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OPS: Begone! Part 2

May 27, 2003 - Nick S

Dave-

I think Tango means that BaseRuns assumes interplay of events in a lineup. That is, e.g. making fewer outs results in more plate appearances which in turn results in more run production. For a whole team or a single pitcher (facing a continuous lineup) this is true. For a single batter it is not - unless he is batting nine times in a row and using "ghost runners" - because he generates extra PA at (mostly) his teammates ability levels, not at his own.

Reliever Usage Pattern, 1999-2002 (June 24, 2003)

Posted 5:31 p.m., June 24, 2003 (#8) - Nick S
  LI is amongst your best work and is the correct way to evaluate the current issue of reliever usage. It's meaning is very clear and cuts right to the real difference between 'starter' and 'reliever', that is, starters have an LI of 1 (more or less, if I recall correctly, and that should be the value as starter's innings are, more or less, randomly distributed into close games and blowouts) whereas relievers do not.

This is work that could actually crossover to the mainstream. BaseRuns and DIPS just don't stand a chance right now (ever?) of being cared about by anyone who isnt' a 'stathead', they are too convoluted in their design, purpose, and meaning. Even if someone can't see quite where an LI number comes from, they can certainly understand it ("See son, at the bottom of the "Fox Box" that flashing, red 2.0, that means that this at bat is twice as important as a normal one.) And, best of all, it is a macho stat that would appeal to players. Relief pitchers seem to want the respect that they are the player to count on in tough situations, currently they associate that with saves, but I'd love to see the day that a pitcher bitches about being sent out for the ninth with a three-run lead because he needs to keep his LI up for salary arbitration :)

SABR 301 - DIPS Bands (July 15, 2003)

Posted 3:59 p.m., July 16, 2003 (#2) - Nick S
  Nice chart. This is generally supportive of the DIPS methodology (i.e. pitchers have little variation in their true ability to prevent H/BIP.) The average groups have right about the 2/3 of pitchers within 1 SD, as would be expected if all pitchers had the very same ability to prevent H/BIP. Towards the low BIP end, the sample is weighted towards pitchers who did poorly at preventing H/BIP, which would, of course, raise their ERA and lower the likelyhood of their staying around the majors. This does not mean that the pitcher's neccessarily had a lower $H ability (although, on average I'm sure they did, just not to any great effect), but rather that they were unlucky. Poor fellows.

The upper end is just the opposite, but with a similar interpretation. It does seem likely that with these large sample sizes, that some pitchers do have a better than average $H ability, but you should note that for a pitcher like Maddux who was 0.008 H/BIP better than average, this comes out to about 5 hits over the course of a season (maybe 3 runs or so). The absolute upper limit for a pitcher is probably less than 0.03 H/BIP, which would be about 20 hits in a season.

Evaluating Catchers (October 22, 2003)

Posted 6:16 p.m., October 22, 2003 (#1) - Nick S
  I like it. You are determining catcher fielding runs in a similar manner to determining batting event values from BaseRuns by doing a small perturbation (e.g. take away 1 single, recalculate), except by removing one catcher from his career. This works because of the massive sample size (as you note, you manage to catch the average baseline, pretty much) and, as such, is not likely to be useful for evaluating individual seasons. It is terrific, though, for evaluating a career in retrospect (i.e. Hall of Fame arguments), and I'm sure everyone (both) people who are reading this section of the site would be curious to know how Mr.'s Rodriguez and Piazza have faired in this metric to date.

Results of the Forecast Experiment, Part 2 (October 27, 2003)

Posted 10:44 a.m., October 28, 2003 (#46) - Nick S
  1) The forecasters clearly do a good job, much better than our eductaed guesses (4 out of 6 forecasters basically topped the distribution).

2) The forecasters that did the best job are probably using very similar methodology to the (as noted above very, very mathematically inclined) monkey. What are the differences between the two forecasters that did poorly and the ones that did well?

3) Tango, what where the batter/pitcher breakdowns for the forecasters and for the monkey? The assumption would be that oddball batter prediction is more on the ball, although we won't really be able to tell that from the small pitcher sample size.

4) It is a good thing you removed Giambi fromt he sample, or I wouldn't have squeaked into the top 20.

Value of keeping pitch count low (October 30, 2003)

Posted 6:30 p.m., October 30, 2003 (#3) - Nick S
  I'd look straight at IP to determine how many innings of relief that pitcher saved the team. High IP is both a function of efficiency (Halladay) and durability (Johnson). So I agree that efficiency is an attribute, but only in asmuch as it affects IP. I wouldn't use efficiency as a measurement of value added when you can drop the level of abstraction and go straight to innings, but it certainly may have predictive value.

If we run a matched pair study (say control ERA (or DIPS ERA) and IP, and vary pitches/batter) which (if either) group fares better in future years, the low pitch/bfp (efficient) or high pitch/bfp (durable) player.

Offensive Performance, Omitted Variables, and the Value of Speed in Baseball (November 6, 2003)

Posted 10:18 a.m., November 7, 2003 (#1) - Nick S
  I skimmed the paper, so maybe I missed his point, but it seems that he looked at calculated values of the SB/CS break even point and said "Teams seem to steal to much, why is that?" and offers that the calculated values are based on run value break-even points, whereas teams base decisions on win value break-even points. While this is a perfectly reasonable statement, I recall (though can't cite) work (I think by MGL) that SB attempts are randomly distributed throughout game situations.

What's a Ball Player Worth? (November 6, 2003)

Posted 10:02 a.m., November 7, 2003 (#6) - Nick S
  Only five comments and pretty much everything I thought when I read this has already been said. I love the "primate studies" section.

Of course we all have the slightly indignant reaction of "Well, they're doing THAT (e.g. Apparently giving all (most) defensive credit to the pitcher) wrong, so why is this stuff in the national media while Tango's website gets 15 hits a month", but this really is a good thing. It is very hard to get any attention unless you have credentials, as most people for most subjects are not able to easily differentiate between wrong, right, good, bad, and revolutionary ideas, so they use credentials of the inventor or critic as a proxy for the idea's merit (and their certainly is a correlation). So when people read this coming from Math PhDs at Yale, they tend to believe that there is something to it. Since I believ we all agree that with a couple minor tweaks this is a perfectly good "95% there" method, it is nice to see it in the national media.

Clutch Hitting: Fact or Fiction? (February 2, 2004)

Posted 6:51 p.m., February 3, 2004 (#33) - Nick S
  Tango-

How did you define clutch situations? That is, I assume you picked a LI threshold above which a situation was clutch, where was that threshold, and have you run the study at various thresholds to see if and how clutch ability varies with LI.

The Scouting Report, By the Fans, For the Fans - Most Similar Fielders (March 18, 2004)

Posted 12:25 p.m., March 19, 2004 (#8) - Nick S
  Great fun to skim through, Tango, thanks for putting this together.

We now know definitively that Ramon Martinez is the nexus of the fielding universe.