Archive for Meta Analysis

So it’s been an xBABIP two weeks and we’re just about through analyzing every aspect of my new equation. Over the last couple of days, I’ve looked at the 2017 BABIP surgers and BABIP decliners, but the majority of the names were fairly obvious. If you posted a .230 BABIP in 2016, you’re probably going to find yourself on a potential surger list, while a .380 BABIP is likely going to get you onto the decliner list. Commenter Tom Cranker suggested cherry-picking a list of fantasy relevant hitters who posted 2016 BABIP marks around the league average (.300) who xBABIP actually believes should have performed significantly better or worse. These guys you wouldn’t think twice about believing their BABIP marks since they aren’t out of the ordinary, but their underlying skills suggest otherwise. Let’s take a look at some of those names.

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Last Thursday, I used my new xBABIP equation to identify 10 fantasy relevant hitters whose xBABIP marks were significantly above their actual BABIP marks, suggesting serious BABIP upside in 2017. Today, I’ll make many of you sad with a list of names who are at risk of major BABIP regression this season, if they don’t improve their underlying skills by a massive degree. By no means do you want to avoid these names, you just simply don’t want to value them assuming their 2016 BABIP marks are actually sustainable. But since someone in your league probably does believe the 2016 BABIP is real, you probably won’t end up rostering them at a fair price.

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A week ago, I introduced the newest version of our ever evolving xBABIP equation, this time incorporating the much-needed shift data. Last Thursday, I identified the 10 fantasy relevant hitters with the greatest BABIP upside in 2017, given the gap between their 2016 BABIP and xBABIP. In the comments, I was asked if I could perform a retrospective analysis to see how the new equation would have done if I ran it heading into the 2016 season.

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The fantasy community is down on Justin Upton. I get it, but it’s a little strange to me given our collective penchant for recency bias. Upton had a monster second half and finished the season an almost-perfect replica of his usual self. (The operative qualifier being “almost.” We’ll get to that in a second.) Sure, it was a rocky year, but hey, Joey Votto had one, too. Dude was batting .213 with a 27 percent strikeout rate (K%) through May…

Right, so Upton was an almost-perfect replica of himself. In a vacuum, his production looks nearly identical to his typical annual accomplishment, down to nearly every statistic except for his batting average on balls in play (BABIP). In my investigation of his woes, I noticed his uncharacteristically low infield hit rate (IFH%). Here’s a list of hitters with higher infield hit rates than Justin Upton in 2016:

• Salvador Perez
• Kendrys Morales
• Albert Pujols
• One hundred and thirty five other players not surnamed Ortiz, Mauer, Seager, Granderson, Cabrera, Votto or Kemp

Yes, Upton ranked among the bottom 6 percent of hitters in terms of infield hits. If there’s a single bone to pick about Upton’s season — well, aside from the insane volatility — it’s that his BABIP failed to get back on track, continuing to linger at a league-average mark. It seems a trend has emerged; accordingly, it’s easy to accept said trend as a new normal, as a resignation of Upton’s fifth tool.

I’m here to make the classic* Infield Hit Rate Defense, or IHRD, as it’s known in the infield hit community.**

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Finally, after unmasking the newest version of xBABIP that accounts for shifts, it’s time to get to the names…you know, the kind of list you could actually use for your fantasy leagues this year! So let’s identify and discuss the fantasy relevant hitters whose xBABIP marks were significantly above their actual BABIP marks. These ten hitters should enjoy a BABIP rebound in 2017, assuming their BABIP-related skills remain stable.

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The subject of calculating replacement levels is a touchy one in ottoneu. Most people who play probably have some opinion on how this process should occur and, if you partake in any of the various ottoneu communities, you have probably witnessed some of the debates from both sides.

I tend to enjoy the process of determining replacement levels and, as I have gone through this process in the past, I wanted to take some time to divulge on my process as we near 2017 draft season. Before we do, we need to acknowledge some methodology or this will quickly turn into a debate of semantics. There are two ways that replacement levels can be defined. This is either as a specific point per game (P/G) or point per inning total (P/IP) or as the nth player ranked at a position. For example, I could say, replacement level for SP is about 4.00 P/IP, or I could say that replacement level for SP is about the 90th SP.

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It’s xBABIP week and on Monday, I unveiled the latest incarnation of my equation, this time incorporating shift data. Then yesterday, I analyzed leaguewide shift data trends and unearthed some interesting tidbits. Today, it’s finally time to talk some names. We’ll begin by looking at the players that enjoyed the biggest gains using the new xBABIP equation versus Alex Chamberlain’s 2015 version that I had been using as my primary go-to, and also the biggest losers.

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Yesterday, I unveiled the newest incarnation of the xBABIP equation, this time tacking on a shift-related component. The defensive shift has been all the rage these past couple of years as teams are utilizing data more and more for any incremental advantage they could find. Finally with the Splits Leaderboard, we have all the data at our disposal to dive into. Let’s jump in, shall we?

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Last week, our own Brad Johnson discussed unpredictable auction values in “Rationality Will Ruin You”. I 100% agree with his premise. Unexpected inflation exists but so does a solution. By charting out the desired and the potential outcomes before an auction, an owner can remove a ton of auction frustration.

Brad examined the elite player section of the draft where the actual auction values are higher than most people’s predicted values. Owners refuse to pay these high values but instead end up spending their resources on near replacement level talent. The following simple chart helps an owner deal with the problem along with uneven hitter/pitcher mix and outlining a personal auction strategy. The process is similar to the one used in Winning Fantasy Baseball by Larry Schechter but with a few more additions. Here’s the procedure.

Step 1. Get auction values

This step could be as simple as using our auction calculator or creating your own projections like our own Mike Podhozer does. To set the pitcher/hitter mix, use the league’s historic mix or just go with standard 70/30 split.

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For years now, we have attempted to better understand the seems-impossible-to-predict metric we all know and love, batting average on balls in play, or BABIP for us nerds who like acronyms. As far back as 2008, we have tried, tried, and tried again to come up with an xBABIP equation strong enough to play with the other xMetrics. Two years ago, I developed what was at the time, the best we had. Then, we were given a gift in the form of data collected by Baseball Info Solutions, some of which Alex Chamberlain used to improve my equation and make it easier to calculate. Then Andrew Perpetua developed a Statcast driven xBABIP. Finally, Alex updated his equation by tacking on a seasonal constant.

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