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  1. Booo!

    Obviously, the Padre offense took a hit with the loss of Adrian.

    But are we ignoring the upgrades with Bartlett, Hudson, Maybin, full year of Ludwick? I think maybe a little…

    Comment by Mike Savino — December 28, 2010 @ 4:04 pm

  2. “Quadrant II consists of teams which are below average at both skills.”

    If you’re going to call them skills, can we get some error bars? How “lucky” is the the strand rate skill?

    Comment by Erik — December 28, 2010 @ 4:07 pm

  3. Those players represent good defensive upgrades, but there’s not much for power there.

    Comment by Pat — December 28, 2010 @ 4:36 pm

  4. What are potential sources of error in the data?

    Comment by David — December 28, 2010 @ 5:00 pm

  5. I always thought Quadrant 1 was the top left quadrant, and then the numbering proceeded clockwise…

    Comment by Resolution — December 28, 2010 @ 5:05 pm

  6. I’ve been a little confused on what the strand rate actually tells us. Isn’t it a combination of luck and skill? For instance shouldn’t we expect the Braves and Reds to move closer to the average in strand percentage?

    Comment by MDB — December 28, 2010 @ 5:06 pm

  7. I’m sure it is some combination of luck and skill, but over the course of a season, I’m thinking of it mostly as a skill. I’ll get into some different ways of measuring this next week.

    Comment by Jesse Wolfersberger — December 28, 2010 @ 5:10 pm

  8. Nope, in math, Q1 is always top right, going counterclockwise.

    Comment by Travis — December 28, 2010 @ 5:15 pm

  9. Well, it’s going to have some luck but a lot of it is still going to be skill. Unfortunately, a part of that skill happens to be OBP however… If you’re getting less outs, it’s easier to drive runners in.

    Comment by B N — December 28, 2010 @ 5:16 pm

  10. Why use strand rate instead of iso for power?

    Comment by jscott — December 28, 2010 @ 6:10 pm

  11. Well whaddya know. Thanks.

    Comment by Resolution — December 28, 2010 @ 6:26 pm

  12. Imagine that the actual events were drawn from a distribution of all possible events… Deming anyone?

    Comment by Barkey Walker — December 28, 2010 @ 7:31 pm

  13. I’ve been thinking about your calling the OBP capital. I think this is very Marxist. He insisted that capital was just the dead labor. He spent a long time trying to prove that you could remove capital from the equation by insisting that it was only the labor that had been put into making it so that there was only one input–labor (and dead labor).

    Comment by Barkey Walker — December 28, 2010 @ 7:36 pm

  14. estimate a c=d production function? why would you expect strand and obp to exhibit crs?

    Comment by John — December 28, 2010 @ 9:32 pm

  15. I’m surprised how well the Dbacks did in team strand %. Even with their power the team set a record for most Ks in a season.

    Comment by Wavaw — December 28, 2010 @ 10:15 pm

  16. A Cobb-Douglas only exhibits constant returns to scale if the sum of the exponents is equal to 1.

    Comment by walt526 — December 29, 2010 @ 12:49 am

  17. Yeah, this is fatally flawed.

    Comment by Oscar — December 29, 2010 @ 3:15 am

  18. In case people don’t know, I have these graphs laid out for every year in baseball history at http://www.baseballgraphs.com. However, I use ISO instead of strand percentage.

    Actually, I prefer the graphical layout I put into this article:

    http://www.hardballtimes.com/main/article/how-teams-score/

    …I think it better addresses some of the underlying issues you’re raising here. I used this format last year in THT’s Graphical Report.

    Comment by Dave Studeman — December 29, 2010 @ 8:10 am

  19. Both factors used are also directly affected by the park’s effect on HR rate. Notice the grouping

    If there were adjustments for neutral environment, might show a bit more ‘true’ skill? But I understand that teams play their games in the real world….anyway, just a thought

    Comment by Nick — December 29, 2010 @ 8:51 am

  20. I have to disagree. Maybin can’t hit worse than Tony Gwynn Jr. and would be hard pressed to be a defensive upgrade (Gwynn was 12.9 runs above average last year according to fangraphs).

    Ludwick hit terribly for San Diego last year and might have a bounce back season…or not.

    Last season, Jerry Hairston Jr. and David Eckstein were the Padres middle infield. Orlando Hudson and Jason Bartlett should easily surpass that production.

    Obviously, Brad Hawpe/Kyle Blanks aren’t going to replace Adrian’s production but the easiest place to find replacement offense is at 1b/DH.

    All I’m saying is that the Padres definitely got worse at 1b but it looks like the team will be better offensively at at least four other positions, 2b, SS, CF, LF.

    Comment by Mike Savino — December 29, 2010 @ 10:38 am

  21. Sorry, I mean “error bars” in the statistical sense. I.e., http://en.wikipedia.org/wiki/Observational_error

    I guess what I’m looking for is some more explanation of the effect of strand rate, simply because I’m not used to seeing it as a tool to evaluate offense. So I don’t know if it’s a measure of luck or skill, or how large a, say, 2% difference in strand rate makes on the success of a team. Sticking error bars on the plot would be one way to address this.

    Comment by Erik — December 29, 2010 @ 11:00 am

  22. They may reach base more, but my point is that there won’t be much of an upgrade in power (except in eckstein’s case of course)

    Comment by Pat — December 29, 2010 @ 11:06 am

  23. and it’s just beneath you to stoop to our level and explain why?

    Comment by chuckb — December 29, 2010 @ 11:14 am

  24. I think over the course of a season, strand rate is an ok measure of how well an offense performed overall; however, I don’t think that it represents a good measure of power. It seems to me to be a better measure of balance and consistency in an offense… of how well the 3rd and 4th hitters are able to perform in an inning.

    It may also be a decent way to consider luck… much like ERA gives an overall idea of how the pitching staff performed, but not a good idea of their true talent or context-independent performance. We can look at an ERA and an FIP and see how much luck and defense factored in, and see how well a pitcher’s results lived up to his potential. By the same token, strand rate feels like a good measure of overall performance, and by looking at the other peripherals, we can get an idea of how well an offense lived up to its potential.

    If we think of the strand rate as offensive efficiency, and the OBP as one contribution to that measure, the graph does give us some useful information. It tells us how efficient they were, and it tells us that they didn’t live up to their potential. It doesn’t actually tell us why, though you infer it is due to power. I think it would be more interesting to use several graphs and change the x-axis. Seeing one with OBP, one with ISO, one with K% and BB%. By comparing all of them, we should see which factors are most correlated, and for each team, see what their deficiencies were.

    For example, on the graph, Cincinatti and Atlanta aren’t that different in OBP, but horribly different on strand rate. I would like to see how they differed in ISO vs strand rate as well. If that is significantly different, we could say that power was a strong factor. If they aren’t, it would just be lineup balance and luck/sequencing. That would make the analysis much more interesting I think.

    Comment by GTStD — December 29, 2010 @ 12:15 pm

  25. Take what I said before (I hadn’t read all the comments yet), and read that link and then be happy. :)

    Those measures layout more specific result with more drawable conclusions… hooray!

    Comment by GTStD — December 29, 2010 @ 12:19 pm

  26. I’m more concerned with a couple other qualities of C-D functions.

    Complementarity – With Strand % and OBP, you would expect a competitive result in output, not complementary or independent. This could be fixed by taking the RBI% (or whatever you want to call 100 – Strand%).

    Diminishing Marginal Returns Everywhere – This only applies if the exponents are less than 1, but I wonder if a cubic production function might be more representative of reality.

    Comment by Kevin — December 29, 2010 @ 1:05 pm

  27. Couldn’t “getting on base” easily be defined as labor?

    Comment by John — December 29, 2010 @ 1:09 pm

  28. So, basically, my D-backs were the BEST of the “average” offenses in the NL.

    Not sure if that’s a backhanded compliment or something to actually applaud.

    Comment by DIVISION — December 29, 2010 @ 1:49 pm

  29. In addition to the players Mike noted, Chase Headley, Nick Hundley and Wil Venable may improve a bit more as they enter their primes. In the end, I wouldn’t be surprised if the Padres score more runs in 2011.

    Really like the chart by the way, and so will my friend who is a Red’s fan.

    Comment by Dirk — December 29, 2010 @ 3:09 pm

  30. What about changes in Multi Factor Productivity?

    Comment by Filton Mriedman — December 29, 2010 @ 3:16 pm

  31. Yeah, I’ve been mulling it over since Part 1 was posted a few days ago and for the life of me I can’t understand why the author is using Strand Rate rather than ISO as a factor of run production. I’m also not quite sure why he’s laying out a four-quadrant graph with league average at the origin as I’m not sure that the unconventional layout really adds anything to the analysis. Maybe his reasons for doing both will be made clearer in a forthcoming post.

    Comment by walt526 — December 29, 2010 @ 3:18 pm

  32. Would it be possible to create indifference curves using this type of data? Maybe compile data from the past several years and use contour lines or something? The only thing is that I would make the strand rate descending instead of ascending so the IC would hold its traditional shape.

    Comment by Mitchello — December 29, 2010 @ 4:57 pm

  33. …the indifference curves would represent the different values of runs scored for each team.

    Comment by Mitchello — December 29, 2010 @ 4:59 pm

  34. Yeah, that’s exactly where I’m heading with this actually.

    Comment by Jesse Wolfersberger — December 30, 2010 @ 12:42 am

  35. Awesome. Sorry if I ruined the surprise lol.

    Comment by Mitchello — December 30, 2010 @ 12:51 am

  36. Same post I put on the AL article:

    Let me start by saying I absolutely love economics. I majored in economics in college, and am now employed by the Foundation for Economic Education (yes I love the Austrian stuff).

    With that said this post is comical. You used microeconomic theory to show that teams who get the most runners on base and strand the least of those runners will score the most runs? DUH! You can replace “OBP” with labor and “strand %” with capital but really you just stated the obvious.

    Economics has its place in baseball. For example, micro-theory can accurately predict contract length/salaries for free agents. But don’t bring economics into an area of the game where it’s not needed just to try and look smart.

    Comment by Phil — December 30, 2010 @ 11:13 am

  37. Phil…you hit the nail on the head. Nice try, but economists have been doing much better work for decades.

    In the the academic sports economics literature they have been estimating production functions for about 30 years, but they are more sophisticated than what we see here. Generally speaking, the “outputs” are wins and the “inputs” vary but generally include things like hits, SB, HR’s, etc. The value of coaching is often measured by comparing actual wins to predicted wins for a given amount of inputs.

    If you are estimating a discrete dependent variable Jesse, you should use a Poisson model btw.

    Comment by Ed — December 30, 2010 @ 11:18 pm

  38. I feel like strand rate might not be the best variable to use in these graphs. If you’re trying to show what certain teams lack, and how they can improve- ie. on-base ability or power- strand rate is a little bit obscure.

    I would like to know what a graph of on-base% vs. isolated power would look like compared to this graph. We know that power helps to drive in runs (and therefore lower strand rate), but so do singles. There’s simply no way we can say that “If you add a power hitter to your lineup, your strand rate will go down.”

    The on-base part of this article works completely: you can logically assume that if you get a guy that walks a lot and gets on base a lot, he’s going to improve your overall team on-base%. It just doesn’t work the same way with strand rate. Graph ISO vs. OBP and see where the teams lie. The teams in Q1 could drive in more runs by adding a power hitter. Teams in Q2 need both power hitters and on base guys. Teams in Q3 would benefit by adding a high OBP guy, and Q4 still represents the best offensives.

    Comment by Adam — January 1, 2011 @ 6:17 pm

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