Archive for Meta Analysis

Edit (3/29/17, 7:55 pm EDT): Brent Hershey of BaseballHQ and Ron Shandler’s Baseball Forecaster (very politely) brought to my attention that this has been done before! By Bill Macey back in 2012. Formerly behind a paywall, it has now been made public for your reading pleasure. I didn’t even know this research existed (so I’m really glad Murphy brought it to my attention); I am always reluctant to ever claim to break ground in this field that progresses so quickly but also has such a rich history of research. Please consider the following research a companion to and external validation of Macey’s work.

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I welcome all constructive criticism. This research is not especially rigorous, but given the nature of the claim — a legitimately significant spring training statistic! — it merits the disclaimer.

I found a statistically significant spring training statistic.

I’d rather not rehash the history of research and speculation regarding The Spring Training Stat(s) That Matter. Just know that, outside the modest results from this Dan Rosenheck piece in The Economist, it’s generally accepted that Spring Training statistics mean virtually nothing, and you’ll read all manners of baseball writers bashing this notion.

The big caveat is most of this research concerns individual players. Mine: team-level statistics. Alas, it’s an inherently different beast with which I’m dealing. Despite small within-year populations (30 teams rather than hundreds of players), the observation-level sample sizes are much larger (hundreds of plate appearances rather than dozens), making the odds of finding meaningful correlations much better despite fewer data points.

Per usual, I buried the lede: a team’s rate of stolen base attempts (calculated from stolen bases [SB] plus caught stealing [CS]) during spring training is actually meaningful. I’ll get to the implications of this later because there are many. First, let’s dig into the guts of the research. I gathered team-level spring training statistics from 2006 through 2016 and paired it with regular season statistics from the same span plus 2005.

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Earlier in the offseason, I examined out how reported Hit tool grades compared to actual MLB batting averages. I called the process a “mess” but figured it had some value. When I implemented the formula on MLB.com’s 2017 grades, commenters had the following to say about the projected batting average values:

“… not enough differentiation there in my opinion”
“… adjust your outputs to create more difference..”
“… hoping the table would be more conclusive…”
“…way too tightly grouped to the mean…”
“…it’s better to have no projection than to project everyone to be average…”
“… regressing too much to the mean…”
“… hit tool grades should be ignored…”
“…hit tool is undervalued in prospect analysis…”

I have no issue with the hit values being regressed to the mean. What I do have a problem with is if the hit tool is not measuring the correct factors. I needed to find out if reported hit grades provide any value. The following is a detailed look at how the hit tool is graded and how it fails to predict one simple factor, a hitter’s ability to get hits.

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Alas, it’s finally time to wrap up the Pod vs Steamer Projections series, which pitted my Pod Projections against the Steamer projections in several fantasy categories, discussing which players I’m significantly more bullish and bearish on. Last week, I identified 13 pitchers I was far more bullish on than Steamer for ERA. In doing this exercise, I realized I was actually forecasting lower ERAs for the majority of the pitchers we both projected. So now turning to the pitchers I forecasted a higher ERA for, there was literally only 21 to choose from, most of which were within 0.10 runs of each other, which is, like, nothing. But here are seven fantasy relevant pitchers I’m a bit more bearish on than Steamer.

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In the middle to late rounds of a draft, pitchers seem to blend together. Picking between two similar pitchers can be difficult. To help with these decisions, I have created a simple cheat sheet to determine which pitcher has an easier path to success based on several outside factors like schedule strength and bullpen quality.

The chart is simple. I went through each factor which may influence a pitcher’s prediction in which they have no control over. I collected projections on each metric and then found the z-score for each value. Greater than 0 is good, less than zero is bad. Then for each team, I added up the z-scores for a final overall value.

The cheat chart is not perfect. It’s to be used as a guide. For example, if a pitcher is a heavy groundball pitcher, the user may not want to add the team’s outfield defense and home park home run factor. A different user may have the perfect projection set except for bullpen and defense. They can ignore the rest of the information. Additionally, a user may want to create their own category weightings. Again, this is just a guide.

To start with, here are the categories and the where I got the values.

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This past Thursday, I spoke at the SABR Analytics conference on my per pitch valuations (pERA).  I originally created them to form an understandable framework for comparing prospect pitching grades and major league results. Some byproducts of the work became useful like the effects of dropping a pitch. Today, I will make available new information I provided at the conference.

For the readers who aren’t familiar with the original work, it can be read in its 2500 word entirety in this previous article. Here is a summary.

• The key is to give each pitch an ERA value (pERA) based on the pitch’s swinging strike and groundball rates. All the values are based on the average values for starting pitcher. Closers will have higher grades because their stuff plays better coming out of the bullpen.
• The pitcher’s control is determined from their walk rate which is separate from the pitch grades.
• Each pitch is placed on the 20-80 scale with 50 being average, 80 great, and 20 horrible.

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This article has happened before, and it will happen again. Every year, fantasy owners pour their resources into players on good teams. It makes perfect sense too. A “good team” scores a lot of runs, prevents runs, or both. That correlates nicely with most of the categories we track in the standard 5×5.

However, good teams also don’t take many risks on unproven talents. They start the season with extra depth, and they acquire more at the trade deadline as needed. Ryan Schimpf and Alex Dickerson wouldn’t have emerged as fringe-roto names if they had been in the Red Sox system. Today, let’s talk about some bad teams.

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Well, it’s Friday. As we head into the weekend, I wanted to take some time to address some ottoneu thoughts and tactics I’ve been ruminating on over the past week. If you’ve read any of my buying generic series, you likely know that I try to entertain all possibilities and look for value when consensus seems to move in one direction. I’m also constantly on the hunt for ways to operate more efficiently. So these are my current thoughts as I consider the ottoneu landscape. Let’s discuss these in the comments. If I’m missing anything, or these tactics seem off, please let me know.

Might David Price present a buying opportunity?

When we released our starting pitcher rankings a week ago, each of Chad, Justin, and myself had David price between $24-$25 as we head into the 2017 Ottoneu season. This was around the same time that a news of a potential injury broke. According to Jim Bowden, there is concern that Price might need tommy john surgery. If he has tommy john, Price will not be a $25 pitcher. However, Price will be reevaluated today and, according to John Farrell, could play catch again tomorrow.

Well it seems like opinions are all over the map on this one.

Let’s start with what we know. Price injured his elbow, leading many to think Tommy John surgery was necessary. However, even though he has received multiple opinions on his elbow from Dr. James Andrews and Dr. Neal ElAttrach,  it doesn’t appear that he is in any hurry to have surgery, and may be on a quicker recovery time than we realize (based on Farrell’s comments). So what does this mean for Ottoneu?

From the Ottoneu Rules Sec V:
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We’re getting ready to post our March fantasy rankings. I believe they’ll roll out starting next week. RotoGraphs isn’t the only place to publish my rankings though. Over at RotoBaller, we’re on our fourth round of updates, having started in December.

To accompany the February round, we ran a series of rankings disputes. Players with wide discrepancies like Albert Pujols – 60 picks between the highest and lowest (me) rankers – were debated. The point was to show how different player evaluation approaches can produce different projections and rankings. Instead, I discovered that I usually agreed with the other guy. Our different rankings had nothing to do with the expected stats. The issue was our managerial preferences.

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Sometimes we know something is a bad investment, but there’s nothing we can do about it. Cars – especially new cars. Hideously expensive and they hemorrhage value as you sink more and more dollars into them. But with a few exceptions, you have to either buy one, waste a couple hours per day on buses, or live in the only U.S. city with a viable subway. So I pay to own a car even though I work 40 feet from my bed. I bought it for something like $15,000 a little over four years ago, and now it’s only worth half that. Soon I’ll have to perform expensive maintenance like replacing all the tires. Dumb. Dumb. Dumb.

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After a short break to tend to family matters, let’s return to the comparison of my Pod Projections to the Steamer forecasts. A week ago, I identified six hitters I was more bullish on for stolen bases, so today, I’ll discuss the hitters I’m more bearish on. To ensure we’re comparing apples to apples, I extrapolated Steamer’s stolen base projections to the same number of plate appearances I’m forecasting for each player.

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