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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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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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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Arguing price is something that has been going down since currency was made out of copper coins and some of the coins had holes in the middle. In today’s world, price negotiators say things like, “$3.15 a gallon for gas?! You’re out of your mind!” or “I’m not paying over $3 for a watermelon, I’m just not!” Whether you’re the type to wait for a sale on underwear or the type to just go and pay what you pay because, well, you need it, all of us can relate to the idea of arguing a price. In my last piece on Kyle Freeland, I made the case that he’s worth $4. I was immediately argued with (politely, that is) about that price, and you know what? I may be overpaying. But, that’s the beauty of price! It’s here, it’s there, it’s really up for debate. So, let’s do it, let’s try this: Garrett Hampson is not worth $8 in FanGraphs points Ottoneu leagues. Here’s why:

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I recently took over an Ottoneu team for the upcoming 2022 season. If you are unfamiliar with Ottoneu, it’s keeper league where you get to make trades from mid-November to January 31st. Any players left on your roster after that you keep, using the auction draft to fill in the missing pieces. Taking over someone else’s team kind of feels like moving into an empty office. There are a few cobwebs in the corner, the previous owner left a really cool pen in the desk drawer and there’s a very stinky sandwich in the staff refrigerator that you somehow feel like is your new responsibility.

The fun part is taking stock of what you have and trying to decide what you want to keep (cool pen) and what you want to cut (stinky sandwich). In this series of posts, I’ll write about the decisions I have to make, how I go about analyzing the data before making my decision, and then what decision I plan to make. You too could be doing this kind of thing, all you have to do is take over someone’s abandoned team and search through the desk drawers when you move in.

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Let’s do some experimenting. Let’s imagine you drafted a team using only one statistic. What style of baseball are you? Do you love the hitters with speed, bat control, and an eye for tactics? Or, are you more of the home run or strikeout kind of fan? Why can’t you be both, you ask? Well because it’s an experiment and you have to choose one or the other. So…go ahead. Which do you choose? The K% Kings or the Barrel Brothers?

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If you haven’t studied the managers who won your league by now, you should. If you won your league, and your competition is smart, you’re being studied. People are trying to find out why the Robbie Ray‘s and the Marcus Semien’s of 2021 caught the eye of those who drafted them and are looking to find next season’s doppelgangers. The Dodgers didn’t take long at all to take their pick. We can make all the models, algorithms, spreadsheets, and crystal ball readings we like, but the most tried and true technique is regression. Players that were unbelievably good…will regress to their true-talent level. Players that were unexpectedly bad…will regress to their true-talent level. This is something you can take to the bank. Rather than choose from 15 of the best players we should take in the first round, let’s think of players that we expect to go in the 5th and 6th rounds. Here’s a look at three players that seem likely to do just that, and could fly under the radar in your 2022 draft.

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Machine learning allows human eyes to take a break from the mundane scrolling and sorting through spreadsheets while still gathering useful insights. What made a top-five pitcher (from a fantasy perspective) so great in 2021? You could answer that easily by sorting through our leaderboards. Robbie Ray? Well, he had an excellent K-BB% (25.2%, league-average 14.6%) Corbin Burnes? His K/9 was a huge 12.61 while the league was only at 8.9. These underlying metrics are important to take note of but can be difficult to analyze all at once. Don’t get me wrong, it can be done. Just look at Michael Simione’s latest piece where he compares pitchers’ underlying metrics. In fact, that’s a lot of fun to do! But, as you come out of your fantasy hibernation and are ready to begin making your draft rankings, you’ll want a quicker way to analyze large data sets all at once. Enter the decision tree. 

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Let’s play a game. We’ll look at one single at-bat and go through each pitch pausing in-between to assess what we think will happen next. The goal of this game is to guess the final outcome; pitch type, pitch location, and result. Let’s get started with our first edition of…Guess!…That!…Pitch!!

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What’s the first thing you do when you get a new spreadsheet? My answer; sort ascending, sort descending. That’s what I did on the 2021 pitching leaderboards. I looked at the plate discipline metrics for qualified pitchers and sorted the sheet by descending swinging-strike rate (SwStr%). The names I saw were not surprising. Corbin Burnes leads the group at 16.6%. He’s followed by Max Scherzer (15.9%) and Robbie Ray (15.5%). José Berríos is not in the top 30. In fact, when you sort the same list by ascending swinging-strike rate, he’s in the top 10, meaning he had one of the lowest swinging strike rates in the league (9.9% to be specific). The lowest swinging strike getters in the league this year were Adam Wainwright (8.1%), Chris Flexen 플렉센 (8.6%), and Dallas Keuchel (8.7%).

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