Author Archive

New SIERA, Part Three (of Five): Differences Between xFIPs and SIERAs

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.

Read the rest of this entry »


New SIERA, Part Two (of Five): Unlocking Underrated Pitching Skills

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.

Read the rest of this entry »


New SIERA, Part One (of Five): Pitchers with High Strikeouts Have Low BABIPs

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.

Read the rest of this entry »