If you had to choose, right now, who will finish the 2022 season with the highest FB pVal, who would you choose? How would you make your choice? Maybe you would start by looking at last year’s top FB pVal finishers. Read the rest of this entry »
In last week’s post, I included a visualization that showed the distribution of projected counting stats by position so that fantasy managers can prepare for drafts. Here’s the example of stolen bases I presented last week:
I’ll admit that the first time I looked at the auction calculator I was overwhelmed and confused. What do all those numbers mean? How come some of them have parentheses around them? What’s an aPOS? I use a snake draft in my league, not an auction. Why do I need dollar values? Well my friend, if you have asked yourself those questions I hope to answer some of them in this post and prove to you that our auction calculator is a very valuable tool whether you play in a 10-team ESPN home league or you’re an NFBC regular.
Let’s play a game. Here are the instructions:
Step 1: Don’t scroll down to the table!
That would be like reading the answers in the back of the textbook before you read the question. You did it in fifth grade, don’t do it now.
Step 2: Imagine drawing a vertical line in the visual below that does a good job of maximizing the four roto counting stats, SB, HR, RBI, and R. Try not to pick rank one, two, or three.
Step 3: Note your player rank. Now you can scroll down to the table to see who you have chosen.
I created these ranks using the default settings of the auction calculator with steamer projections to sort by dollar value. Note that if you wish to do something similar, you should input the parameters of your league accordingly. The point of this exercise is to try to identify players who could be good pickups later in the draft. We so often see tables and spreadsheets and mark single statistics. But, it’s difficult for the human eye to take in all that information and process it. This visual is helpful because it shows us not how single stats like stolen bases decline across the ranks, but how all the counting stats vary across the ranks. For example, if you chose rank 8, you would get a nice little bump in projected (steamer) stolen bases with close to average HR, R, and maybe a little more RBI than some of the lower ranks. Who is this mysterious rank eight? Scroll down to the table to find out.
You, like me, may think, “I can wait for stolen bases. Shortstops seem to have a lot of them.” This visual proves that theory somewhat:
That long tail moving to the right of the shortstop (light blue) distribution shows us that we could potentially pick up a decent amount of steals with a late shortstop pick. However, rank 15 in the first visual shows how much you would sacrifice in projected runs and RBI if you were to employ that strategy. Who is this mysterious rank 15? Scroll down to the table to find out.
I’ve done some mock drafting this offseason using the Fantasy Pros draft simulator and I’ve found that if I don’t get stolen bases early because there are so many good hitters and pitchers that I take instead, I come out very SB-lite. I’ve also noticed that the simulator reaches for base stealers like Tommy Edman, Jazz Chisholm Jr., Akil Baddoo, and Tim Anderson much earlier than I would expect. That’s likely to happen in my actual draft as well. There’s nothing worse than finishing a draft and being really excited about your team and then realizing that you are depleted in a single category, especially if you don’t want to employ a punt strategy. Hopefully, these visuals and a few mock drafts will help you identify optimal draft positions for certain categories. I’m happy to do this same exercise for another position, but only if you’re nice to me in the comments section.
| Rank | PlayerName | POS | ADP | PA | HR | RBI | R | SB | Dollars |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Fernando Tatis Jr. | SS/OF | 1.8 | 677 | 46 | 113 | 115 | 26 | $43.80 |
| 2 | Trea Turner | 2B/SS | 2.0 | 681 | 25 | 88 | 103 | 27 | $31.10 |
| 3 | Bo Bichette | SS | 5.4 | 668 | 30 | 97 | 99 | 17 | $29.70 |
| 4 | Tim Anderson | SS | 34.9 | 692 | 23 | 75 | 95 | 19 | $21.50 |
| 5 | Marcus Semien | 2B/SS | 31.8 | 681 | 30 | 84 | 96 | 11 | $17.10 |
| 6 | Francisco Lindor | SS | 48.7 | 667 | 30 | 93 | 89 | 13 | $16.90 |
| 7 | Wander Franco | SS | 53.5 | 651 | 19 | 84 | 85 | 10 | $16.20 |
| 8 | Trevor Story | SS | 41.0 | 655 | 27 | 88 | 85 | 20 | $16.10 |
| 9 | Xander Bogaerts | SS | 42.8 | 649 | 24 | 92 | 85 | 6 | $15.50 |
| 10 | Corey Seager | SS | 72.1 | 596 | 25 | 82 | 85 | 3 | $14.80 |
| 11 | Jorge Polanco | 2B/SS | 79.3 | 654 | 24 | 83 | 85 | 11 | $14.30 |
| 12 | Carlos Correa | SS | 91.8 | 621 | 28 | 88 | 84 | 1 | $14.20 |
| 13 | Gleyber Torres | SS | 149.6 | 632 | 23 | 82 | 81 | 14 | $14.10 |
| 14 | Robert Witt | SS | 90.9 | 545 | 24 | 75 | 71 | 18 | $12.80 |
| 15 | Jazz Chisholm Jr. | 2B/SS | 79.2 | 568 | 22 | 66 | 73 | 22 | $8.20 |
| 16 | Jake Cronenworth | 1B/2B/SS | 111.9 | 644 | 17 | 79 | 78 | 6 | $7.60 |
| 17 | Willy Adames | SS | 133.8 | 628 | 23 | 76 | 81 | 7 | $7.40 |
| 18 | Oneil Cruz | SS | 224.2 | 457 | 21 | 64 | 57 | 14 | $7.00 |
| 19 | Dansby Swanson | SS | 123.1 | 665 | 22 | 73 | 85 | 10 | $6.60 |
| 20 | Brendan Rodgers | 2B/SS | 152.1 | 586 | 21 | 75 | 78 | 2 | $5.00 |
Last week I pointed to the great research done before me that shows that a player’s BABIP is likely to regress to their previous 3-year average. What’s the point of doing this before drafting? Well, it allows you to see players who may be over or undervalued. In the case of last week’s post, you can find players who may have gotten lucky and will likely do a little worse, hit-wise, in 2022. For example, last week’s analysis showed that Starling Marte, Brandon Crawford, and Kevin Kiermaier topped the list, in that order, of hitters who outperformed their 3-year (2017-2019) BABIP in 2021. Furthermore, I performed a cluster analysis that tried to explain just how these players overperformed. That allowed us to figure out if there was a skill change or not. Let’s do the same thing for players who underperformed in 2021.
Work done by Voros McCracken has shown that a batter’s previous 3-year BABIP is a good predictor for next year’s BABIP, it is known. Here’s a quote from our own glossary:
..changes in BABIP are to be met with caution. If a batter has consistently produced a .310 BABIP and all of a sudden starts a season with a .370 BABIP, you can likely identify this as an instance in which the batter has been lucky unless there has been a significant change in their style of play.
Those who have been smart enough to pick up a copy of the 2021 Baseball HQ Forecaster (and likely posted a picture of it to their Twitter) have already read through the “Other Diamonds” portion of the text. It’s my favorite part. In it, there is a section called Paradoxes and Conundrums and it states that a player’s year to year improvements can be labeled as, “a point in a growth trend, an isolated outlier or a complete anomaly…” I loved that line when I read it. But, so much strange happened in 2020 and I think it should have some say. In this post, I’ll go over three pitchers who increased their K% from 2020 to 2021 but whose gains might be skewed by the 2020 season. Two of these pitchers really just rose back up to where they were in 2019. An increase in K% is wonderful but is it a trend, an outlier, or, to incorporate my own twist, to be expected?
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.
The experiment from last week’s post on modeling for strikeout rate continues this week with a look at walk rate:
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: