Here are four players who were projected by Steamer to steal at least 15 bags and are outperforming their wOBA projection:

Now, before you get all “snarky-comment” on me, Badoo and Thompson each only have 15 PAs and will be omitted from this analysis due to such a small sample. Only Myles Straw (46 PA) is a qualified batter, but Mateo (33 PA) is close. Yes, it’s early but just try to go into this with an open mind. For if you drafted, Myles Straw for example, you are probably pretty proud of yourself, sitting upon your thrown enjoying the grapes that are being hand fed to you while also being fanned to cool off from such a hot start. But, what can we expect from these surprises moving forward? If these are real gains, then we should expect even more stolen bases.

Myles Straw, CLE: BABIP is way up, but so is his BB%

Just look at the differences in his projected and actual batting average and wOBA. Over time, Straw will get closer and closer to that projected number. The real question is not, “Will this last?”, as much as it is, “Will he end up north or south of his projected numbers?” So far this year, he’s had a number of ground ball singles to the pull side. Here’s an example:

Straw is obviously very, very fast. But if Volpe is playing a little closer in and the third baseman doesn’t make an attempt on the ball, maybe it’s an out. It’s hard to say just how difficult of a play that was to make, but it doesn’t seem difficult to predict it doesn’t happen over and over again. It’s part of the reason Straw’s BABIP sits at .414 (current 2023 MLB average: .300). It’s completely unsustainable. Here’s a look at his spray chart and you can see a few ground ball singles to the pull side helping to inflate his BABIP:

Click to enlarge

Straw is destined to regress, but how far will he regress? In order to answer that, we have to see if there has been any significant change in his approach that might suggest he has made a change. Let’s look at his O-Swing% to see if maybe he’s better at identifying bad pitches:

No change there. How about his approach in different counts?

There’s some suggestion here that he has improved in 3-0 and 1-1 counts, but the sample is simply too small to make much of a conclusion from. But, if you look at his BB% towards the end of last season, he was trending in the right direction and appears to have picked up right where he left off. He’s done that before, just look at what he did in the second half of the 2021 season when he reached a peak 22.7% walk rate!

The early returns on Straw have been terrific and if you are rostering him, put him in your lineup until the well runs dry. He is an excellent base-stealer with a career 88% stolen base success rate. For context, Trea Turner has a career 85% stolen base success rate, though Turner has attempted significantly more 2B robberies. Only time will tell if his OBP (.449 2023, .326 Career) gains in the form of BABIP, wOBA, and BB% are more luck than skill.

—

Jorge Mateo, BAL: wOBA is up and plate-discipline trending in the right direction.

I went to my first Grapefruit League game this spring and was impressed with how good Mateo’s batted balls were looking. He just kept smashing the ball, but right at a defender. He’s always had issues with plate discipline but at the end of last season, he started to bring his rolling averages down on swings and swings outside of the zone:

If he can work on his approach, specifically when he’s behind in the count, he could see OBP gains that would directly impact his stolen base accumulation. He is at his best when he’s ahead in the count, like in 3-1 and 2-1 counts, but he could be even better when ahead in the count. There’s no reason he shouldn’t be at or above league average in 3-0 counts. There is no reason, at all, that Mateo should be given the green light in 3-0 counts. Even if he takes and gets to a 3-1 count, he will be at his very best. So far this season, Mateo has faced four separate 3-0 counts and has taken a called strike on the next pitch in each plate appearance. That’s good. Those four plate appearances ended with a walk, a pop out, a strike out, and a hit by a pitch.

While Mateo’s plate discipline metrics are trending in the right direction, he is outperforming his expected stats, which is the opposite of what I observed in Sarasota:

AVG:.286 xAVG:.231
–
SLG:.500 xSLG:.426
–
wOBA:.383 xwOBA.324

Even still, his .324 xwOBA is just below the current league average (.328) and that’s a step in the right direction as his career-best came in 2021 when he put up a .287 xwOBA. He’s barreled the ball twice already, but that doesn’t come close to league leaders Matt Chapman and Bryan Reynolds, who each have 12 on the year. Let’s take a look at Mateo’s barreled balls:

–

Barreling balls for home runs is good fun, Mateo just needs to do it more consistently. At the end of last season, he was putting the ball on the ground less often and hitting it harder more often, and while those trends seemed like they might continue in 2023, he’ll need to increase his launch angle more consistently to make an offensive impact:

One thing is clear, base-stealers are stealing bases at rates that suggest the projections could be way off the mark by the end of the season. Now is the time to find base-stealers who have made some kind of approach or skills change that get’s them on base more often. Each one of Straw, Mateo, Badoo and Thompson should be added if they are available. If the sample size gets larger and the gains smaller, you can always drop them.

*Stats in the opening table were created on Wednesday, April 12th.

Much like Hot Right Now, Cold Right Now will be a weekly Ottoneu feature, primarily written by either Chad Young or Lucas Kelly, with a focus on players who are being dropped or who maybe should be dropped in Ottoneu leagues. Hot Right Now will focus on players up for auction, players recently added, and players generally performing well. Cold Right Now will have parallel two of those three sections:

1. Roster Cuts: Analysis of players with high cut% changes.
2. Cold Performers: Players with a low P/G or P/IP in recent weeks.

There won’t be a corresponding section to Current Auctions because, well, there is nothing in cuts that correspond to current auctions.

Roster Adds

Darick Hall, Leagues with a Cut (7 days) – 33.97%

RotoWire estimates a return to action by mid-July after Hall goes through surgery to repair a torn ligament in his thumb. It’s a shame since Hall was going to get a chance to be the everyday first baseman for the Phillies after Rhys Hoskins‘ knee injury vacated the spot. Now, the Phillies have shifted things around the infield placing Alec Bohm at first and sliding Edmundo Sosa over to the 3B position. That leaves Kody Clemens and Josh Harrison as the IF/OF utility options to fill in positions when needed, with Harrison likely filling a platoon spot against righties. In any case, managers who rostered Hoskins, then Hall, and are now looking for replacements may have to split the playing time between a few players and may need to find players with sneaky 1B eligibility. Here are a few to consider:

Carlos Santana, PIT – 1B, Roster 5.77%

Harold Castro, COL – 1B/2B/SS/3B, Roster 2.24%

Garrett Cooper, MIA – 1B, Roster 23.08%

J.D. Davis, SFG – 1B/3B, Roster 38.78%

–

Aaron Ashby, Leagues with a Cut (7 days) – 47.12%

The young lefty just couldn’t get his shoulder right in Spring Training after seemingly giving it every attempt. It was announced last week that Ashby will likely miss the full 2023 season and Ottoneu managers unwilling to keep him in the IL spot for the whole year have made cuts. For those in re-build mode this season, Ashby may be a nice hold target but don’t expect that it will be a quick turnaround. Shoulder injuries can be so tricky to get right. Ashby is now rostered in 55.45%, so the majority has chosen to keep him on the roster.

Ken Waldichuk, Leagues with a Cut (7 days) – 27.56%

Two stinkers and managers are done:

In points leagues, those home runs did serious points damage and in roto-leagues, the hit to ratio stats must be painful. Waldichuk put the ball in the zone too often across his two starts (46.7% vs. MLB 2022 SP Average, 41.6%) and he relied on his fastball too much throwing it 58.1% of the time. While many managers must be hurt deeply by these two starts, I don’t know if it’s fair to make a cut after two bad starts. Waldichuk is the second listed prospect on THE BOARD for Oakland and 85th overall in the league. There’s still a lot of talent and potential and he’s worth stashing and sitting.

Jared Shuster, Leagues with a Cut (7 days) – 23.08%

Similar to Waldichuk, Shuster is being dropped after a rough couple of starts:

While you could chalk it up to BABIP bad luck, the walks and lack of strikeouts are an issue. Shuster was optioned to AAA and will work on a few things in the minors, but should be back up eventually this season. He was a first-round pick for the Braves in 2020 and was the number-one ranked prospect in the organization as of last year. He’s a pitcher to stash for sure.

Cold Performers

Javier Báez, 33 AB, 2 BB, 4 H, -0.51 P/G

Baez’s K% is down from his career average and his BB% is up from his career average and his BABIP is at a career-low .154. I realize it’s too soon to be making comparisons between the 2023 season and a player’s career, but it is something to note. He’s already hit a ball over 100 MPH but he’s been putting the ball on the ground (GB%) 65.1% of the time. Before you get all “change of approach and bad luck!?” on me, note that his O-Swing% currently sits at 50.6%, which would indicate business as usual. I think what we are seeing here is simply a small sample of Javier Baez and while he will get the bat going eventually, he’s starting the year out cold. He’ll likely hit 20 home runs with a batting average below .250 and this is just a snapshot of that season.

Josh Bell, 35 AB, 8 BB, 3 H, 0.33 P/G

Bell is taking his walks (18.2%), but he’s also striking out a lot (27.3%). Bell has been swinging outside of the zone more often and making contact outside of the zone less often compared to his career numbers. Similar to Baez, he’s already smoked a ball at 108.5 MPH but his average launch angle thus far in 2023 is a sad -6.2. His career average launch angle is 8.4. One thing to note is that Bell has seen a relatively high percentage of breaking and offspeed pitches so far this season at 47.5%. It could be pressing, it could be cold weather and it could be a small sample, but Bell is slumpi–, ok don’t call it a slump. Bell is struggling to get his bat going to start the year.

Carlos Correa, 33 AB, 3 BB, 6 H, 1.93 P/G

With such a small sample of at-bats, it’s difficult to not sound like a broken record when analyzing slow starters. But, Correa already has a ball hit 112.0 MPH, his average launch angle is 24.3 degrees, he is hitting the ball hard 39.1% of the time and his xBA, xSLG, xwOBA are all higher than his actuals. His Z-Swing% and Z-Contact% are lower than his average and he’s taking called strikes at a higher rate than usual at 22.1% (18.4% career). Perhaps he is working on a more patient approach to start out the year as he works to get back into the rhythm of game action after a strange and roller-coaster offseason. All peripherals point to Correa righting the ship.

The 2023 version of the Bullpen Report includes five different sections, as well as the closer chart, which can be found at the bottom of the page.

We will always include a link to the full Closer Depth Chart at the bottom of the Bullpen Report each day. It’s also accessible from the RosterResource drop-down menu and from any RosterResource page. Please let us know what you think.

1. Notable Workloads: Primary closers or valuable members of a closer committee who have been deemed unavailable or likely unavailable for the current day due to recent workload.
2. Injury News
3. Outlier Saves: Explanation for a non-closer earning a save during the previous day.
4. Committee Clarity: Notes on a closer committee that clarify a pitcher’s standing in the group.
5. Losing A Grip: Struggling closers who could be on the hot seat.

The “RosterResource” link will take you to the corresponding team’s RosterResource depth chart, which will give you a better picture of the full bullpen and results of the previous six days (pitch count, save, hold, win, loss, blown save).

Click HERE to view the full Closer Depth Chart.

Read the rest of this entry »

The 2023 version of Ottoneu Hot Right Now will include three different sections:

1. Current Auctions: A closer look at players being auctioned at a high rate.
2. Roster Adds: Analysis of players with high add% changes.
3. Hot Performers: Players with a high P/G or P/IP in recent weeks.

The FanGraphs Ottoneu team plans to run this feature weekly, updating fantasy managers on the biggest movers in Ottoneu leagues with an analysis of how these players could or could not help your roster.

Read the rest of this entry »

Here’s a list of relief pitchers, provided by Roster Resource, who could potentially earn saves and fill the closer role for the White Sox in 2023:

Reynaldo López
Kendall Graveman
Aaron Bummer
Joe Kelly
Jake Diekman

Which did you take for your fantasy team? López and Graveman are the relievers on this list who currently have the black border around their names that signifies shared closer duties on the Bullpen Report (now streaming). Those who chose Reynaldo López were victory-dancing all around the living room on opening day as the 29-year-old Southside reliever recorded his first career save. It wasn’t a dominant performance by any stretch as the big righty gave up a monster home run, but to be fair, it was a Yordan Alvarez home run:

At the end of last season, I wrote about López’s incredibly low FIP and argued that it was unsustainably low due to an unrealistic HR/FB rate. His 2022 HR/9 in 63.1 innings of relief was 0.1, he only gave up one homerun all season. In his first appearance as a reliever in 2023, López made me seem like a smart guy. So that’s it, right? López is the same reliever he was last year, except now he’ll be eaten up by the regression monster. Not so fast. Check out his increased velocity so far in 2023:

That much of a velocity increase really stands out and it comes on top of the increased velocity he displayed last year as he transitioned more into a full-time reliever. López has always been a hard thrower relying on his fastball and while he throws a curveball and a changeup, it’s the changeup that has been his best secondary offering. He has never really leaned on the pitch too heavily, throwing it 3.7% of the time in 2022, but the pitch earned a 15.2% swinging-strike rate (PitcherList) last season. That’s better than the league average swinging-strike rate on changeups among relievers, 13.9%. Even it has seen increased velocity so far in 2023:

The last time I wrote about López I noted the increased velocity on each of these pitches and how that led to increased whiff rates. Now, López has added even more velocity and a big question is, can he continue to add velocity without sacrificing control? His strikeout rates have been on the rise and so has his command (K/BB), but how will the added velo and increased appearances in high-leverage situations affect his ability to command the strike zone?

He was put to an early test on Sunday (4/2) afternoon when he entered the bottom of the ninth with a five-run lead and things got shaky. After walking the lead-off man, César Salazar, López threw a wild one that let him take second:

After another walk to Jeremy Peña, López got Alex Bregman swinging on a 100 mph fastball:

Even though López got out number two on a Kyle Tucker fly ball, he gave up two rbi singles thereafter, balked a runner over to the third base, and made his manager bite his nails. Things finally came to a close with another fly ball, this time without leaving the infield. So, while increased velocity is great, López’s command doesn’t look locked in just yet and he has a history of high walk rates. In 2020, his BB% crept up to 12.4% in his 26.1 innings as a starter. The 2022 league average BB% among starters was 7.5%. That seems to always be the “Strange Case of Dr Jekyll and Mr Hyde” for López; good, hard stuff, but touchy control. But, Reynaldo López is no longer a starter. Now, he has the freedom to focus his efforts on short, high-leverage stints, letting his velocity and stuff challenge hitters. For those of you who have fully converted to the ways of Stuff+, here are López’s early 2023 numbers:

stf+ FA – 148
stf+ SL – 133
stf+ CH – 108

While I don’t really know what those numbers mean after two full innings, they are all pretty high. I assume that’s good. As with all things, we’ll just have to wait and see what happens. I’m rooting for López and if he’s available in any of my leagues, I’m adding him. White Sox manager Pedro Grifol has been impressed with Reynaldo López calling him a “special talent”, but he hasn’t named any reliever as the team’s closer in Liam Hendrik’s absence and that will likely be the case all season. Reynaldo López’s potential has always been there and if fantasy managers have the roster spot available and can keep close tabs on him, I believe he has a lot of strikeout and save/hold/win upside for 2023.

Aaron Judge only has one home run as of this writing in the 2023 MLB season. What the heck!? Shouldn’t fantasy managers rostering Judge have at least three or four home runs by now?! Wait…wait…relax. Ok? It’s been a long offseason, but we made it. We’re here. Now you get to wake up and read real stats! If the anticipation of opening day has clouded your judgment, I’m here to provide some insight into how your category totals should line up throughout the first few months of the season. Read the rest of this entry »

The 2023 version of Ottoneu Hot Right Now will include three different sections:

1. Current Auctions: A closer look at players being auctioned at a high rate.
2. Roster Adds: Analysis of players with high add% changes.
3. Hot Performers: Players with a high P/G or P/IP in recent weeks.

The FanGraphs Ottoneu team plans to run this feature weekly, updating fantasy managers on the biggest movers in Ottoneu leagues with an analysis of how these players could or could not help your roster.

Read the rest of this entry »

On July 17th of the 2022 season in Minnesota, Dylan Cease dealt. He threw seven innings, only gave up one hit, and recorded eight strikeouts. His showing left a game score of 83. It wouldn’t be his highest game score of the year (94), in fact, it wouldn’t even be his second-highest (90), but it was a great outing nonetheless. I’m going to use this game as way of continuing my analysis from last week on what we can measure from a pitching mechanics standpoint using statcast pitch-level data. Like in last week’s post, I took the following variables from Cease fastballs on that great start, July 17th:

‘release_pos_x’, ‘release_pos_z’, ‘release_spin_rate’, ‘release_extension’, ‘spin_axis’

I then conducted a principal component analysis in order to bring these five columns of data down into two. That allows me to then plot the data points on a scatter plot like so:

The graph above shows two principal components of all of Cease’s fastballs thrown on July 17th. I am interested in understanding if the spread, or variance, of these data points, relates in any way to performance. A helpful suggestion from FanGraphs member, “couthcommander” came in last week’s post:

“[C]an you…change the point-character shape based on inning?”

I chose a slightly different route and changed the color of the points based on the inning. I was expecting to see the darker points (later innings) on the outer edges of the scatter plot and lighter points (earlier innings) tighter around the center, but it’s hard to notice much of a pattern from this one game. Let’s visualize it in a different way. Rather than directly plotting the two principal components as X and Y, I calculated the variance of each by inning and compared the two components:

Click to enlarge

The first principal component shows higher variance as the game goes on through the fourth inning, but then comes back down for the fifth and seventh. A similar pattern is shown in the second component but only through inning two. The variance in PCA2 jumped in inning five but came back down in inning seven. No fastballs were thrown in inning 6.

It’s important to remind ourselves of what we’re actually looking at here. PCA1 finds a new axis of variation in this multi-dimensional dataset. Imagine a straight line being drawn through a multi-dimensional scatter plot. This new “principal component” does its best job of explaining as much of the variability in the dataset as possible. By that logic, PCA1 is just a little more informative than PCA2. The bar chart is telling us that as the game increased, that component become more variable through the fourth and then stabilized in the fifth. But remember, this is only explaining the following:

‘release_pos_x’, ‘release_pos_z’, ‘release_spin_rate’, ‘release_extension’, ‘spin_axis’

So the question is, does it matter? Does the variance of a component measure of these five features correlate with success? We can look at the components of Cease’s start before and after the great July 17th start.

–July 12th @ CLE: Game Score 66–
PCA1 = 3.3
PCA2 = 0.3

–July 17th @ MIN: Game Score 83–
PCA1 = 1.8
PCA2 = 0.3

–July 24th VS CLE: Game Score 63–
PCA1 = 2.1
PCA2 = 0.2

Variance = STD(PCA)^^2 x 10,000

While this is in no way conclusive evidence, it’s a start. The variance of PCA1 was lowest on July 17th. The next step in this analysis, as always, is to bring in more data! I will work towards answering the question, does a low variance PCA1 or PCA2 correlate with better performance? If it does, fantasy managers could use this information, if it is tracked and made available, to determine hot spots in a season where pitchers are locked-in. Thanks for participating in this data journey with me. We’ll see where it takes us.

ZiPs 2024 gives us some insight as to how prospects will perform if and when they make it to the big leagues. If we can get a general sense of how a player will perform with projections, we can get a general sense of how much they should be valued. To call this process an oversimplification is to look up at the sun and say, “Bright!” Yes, it is an oversimplification, that’s a given. First, we’re trying to predict not only the future performance of a player who hasn’t actually done it yet. Next, we’re trying to determine how much that performance will be worth without any real context. Where will they play? Who will be on their team? Are they as mentally strong as they are physically strong? Finally, we’re assuming they’ll be healthy.

This oversimplified process can only give us a sense of who might perform like a big leaguer in 2024 and since I’m writing from a FanGraphs points scoring system viewpoint, we can make comparisons with other, more established pitchers. Here’s a reminder of my process. First, I find prospect pitchers yet to debut using The Board. Next, I bring in the ZiPs 2024 projections for the players on that list. Not all of them have projections. After that, I convert their projected stats into FanGraphs Ottoneu points. Finally, I throw the prospects and their projected points into Justin Vibber’s Surplus Calculator output for 2023 and make comparisons. The result tells me how these pitchers will perform in 2024 if they are in a pool of 2023 projected players. The dollar value given assumes that next year’s player pool will be much like this year’s player pool. Here’s an example:

Pfaadt is already grabbing the attention of Ottoneu players as his current FanGraphs points average salary is $4, or $3 Median. Will he increase in value by the end of 2024? ZiPs likes his chances and you can compare his projected points total for 2024 with this year’s Jordan Montgomery. If you pay over the average now, let’s say $6, and this projection comes to fruition, you’ll have a good chance of generating value in 2024. There is, however, another scenario where ZiPs is off the mark and he only brings in $4 in 2024. In that case, you’ll be overpaying. Here are the rest of the 2024 ZiPs projected prospect pitchers and what their value could be at the end of the 2024 season:

Let’s compare these estimated 2024 values with some current (2023) average/median Ottoneu salaries:

Current FanGraphs Points Leagues Avg./Med.:

Kodai Senga – Average: $15 / Median: $15
Grayson Rodriguez – Average: $4 / Median: $6
Taj Bradley – Average: $3 / Median: $3
Kyle Harrison – Average: $3 / Median: $3
Ricky Tiedemann – Average: $3 / Median: $3
Robert Gasser – Average: $2 / Median: $3
Tanner Bibee – Average: $2 / Median: $1
Gavin Stone – Average: $2 / Median: $2

This is just one way of trying to look into an uncertain future; mashing a bunch of different spreadsheets together and then estimating a value. Is it worth doing, or would you rather just pay a few dollars now to see what happens later? I think this analysis helps us do both. Remember that the goal is to identify future value and not current value. It allows us to prospect on players because we like them or we believe in them or we saw them at a AA game and were impressed. But, it also allows us to put some kind of filter on how we are rostering and for how much. Are you rostering Taj Bradley for $7 because he was bumped up during arbitration, or you got him in a rebuild trade deal when someone else realized his salary was too high? It may be time to re-examine that hold because, by this analysis at least, he won’t reach that value in 2024. Everyone has a strategy and this is just one approach, but it’s utilizing analytical tools and projections from smarter people than myself to provide insight and that can’t be a bad thing.

The second word in the “music to many people’s ears” term, Spring Training, is an important one to consider. Pro ball players are training. They are preparing for the season. What types of things are they working on? Beat writers report out every year that pitchers are tinkering with new grips, different release points, varying arm slots, diets, cleats, the list is endless. This is assumed to be even more evident in pitchers. As they ramp up to game-ready status, what exactly are they ramping up and can it be quantified by a writer with only so much publicly available data at his fingertips? Away we go in answering that question together.

With statcast data available in spring training ballparks, we can access pitch-level data from the good folks at baseball savant. God bless them. There are a few metrics that measure what I would consider pitcher mechanics and here they are:

[‘release_speed’, ‘release_pos_x’, ‘release_pos_z’, ‘effective_speed’, ‘release_spin_rate’, ‘release_extension’,’spin_axis’]

These seven variables are incredibly manageable from a data perspective when compared to some of the more advanced biomechanical data teams and private company analysts are working with today. However, it can be really difficult to notice patterns from game to game just by looking at a spreadsheet:

Yes, you could look at this and make general assumptions. But, what if we want to visualize this? What if we wanted to hyper-analyze this so that the only people who really know what the heck is going on are the ones who are too busy playing the game in hyperspace? Bring in principal component analysis!

I’ve used this technique for a few articles here on FanGraphs. In this case, a principal component is being created based on multi-dimensional data, like the spreadsheet above with numerous columns, to create a new column. It cuts through the data and builds new “axes of variation” to better explain multiple data points. A more simplistic way of explaining this is that it’s taking multiple columns in the spreadsheet and condensing them into one. If we then create two of those new, condensed data columns, or principal components, then we can create a visualization. If this is too much data talk for you, hopefully, it gets better as I bring in the baseball.

Let’s start with the young, yet-to-debut major league pitcher, Grayson Rodriguez. How do the metrics above look, game by game as he ramps up for a season in which he expects to debut? I’ll create two principal components to help summarize a dataset similar to the table above and I’ll plot them on the x and y axis of a scatter plot, like this:

What we see is two variables, principal components one and two, explaining all the variables listed at the top of this article for one Gray-Rod Spring Training game’s worth of fastballs. It’s not very exciting. But, bring in a second game’s worth of fastballs to the visual and the excitement levels go through-the-roof!

…Ok, maybe it’s not that much more exciting. But, at least we can now see a little more of a story starting to develop. Ideally, since these variables are mostly repeatable we should see the blue and red dots sit closer together. What’s up with that game 2 outlier at the top of the second plot? We can compare that pitch with the averages of the other pitches in that game to analyze it further:

It seems that this pitch had a higher release, effective speed, and release spin rate. Is this significant? I really have no idea. It could just be noise. I would love to know if Rodriguez would have noticed any difference after that pitch was thrown. Would he have admitted that he really wanted to get that guy out? Let’s go to the video to see what the situation was:

…Oh, wait. We can’t because MASN doesn’t want to film in sunny Florida. Luckily, we can still look at the savant video-less page here. On a 2-0 count against Spencer Torkelson, maybe Gray-Rod reared back and put a little extra mustard into making sure he didn’t get to 3-0. We’ll likely never know.

How might this compare with a pitcher who is more established? Let’s conduct this same analysis on two of Max Scherzer’s spring outings this season and compare:

Scherzer shows a little tighter spread between all of his pitches and lacks the clear outliers showcased by Rodriguez. The more interesting part to me is that the pitches get closer together from game one to game two. Could that mean anything? Could he be getting ramped up and more consistent, more repeatable?

Now the ultimate question in baseball analytics, how can we actually use this to win? I believe checking in on pitcher components throughout the season may be able to help us identify fatigued players who need rest in order to get their components back into a form that is more in line with areas of succes. This would require measuring the game by game spread or variation of the points. If that number is larger, is that a measurement of inconsistency? If it is lower, does it correlate with success? This analysis really brings up more questions than it answers, as per usual:

What if we changed the colors of the data points in the visualization to reflect individual start game scores?

Are tighter pitches (less spread among single games point locations) better?

What could be done with more data? Can this analysis be applied to biomechanical data?

How does it apply to non-fastballs? Do certain pitchers struggle with repeated motion on certain pitches and not others?

If this post is a thread in that old spring training baseball jersey you pulled out of the closet for your trip to Florida, then let’s start pulling until there’s nothing left and you’ve gotta borrow sunscreen from the shirtless guy next to you. My hope is that with a little more time and research, I’ll be able to utilize this analysis to detect in-season struggles by starting pitchers.