Archive for Meta Analysis

Before taking a short vacation, I reviewed the first preseason Pod vs Steamer Projections series posts, focused on home run upside. That article discussed the hitters whose home run Pod Projection was significantly greater than his Steamer projection. Today, I’ll recap the players I identified as possessing significant home run downside compared with Steamer. With the record setting home run total, this shall be interesting.

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I’ve finally compiled the 2017 Disabled List (DL) information. The main change from the last few seasons is the transition from the 15-day DL to 10-day DL and the subsequent increase in DL trips. With the total trips up, the number of days lost is down which makes it tough to draw any major conclusions. It’s time to dive into the numbers.

First off, I collected the information from MLB.com’s transaction list. I like to use this list because it is easy to go back and check. I waded through it and it wasn’t pretty. It took me twice as long to compile the data compared to previous seasons. I would just like to give a big thank you to ProSportsTransactions.com for having most of the missing data.

With my venting out of the way, here is how the days missed for pitchers and hitters compare over the previous 4 seasons.

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A few weeks ago, I began the process of determining an underperformance metric. In the article, I laid out the groundwork determining the drop off in plate appearances (PA) and production (wOBA). With these thresholds, I created several metrics, each with its own advantages and disadvantages. I’m not setting the values into stone yet but I’m getting closer to a solution. I’ve found a few value I like better than others.

In the original article, I found fantasy owners considered a drop in 220 PA from 600 PA (37% drop) and of 0.035 wOBA from .350 wOBA (10%) to be the thresholds. I didn’t mess with these two values. Besides the pair, I wanted to know when both occurred. Additionally, from a discussion in the comments, I found when either PA or wOBA thresholds where met and when both dropped close to, but not over, the thresholds. This value (called Minor Drop) I found to provide the most overall value.

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Humans are kind of a mess. One of our many failings as a species is a tendency to oversimplify complex issues. I think of it as the “good versus evil conundrum.” I’m sure academia has a better name for it. Basically, we prefer wholesome heroes and fell villains rather than the equivocating mess we typically find in reality.

Good guys sometimes do bad things. The venerable Abraham Lincoln suspended habeas corpus during the Civil War to deal with dissidents. Most of the so-called evil folk in history were trying to make a better world for themselves. They were the good guys in their story. Our tendency to ignore the gray area between good and evil – i.e. the nuance – can be found in baseball (and fantasy baseball) too.

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Before the 2017 season, I decided to switch up my typical “Steamer and I” series posts where I discuss one player in which my Pod Projections differ from Steamer. Instead, I compared my projections in specific fantasy categories to identify upside and downside guys my forecasts hinted at versus Steamer, representing the crowd. Let’s begin our recaps of the new series with the home run upside group, which are those hitters I projected for significantly more home runs than Steamer. Note that I extrapolated the actual Steamer home run projections to match the same number of at-bats I projected, so playing time differences wasn’t a factor.

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Yesterday, I reviewed my list of 2017 HR/FB rate surgers, utilizing my Statcast-fueled xHR/FB rate to identify the guys with significant upside. Today, I’ll recap the list of HR/FB rate decliners. Having not yet looked at my list, I’m nervous the leaguewide spike to record-setting home run numbers is going to make me look silly. Let’s find out!

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Once upon a time, we basically knew who everybody was prior to the season. Sure, that’s not a foolproof statement. Pitchers are always inconsistent. Some hitters decline due to age or injury. Others rebound due to a dead cat bounce or better health. Rookies are random number generators. But the point stands – players mostly played within the confidence intervals of their projections.

We’d talk about finding the Ben Zobrist or the Jose Bautista as your big opportunity on the waiver wire. You basically had a couple chances at rostering a waiver wire savior – plus some of those random rookies. In recent years, supply has increased. The 2017 campaign featured more free studs than I care to list. We had a new toy every week.

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Geez, I could have blindly selected a handful of hitters whose HR/FB rates were due to rise in 2017 and probably would have hit on the majority! As I continue to recap my preseason lists, let’s move on to my 2017 HR/FB rate surgers. I compared my 2016 Statcast-fueled xHR/FB rate to the hitter’s actual HR/FB rate and identified six hitters whose xHR/FB rates were significantly above their actual marks, suggesting serious upside. Let’s see how these hitters performed.

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This past weekend, I was in Phoenix for Baseball HQ’s First Pitch forum. It’s an intensive few days of catching up with old friends and focusing on the upcoming fantasy baseball season. There was an underlying theme of the weekend, the fantasy baseball game is being forced to change. Some game facets have experienced some massive adjustments. The following are some of the meta-trends which have quickly popped up over the past few seasons.

Home runs are way up

A few days ago I wrote the following incorrect statement about Carlos Martinez for a 2018 player preview.

His 1.19 HR/9 will likely drop back below the league average.

It was pointed out to me, his home run rate was below league average. I was for sure it was not near 1.20 but I was wrong. Here are the recent league-wide HR/9 values.

2014: 0.86
2015: 1.02
2016: 1.17
2017: 1.27

I remember when the HR/9 hovered around 1.0. Not anymore. Some other pitching stats are feeling the effects of the jump like ERA, but the root cause is more home runs.

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The winning formula for 2017 was exceptionally simple. Here’s a bullet list guaranteed to finish second or better. Every single one of us had the opportunity to execute this plan – even most rebuilding owners in keeper formats.

• Acquire a few stud hitters with stolen base potential – think Jose Altuve or Paul Goldschmidt. Use the Stars and Scrubs meta in auction leagues.
• Pick up two or more stud pitchers from the Kershaw, Scherzer, Sale, and Kluber bucket.
• Make value gambles on players like Jose Ramirez.
• Work the waiver wire. In most leagues, Aaron Judge was (very briefly) free. Lesser sluggers like Tommy Pham were cheaper than free. Arms like Alex Wood, Jimmy Nelson, Zack Godley, and Charlie Morton also cost nothing and were exceptionally obvious additions.
• Buy low on Craig Kimbrel in-draft, churn waiver wire closers for the rest of your saves.

While you may not have had access to a specific player like Altuve or Kershaw, the general approach still holds true. And you could have gotten even more bang for your buck by selecting Bryce Harper and Giancarlo Stanton for a relative bargain.

Now that we know how to win in 2017, how do we apply this information to next season?

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