misses resulting in strikeouts, a strong walk rate, and excellent power.

Naturally, fantasy owners are expecting the sun, the moon, and the stars from him in 2016, as his NFBC ADP currently sits at 11th overall. He has been selected as early as fourth (!!!!!) and as late as 22nd. Are they crazy for their super optimism, or justified to believe an offensive explosion is on its way?

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Welcome to the return of the Steamer and I series. I debuted Steamer and I last year and have decided to bring it back for an encore performance. In the series, I pit my Pod Projection against Steamer, comparing wOBA forecasts for hitters and ERA for pitchers. I’ll choose several fantasy relevant players to discuss on each end of the spectrum – those I am far more optimistic on and those I am more bearish on.

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In the real world of baseball, the Orioles recently agreed to terms with Yovani Gallardo on a $35 million contract. Then they ran their classic bait-and-switch physical, cried “shoulder,” and leveraged Gallardo down to a $22 million guarantee. This is not the first time the O’s have killed a player’s leverage. Usually they go on to sign with another team for much less money.

You’d be hard pressed to find any 30-year-old pitcher without some kind of radar blip in their shoulder or elbow. In his prime, Gallardo threw 93 mph. This was back when 93 mph rated among the hardest throwing starting pitchers. Now he throws 90. There’s a pretty decent chance that whatever’s wrong with his shoulder is already assimilated into his game. Furthermore, given the contracts signed by Ian Kennedy and Mike Leake, decline appeared to already be priced into the original $35 million figure.

Gallardo and his agent should have anticipated the Orioles. They should have laid groundwork to co-opt the false leverage gained by the Orioles with the physical. Before any figures are exchanged, it’s the agents job to say, “we know you’re going find something when you run your physical. Let me be clear, we aren’t renegotiating.”

That statement doesn’t mean what it says. Here’s the sub-text. If you, the Orioles, find something in my client’s physical, we’ll budge. A little. Like a very tiny bit. Maybe we’ll accept a small percentage of the contract as very easy to trigger incentives. It depends what it is too.

Put another way, do you think Scott Boras would have taken a $13MM reduction in guaranteed pay? Doubtful.

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Two years ago, I shared with you an updated version of my xK% equation. The formula uses a trio of strike type rates found at Baseball-Reference.com, including a pitcher’s looking, swinging, and foul strike percentages, along with his overall rate of strikes thrown. With an adjusted R-squared of 0.913, it explains a very higher percentage of a pitcher’s strikeout rate. Its best use is early in the season when the plate appearance (the K% denominator) sample size is still small, as xK% uses total pitches as the denominator, so we can reach a reasonable sample size to analyze much more quickly.

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With drafts fast approaching, it’s time to start talking auction strategies. Sometime soon, I’ll offer advice on nominations and common early bidding patterns. Today, let’s begin with the end of the draft.

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Two years ago, I shared with you an updated version of my xK% equation. The formula uses a trio of strike type rates found at Baseball-Reference.com, including a pitcher’s looking, swinging, and foul strike percentages, along with his overall rate of strikes thrown. With an adjusted R-squared of 0.913, it explains a very higher percentage of a pitcher’s strikeout rate. Its best use is early in the season when the plate appearance (the K% denominator) sample size is still small, as xK% uses total pitches as the denominator, so we can reach a reasonable sample size to analyze much more quickly.

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A few weeks ago, I dug through some PITCHf/x data, courtesy of Baseball Savant, and calculated the BABIP (batting average on balls in play) on ground balls to the pull side by velocity for hitters by handedness in 2015. There are a lot of prepositional phrases in that last sentence, but instead of trying to further clarify it, I’ll summarize the findings: right-handed batters hit for a higher batting average on pulled ground balls at every batted ball velocity than did left-handed batters in 2015.

It’s a long time coming, and I’m here to present the same analysis but for ground balls to the opposite field.

But, first, some quick housekeeping. Following my recent ADP (average draft position) research, I asked readers to predict which players’ end-of-season (EOS) rankings would outshine their ADPs for 2016, given some certain conditions. Twenty-seven readers responded and the results are in, ranked by frequency of votes:

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