SIERA’s updated version was unveiled Monday at FanGraphs, and as part of the release, I’ve been taking readers through its ERA-estimation process. I’ve written about SIERA’s ability to predict BABIP and HR/FB, and then I broke down its formula and structure. But like any good analysis, it needed to be tested against other estimators.

So far, the results are pretty conclusive. In fact, SIERA might be the best tool yet to help us understand and better-interpret pitching performance. Read the rest of this entry »

Who’s up and who’s down? Which pitcher will improve upon a stellar season, and who is headed for the trash heap? SIERA and xFIP attempt to answer these questions from year to year, but they’re not totally interchangeable metrics. Why? The biggest difference is the way each uses strikeout rates.

That’s not to say that the two statistics don’t generally say the same thing. In fact, they’re much more reliable – and calculated much differently – than traditional ERA. For a quick example, take a look at the top 10 pitchers in all three metrics during the past four seasons.

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Hidden statistics are the bread-and-butter of any good analysis, but most DIPS models rarely go beyond the obvious to find a player’s true value. FIP can take you most of the way by looking at the three true outcomes (home runs, walks and strikeouts) and xFIP adjusts FIP to what it would be with a league-average HR/FB rate. But neither of those systems considers how well pitchers control more volatile statistics — the ones that take up the other 70% of plate appearances. Now, with the new SIERA here at FanGraphs, we’re finally gathering the kernels that’ll help all of us figure out the small things that make good pitchers, well, so good.

A year after its release, SIERA has undergone some important changes, which we’re highlighting this week. I think you’ll like what you see. FanGraphs’ new-and-improved ERA estimation system now uses different proprietary data, takes more interactions and quadratic terms into account when reaching its conclusions, treats starters and relievers differently and adjusts for run environment. In other words, the new SIERA does an even better job analyzing pitching skills.

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Predicting baseball statistics is a tough job, especially when it comes to pitchers.

For every Roy Halladay pitch machine, there are 10 James Shieldses – guys whose ERAs change a run or two every year. Basically, it’s a crapshoot when it comes to figuring out the next ace – or the former ace-in-waiting who’ll lose his job by the all-star break. Don’t believe me? Consider this: In the past 11 years, four hitters have led the major leagues in WAR; eight pitchers have led the majors in ERA.

So while the league-leading run producers might be predictable, league-leading run preventers change almost every year. But hope isn’t completely lost when it comes to figuring out the next pitching superstar – or dud. Some pitching stats are very predictable, and focusing on those few numbers might lead us to a better system to evaluate talent.

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Adam Dunn as has had an unually huge drop in his production so far this year, especially in relation to his power. Over the rest of the season, people should expect some level of increase production because he really doesn’t that much further to drop. By looking back at players that had similar drops in power, the amount he should rebound can be estimated.

I wanted to look at how a hitter performed in the second half of the season and the next year after a huge power drop in the first half of the season. Thanks to our own Eric Seidman, I got a list of the players after 1973 that had at least 1200 PA in the 3 prior seasons, 100 PA before or on July 1st, 100 PA after July 1st and had a similar drop in production as Adam Dunn.

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Consider Alex Remington’s excellent pieces on Kim Ng and Justine Siegal and Marisa Ingemi and Kate Sargeant my preamble.

On April 2, 1931, a 17-year-old girl struck out two of the greatest hitters in baseball history. Jackie Mitchell struck out Babe Ruth and Lou Gehrig swinging and then walked Tony Lazzeri. Shortly thereafter, baseball’s then-commissioner and all-around inflexible gentleman, Kenesaw Landis, disallowed Mitchell’s contract, ending her tenure with the Double-A Chattanooga Lookouts.

By all accounts, Mitchell threw one pitch throughout her career — a “dropball” or sinker, reportedly taught to her by Hall-of-Famer Dazzy Vance. Despite her incredibly young age, she located the pitch effectively and worked as a middle-reliever during her career (which included chiefly independent league and barnstorming appearances following her departure from MiLB).

Babe Ruth, physiologist extraordinaire, remarked after the game:

I don’t know what’s going to happen if they begin to let women in baseball. Of course, they will never make good. Why? Because they are too delicate. It would kill them to play ball every day.

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Why do so many Major League Baseball players come from the Dominican Republic (DR)? Why does economically strong, population-rich Japan produce so few MLB players? Why does baseball-loving Colombia have so few MLB alums (nine total)? Well, as it turns out, the answers are not so easy to find.

My previous two articles — one on East Asian talent, th’other on the relationship between height and OBP — have generated a lot of good discussion about what determines where a baseball player comes from.

In the first piece, I proposed that teams should invest in Chinese (and Indian) baseball academies to take advantage of the exceedingly large pool of athletes in those areas. However, several commentors suggested that baseball culture, not population size, determines the talent pool.

I found this a most intriguing analysis, so I went back to the ol’ opening day roster/injury list of foreign born players.

This is how it breaks out:

It does not require an electron micrometer to see the DR and Venezuela give the MLB lots o’ players. These two nations composed 17.5% of opening day rosters, but have a combined population smaller than Korea or Columbia individually.

So what does determine MLB talent sources? Well, it certainly does not appear to be population:

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Over the past decade, a tremendous amount of progress has been made in quantifying if and when managers should do everything from sacrifice bunt to issue an intentional walk, but one area that has been (at least to my knowledge) overlooked is when to pinch run. One of the main reasons pinch running hasn’t been investigated to the same degree as these other strategies is that there are a lot of moving parts to look at- how much more valuable is the pinch runner than the previous runner? Will removing the starter hurt the team’s defense? What’s the opportunity cost of losing an available bench player? While the answers to each of these questions are needed for a complete analysis of when to pinch run, there is one question we can answer relatively simply- how often can we expect the pinch runner’s spot in the order to come up again later in the game?

The more often a pinch runner can be expected to have to come to the plate, the less appealing pinch running is likely to be, as the players who are most often removed for pinch runners are generally very strong hitters (or catchers), and pinch runners are generally light hitters.

Take for example the White Sox-Twins game on April 9th of last year. Tied in the bottom of the 8th inning, the White Sox elected to have Mark Teahen pinch run for Paul Konerko following Konerko’s 1-out single. The next two White Sox hitters went down in order, and two innings later Teahen stepped to the plate in the bottom of the 10th with men on first and second and one out. Teahen grounded into a 6-4-3 double play and the White Sox went on to lose in 11 innings.

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NOTE: This data may not include every player, only those with recorded heights.

In my two recent articles, concerning little people in baseball and east Asians in baseball, many commentors got hung up on the height issues.

“What about just short people?” said some. “Maybe they are a present inefficiency? Why do you keep ignoring short people!?”

Others asked: “How do you expect China to produce legitimate talent? I watch Southpark. I know that Chinese people are too short for baseball.”

Well, okay, let’s explore these issues.

Chinese People Are Too Short to Play Baseball
This is wrong. And not just racistly, but legitimately too.

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Over the weekend Buster Olney reported that Major League Baseball and the players’ union discussed a possible plan for realignment. The plan would move the Houston Astros, or possibly the Florida Marlins, to the AL so that both leagues would have 15 teams; eliminate the three divisions within each league; have the top-five teams from each league go to the playoffs; and require an inter-league game every day (because of the odd number of teams in each league). There is lots to consider with such a realignment — fairness, logistics, etc. — but I wanted to look at a single aspect: how will this plan would affect the number of competitive playoff races at the end of the season?

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