The Natural
Welcome to the third edition of Plot the Ball — a newsletter where I offer data-driven answers to interesting questions I have about the world of sport.
This month — after Major League Baseball’s work stoppage finally ended, and Spring Training began — I’ve been thinking about reigning American League MVP Shohei Ohtani, and wondering whether there’s anything interesting left to say about one of the most widely discussed seasons in the sport’s history.
Just how gifted is Shohei Ohtani?
In a similar manner to cricket, baseball is a sport peppered with contextual quirks that make the assumption that the game is the same wherever it’s played seem foolish.
Playing areas at some major league ballparks are idiosyncratically small and contorted, for instance — while at others atmospheric conditions lead to the ball travelling through the air in ways that can confound visitors’ expectations.
A major component of the analytics movement within baseball, in turn, consisted of attempts to account for such foibles in order to come up with better standardised measures of player and team performance.
Given the easily quantifiable nature of the action that takes place between the game’s white lines, that movement has arguably become more embedded in the soul of baseball than that of any other sport.
Consequently, baseball can perhaps claim to have the best grasp of any major sport on the true drivers of on-field success — and, it follows, of individual excellence.
At the end of last season, the message from those advanced metrics was clear: no one contributed more positively to their team’s success than the Los Angeles Angels’ Shohei Ohtani.
The 27-year-old Japanese superstar did something that no one in top-level US baseball had done since the 1920s: hit like an elite batter on one side of the ball, and throw like a leading pitcher on the other.
(After signing with the Angels out of Nippon Professional Baseball in 2017, he threatened to do just that in his rookie year until he began to deal with elbow issues that would eventually ruin most of his 2019 and 2020 seasons.)
One of the beguiling things about Ohtani, however, is that his excellence is just as easily communicable to those less familiar with baseball’s arcane accounting systems.
With bat in hand, there was — in simple terms — no one last season who struck the baseball more cleanly than Ohtani.
MLB’s advanced pitch-tracking system — known as Statcast — uses Hawk-Eye technology to collect information on every in-game movement (of both ball and player) in every major-league ballpark.
Every time a batter makes contact with a pitch, it records — among other data points — the speed and trajectory at which the ball travelled off the bat.
That information can then be used to identify the most sweetly struck batted balls — which MLB classifies as ‘Barrels’. (Officially defined, they are batted balls “with the perfect combination of exit velocity and launch angle”.)
In 2021, as the chart below shows, no one connected perfectly with the ball more frequently than Ohtani did — and only six players hit the ball further than he did, on average.
On the other side of the ball, he was comfortably one of the best pitchers in the Majors when it came to throwing hard and making batters swing and miss.
He struck out 29.3% of all the hitters he faced in 2021 — putting him in the 85th percentile of MLB pitchers.
That was a step back from his rookie season, however, when he ranked in the 88th percentile by the same measure.
Whether those elbow issues — which had to be rectified by a major medical procedure known as Tommy John surgery — have caused lasting damage to Ohtani’s throwing arm may be cause for concern.
The chart below shows that — as well as his strikeout rate — the average speed of his fastball also declined between 2018 and 2021. Last year he was still in the upper echelons of the Majors by both metrics, but clearly not to the same degree as in his rookie season.
Ohtani did look to develop his pitching in other ways last year after returning from injury — primarily by trying to impart more spin on the ball, and therefore generate more movement as it travels through the air towards the batter.
Statcast data does show some improvement in this regard in 2021 — but not enough, judging by his strikeout % (and a related metric known as whiff %), to counteract that loss of velocity.
In a sense, this analysis is an indication of how — in a sport which has integrated data as comprehensively as baseball — it will always be clear even to players as good as Ohtani where they can improve.
However, we shouldn’t distance ourselves too far from the most joyous thing that this advanced data allows us to do: marvel at just how naturally gifted an athlete he truly is.
You don’t need to be a sabermetrician to appreciate Shohei Ohtani’s exceptional ability to hit and throw a baseball. Even the simplest of measures can communicate his skills clearly enough by themselves.
Further reading
Fabian Ardaya and Molly Knight of The Athletic on the process that brought Ohtani to the USA and the Japanese journalists who now follow his every move there
Tim Keown of ESPN on the promise of Ohtani’s rookie season in the USA and the unprecedented nature of his 2021 AL MVP campaign
Neil Paine of FiveThirtyEight on the promise of the beginning of Ohtani’s career in Japan and the (almost) unprecedented nature of his 2021 AL MVP campaign
Ben Lindbergh of The Ringer on how those seasons in Nippon Professional Baseball laid the foundation for his MLB career
Daniel Riley of GQ on the burden now borne by Ohtani as one of the faces of professional baseball
Technical notes
You can find the code for this piece on GitHub here
At the risk of sounding like a broken record, it was once more incredibly pleasing to find an R package that made obtaining the data for this piece very easy: in this case, Bill Petti’s
baseballr. (The package also appears on a comprehensive list of R resources for sports analytics put together by Dom Samangy, which you can find here.)Statcast data is really interesting to me given the level of detail that’s available — and I was initially tempted to try and do something more visually ambitious, like using their exit velocity and launch angle figures for all batted balls to create some sort of hit trajectory chart. However, when I thought about it such a chart wouldn’t really have fit with the purpose of the piece: I was trying to communicate that Ohtani was an outlier, and — given that (for launch angle at least) the optimal place to be is somewhere in the middle of the distribution rather than at its tail — the exceptional nature of his skills wouldn’t have been visually obvious.
While the statistic — ‘barrel %’ — which I settled on to communicate his batting exploits is (hopefully) intuitive enough once explained, it’s still likely an unfamiliar metric to an overwhelming majority of people reading the piece. I was weighing up whether or not to include a definition in the body of the chart itself, but in order to keep things feeling clean I settled for defining terms in the copy and leaving extraneous detail out of the visual. This is a situation where I think my decision would have been different if the context in which the chart was likely to be viewed was clearly different; if it was primarily for posting on social media, for instance, including more detail in the graph would probably be the right call.
At the risk of sounding like a broken record about sounding like a broken record, I was also thinking a fair amount again about colour when putting these charts together. First of all, using the primary colour of Ohtani’s team to plot his data specifically seemed an obvious choice. However, other individual data points — while interesting — just weren’t that important to the story I was trying to tell: their distribution was what mattered. I settled on using a shade of grey (actually one of Ohtani’s team’s complementary colours, but neutral enough for this purpose) to communicate them — and I’m pleased with how the important data points really stand out on each plot. (A recent Datawrapper blog post — 10 ways to use fewer colors in your data visualizations — explains this particular technique well.)
Next month — to coincide with the end of March Madness — I’ll be looking at the impact that one of the USA’s best college sports programs has had on one of the world’s premier professional basketball leagues.