Former MLB first baseman Nate Freiman is making a baseball comeback – but not on the field.
Freiman, who you might recognize as the tallest position player to ever play the game, has developed a hobby with which few can relate: he crunches numbers, just for fun. He published his first analytical piece on Fancred in April, then published ‘A player’s take on xwOBA’ on Fangraphs in late May, and has since taken to Twitter to publish multi-dimensional graphs and discuss a new metric: average run differential per pitch.
The new stat came to life when he sat down with 87 columns’ worth of downloaded data from Baseball Savant.
What is average run differential per pitch?
“I was just playing around with it, and for two of the columns, for every pitch, it tells you the score of that pitch,” Freiman told Yahoo Sports. “I was using R, and you can group it by the team, and by inning and all that stuff, and basically take an average of the run differential on every pitch the team throws.”
Freiman’s knowledge of R, a “high-level” programming language and software environment for statistical computing (often employed by baseball front offices) is self-taught. Unsurprisingly, he found that good teams have high average run differential per pitch, but what his research shows is why.
“I actually broke it down by inning. I was looking at it, and Mark Simon of Sports Info Solutions tweeted back at me, and he guessed Max Scherzer [would have a high average run differential per pitch]. That seemed like a really good guess, but it turned out he wasn’t even in the top 10. I was interested in why,” he said. “It turns out, if you’re taking an average of run differential per pitch, even if you’re getting good run support, if that run support comes later in the game, it’s going to be weighted by the score earlier in the game.”
Thus, if the game is tied for, say, four innings, then a team takes the lead, the stat will be heavily weighted towards a tie game. He noted that the stat contains information that is reflected in WPA and other pitching metrics, but Freiman “just had fun with it.”
— Nate Freiman (@natefreiman) May 29, 2018
He further explained: “The top 10 are the usual suspects – Corey Kluber is number one – but what I like about it is it takes into account your pitching and your team’s offense.”
What run differential per pitch indicates is if a pitcher is effectively holding an opposing team while his own team is racking up runs. If a pitcher is effective but his team is not scoring, his run differential per pitch will be correspondingly low.
Numbers would have complicated his playing career
But taking a step back from the new stat, which he believes is unlikely to catch on in public use, the obvious question is if Freiman could have benefitted from this hobby while he was in the majors.
He was quick to answer.
“No – I would have been in a lot of trouble,” Freiman said. “I would already go up to the plate thinking way too much, and most of my teammates would corroborate that … plus, my last year in the Big Leagues was ‘14, so I didn’t have Statcast. I would have just totally nerded out over all the outputs, but it’s probably better off that we didn’t have it when I was playing.”
Freiman, now 31, had a nine-year professional career; he amassed a total of 116 Big League games over two seasons, one of which included an 18th inning walk-off hit against legendary New York Yankees closer Mariano Rivera.
He was in Oakland for the team’s 2013 AL West championship and 2014 wild card berth, and followed those up with minor league stints with the Red Sox and Nationals. Freiman then played in the Mexican League in 2017, and was a member of Team Israel in the 2017 World Baseball Classic, but never made it back to the majors before officially retiring in March.
Along the way he had two sons with his wife, professional golfer Amanda Blumenherst, who he met while both attended Duke University for their undergraduate degrees – and both were star athletes (Freiman still holds the school’s all-time home run record). Now, the family will head back to Duke as Freiman pursues a graduate degree.
“Being a senior sign [in the MLB Draft] comes with some drawbacks,” Freiman said. “But what was nice is when I retired, I already had my degree. I didn’t need to go back or anything.”
He will work to add an MBA to his undergrad studies in history and math, with no plans of playing again, necessarily. But baseball experience happens to be conducive to other endeavors.
Regardless of future career, baseball savvy is a plus
“With the way the world’s changing, baseball is a pretty good microcosm of what’s happening on a macro level with the business world in terms of data, and it’s seeming like a totally indispensable skill to be data-literate,” said Freiman.
His increasingly complex graphs cue his Twitter followers in to what data literacy can entail:
Visual of a 9-dimensional graph. Using @fangraphs batted ball data, plotted starting rotations in K/9, BB/9, K/BB, WHIP, BABIP, soft c%, med c%, hard c% and HR/FB. Used principal components to graph in 2D. Some of these are results of changing game, but still. #outlier @astros pic.twitter.com/XBnhtzuZA2
— Nate Freiman (@natefreiman) May 27, 2018
Granted, Freiman does have an attractive baseball résumé, should he want to return in a front office role. He would join a handful of other former players currently employed to help separate “the signal and the noise” in baseball analytics.
In other words, one of the biggest challenges teams face when it comes to analytics is figuring out what actually matters on the field, and what’s just senseless – but often fun – number manipulation.
He pointed in particular to former teammate Sam Fuld, who now works for the Philadelphia Phillies as the Major League player information coordinator, and former New York Mets pitcher Jeremy Hefner, who now is an advanced scout for the Minnesota Twins.
“That’s the big question in baseball, hands down – and actually in any business. Information is just information. It’s what you do with it that counts,” Freiman said. “Not just in Major League baseball; College baseball, minors organizations – everyone has this information and it’s about what you do with it.”
It will ultimately be up to individual players to decide how much to personally embrace predictive stats like xwOBA and xSLG, which Freiman discussed extensively in his debut articles.
“I don’t think it will hurt,” he said. “I happen to like numbers and I’m trying to get into this coding thing. The world is changing, and I’m trying to keep up with it, basically. For me, it wasn’t about trying to gain competitive advantage, it’s more about understanding how the industry and the world around me is using data, and to keep up with the current.”
And as to how a lay person should get started learning analytics?
“I’ve actually been going around and asking everyone I can that exact same question,” Freiman said, “So I’ll just repeat what I’ve heard, and that’s not be afraid of the data: just pick it up and start exploring.”
So Freiman will keep doing just that – and hopes to publish another Fangraphs piece shortly.
“Front offices have really sophisticated modeling with many, if not dozens, of parameters,” he said. “I’m not at that level yet, but I’d like to be, and I’d like to learn.”
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