I worked out LWTS. This too was a relearning process, in that I copied the formulas, rechecked the inputs and weightings and applied the data into a spreadsheet. Then I puzzled over HOW the final runs numbers could be so low.
Headslap: LWTS is designed to estimate RUNS ABOVE average. That called for a review of AVERAGE for the year in question, which calculated to ,1213 runs created per plate appearance. A fulltime player with 600 PA would [on average] "create" 72.78 runs. Rather than use a fixed figure, I applied the .1213 factor to the actual PA for each player. Then, adding LWTS to the RC base, I got a LWTS calculation of RC for each. The results:
M. 135.5
A. 133.2
H. 127.9
K. 119.2
L. 117.5
J. 116.8
F. 114.6
E. 112.1
I. 108.5
G. 107.4
B. 105.1
C. 100.6
D. 95.6
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Originally posted by leewileyfan View PostAnyone care to chip in with LWTS or BsR or some other metric?
Edit: Oops, I just noticed on Tangotiger's wiki that base runs are A team run estimator. To estimate for individuals, you supply an actual or theoretical (e.g. all average) team and compare results with and without the player's BsR input. Sorry, but learned something.
A 138
M 133
H 122
L 119
J 116
K 116
E 114
F 111
G 103
I 103
C 100
B 99
D. 86Last edited by Jackaroo Dave; 02242012, 11:55 PM.
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My favorite quickanddirty metric when I'm mobile is SLOB: SLG X OBA. It's usually close to OPS, but when OPS are equal, it favors the batter whose component scores are closer together, i.e. almost always the player with higher OBP (appropriately). To get runs created, just multiply by atbats. .200 is a hall of fame score, .333 X .420 = .140.
A .241
M .232
H .210
K .206
L .195
B .187
J .183
G .172
D .170
I .167
E .164
F .161
C .160
No big surprises. The order of the last 4 or 5 seems pretty arbitrary, as they are within .012 of one another.
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Ubiquitus, in Post #40 above, calculated wOBA for the thirteen players and listed them in declining order by wOBA. I, too, worked wOBA on them, getting different results {I readily admit the possibility that either I erred on inputs , OR that we used different weightings}. The results are essentially the same, as to standing order, with a minor change. Column 1.
wOBA
A. .402
M. .379
H. .371
K. .358
L. .355
J. .349
B. .339
G. .335
E. .333
F. .332
I. .329
D. .323
C. .321
Then, One version of RC: [shortcut: (TB+BB)*BA
RC shortcut
M. 134
A. 118
H. 112
K. 107
L. 100
J. 99
I. 98
B. 97
F. 93
E. 93
G. 93
C. 90
D. 88
Then, shuffling them again [not by a metric] but by actual MVP votes that season MVP vote standing in brackets, N = no MVP votes:
1. A [5th]
2. M [8th]
3. H [10th]
4. K [11th]
5. L [14th]
6. D [15th]
7. E [23rd]
8. B [25th]
C, F, G, I and J = NLast edited by leewileyfan; 02242012, 02:28 PM.
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Originally posted by leewileyfan View PostHowever, if we might keep the season secret for the time being we might get some evaluating responses.
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Originally posted by Jackaroo Dave View PostLeewileyfan, what particular questions or issues prompted your posting these data lines? I realize, of course, that anything interesting will be of interest, but are we still looking to see whether BA is underrated, or comparing a bunch of evaluative tools, and if so to what end, or did you have something else in mind?
You mentioned some surprising results. Do they have to do with your version of runs created? How about a little hint?
Take note of Ubiquitous' entry for the players by WOBA. Others may use LWTS, BsR, various RC formulas.
That's what I had in mind. Evaluate these players by your favorite metric[s]. We could have a great discussion. It will all revolve around what each poster thinks is significant. [Good question. Thanks.]
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sorted by wOBA
Code:wOBA A 0.448 M 0.440 H 0.429 K 0.416 L 0.409 B 0.400 J 0.397 D 0.388 E 0.388 G 0.384 I 0.381 F 0.376 C 0.370
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Originally posted by leewileyfan View PostWell, no problem if you'd like to evaluate each by whatever metrics you like. No harm done. However, if we might keep the season secret for the time being we might get some evaluating responses.
You have an edge. You can look up all the data to calculate wOBA if you like. [I did].
You mentioned some surprising results. Do they have to do with your version of runs created? How about a little hint?
There are some amazing batting lines in here, by the way, and the two from this year that are most so aren't even included.
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Originally posted by Joltin' Joe View PostI actually know what year and league this is because I recognized the line from the first player! Does this mean I read and watch too much baseball? :ooo: Although being a Yankee fan and this year being very historic did make it a little easy, but still....
You have an edge. You can look up all the data to calculate wOBA if you like. [I did].
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Originally posted by leewileyfan View PostSomeone asked for an example, using real MLB hitters in actual seasons of play. I decided to do just that. Listed below are thirteen Major League players, same league, same season and the numbers they posted.
I will try to provide all information that might allow posters to apply whatever run creation or other formula they favor to evaluate the players, maybe even list them, toptobottom in run productivity or overall batting value. Meanwhile, I have applied my own RC formula. Then I dug deeper and calculated wOBA for each, with some surprising differences.
Here are the players and their numbers:
A. 610 PA; 507 AB; 151 H; 102 BB; 294 TB = .298/.416/.580
B. 580 PA; 513 AB; 165 H; 60 BB; 240 TB = .322/.399/.468
C. 644 PA; 581 AB; 156 H; 51 BB; 282 TB = .269/.330/.485
D. 565 PA; 494 AB; 160 H; 61 BB; 210 TB = .324/.401/.425
E. 687 PA; 590 AB; 153 H; 92 BB; 269 TB = .259/.360/.486
F. 703 PA; 584 AB; 149 H; 113 BB; 251 TB = .255/.376/.430
G. 632 PA; 546 AB; 155 H; 80 BB; 249 TB = .284/.377/.456
H. 629 PA; 501 AB; 159 H; 121 BB; 233 TB = .317/.452/.465
I. 653 PA; 569 AB; 169 H; 82 BB; 247 TB = .297/.386/.434
J. 653 PA; 552 AB; 152 H; 95 BB; 264 TB = .275/.385/.478
K. 615 PA; 518 AB; 161 H; 82 BB; 263 TB = .311/.406/.508
L. 632 PA; 538 AB; 149 H; 81 BB; 279 TB = .277/.377/.519
M. 643 PA; 585 AB; 199 H; 50 BB; 343 TB = .340/.396/.586
There they are. Real season. Real Players.Last edited by Joltin' Joe; 02232012, 08:58 PM.
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Someone asked for an example, using real MLB hitters in actual seasons of play. I decided to do just that. Listed below are thirteen Major League players, same league, same season and the numbers they posted.
I will try to provide all information that might allow posters to apply whatever run creation or other formula they favor to evaluate the players, maybe even list them, toptobottom in run productivity or overall batting value. Meanwhile, I have applied my own RC formula. Then I dug deeper and calculated wOBA for each, with some surprising differences.
Here are the players and their numbers:
A. 610 PA; 507 AB; 151 H; 102 BB; 294 TB = .298/.416/.580
B. 580 PA; 513 AB; 165 H; 60 BB; 240 TB = .322/.399/.468
C. 644 PA; 581 AB; 156 H; 51 BB; 282 TB = .269/.330/.485
D. 565 PA; 494 AB; 160 H; 61 BB; 210 TB = .324/.401/.425
E. 687 PA; 590 AB; 153 H; 92 BB; 269 TB = .259/.360/.486
F. 703 PA; 584 AB; 149 H; 113 BB; 251 TB = .255/.376/.430
G. 632 PA; 546 AB; 155 H; 80 BB; 249 TB = .284/.377/.456
H. 629 PA; 501 AB; 159 H; 121 BB; 233 TB = .317/.452/.465
I. 653 PA; 569 AB; 169 H; 82 BB; 247 TB = .297/.386/.434
J. 653 PA; 552 AB; 152 H; 95 BB; 264 TB = .275/.385/.478
K. 615 PA; 518 AB; 161 H; 82 BB; 263 TB = .311/.406/.508
L. 632 PA; 538 AB; 149 H; 81 BB; 279 TB = .277/.377/.519
M. 643 PA; 585 AB; 199 H; 50 BB; 343 TB = .340/.396/.586
There they are. Real season. Real Players.
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Originally posted by dominik View PostI think it is pretty safe to say that if SLG is equal the guy with the higher BA and lower ISO will produce more.
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Not sure that this will add much to the discussion, but shoot me...I like to talk about numbers!
If you look at baseball and want to 'predict' scoring, you have at least two theoretical options:
1) find actual baseball results (hopefully a LOT of them and hopefully under the same conditions regarding rules, parks, etc.), run a regression and declare the formula viable. It can and should be tested on similar data that was not part of the set you used to test it's predictive value.
2) create a statistical model of baseball using actual data. E.g., if a runner is on second, the batter hits a single, the odds are xx% of the time that the runner will score, stop at third, stop at second, the batter will stop at first, make it to second on a throw, get thrown out at second, either runner will advance on an error, etc.
Obviously, step #2 is insane for modeling a league, but valuable if one wants to model a single situation like, runner on first, no outs, average hitter at the plate and on deck, do I sac bunt or swing away.
I'm still not up to speed on base runs, but ultimately, all regression models are strong at modeling large amounts of data, but much weaker when one applies it to small amounts of data (e.g., 1 home run) or abberant data (e.g., a .180 batting average with .550 slugging average). In reading on base runs, it apparently has weaknesses as well, but I can't say if they are due to the same flaws.
Notwithstanding any of this: models are not reality and their validity only comes from whether that do what they purport to do, and their value from whether we need them to do that at all.
It's unlikely that any model can tell us whether one player is really 'better' than another unless we start with the assumption that we already know what 'better' looks like. Models that tell us that a player's stats are what we expect will produce 'x' number of runs are great, but still we all know that a homer with the bases loaded is not the same as a solo homer. Many (all) of the models we use ignore that and assume homers all have the same value because they are assumed to be random. I'm not sure that I think that's true, and I'm fairly certain I haven't seen anyone prove it's true.
Better players are possibly (simply) players who play better than other players all the time, or they may be players who play the same as other players most of the time, but are much better than other players when it's more important (e.g., bases loaded, team behind). I don't know that many models try to model that second behavior.
Down from my soap box!
I think BA, OBP and Spct are too simple to be taken at face value when players are fairly close in quality, even after normalization. They are better than counting stats, but there's a deeper level we likely should add by joining in situational performance.Last edited by drstrangelove; 02222012, 11:36 AM.
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