Archive for Meta Analysis

Comparing Various Website Drop Lists

First, sorry about no drop list article last week… more on that later. Second, today’s experiment stems from HappyFunBall’s comment from a couple of weeks ago.

I was well into the analysis last week and found I was not answering the right question and had the wrong data. The drop comparisons need to be a little forward-looking, not backward. The data collected from the sites was after the NFBC FAAB ran. The other sites I used allow daily or quick-draw pickups, so some of the news from the Monday was getting baked in. I wanted the rates to be taken from similar timeframes.

My original question focused on which players I should analyze so owners can gain an edge. The edge could be knowing who to drop because they are no longer fantasy-viable. Also, the hope is to find some players whose owners gave up too early on. The hope is that one of the other platforms can provide more insight than the NFBC Main Event.

Several issues exist with this study. First, it’s just one week and a busy week at that with the trade deadline. The increased activity may help to offset the lack of moves since it’s later in the season. I’m not 100% sure but I may run the same analysis again but I need to do it before the head-to-head playoff start and main teams get abandoned.

Finally, the table is a mess. A big one. What Happy Fun Ball and I hoped for didn’t materialize but a few interesting observations can be made.

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Otherish Content: Blurring The Line Between Selfish and Selfless

Nearly every article I write, I do it because I find the content useful. It’s an article I wish others would write but don’t. Because no one was writing about lineups is why I started writing one. Last year I tried writing both the AL and NL versions rotating weeks but the two-week gap between leagues was too long. Too many changes happened. This year Al Melchior took NL teams and I went with the AL ones. Because of the extra research, I’ve noticed my mixed-league teams lean AL.

It’s not just my lineup article providing self-centered content. My closers rankings and FAAB projections help me make decisions and I hope they help others.

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Speed and BABIP

On Monday and Tuesday, I identified and discussed a slew of hitters who were underperforming or overperforming their xBABIP marks, a backwards-looking metric I developed to try cutting through the noise to determine what a hitter’s BABIP should be given various underlying skills I deemed important. One of those skills was speed. Obviously, a faster hitter has a better chance to beat out an infield grounder for a hit than a slower batter. The problem is, how do I know how fast a hitter is once he makes contact and exits the box en route to first base?

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Potential Second Half 2019 BABIP Decliners

Yesterday, I identified and discussed 12 hitters whose xBABIP marks were significantly higher than their actual marks, suggesting potential dramatic upside for the rest of the season…assuming they maintained those underlying skills. Now let’s check in on the opposite group of hitters — those whose BABIPs have significantly exceeded their xBABIP marks, suggesting the potential for serious downside the rest of the way if they are unable to improve those underlying skills.

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Are Foul Balls Good or Bad?

I’ve had the question written on my whiteboard for ever: are foul balls good or bad? It’s a glass-half-empty, glass-half-full conundrum. The former group might think a foul ball is simply a barely-missed opportunity at in-play contact. The latter group might view that same event as a positive — that the poor quality of contact on a foul ball is indicative of an ability to induce poor contact quality in general, and it’s not inherently different from a swinging strike.

In my heart of hearts, it makes more sense to me that a foul ball is closer to in-play contact than not. Considering the diameter of both a bat and a ball, and the nearly physically impossible feat of connecting the two in motion, a foul tip has a margin of error of mere inches, whereas a swinging strike, fully sans contact, can have a margin of error measured in feet. Yes, it seems like getting a piece of the ball suggests, from the pitcher standpoint, makes the glass appear more half-empty than otherwise.

I wanted to finally tackle the subject, but I didn’t really know how. I first looked at the outcome of the pitch directly following foul and non-foul pitches, but it was a bit noisy (although, to be fair, I may have missed clear patterns in that noise). I imagine the effects spawning from a foul ball are not exclusive to the next pitch; rather, they may manifest two or three or even four pitches deeper into the plate appearance. In other words, a pitch-sequencing analysis might be prohibitively difficult, at least for someone like me who lacks the brainpower or mental stamina to pull it off.

Instead, I opted for something a little easier yet arguably just as telling. Read the rest of this entry »


Fly Ball Launch Angle Matters

On Tuesday, I tried to figure out why Jose Ramirez‘ home run power has disappeared. After much research that led to multiple dead ends, I discovered that his fly ball launch angle (LA) had increased significantly this season to a its highest mark in the Statcast era. I opined that perhaps his fly balls had become overly elevated and were too close to a pop-up than a line drive, driving down his HR/FB rate. This discovery piqued my interest in fly ball LA, so I decided to embark on a not-too-scientific study.

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Jose Ramirez, 4 Homer Man

Yesterday, I analyzed Ketel Marte’s power breakout, identifying what has driven his 20 home run total. Today, we’re going to flip to the sad side of 2019 performance. Jose Ramirez has been one of the season’s biggest busts, after he delivered both elite fantasy and real baseball production over the past two years. Now, nearly half way through the season, he’s sitting with just four dingers. He’s managed to swipe 16 bases, despite a sub-.300 OBP, though, so at least us fantasy owners are getting something for our hefty investment. Let’s find out what happened here.

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Minors to the Majors: Hitter Metric Correlations

On Tuesday, I published the results of my research calculating correlations for various pitching metrics as pitchers transition from the minors to the Majors. Today, we’ll look at the hitters. Just like for pitchers making the jump from the minors to the Majors, hitter correlations are very similar to what Matt Klaassen calculated for MLB batters year-over-year. That may be a surprise, but it does mostly suggest that there’s not as much development from the minors to the Majors as we might expect. A baseball player is who he is, for the most part, on average. Now let’s get to the details.

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Expectation for Hitters Returning from Tommy John

Last season at BaseballHQ ($$), I found the expected results for hitters returning from Tommy John surgery (TJS). The research was completed with traditional stats and no StatCast information was utilized. The reason was that only one regular, Christian Vazquez, had Tommy John surgery since there was publicly available StatCast data. That changed when five hitters had the surgery last year. With the return of Didi Gregorius, all have made it to the majors. Sadly the early returns are unspectacular with Corey Seager and Shohei Ohtani struggling.

In his first three seasons, Seager posted a .876 OPS but past two seasons it has only been a .793 OPS. I included last season because he struggled while trying to play through the injury. For Ohtani, he posted a .925 OPS last year and just a .742 OPS this season. These values line up with the original TJS article.

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Minors to the Majors: Pitcher Metric Correlations

Yesterday, I identified and discussed 11 starting pitchers significantly outperforming their SIERA marks, suggesting potentially major impending regression. One such pitcher on the list, ranking second in SIERA outperformance, was Mike Soroka. Through 10 starts and 65.1 innings, Soroka has allowed an amazing microscopic HR/FB rate of just 2.9%, which easily leads all qualified pitchers in baseball.

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