Archive for Meta Analysis

This year’s new faces from foreign leagues on the offensive side come to us from the KBO League of South Korea. First baseman Byung-ho Park was signed by the Twins and is expected to serve as the team’s primary designated hitter, as Joe Mauer is entrenched at first. The Orioles signed outfielder Hyun-soo Kim and he figures to play left field on an every day basis, though as a lefty, could end up being platooned. We have precious few players historically who have come over from the KBO League to look to in order to assist with our translations. So forecasting these two players is extremely difficult. They are essentially just educated guesses, and although all projections technically are, these are far less educated ones!

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As a standings gain point disciple, one of the questions I get asked most is if I know where to get final standings data (and SGP denominators) for AL- and NL-only leagues. You can find reliable mixed-league data if you search hard enough on the web. But you won’t find much, if anything, about the only-league format.

Thanks to a tip from Mike Gianella, I finally got my hands on a nice set of AL- and NL-only standings. Mike suggested I check with the guys that run OnRoto.com. If you’re not familiar with OnRoto, they’re a league hosting and stat service that caters to hard-core and old-school rotisserie leagues… Meaning they host a lot of only leagues.

OnRoto has also historically hosted the various Tout Wars competitions. So this is legit data from a trusted fantasy resource. In this post we’ll be looking only at the AL data (NL coming soon). I was able to obtain the standings for 76 different 12-team AL-only leagues and here’s an analysis of the data… Read the rest of this entry »

Last year, I unveiled the xHR/FB rate equation I developed that I use to help guide my HR/FB rate forecast for my Pod Projections. We’re all familiar with the average batted ball distance component of the formula. Also included is the hitter’s standard deviation of distance (SDD) of his fly balls and home runs, which has a rather high year-over-year correlation, though not as hefty as distance. That means that the leaders generally remain near the top and the bottom dwellers will rarely surge into the top quartile.

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As the founder and commissioner of my local fantasy baseball league, perhaps the most difficult part of my job is to police owner inactivity. That actually falls under the larger challenge of simply finding good owners. One of the traits that makes an owner a strong asset to the league is that he is active. By active, I mean that his lineup is always set with healthy players, he manages his DL slots, and he makes transactions that are logical, like dropping the guy just demoted to the minors who is clearly not going to accrue any value for the rest of the season. Every year, we have owners either drop out for personal reasons or kicked out because they violated my most important rule of remaining active. Why is it so difficult to find an owner that pays attention for the entirety of the season?!

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Yesterday, I shared the fantasy relevant 2015 leaders in average absolute angle (AAA), one of the three components of my xHR/FB rate equation. If you don’t feel like clicking back to yesterday’s article, I will repeat what this mysterious metric is all about.

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Yesterday, I finally launched my never ending series of lists as we look toward the 2016 season by reviewing those fantasy relevant hitters who gained at least 15 feet of batted ball distance from 2014 to 2015. As you may have guessed, today I’ll look at the other end, those hitters who lost the most distance. Since there were surprisingly fewer extreme decliners, the list is going to include those who lost at least 10 feet of distance, rather than 15 feet like the gainers list included.

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Finally, it’s time for me to start looking toward the 2016 season with list after list after list. You love lists, right? Of course you do! We’ll start preview season with a look at one of my favorite metrics – hitter average fly ball and home run distance, or simply hitter batted ball distance. A long three years ago, Chad Young and I embarked on a quest to predict HR/FB rate and found that a hitter’s batted ball distance available at Baseball Heat Maps is highly correlated with HR/FB rate. No duh. The further a batter hits the ball on average, the more home runs, shocking!

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Yesterday, I borrowed PITCHf/x data from Baseball Savant to investigate how changes in batted ball velocity affected batting average on balls in play (BABIP) to a hitter’s pull side. If you’re too lazy to click, the short of it is: more velocity coincides with a better batting average. However! Lefties consistently fare worse than righties on ground balls to the pull side at all batted ball velocities.

This phenomenon can perhaps be attributed to the defensive shift. Or to the ease with which second and first basemen can convert singular outs at first base compared to their shortstop and third base counterparts due to the distance (and, thus, difficulty) of the throw. Or, most likely, to both.

But that’s not why I’m here. I’m not in the business to speculate — not today, at least. I’m just here to provide the facts in the form of some numbers I crunched in Microsoft Excel that, if you read yesterday’s post, you will probably find interesting. It has a nifty graph, if words aren’t your thing.

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Baseball Savant, a website maintained by Daren Willman, is a thing of beauty. Aside from some great leaderboards and applications, Willman hosts a database of PITCHf/x data. Using it without a game plan is like entering the Amazon without a machete — it can be unwieldy and overwhelming. Navigating just right bears ample fruit, however. I would like to share some of my fruit with you.

Because in 2015, PITCHf/x data began including batted ball velocity for most balls in (and out!) of play. Batting average on balls in play (BABIP) is a critical component to player success, and while there has been plenty of focus on it in the last decade — more so than, say, pitch framing, which is a popular but still-raw area of research — the baseball community would still benefit from a better understanding of BABIP, especially in light of more frequent employment of defensive shifts.

Intuition tells us that a harder-hit ball in play will have a greater probability of resulting in a hit. (Indeed, my expected BABIP equation from last year that helps corroborate such a claim.) Specifically, in regard to ground balls and defensive shifts, a hard-hit grounder will have a much greater chance of clearing a crowded first-base line than would a softly hit grounder.

Enter Baseball Savant and its very granular PITCHf/x data. Read the rest of this entry »

Last week, I examined how a decline in pull rate (Pull%) and contact rate (Contact%) may be a precursor to overall decline. The idea was that if the hitter was worse at doing either or both, they could be in for a larger than normal decline. The research was an initial stab at the data and I got some great comments for future areas of stuady on it. With a few tweaks, I was able to take the ideas and refine the research in order to have a better understanding of what can be a good sign of a major decline.

Idea #1 – Use O-contact% instead of Contact%

Reader Brendan stated the following:

It would be interesting to take this concept and run an ANOVA amongst the 4 groups + control for o-zone contact rate AND z-contact rate separately, being that o-zone contact % is the one peripheral that drops off the cliff dramatically for older players. We’d get an idea about the extent to which overall wRC+ is affected by o-zone contact decline. Hardy’s 2014 and 2015 o-zone contact rate was much lower compared to recent years.

So I analyzed the data with various combinations of Contact%, O-Contact% (outside the zone contact rate), and Z-Contact% (inside the zone contact rate). I looked at several different combinations and none were really any better than Contact%. I decided to move forward with just Contact% for the rest of the analysis.

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