In the pre-PitchCom era, major league teams had more rigorous protocols for protecting their signs than your bank has for securing your account. It wouldn’t surprise me to learn that some teams’ custom PitchCom audio clips are read in a modified pig latin created by a pitching strategy staffer. That the hitter doesn’t know what pitch is coming is considered a huge advantage for the pitcher. And it’s not only pitchers who think so — just ask the 2017 Astros.

Sign-stealing aside, hitters stand in the box pondering which pitch might come hurtling their way mere seconds later. What that pondering looks like depends on the hitter. There’s Nick Castellanos and his “glorified batting practice” approach, in which he looks for the ball and hits it as hard as he can. But there’s also Carlos Correa, who starts his day studying pitcher tendencies in the video room.

For their part, pitchers set the difficulty level on the hitter’s guessing game. That terms like “fastball count” and “pitching backwards” exist tell us that pitchers follow (and, at times, purposefully upend) conventional tactics to sequence their pitches, and believe that certain pitch types are optimal in certain counts. Strategies become standard practices because they’re effective, but an over-reliance on one or two strategies can lead to predictability. Become too predictable and a pitcher effectively sets their opponents’ guessing game on “easy” mode. But does making it easy for the hitter to sit on a certain pitch automatically make the overall task of hitting easier? Does keeping a hitter guessing always ensure effective pitching? Read the rest of this entry »

Previously on Dragon Ball Z, we discussed whether it’s better to run hot and cold like a reheated frozen burrito or show up at the plate with the comforting and consistent warmth of a hearty helping of mac and cheese. Specifically, when you’re a hitter trying to microwave some offense in the playoffs. The tl;dr of that article: When comparing streaky hitters to their more consistent colleagues, the streaky hitters came closer to replicating their regular season numbers in the postseason. Despite the fluky nature of playoff series and their bite-sized samples that leave no space for slumps, hitters prone to slumping still bring enough electricity when they do get hot to maintain a charge in their individual numbers.

But individuals don’t win the World Series, teams do. In the context of a team playing a sport where the superstars don’t necessarily factor into every plate appearance, individual performances don’t carry the same weight that they do in other sports. Not only do teams need contributions from multiple hitters in the lineup, but the sequence of those contributions matters too.

In my last article, I used wOBA, which is derived from the run values associated with specific events (i.e. walks, doubles, home runs), to measure individual output. In turn, run values are historical averages of the number of runs scored following the given event. Those historical averages assume that what follows a given plate appearance is a league-average hitter doing whatever is most statistically likely. But that’s not how it works irl. The player on deck might be better or worse than league average, might have distinct tendencies toward hitting the ball on the ground or in the air, might be 0-fer their last eleventy-billion, or might be hotter than soup in the summertime. Read the rest of this entry »

As the man who inspired Brad Pitt’s most memorable role once said, “My shit doesn’t work in the playoffs.” Assuming Billy Beane wasn’t explaining an October Metamucil purchase to a grocery store cashier who simply asked how his day was going, what Beane likely meant was that the statistics used to construct his major league rosters don’t accrue large enough samples during postseason series to eventually even out in his favor. Over the course of 162 games, a team’s production settles into a reasonable representation of the squad’s true talent. But zoom in on any random seven-game stretch and the team on the field might look like a bunch of dudes in baseball player cosplay.

What applies to team outcomes applies just as well to player outcomes. A player with a perfectly respectable stat line in the regular season might morph into a pumpkin as the calendar shifts to fall, or on the flip side, an unlikely hero may emerge from the ashes of a cruel summer and put the whole team on his back.

With the law of averages in mind, I’d always assumed that the more consistent hitters would be better positioned to perform well in the playoffs. My thinking went like this: The natural variation in these hitters’ performances would never wander too far from their season-long average, making them the safer, more predictable options. Whereas streaky hitters — the ones with high highs, low lows, and steep transitions between the two — would be too reliant on “getting hot at the right time” to be the type of hitter a front office should depend on in the postseason.

Reader, I was incorrect. Read the rest of this entry »

The other week, I was talking to a friend as she fretted over her lack of productivity. Her struggle wasn’t with laziness or a lack of motivation, but rather a severe case of burnout, following her around the way No-Face follows Chihiro in Spirited Away. In the moment, I could see her desperately reaching for more energy to get through the day, like when the chip bag is nearly empty, so you bring it to your mouth and tilt your head back to suck down whatever salty goodness remains.

But instead of encouraging her to power through, I went a different way.

“Y’know, it’s okay to take a break.”

She laughed and said, “That’s what my therapist keeps telling me.”

If you’ve ever described yourself as a perfectionist or a people pleaser, or tied your self-worth to your measurable output, you know taking breaks can be hard.

Athletes learn from the beginning to idolize hard work. Hard work is the salve for every ailment. Wanna get stronger? Work harder. Wanna play better? Work harder. Wanna go pro? Work harder than everyone else. The hardest workers earn their own dedicated titles and recognition, separate from their actual production. Cal Ripken Jr.’s consecutive games played streak earned him the Ironman title. MLB gives out a Heart and Hustle award to whichever player’s heart tells him to hustle the hardest. And every year, we read stories about players and coaches who are the first to arrive to work each morning and the last to leave at night. Read the rest of this entry »

The Pittsburgh Pirates spent part of deadline day sprucing up their offense by acquiring outfielder Bryan De La Cruz from the Miami Marlins and infielder Isiah Kiner-Falefa from the Toronto Blue Jays. Neither hitter provides the superstar impact to fully rehabilitate Pittsburgh’s fixer-upper lineup. Instead, they yield an effect similar to tearing out the wood paneling, scraping the popcorn ceilings, and applying a couple coats of paint in a shade called something like Chantilly Cream to zhuzh up the place. Perhaps this offseason, Pirates’ GM Ben Cherington will put in a call to Chip and Joanna Gaines to facilitate a full renovation, but until then, this certainly makes the space the Pirates currently occupy nicer.

De La Cruz and Kiner-Falefa join the Pittsburgh clubhouse amidst a handful of other additions and subtractions over the last few days. The team acquired two lefty relievers in Jalen Beeks and Josh Walker alongside Nick Yorke, a post-hype hitting prospect who is ready to compete for big league playing time, according to Eric Longenhagen’s breakdown of all three acquisitions. Additionally, the Pirates dealt from their starting pitching depth by sending Martín Pérez to the Padres in exchange for Ronaldys Jimenez, an 18 year-old left-handed pitcher currently in the DSL.

On the whole, Pittsburgh’s deadline activity amounts to a series of one-for-one trades that marginally improve their current situation, without upsetting their long-term construction plans for building Barbie’s Dream Roster in the future. (Or because it’s the Pirates, whatever ownership deems is Kenough.) Read the rest of this entry »

Have you ever had a friend enthusiastically recommend that you watch a TV show and then say, “It takes a few episodes to get going, and the timeline gets weird at the end, and one or two of the main characters can be kind of annoying, but other than that it’s SO GOOD.” And initially you might be put off, thinking that a truly good show wouldn’t require that many qualifiers. Sometimes you’re right about that, but sometimes it turns out the show is Parks and Recreation and even though the first season is about as appealing as living in a pit, the rest of the show is an absolute treat.

Sometimes small components of a larger body of work do a poor job of representing the work as a whole. The oddities that occur in small samples are likely not a new concept to FanGraphs readers, nor will it shock anyone when I note that what constitutes a small sample depends on what exactly we want to measure. Recently, the fine folks at MLB Advanced Media gifted us with a handful of new metrics that make use of Statcast’s bat tracking technology. Every time we dig into a new metric, we must consider the appropriate serving size to satiate our hunger for knowledge, lest we find ourselves hangrily generating takes that we later regret.

For this article, we’ll attempt to determine appropriate sample thresholds for measuring a hitter’s average bat speed; so that players without bats don’t feel left out, we’ll do the same for sword rate from the pitcher’s perspective. For many metrics, the sample size is measured in pitches or plate appearences, but since both bat speed and sword rate are tied specifically to bat movement, their samples will be composed of swings. To determine reasonable sample sizes, I used the split-half correlation method. The idea is to randomly select two samples of size X from a player’s collection of swings, calculate the player’s average bat speed or sword rate for both samples, lather/rinse/repeat for a bunch of players, then take the full set of two-sample pairs for all players and see how well they correlate. We complete the experiment by repeating the process for progressively larger sample sizes. And just to be super thorough, we’ll re-run the experiment several times and average the correlation values. Read the rest of this entry »

Remember back in 2021 when Gen Z tried to tell everyone to move their side parts to the middle and swap their skinny jeans for a looser variety? While most Millennials responded with outward indigence, offline they begrudgingly tried on high-waisted mom jeans and posted up in the bathroom blowing out their hair in a new direction. But before long they let their hair go back to lying in the manner to which it had become accustomed and eschewed jeans completely in favor of athleisure-wear. Even as many of us considered complying with the directive of our teenaged overlords, it felt absurd that people who haven’t even finished developing their prefrontal cortexes are left in charge of dictating what’s cool. As it turns out, though, that’s exactly why teenagers decide what’s cool. Teenagers are the only members of society with the time, energy, and lack of rationality to care so deeply about something that matters so very little.

Those who stuck to their dated stylings and weathered the petty hail storm of Zoomer mocking were vindicated a couple of months ago, when the celebrity and influencer cohort brought back the side part, declaring it on-trend once more. Around that same time another trend was taking hold among the baseball commentariat: Using strength of schedule to determine which teams had actually earned their W-L records. Mostly, this meant arguing that the Phillies weren’t a top team in the league because they’d played a soft schedule. The discourse eventually spawned multiple articles arguing that while yes, Philadelphia hadn’t exactly been slaying dragons while walking a tightrope, its act wasn’t entirely smoke (generated by the clubhouse fog machine) and mirrors either.

Strength of schedule is not typically a prominent talking point when comparing MLB teams. It might occasionally come up when comparing September schedules in a tight postseason race, but as a phrase uttered in May, it’s typically part of a college baseball discussion, or because you’ve wandered into a BCS-era college football forum. College sports need strength-of-schedule metrics because teams don’t all play one another and the variation in team quality spans the Big Ten’s new geographical footprint. But in the major professional leagues, the schedule is fairly balanced, and even though the White Sox and Rockies exist, dominating the worst teams in MLB presents a tougher task than rolling over the University of Maryland Baltimore County Golden Retrievers. Read the rest of this entry »

By June of 2013, Baltimore’s beat writers had established their favorite in-season stat to track: Manny Machado’s rapidly rising doubles count.

Machado was in the midst of his first full season in the majors and on pace to make a run for the single-season doubles record. He finished the season with 51 (a several-way tie for 51st all-time), but in the moment, he represented a rookie with sky high potential, standing in the shadow of a mountain representing his own potential peak.

As he dumped double after double into the outfield, Orioles broadcasters noted for fans that the 20-year-old Machado was still developing physically, so his power tool was poised to level up as his career progressed, and when it did, some portion of those 51 two-baggers would convert to round-trippers. The Doubles to Dingers developmental arc is a real phenomenon. After hitting a double once every 14 plate appearances and a home run once every 50 in 2013, Machado followed up that performance in ’14 by doubling just once every 25 PA while upping his homer rate to once every 30. More recently, the current Baltimore broadcast booth has applied the Doubles to Dingers arc to Gunnar Henderson, another promising second-year player on the left side of the Orioles’ infield. However, as Henderson has increased his home run rate in his second full season by over 50%, his doubles rate has held steady, suggesting he’s leveled up his hit tool alongside the power surge, which doesn’t quite fit this narrative arc.

So which players have best exemplified the Double to Dingers storyline, and how did their stories play out afterward? We don’t need to rely on broadcasters, who have a tendency to fuel fan optimism, because this is extremely quantifiable. To identify players on this path, I started the search in 1988 and looked only at players who at the time had three or fewer seasons of at least 100 PA in the majors. I compared their ratio of dingers to doubles during the season in question to their career ratio for all seasons prior. Only samples of at least 300 PA were considered when calculating the ratio. Players also needed to be hitting doubles in at least 4% of their PAs prior to unlocking the next level of power, and after leveling up, their new homer rate should settle in above 2% of PAs (thresholds chosen are round numbers at or just below league average because this really only matters for hitters producing at a baseline level of competency in both key categories). To further ensure that the change in their dingers-to-doubles ratio is caused by a somewhat proportional change to the frequency of both doubles and dingers, their homer rate needed to increase by at least half a percent; likewise, their doubles rate must have decreased by at least half a percent. Read the rest of this entry »

Of all 193 relief pitchers with at least 20 innings pitched this season, exactly three have thrown four distinct types of fastballs a minimum of 20 times each: Reed Garrett, Chris Martin, and Cole Sands. They all have one non-fastball offering, but none of them throw it more than a quarter of the time. Justin Choi wrote recently about the strategic options available to pitchers with more than one fastball, but four? Four whole fastballs? These guys feel like doomsday preppers getting ready for some apocalyptic scenario where money is now worthless and fastballs are the new currency.

But anytime a new strategy pops up in baseball, it’s worth checking to see if the outliers are onto something others should attempt, or if their “one weird trick” to pitching works only for them. Shoot, maybe it doesn’t even work for them all that well. Regardless, we’re gonna get to the bottom of what’s going on with these pitchers and all the fastballs they’re hoarding.

Reed Garrett

Garrett has thrown 34.2 innings for the Mets so far this season, posting a 3.12 ERA and a 3.17 FIP. He’s struck out 37% of the batters he’s faced and walked 12%. His performance this year has earned him an ERA- of 81, firmly better than average. What the averages aren’t telling you is that Garrett started the season with a 0.57 ERA in March and April, a ridiculous run that earned him a full breakdown on his evolution from last season by Ben Clemens on April 23. But that April ERA had to buy new pants after swelling to 6.08 in May. His performance has regressed somewhat in June, settling somewhere between those extremes. The current version of Garrett is probably more representative of what the Mets should expect from him moving forward.

The table below shows a breakdown of Garrett’s pitch repertoire with the usage and a few metrics for evaluating each offering (run value per 100 pitches thrown, xwOBA, Stuff+, and Location+). The two most common fastball types (four-seamers, sinkers) that most pitchers feature at the center of their arsenals are the pitches he throws the least. But the metrics linked to Garrett’s outcomes — either actual outcomes (RV100) or expectations based on the characteristics of the outcomes (xwOBA) — agree with his decision to de-emphasizing those pitches. They like Garrett’s four-seamer the least, even though it has his highest velocity and second best Stuff+. The pitch’s Location+ score reveals its critical flaw: a lack of command. Stuff+, RV100, and xwOBA agree that his sweeper and splitter are his two best pitches. Based on usage, Garrett agrees with that assessment.

His pitches mostly hover around league average in terms of individual characteristics, but the sweeper and splitter are both a tick or two harder than average and generate a bit more spin leading to more horizontal break, which is likely why Stuff+ likes them more than the rest of Garrett’s arsenal.

He makes the most of middling pitches by playing them off one another. The sweeper and cutter mirror the spin direction of the sinker and the splitter. As a result the pitches look similar out of the hand but fork in four different directions as they approach the plate to keep the hitter guessing (see movement plot below). So even if hitters guess the horizontal direction correctly, they’ve still got two similarly spinning pitches that fan out vertically as they approach the plate.

Garrett deploys all of his pitches no matter the handedness of the hitter, but he does vary the flavor of his approach. To lefties, Garrett likes to fill the zone with his cutter and dangle the splitter down and away when looking for a chase. To righties, he keeps the hitter off balance by throwing the sweeper to a variety of locations, but then comes down and inside at varying speeds with the splitter and the sinker.

The flowchart below gives us an idea of Garrett’s sequencing habits. He tends to start hitters with a cutter or sweeper. Once ahead in the count, he’s more likely to play around on the periphery of the zone with his sweeper and splitter, whereas while behind in the count he rolls with the four-seamer and cutter as more zone-friendly options. The wOBA values for plate appearances passing through each given count indicate the approach works well in early counts and with two strikes, but not as well when the count forces him back into the zone, in part because his four-seam command limits his ability to actually hit the zone with that pitch when circumstances demand it.

Here’s a representative example of how hitters respond to Garrett’s two-strike splitter.

Looking at swing metrics by pitch type, each pitch adds a valuable tool to his kit. The splitter is Garrett’s best combo play for inducing swings (56% swing rate) without courting disaster. The pitch owns his best swinging-strike rate (30%) and second lowest hard-hit rate (20%) when batters do connect. He gets batters to swing at 74% of the sinkers he throws in the zone, he uses the sweeper to induce weak contact (17% hard-hit rate), and turns to the cutter to mix things up. The four-seamer is the weak link in the chain so long as it keeps taking the scenic route to the catcher’s mitt.

Chris Martin

Martin has thrown 21.1 innings for the Red Sox in 2024, logging a 4.22 ERA with a 3.90 FIP. He’s struck out 28.2% of the batters he’s faced while walking just 2.4% of them. He has been on the IL since June 5 while proactively seeking help with anxiety.

Again, we’ll start with a synopsis of each pitch he throws according to the value metrics. Stuff+, RV100, and xwOBA all like his splitter best. The pitch is very similar to Garrett’s splitter from a velo/movement/spin perspective, but he doesn’t throw it nearly as much. His four-seamer is his next best pitch by RV100 and xwOBA, but fourth best by Stuff+. However, he locates it well enough to still get results. Martin’s cutter is his consensus third-best pitch, striking a balance between stuff and command to get the job done. Like Garrett, Martin’s non-fastball pitch is a sweeper, but unlike Garrett, he throws it so infrequently that it’s hardly worth discussing.

His pitch characteristics all hover around average, thrown maybe a tick or two harder, but with slightly less spin and therefore less movement. What helps overcome somewhat middling profiles is a distinct release point created by his long levers. Though his delivery is composed of a pretty standard three-quarters-ish arm slot, the arm attached to his 6’8” frame allows him to release the ball several inches higher and farther to his right than other pitchers throwing from a similar slot.

Rather than mirroring the spin on his offerings like Garrett, Martin takes a different approach to cultivating deceit. The puzzle for his hitters is more akin to spotting the difference between two nearly identical photos. All of Martin’s pitches spin in a similar direction, and his four-seamer, sinker, and cutter do so at almost the same spin rates. Where they differ is in the amount of active spin, or the amount of spin contributing to the pitch’s movement. The four-seamer, as one might expect, has the most active spin and the most rise. The sinker has a little less active spin and creates more horizontal break and more drop. The cutter drops in a comparable fashion to the sinker, but refuses to follow his fellow fastballs and break toward the third base side. Then there’s the splitter that spins at a much slower rate and with less active spin, which translates to roughly the same amount of horizontal movement as his four-seamer, but with even more drop than the sinker. Yet another carbon copy, but with a small but crucial edit.

Martin uses the same theory to guide his approach to both righties and lefties: Fill the zone with the primary fastball(s), use one of the secondary fastballs as a threat inside, and pepper the bottom of the zone with splitters. Against right-handers the four-seamer and cutter are the pitches he consistently throws to all parts of the zone and the sinker backs the hitter off the inner half of the plate. Against left-handers, Martin stays away from the sinker, so the cutter becomes the weapon he aims inside, while the four-seamer and the splitter maintain their existing roles.

The job of each fastball is further etched in stone by Martin’s sequencing, visualized below. He starts an overwhelming majority of hitters with the four-seamer or cutter and sticks to those zone-friendly pitches if he falls behind in the count. But if he gets ahead, Martin starts mixing in the splitter and sinker. His results tend to be better if he gets to those splitter/sinker counts, but it’s unclear whether that’s because of the effectiveness of those pitches or because he gets too predictable in unfavorable counts.

The swing metrics indicate Martin’s cutter is his best option for getting swings (55% swing rate) that lead to either strikes (13% swinging-strike rate) or weak contact (27% hard-hit rate). The splitter is his overall best bet for a swinging strike (19%), but when hitters do make contact, it yields the highest hard-hit rate (70%). The sinker is most effective when thrown in the zone because it has the lowest out-of-zone swing rate (18%) and in-zone contact rate (78%) compared to Martin’s other offerings. And avoiding contact is key, since the sinker has the second highest hard-hit rate (67%) of the bunch.

Cole Sands

Sands has pitched 32 innings for the Twins this season. Those innings have amounted to a 4.22 ERA and a 3.30 FIP. His strikeout rate sits at 28% and his walk rate is a measly 3%. Sands’ season trajectory mimics Garrett’s: on a rocket to the moon in April, a crash landing in May, and now back up and cruising at altitude in June. At his peak, Sands was striking out Shohei Ohtani on three pitches, and Minnesota was considering stretching him out to start while managing injuries in the rotation; now he’s settled into a multi-inning relief role.

Digging into Sands’ repertoire via the pitch evaluation metrics, his cutter, curveball, and splitter all clock in right around average according to Stuff+, but RV100 favors the four-seamer and hates the curve and split. Comparing the curveball’s xwOBA (.305) to its wOBA (.372) suggests the pitch’s actual outcomes have been a bit unlucky compared to what’s expected based on the batted ball characteristics, which in turn is likely deflating its RV100. Meanwhile the four-seamer and sinker both have better wOBAs when compared to their xwOBAs, suggesting some good luck has swung their way and their RV100s might be a little full of themselves. Luck doesn’t explain the metrics’ diverging opinions on the splitter, suggesting something is amiss with Sands’ execution. Hopefully, this contradiction will untangle itself as we proceed.

In terms of the movement profile broken down in the table below, Sands, like Garrett, mirrors the spin of his breaking ball relative to the four-seamer, sinker, and splitter in an attempt to disguise their true identities until it’s too late for the hitter to react. And concealing those identities is necessary because, as with the other two pitchers, Sands’ pitch characteristics are far more average than overpowering. The furthest he deviates from average is with his extension, but unfortunately he deviates in the wrong direction. His 5.8-foot extension puts Sands roughly six to eight inches below league average. Releasing the ball farther from home plate gives the hitter more of a chance to identify the pitch’s trajectory, which likely explains the lower Stuff+ scores relative to what Garrett and Martin receive for comparable pitches. And while we’re talking pitch trajectory, the extra couple inches of drop on his splitter relative to an average right-handed offering of the pitch might be too much of a good thing; at times it dives too far, too quickly to really tempt hitters.

How the pitches move relative to one another is basically a hybrid of what we’ve seen so far from Garrett and Martin. The fastballs land on the movement plot in roughly the same orientation as the other two, aside from being stretched more vertically. Sands’ curveball operates similarly to Garrett’s sweeper, just with more drop.

Like Martin, Sands doesn’t throw his sinker to lefties, but beyond that omission, Sands attacks hitters in the exact same manner regardless of handedness. He aims to fill up the zone with his four-seamer, works arm side with the cutter and sinker, and keeps the ball down and/or to the glove side with the splitter and curve.

Sands mostly sticks to the standard sequencing playbook, but he’ll reach for any of his non-splitter offerings to begin a plate appearance. If he gets ahead, expect a heavy mix of splitters and curveballs; if he falls behind, expect him to thrown mostly cutters and four-seamers. His adequate command keeps him competitive, since even after falling behind, the average outcomes remain respectable and in line with the more favorable counts.

The swing metrics suggest Sands’ cutter is his best option for inducing weak contact (51% swing rate, 32% hard-hit rate), the four-seamer has the lowest in-zone contact rate (80%) to pair with the second highest in-zone swing rate (71%), and the curveball is best for forcing swings out of the zone (35%) that lead to either a strike (14% swinging-strike rate) or weak contact (25% hard-hit rate).

***

With the four-fastball approach to relief pitching now fully dissected on the lab table before us, I can’t truly say we’ve discovered the next big thing that pitchers everywhere will be rushing to replicate. Though Garrett, Martin, and Sands are the only three relievers doing this out of almost 200, their approach is not as novel as those numbers suggest. What they’re actually doing is leaning on all of the classic pitching fundamentals: changing the hitter’s eye level, attacking the zone to get ahead in the count and then make the hitter chase, varying speeds, varying locations, keeping the hitter off balance. Most relievers execute these fundamentals using one or two overpowering pitches, or in lieu of dominant stuff, they cobble together a few crafty junk pitches. Garrett, Martin, and Sands pitch as if they were junkballers, but instead of throwing knuckleballs or Bugs Bunny changeups, they take their collection of middling fastballs and deploy them as junkballs. They mix and match movement profiles and velocities so hitters can’t sit on certain pitches or locations. They do all the same stuff every pitcher does; they just dress it up a little different. Which in and of itself is novel enough to still be impactful. After all, 10 Things I Hate About You is a singularly great movie, but it’s also a classic Shakespeare play, just dressed up a little differently.

Season one of One Tree Hill is a perfect season of television, and I will not be entertaining arguments to the contrary. In it we meet Nathan and Lucas Scott, the sons of hometown basketball hero, Dan Scott, who runs a local car dealership. Nathan was raised in the traditional nuclear family structure by Dan and his college sweetheart and wife. Lucas was raised in a single-parent household by his mother, Dan’s high school sweetheart. Despite sourcing their foundational genetic material from the same DNA pool, Nathan and Lucas are depicted at odds with one another in several key ways. Nathan is his father’s golden child and characterized as hyper competitive, entitled, and emotionally stunted; Lucas receives no acknowledgement from Dan and skews more intellectual, reserved, and empathetic. Both are super good at basketball and both crave the approval of their father. Nathan seemingly has it all, but presents as lonely and ill at ease in his environment. Lucas drew the short straw, but is mostly content and supported by several meaningful relationships.

The whole concept is a pretty straightforward exercise in nature vs. nurture, and if you haven’t seen One Tree Hill, don’t worry, I haven’t spoiled anything; this is all part of the show’s initial setup in the pilot episode. What the viewer is intended to puzzle out as the season unfolds is how much of Nathan’s arrogance and aggression is a reaction to his surroundings and how much is an inherent part of his character. And on the other hand, can Lucas, against his father’s wishes, learn to thrive in new surroundings as he steps into the spotlight of varsity basketball? Or is he more naturally suited to exist in the shadows?

I recently read almost six years of scouting reports, statistical breakdowns, and interviews covering two prospects from the 2018 MLB draft in an attempt to to understand the how and why of each player’s career arc. More on that later, but for now, I want to emphasize how much easier it is to analyze a teen soap opera. And it’s not that the scouting reports were unclear, or that the statistical analysis was misleading, or that the players misrepresented themselves in interviews. It’s that taking 18- to 22-year-olds and turning them into big leaguers is a hard thing to do under the best of circumstances. Read the rest of this entry »