You need a little holiday gift for yourself, don’t you? Sure, you’ve probably received your copy of the Forecaster by now and you still have that racy romance novel to finish, but you should add a little more baseball analytics to your que. Go ahead, buy yourself a copy of FanGraphs’ own Mike Podhorzer’s Projecting X 2.0. I recently did and was not one bit disappointed. Many fantasy baseball touts refer to their “projections” often. You’ll hear pundit A say they are working on their projections and pundit B say their projections are vastly different than others, but it’s not always clear what that work entails or how they got those very different projections. Projecting X gives readers a better understanding of how projections should be created and empowers them to take on the task themselves. Regardless of who’s cooking your dinner, it’s always important to know the ingredients.

When writing about K% projections, Podhorzer sites his own research and some of Jeff Zimmerman’s research to identity three plate discipline metrics that help evaluators assess changes in hitter K%. Those three metrics are Z-Swing%, Contact%, and Zone%. Taking it one step further, Zimmerman’s age-curve research showed that hitter K% typically declines through age 24. Podhorzer’s e-book motivated me to take a look at these three metrics among young hitters from the past two seasons. Here are the hitters who accumulated at least 75 plate appearances in both 2021 and 2022 and finished 2022 as a 24-year-old:

If you look at how I’ve calculated these year-to-year differences you’ll see that I’ve made it so the numbers make sense from a positive/negative standpoint. In this case, Luis Robert saw the ball in the zone less often (-4.2%) and made contact more often (1.7%), but swung in the zone less often (-1.7%) while lowering his K% (-1.4%). Scanning the rows will show you that nine out of 12 players “did what they were supposed to do” and lowered their K% in their 24-year-old season.

While we see a few established, elite players listed in the table above, there are certainly a few intriguing players from a sleeper standpoint as well. Taylor Trammell lowered his 2021 K% of 42.1% down to 28.2% in 2022. While 28.2% is still significantly over the 2022 MLB average of 22.4%, it’s an improvement. It’s nice to see the likes of Jazz Chisholm Jr. and Brandon Marsh on this list and it’s also important to remember that some of these players were ONLY 24 for most of the 2022 season! If we kick it into experimental mode and drop the PA threshold down to 20 in each season, we see a few more interesting players:

I rostered Bruján in a few deep leagues and was tremendously disappointed, but he’s a young player who is still developing. Yes, he dropped his K% between 2021 and 2022, but like Trammell, he finished 2022 with a K% that still needs to come down (22.8%). Use caution when seeing those decreases in young players and be sure you don’t just assume they’ve figured it out. Hopefully, these two tables give you threads to pull on, rankings to adjust and perhaps, a little itch that can only be scratched with learning how to project player performance on your own. When the 2023 winter cold winds blow, download a copy of Podhorzer’s book and find yourself little gems like this to occupy your off-season hours.

How would you define Jeff McNeil as a hitter in just a few words? If you had to place him in his own “group” of hitters, who else would you place him with? Last week, I used a cluster analysis to find a player that might compare to Luis Arraez and in turn, help provide some approach recommendations for increasing his power. This week, I’ll use that same cluster analysis, with just a few tweaks, to determine what combination of Statcast and plate discipline metrics increases roto value on average. Let’s start with a refresher on my process.

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The 2022 American League batting title was won with .316. It was the lowest batting average to earn the award in the American League since Carl Yastrzemski hit .301 in 1968. Rod Carew earned the best AL batting average in 1972 with .318 and Tony Gwynn hit .313 in 1988 to earn the NL award. But typically, the batting title is awarded for a higher average. The average batting average of players winning the batting title in both the AL and NL over the past 50 seasons has been .345. Arraez’s .316 average was impressive, but it probably won’t benefit your fantasy team when quite enough when it brings only 8 home runs along with it.

Is there room for more power in Arraez’s approach? Don’t tinker with a good thing is what I immediately think, but then again, will .316 and probably slightly below (Arraez steamer 2022: avg .305), continue to top leaderboards? Furthermore, Arraez is up for arbitration prior to the 2023 season and won’t be a free agent until 2026. He has plenty of room to work for a few extra dollars in the power department. Shoot, he even said he wanted to add power himself when speaking with two of the most powerful in Giancarlo Stanton and Aaron Judge at the All-Star game (0:36):

So, what can he do? How can Luis Arraez add a little more power without changing who he is? I’m not a swing expert, but I did stay at a Holiday Inn last night and I know how to run a clustering model on high-dimensional data. But we’ll get to that in a minute.

Let’s start with who he is. First, he’s a man who does not strike out. He had the lowest K% at 7.1% among qualified hitters in 2022. He also never swings and misses. His 2.5% SwStr% was also the lowest among qualified hitters and lower than the new kid on the block Steven Kwan’s second place 3.1%. Second, he doesn’t steal bases. Four bags in 2022 and two bags in 2021 didn’t accentuate Arraez’s ability to get on base. Lastly, he doesn’t hit for power. His .104 ISO ranked 12th from the bottom among qualified hitters in 2022. From a fantasy perspective, Arraez is not necessarily a one-sided player, but he’s close. He got on base enough times to be driven in to score enough times and both his mR and mAVG returned positive value according to our auction calculator:

So where does this profile place him amongst his peers? Well, looking at a lot of columns in a spreadsheet can make it difficult to put a single label on a player. There’s just too much to sway your opinion. In order to combat this and help us create a more summarized view of many metrics, I’ll use a Principal Component Analysis (PCA) to “increas[e] the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data”. I created two sets of variables, one mostly batted ball, and plate discipline and the other Statcast metrics with a few non-Statcast metrics that more or less define power. Here they are:

Batted ball and plate discipline metrics
LD%, GB%, FB%, Pull%, Cent%, Oppo%, Swing%, Contact%, Zone%, SwStr%, CStr%

Statcast/power metrics
HR/FB,EV, maxEV, LA, Barrels, Barrel%, HardHit, SLG, xSLG

With a PCA I’m able to reduce these lists to two numbers which can then be passed through a k-means cluster analysis, grouping players into nice segments for visualization. Typically, a cluster analysis is used to gather insights on unlabeled data and it is a type of unsupervised learning. In this case, we’re using it to make comparisons we otherwise wouldn’t have:

Arraez finds himself, surprisingly, in the high-power end of cluster 1. To better understand why that is, we can compare his Statcast/power metrics with the averages from cluster 1. In addition, I’ll throw in that player all the way to the left, Tony Kemp, to help us compare Arraez with his cluster-mates:

Now we have a group for Arraez that makes sense. Next, let’s look at a few players who are higher up on the power scale, but aren’t changing too much in the batted ball/plate discipline area. Here’s our cluster image from before but with two new names identified that might be able to help Arraez inch over to the next cluster:

I am not saying that Luis Arraez should just go up there and try to be more like Josh. But I am using him as an example to determine what makes his profile more powerful. Josh Bell, 6′ 4″ / 255, and Luis Arraez, 5′ 10″ / 175, are different. While I don’t expect Luis Arraez to just suddenly increase his exit velocity, I am certain he has the skills to change his approach. One place to start would be adding more pull.

Josh pulls the ball more. Josh also swings and misses more often. But while not swinging and missing is really impressive in this day and age, how valuable is it from both a fantasy perspective and a real-life perspective? Increasing his swinging-strike percentage while also increasing his slugging percentage would benefit everyone involved. Arraez is already hitting the ball with decent slugging results when it’s put inside, though he could improve on high-inside pitches, and all of his 2022 home runs came off pulled balls:

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While watching a player who can spray the ball all over the field is fun, Arraez’s numbers aren’t great when going oppo. He slugged .638 when pulling the ball but when he slapped the ball the other way in 2022, he had mediocre results and his slugging percentage was brought down to .364. Just look at how many outs he hit into the opposite direction:

In 2022, Arraez’s HardHit% increased from 27.8% to 30.6% when he pulled the ball. When he was ahead in the count and pulled the ball, it jumped to 32.5%. Given a little more freedom from the worry of striking out, he added more power. But, here’s where things get a little odd. Arraez put the ball in the air more often than Bell and Nimmo in 2022 and his average launch angle was higher as well. If we look at his baseball savant radial chart isolated to singles, doubles and home runs (he only hit one triple in 2022), he clearly knows how to elevate the ball to hit for power:

But, without the exit velocity to take the ball out, he ends up with a lot of fly ball outs. Looking at the table above, he’s putting the ball in the air more often than Nimmo and Bell but with a significantly lower HR/FB rate.

Let’s summarize. Luis Arraez could be more valuable if he hit with a little more power. One way he might add power is to start pulling the ball more and leveling out his swing ever-so-slightly. This may cause him to swing and miss more often, but he can afford it. Arraez earned nearly $13 in 2022 and we should expect that to increase if he can adjust. It may seem nuts, but Luis Arraez needs to start swinging and missing more often.

Here are two players who accumulated over 450 PAs in 2022 and a few stats to accompany them, choose one for your fantasy team:

Player A, DH
128 wRC+
18 HR
0 SB
FanGraphs Auction Calculator YTD 2022 Value (Default settings): $1.5

Player B, DH
119 wRC+
16 HR
0 SB
FanGraphs Auction Calculator YTD 2022 Value (Default settings): $9.6

Surely, you wouldn’t just choose player A without wanting to know more but if you had to choose, you absolutely had to, you would choose player A, right? You would do this because Player A’s wRC+ is higher and he hit more home runs. But now look at the rest of the roto stats each player accumulated in 2022:

Player A, DH
128 wRC+
18 HR
0 SB
.238 AVG
47 R
59 RBI
FanGraphs Auction Calculator YTD 2022 Value (Default settings): $1.5

Player B, DH
119 wRC+
16 HR
0 SB
.274 AVG
76 R
62 RBI
FanGraphs Auction Calculator YTD 2022 Value (Default settings): $9.6

With this new information, you would have to choose Player B. You would surrender two home runs for 29 more runs and three more RBI. But, is it fair to say those runs and RBI minus two home runs are worth $8? And why is Player A’s wRC+ 9 points better? Let’s start digging and then we’ll see if we can find our way back out.

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wRC+ is by our definition, “the most comprehensive rate statistic used to measure hitting performance”, but it places heavy emphasis on runs. Here are the correlations between wRC+ and fantasy stats from the 2022 season (over 200 PAs):

Player A wOBA: .349
Player B wOBA: .343

Intuitively that makes sense. The statistic is called weighted runs created plus. But, it can be confusing if we consider our two examples above. Player A literally scored less runs himself and drove in less runs than Player B, yet he has a larger wRC+.

Let’s do some nitty-gritty mathematics and calculate each players wRC+ manually to see if we can surmise what’s going on:

wRC+ = (((wRAA/PA + League R/PA) + (League R/PA – Park Factor* League R/PA))/ (AL or NL wRC/PA excluding pitchers))*100

Player A wRC+ = (((14.4/461 + .114) + (.114 – .975 * .114))/ (10,600/91,118))*100 = ~128
*Played in the NL

Player B wRC+ = (((15.7/596+ .114) + (.114 – 1.06 * .114))/ (10,227/90,850))*100 = ~119
*Played in the AL

Even if we changed this calculation and placed player A in Player B’s league, creating the same denominator for both players, Player A would actually increase in wRC+. No, the issue is not league-specific or even park-specific, it is in the plate appearances. Player A was simply more productive in wRAA because he didn’t get enough plate appearances to maybe go through a few additional slumps. So, Player A has a better wRC+ because he was more productive with the plate appearances he was given, even though Player B was able to accumulate more statistics. That’s really the essence of what’s going on here. Player B accumulated more raw stats for your fantasy team than did Player A.

But, does it mean that if a player finished the season with an above average (over 100) wRC+ they have returned positive value? Well, it depends on how much you’ve paid as the fantasy manager, but in general, no.

There can be plenty of situations, most plate appearance based, where a player may post above average wRC+, but does not bring positive value or is no better than a replacement-level player. Take, for example, Lars Nootbaar who posted a 125 wRC+ in 347 plate appearances with an 8.9 wRAA. That’s above average. But over the course of an entire season, there were plenty of other outfielders who would have been better for your fantasy team. If you had an injury and were able to replace that injured player with Nootbaar for those 347 plate appearances, swell! But, when compared to the player pool given by the auction calculator, Nootbaar’s 2022 season cost fantasy managers $0.50. On the flip-side of this argument is Jose Trevino. Though significantly hurt by his poor production (-4.7 wRAA/91 wRC+) he still generated $4 in value because of the large positional adjustment he receives for being a catcher. This just goes to show how important using the right statistic is when it applies to your fantasy team. Had you looked at Trevino’s wRC+ and thought, “Oh, he’s below average”, you would have missed out on a $4 catcher.

The main rule of thumb for when you find yourself down in a hole and looking to get out is to stop digging. Have we found something in the shovels full of dirt? Fantasy managers should not blindly use wRC+ as a metric for selecting fantasy value. Plate appearance accumulation is still king. But, if you’re the type to zig while everyone else zags, you may be able to use wRC+ to find fringe-level players who others have looked over. If you look at the 0 line in the scatter plot above, just above it are players who were worth $1. In addition, there are a number of players who returned negative value but were very productive at the season’s end. While wRC+ has become more and more common as a one stop shop type of offensive production metric, perhaps it is falsely molding your competitor’s impression of a player and therefore can be used to your advantage.

Player A: Daniel Vogelbach (park factor was the average of Pirates and Mets)
Player B: J.D. Martinez

It is now that time of year when your inbox pings and dings with notifications from Ottoneu. Is that a good trade proposition? Should you keep player A? Are you over-paying player B? These are all questions you should be asking yourself this off-season. Luckily, FanGraphs has all the data you need to make informed decisions. Here are three easy steps you can take this offseason to ensure you’re ready for your Ottoneu re-draft.

1. Merge your league rosters with new 2023 projections.

In order to do this you need two things:

(a) a fresh .csv download of auction calculator values (both pitchers and hitters) using steamer projections, tuned to your specific league settings.
(b) your league csv, which you can easily download by clicking on the tools icon at the top of your Ottoneu league page.

From there, you’ll need to merge your new 2023 values with your current players. Here’s an example from one of my teams:

You can do two things with this table. First, you can compare the salary you’re paying a player with what Steamer 23′ thinks a player is worth. Clearly, I am overpaying Taylor Walls. Steamer thinks he’ll be worth negative value, so realistically if I wanted to keep Walls going into 2023, he would need to only be $1 with a lot of upside for that keep to make sense. Second, you can use the same process to see where your league mates stand. If you know your competition is underpaying a few players, you could target them as off-season trade candidates.

2. Use the roster organizer to place cuts on any over-paid players and decide if any players have trade value.

From the way my top five over-paid players table looks, I’ll be clicking over to the “Roster Organizer” tab and making a few cuts. This is a nice tool because it allows you to see how your budget changes depending on what cuts you make. Not all over-paid players need to be cut, but most of them should be cut or traded. Maybe you’re only overpaying by a few dollars and you think a league mate might be interested, slot them into the trade category. On the flip side, you can look for a few players that you are underpaying, but don’t believe in. It’s possible your hunch could be right, but it’s pretty hard to outsmart the projection systems.

3. Analyze your team’s needs for the upcoming season.

Now that you have your roster all organized and tidy, it’s time to figure out what you need for next season. Take your team exactly how it is and calculate the points your players will provide per category. My Ottoneu leagues are all FanGraphs points leagues, so I can take a steamer projection .csv, merge on my players and total out the points they will score based on the projections:

Without being able to compare to other teams, this bar graph only gives me a general sense of my offensive projections, but what I can tell is that my team is full of high-average players. Here’s a breakdown of where my points are coming from:

From what I see above, I believe I’ll need to shop for some power this off-season. It’s time to start looking for low-power/high-average players that I may be overpaying and use them as trade chips to acquire more power.

Breaking your and your league mates’ teams down in this way will give you the opportunity to make informed keep/cut, trade, and draft strategy decisions.

Tye Tolberman sat in his favorite armchair on the coldest day of mid-January in western Maryland, steam coming off his morning coffee as he looked out the window. He took one glance at the ice covering his driveway and decided he just wasn’t going outside today. On his side table sat a pile of unopened letters, a checklist of things needed to be done, and a printed-out spreadsheet a friend had given him the last time he went east to see the Bowie Baysox play last summer.

“It fell out of some scout’s binder the last time I was here. He went flying out of there after the starting pitcher got taken out, so I couldn’t catch him to give it back”, his friend told him as they sat and took in a night game. “Anyways, you can have it if you want. I’m not wasting my time with that fantasy baseball crap this year. I’ve got too much real life to live”, and he placed it in the hands of ol’ Tye Tolberman, manager of the going-on 14-year-old “Buck’s Bucks” fantasy team.

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In 2022 the average number of plate appearances among big leaguers was 121. A plate appearance marks any time a player walks up and digs into the batter box. Each spit, every toe twist, and all of the glove-tightening times in the box accumulate together into plate appearances. Let’s start with the easy one. Who had the most? Marcus Semien. He had 724. In fact, he had exactly 724 last season as well. That’s roughly 4.5 plate appearances per game.

That’s absurd.

He played in 161 games in 2022 and he wasn’t even the player who played in the most games this year. That would be 162 games and, actually, two players did it; Dansby Swanson and Matt Olson. Teammates! The Ironman Cal Ripken, Jr. averaged 4.3 plate appearances (12,883) per game (3,001) for his career. What Semien, Swanson, and Olson did this year is special. It should be an award in itself. If it is already and I don’t know it, don’t blast me. But I’m pretty sure people would just say, “Oh, an award just for showing up?” and I would argue that showing up is rare these days.

Here’s a histogram of 2022’s every plate appearance:

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What was the best possible fantasy combination of infielders (1B, 2B, SS, 3B) this season? That is an easy question to answer if you use the year-to-date settings on the auction calculator and you simply look at the best player at each position. It would look like this:

1B: Paul Goldschmidt, $33.7

2B: Jose Altuve, $25.2

SS: Trea Turner, $32.5

3B: José Ramírez, $31.2

But, if you were in a 12-team roto snake draft and were able to get both José Ramírez and Trea Turner, you likely played in a league full of clowns. J-Ram’s average draft position (NFBC) was 3.2 while Turner’s was 1.2. It’s unlikely any fantasy teams had both of those top players. So, what was the best possible infield within reasonable ADP? Here’s how I tried to answer this question.

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• 2022 pVal Check-In #1.
• 2022 pVal Check-In #2.

How do you really know how well a pitcher’s fastball will perform before the season starts? You don’t. You basically just make an educated guess. Choosing good fastballs from the previous year seems to be a good way to go. But, choosing changeups that performed well in the past can bite you. While it may seem silly to get this micro, this niche, it can be a lot of fun to make picks before the season begins, and then forget about them. There’s no IL, no waiver claims, and no bench to ride. It’s just your preseason picks and the ever-rolling season and when it ends, you get to look back and either ask yourself, “What was I thinking?”, or, you get to puff your chest out and spend the winter months assuring yourself that you are a pVal prediction wizard. Pitching coaches should really be calling you when spring training kicks off again. Let’s take a look at where each of us FanGraphs contingent pVal competition participants finished out the 2022 season. As a reminder, here are all the picks you can make, provided to us by our friends at Pitcher List.

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To qualify for this totally made-up award, a player must have been projected to earn positive value in the stolen base category (mSB) on our Auction Calculator (default settings, steamer projections) and negative value in every other category. They then needed to earn positive value in at least three of the four non-SB categories (mR, mHR, mRBI, mAVG).

Finalists: Bobby Witt Jr., Daulton Varsho, Dansby Swanson

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Finalist #1: Bobby Witt Jr., Projected negative: mAVG, mHR, mRBI, mR. Earned positive: mHR, mRBI, mR, mSB

Surely there will be plenty written about Bobby Witt Jr.’s rookie season in fantasy realms this offseason. His preseason projections were likely low due to the uncertainty of his playing time/call-up situation. It was clear that he was going to be good. But, would you have guessed he’d finish as the fifth-best third-baseman by auction value earned? It was clear that he was going to steal bases, and in the end, that’s what drove his value. If you look at any one of his 15-game rolling charts not involving base running, it would tell you a story of a 22-year-old rookie who has a lot of potential but may not have lit the world on fire in his first year. It happens.

Witt outperformed all of his roto-category projections except for his batting average. He was projected to hit .257 and he hit .254. Not bad, steamer projections. Compared to his 2021 AAA stats, his line-drive rate was down and his ground ball rate was up. He also put out fewer fly-balls for home runs than he did in AAA. However, he still showed that he has the 22-year-old ability to absolutely smoke the ball with his 92nd Statcast percentile maxEV. But, he had a difficult time with major league four-seamers, putting up a -7 Statcast run value and a 23rd worst (among qualified hitters) -2.6 pitch-info p-val on wFA. However, Witt accumulated 632 plate appearances in his rookie season (second behind Steven Kwan) and that experience must be worth a few extra dollars going into 2023.

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Finalist #2: Daulton Varsho, Projected negative: mAVG, mHR, mRBI, mR. Earned positive: mHR, mRBI, mR, mSB

One thing to keep in mind is Varsho’s increased projected value due to his eligibility at catcher. The positional scarcity earns him a higher value. But, Varsho was a catcher-eligible player who was expected to run and he did not disappoint, stealing a career-high (MLB) 16 bases. He also put together a close-to-full season with 592 plate appearances in 151 games. He was a workhorse this season and everything but his batting average (.234) showed it.

However, these roto-value gains don’t seem to be accompanied by much underlying Statcast data:

He did catch 31 games in 2022, so depending on your league rules, he may or may not be eligible again at the catcher position in 2023. Regardless, no matter which way you look at it, 27 home runs and 16 stolen bases at the catcher position in 2022 play nicely on any fantasy team under any league parameters. For a player who has only had roughly one and a half seasons at the major league level, I’m looking forward to seeing his 2023 projections.

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…and the award goes to…

Finalist #3: Dansby Swanson, Projected negative: mAVG, mHR, mRBI, mR. Earned positive: mHR, mRBI, mR, mSB, mAVG

As Swanson heads into free agency this offseason, his 2022 totals indicate that he intended to make a statement.  In 2022 he put up the most plate appearances of his career, scored the most runs, hit the most RBI, and stole the most bases. He also put up his lowest BB%, which didn’t help those rostering him in OBP leagues. His K% has been on an upward trend in the past three seasons, but so have his home run totals, FB% and SwStr%.

Is he selling out for power? Maybe. But he also outperformed his batting average projection (steamer) of .245 by over 20 points (.277) and the 2022 major league average was .243.

Don’t call him a rabbit, but he did run, stealing a career-high 18 bags. However, he’s not being given this award because he stole bases, he’s being given this prestigious award because he did everything else.

Congratulations to Dansby Swanson on an amazing season and to any fantasy managers that rostered him. Next week, I’ll begin my dishing out these highly coveted awards to pitchers.