Archive for Roto

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.

Read the rest of this entry »

Last week, we kicked things off in our initial 2023 fantasy baseball ranking with a surprisingly fun group at catcher, while also alerting me to a previously unknown (and unexpectedly vociferous) Sean Murphy army. I’m sorry, okay! Let us all now move on in peace to the slowest of the corners, and see what’s happening over at first base. Read the rest of this entry »

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:

–

–

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.

—

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

My goodness, what times we are a-livin’ in! Not only can huckster frauds manage to make billions of dollars go up in smoke on any given day but we actually have multiple fantasy options at catchers that are really good. Well, at least ones that won’t make us want to go cry in a corner like we just lost all of our savings to a huckster fraud. What a world!

Whether one catcher or two, 12 teams, or more, catcher has mostly been a wretched den of iniquity, forcing us to generally hold our noses and hope for the best. But not this year, my friends; not this year. By my count, we have five (count ’em, five!) catchers, who if they were to finish as the Top Guy, wouldn’t really surprise us. Five! Now, it’s obviously a different kettle of fish in two-catcher leagues but in the one-catcher, 10-12 team leagues that are the standard for a majority of the fantasy community, this is huge. Read the rest of this entry »

Last week on “Steamer Says”, we spent some talking about the best way to look at and use preseason projections, as the first ones of 2023 have dropped, thereby necessitating we quickly label some busts and steals, post haste! We’ll skip right to the players this time, looking at three more players that these first projections believe will underperform their current draft prices.

Keep in mind that projection systems care more about hurting feelings than they do about numbers and that we should take any disrespect toward players we like very, very personally. Read the rest of this entry »

The first Steamer dropped earlier this week, with Jared Cross and dem boys blessing us with the initial set of projections for 2023. Our long international nightmare is over; there’s fresh data to argue about. Read the rest of this entry »

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:

Read the rest of this entry »

Okay, just one-nneee more look at a couple of early sleepers, and then we’ll take this poor horse to the upstate farm it’s always dreamed of. This time out, let’s get heavy with the dongs, searching for some of that elusive late-draft power. Read the rest of this entry »

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.

Read the rest of this entry »