Archive for Roto

The age of the reliever is going to continue in 2022, if early ADP is any indication, with much of the position gaining steam as we move deeper into the offseason. And given how diluted the position continues to be as saves get less concentrated by each individual, is it that surprising?

Whether for saves now, or rare-earth metals in a dystopian future, it’ll usually behoove you to adjust your expectations about what people are willing to do when it comes to acquiring scarce resources.

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My recent catcher facts article brought in some positive feedback so I am going to do the same format for each position. Moving on to the first base position I want to discuss a bit of strategy first. First base seems to be rather deep and it also holds a lot of late power options with the likes of Josh Bell and Joey Votto. If you decide to get someone early Paul Goldschmidt seems to be the only one who provides early speed and might be the best option. Whether you decide to grab one earl or late here are some fun facts and opinions I gathered while preparing for drafts.

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One of the greatest fantasy tutorials I’ve ever received came in Jeff Zimmerman and Tanner Bell’s, The Process. In the book, there is a breakdown of two very important valuation systems; standing gains points and z-scores. Our auction calculator, for example, is built around z-scores. For a further dive into both, I highly suggest purchasing a copy of the book. In general, z-scores help us understand how good player A is compared to the rest of the draftable player pool and it can be used as a great jumping-off point for your rankings. I use the word “rankings” because they are not projections and that’s the beauty in z-scores. You are not trying to outsmart projections. Instead, you are using a projection system of your choice to create your rankings. In this post, I’ll be creating z-scores for shortstops in 2022 using Steamer projections.

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The experiment from last week’s post on modeling for strikeout rate continues this week with a look at walk rate:

1. I’ve limited to players with at least 120 PAs because it is a good point of stabilization for hitter BB%.
2. I’m using 2017-2019 as a training set and then deploying my model on 2021 data to look for differences between model predictions and actuals.
3. My model only tells us what should be expected from a hitter who accumulates at least 120 plate appearances in a season based on what other players have done in the same situation from 2017-2019. 2020 is excluded. The predictions of this model should not be confused with expectations.

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Here is the first run of my top-150 starting pitcher rankings for 2022. I create these by turning my personal projections into dollar values and using those, along with non-projection factors, to rank accordingly.

These will change, as my projections do, over the course of the offseason, with many being driven by adjustments in playing time projections more so than changes in talent rates. ADP information is taken from NFBC, previous values are calculated with FanGraphs auction calculator. Read the rest of this entry »

Trust me I know the catcher position is insanely boring and especially boring when you have to analyze the players. Well, as I prepare for my draft season I did break down every possible catcher for the fantasy season. Here are some fun facts and opinions I gathered while doing so, maybe this can help ease your pain.

When you think of power in terms of catchers you think of course think Salvador Perez and Mike Zunino. Perez broke the catcher record with 48 home runs while Zunino put up an impressive 33 home runs. But only Zunino had a higher max exit velocity than Willson Contreras who had a 115.4 Max EV.

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Experiment alert! Prepare yourself to digest a very simple linear model that looks at plate discipline data. I’ll do some explaining of the model along the way, but here are a few points to cleanse your already superb palate before sampling the charcuterie:

1. I’ve limited to players with at least 60 PAs because it is a good point of stabilization for hitter K%.
2. I’m using 2017-2019 as a training set and then deploying my model on 2021 data to look for differences between model predictions and actuals.
3. My model only tells us what should be expected from a hitter who accumulates at least 60 plate appearances in a season based on what other players have done in the same situation from 2017-2019. 2020 is excluded. The predictions of this model should not be confused with expectations.
4. Hasn’t this been done before? Probably.

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xFIP is an underlying metric that can be used as a predictive ERA estimator. By definition of Fangraphs: xFIP is a regressed version of Fielding Independent Pitching (FIP), developed by Dave Studeman from The Hardball Times. It’s calculated in the same way as FIP, except it replaces a pitcher’s home run total with an estimate of how many home runs they should have allowed given the number of fly balls they surrendered while assuming a league average home run to fly ball percentage (between 9 and 10% depending on the year).

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There is more to pitching in fantasy than just strikeouts but ratios and wins can be fickle little elves from season to season, mischievously relying on more things beyond a pitcher’s control. The whiffs, however, tend to be more predictable year-to-year. Or, at least, it’s easier to make more direct connections between changing results and changing stuff.

Strikeout rates also start to stabilize quickly, at around 60 batters faced. Note that they only start to stabilize at that point, so while the sample may start being meaningful, it becomes much more so with more batters faced. But the comparatively lower threshold for strikeouts makes it more possible to compare them with 2020’s mini-season.

Here are the starting pitchers in 2021 (min 75 IP) that saw at least a two-point increase in their strikeout rate compared to 2020.

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A player’s spot in the lineup is crucial to the amount of RBI they accumulate. What’s even more important is the production of the player hitting in front of that player. Have I stated the obvious? Probably. But, it’s easier to write down analytical laws in an agreeable way than it is to actually take a look at the data. In this post, I’m going to investigate the RBI production of players who had at least 100 plate appearances on teams in the NL West according to their most common lineup spot. Let’s see if hitting in the three spot is more productive from an RBI standpoint than the one spot, as they say it is.

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