(Special thanks to Tom Tango for working through the conceptual and analytical issues on this article with me)

After seven outstanding seasons as one of the National League’s premier hitters, Prince Fielder signed a nine-year $214 million deal to play first base for the Detroit Tigers. During his years in Milwaukee, Fielder averaged a .391 wOBA, 32 home runs (.0546 HR/PA) and posted a .257 ISO. Certainly, no one could argue about his productivity. But with a change to a new team —and more importantly, a new park — there are questions about whether Fielder’s offense will be impacted.

If Park Factors are to be believed, he should be in for a decline. By just about any model, Detroit is roughly even offensively overall, but a much tougher hitting environment for left-handed hitters than Milwaukee. That means we should expect Fielder’s offensive performance to decline more than basic aging and regression would predict. Since the Park Factor change only impacts half of a player’s games each year, the theoretical ratio between change in factors and change in performance is 2:1. Essentially, we’d expect a wOBA to decrease by 1.5% and home runs to decrease by 15%. There are a number of different Park Factor formulas, but the general pattern looks similar regardless of the factors you look at.

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During Yesterday’s Phillies and Pirates game, Phillies announcer Garry Matthews Sr. stated that he believes fastballs are almost always the next pitch thrown after a pitching coach comes to the mound to talk to his pitcher. Mike Axisa caught this and passed it along, and the topic seemed interesting and worthy of research.

The logic behind Matthews’ statement seems valid. A pitching coach most often comes to the mound when a pitcher is struggling, and a pitcher’s fastball is often the pitch he can most easily get over for a strike. But if this actually were the case, hitters would sit on fastballs each time the pitching coach came out. Hitters perform exceedingly well in fastball counts already, and this would basically be free knowledge that a fastball was coming if teams and players operated in this manner. I suspect that teams would not be this predictable.

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This is the last of four stories on Major League Baseball and social media. You can read the first three parts here, here and here. Full disclosure: Major League Baseball Advanced Media employs FanGraphs contributor Paul Swydan, who wrote this series.

Major League Baseball and its Internet arm — Major League Baseball Advanced Media — started slowly in social media, but the pair has made incremental progress. Technologically, things are running smoothly, and last season the league had lots of success with its Fan Cave, among other initiatives. But what’s in the league’s future?

Certainly the best way for MLB to push the online envelope is to offer good content. But as we’ve seen with countless reality TV shows, what seems fun and exciting one year can soon becomes stale. MLB understands this. “We want the Fan Cave to continue to evolve, so that it’s fresh and unique,” MLB spokesperson Matthew Bourne says. This season, instead of MLB picking Cave finalists on its own, the league is giving fans their say. The league recently concluded a voting period that saw the initial 50 finalists culled down to 30. So far, the results have been promising: MLB’s public relations team said they received more than 1.2 million votes in roughly one month.

All 30 finalists headed down to Spring Training in Arizona this past week, and the league now is deliberating on who will make the final cut heading into the regular season. Once the group — which MLB has promised will include at least one woman — is chosen, fans will once again have the chance to vote off contestants until only two remain in October. “This is an engagement with our fans through social media, and what they say is very important,” Bourne says.

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One of the many insights to come from Bill James was the fact that a team’s winning percentage could very easily be estimated based simply on the difference between the runs they scored and the runs they allowed. And while James’ Pythagorean Expectation cannot account for all variation in team performance, it does a fantastic job.

One possibility that is not accounted for is that teams may distribute their runs differently, game to game, than others throughout the season. It’s possible that two teams with identical run differentials could have significantly different records. Here’s a short example:

Assume two teams, A and B, both with a run differential of 0 (both score and allow 29 runs) over the course of a 10-game series against each other. The Pythagorean Expectation tells us that both teams should have a record of 5-5. However, in this scenario, team B wins 6 out of 10.
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This is the third of four stories on Major League Baseball and social media. You can read the first two stories here and here. Full disclosure: Major League Baseball Advanced Media employs FanGraphs contributor Paul Swydan, who wrote this series.

As the social-media revolution began, few major league franchises were fortunate enough to have a championship-caliber team. And perhaps only one was down the street from a company leading that charge. In 2010, the San Francisco Giants went on a historic World Series run while its neighbor was going on a run of its own. That company was called Twitter.

The close proximity between the baseball Giants and the social-media giant gave the team the online head start that perhaps no other team enjoyed — though several teams have now been able to replicate. And the rewards are still rolling in for those franchises.

Case in point: one of the first Tweetups organized by a club was one that the Giants hosted with Twitter founders Biz Stone and Jack Dorsey, “They have been instrumental in helping us understand how to use Twitter to communicate and engage with fans,” says Bryan Srabian, the Giants’ social media director. Twitter, too, most certainly understood the value of a live baseball game.

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This is the second of four stories on Major League Baseball and social media. You can read the first story here. Full disclosure: Major League Baseball Advanced Media employs FanGraphs contributor Paul Swydan, who wrote this series.

While other leagues have seen attendance dips in the past few years, Major League Baseball has held strong. And though that success initially didn’t translate online quite as well — as the first part of this series indicated — baseball has begun pumping social media fastballs. Among its best decisions was allowing fans to share video.

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This is the first of four stories on Major League Baseball and social media. Full disclosure: Major League Baseball Advanced Media employs FanGraphs contributor Paul Swydan, who wrote this series.

The evening of Nov. 11, 2010, turned into a pretty frustrating one for Kyle Scott. On that night, Scott, who runs the popular Philadelphia sports blog Crossing Broad, got an email from YouTube telling him that several baseball videos he’d posted were being removed from the site. While the videos were short — none exceeded 30 seconds — and contained scant game footage, they’d apparently gotten the attention of Major League Baseball Advanced Media. It wasn’t the first time that Scott had run afoul of MLBAM, but he was frustrated enough by the situation to write about it the next day. “They were short clips that we used for a quick laugh,” Scott says now. The Internet site The Big Lead picked up Scott’s story, and Scott says most readers “sympathized with our frustrations.” That MLBAM put the kabosh on Scott’s videos seems counterintuitive for a sport that’s constantly trying to expand its brand — and 15 months after getting the YouTube email, Crossing Broad averages nearly 1 million page views a month.

So is MLB a big-league bully — or is it simply protecting itself? And how does the league stack up against its peers on the American sports landscape? To figure that out, you first have to take a look at Scott’s case — or more specifically, to YouTube, where the league’s social-media firestorm began. Not only did MLB not post their own videos on YouTube, they actively sought to remove videos that fans had posted — a decision that ran counter to other sports leagues, which never took such heavy handed measures. Sometimes, as in Scott’s case, the deletions left a very public trail — and that critical fallout can have a lasting effect. But while MLBAM could have been more diplomatic about its position, the league’s online media arm had a practical business reason for taking such a hard line: the moneymaker called MLB.tv.

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With disabled list information available going back 10 years, I have decided to examine some league wide and team trends.

League Trends

To begin with, here are the league values for trips, days and average days lost to the DL over the past 10 years.

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Dare to dream.

On Thursday, we looked at Fielding Independent Offense (FIO) — as well as the Should Hit formula — and decided to toss stolen bases into the equation. The result were, let’s say, brow-elevating.

Today, we are going to put that result — the FIO formula — into action.

In the timeless words of Sir Samuel Leroy Jackson: “Hold onto your butts!”
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IT’S SO *** **** HARD TO THINK
WITH ALL THESE DUCKS EVERYWHERE!

In August of 2011, I introduced Should Hit (in three iterations: ShH, SHAP!, and Complete SHAP!). Should Hit is essentially a simple regression of walk rates, strikeout rates, home run rates, and BABIP on weighted runs created plus (wRC+). In both its calculation and its simplicity, it is very similar to FIP — but its uses and impact are quite unlike FIP.

Like FIP with groundball pitchers, the formula has some biases — known, accepted (by me, at least) biases. For instance, because it ignores doubles and triples completely, Should Hit naturally undervalues players who excel at the extra bags and overvalues to the sluggers stuck at first. It presumes a certain number of doubles and triples for every player based on their home run rate and other peripherals — all poor proxies for something that is a verifiable skill or weakness in many players.

Ultimately, though, the tools (ShH and its brethren) work rather well. For the curious thinker, ShH can admirably predict what a player might hit with a normal/career BABIP or if their BB% or K% or HR% changes. However, at the time of its uncovering, I was wrongly under the impression that the current FanGraphs iteration of the wRC+ formula did not include stolen bases. It mattered little to me at the time — the only reason I thought the uncovering was so interesting to begin with was that only four peripherals could explain almost 93% of the variation within wRC+ (and that is still amazing to me!)

But today, we are going to add in SBs and stand back with a decanter of thought and ask ourselves: “What the hell did we just make here?”
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