Announcement

Collapse
No announcement yet.

BA and OPS

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • #16
    Originally posted by Ubiquitous View Post
    If you have two players with similar SLG then the one with the lower AVG is actually putting more runs on the board since he is being more efficient with his hits.
    Efficient? More like more bang for his buck or something like that. And how can we be sure he is putting more runs on the scoreboard when nearly all of those runs are team dependent?
    Your Second Base Coach
    Garvey, Lopes, Russell, and Cey started 833 times and the Dodgers went 498-335, for a .598 winning percentage. That’s equal to a team going 97-65 over a season. On those occasions when at least one of them missed his start, the Dodgers were 306-267-1, which is a .534 clip. That works out to a team going 87-75. So having all four of them added 10 wins to the Dodgers per year.
    http://www.youtube.com/watch?v=p5hCIvMule0

    Comment


    • #17
      Empirically, per the RC formula...

      Assuming I understand the discussion

      two players with the same PA, same AB (i.e., same walks, HB, etc.), same s.pct (i.e., same TB) will favor dramatically the player with the higher batting average. The reason is the higher BA is achieved with fewer outs. While it might 'seem' to defy logic, it's because we are used to thinking of the game the wrong way.

      The most precious commodity in the game is outs. Hitting a lot of MR, for example, while making a ton of outs, is far worse than hitting a ton of singles and making much fewer outs.

      Code:
      																																	Outs	TOT							
      	PA	AB	H	2B	3B	HR	AVG	BB	SO	TB	OBP	SLG	LH	Ex/LH	OPS	Made	RC	HBP	SB	CS	SF	SH	GDP	IBB
      2011	86482	77586	20004	4005	411	2274	.258	6986	15588	31653	.323	.408	6690	11649	.731	60467	10060	738	1600	619	634	530	1721	472
      																								
      BA	86482	77586	20004	0	0	0	.258	6986	15588	20004	.323	.258	0	0	.580	60467	7377	738	1600	619	634	530	1721	472
      																								
      Spct	86482	61556	3970	0	0	3970	.064	23016	15588	15880	.323	.258	3970	11910	.581	60471	6463	738	1600	619	634	530	1721	472
      Here is the 2011 Al season. Top line is data using the RC formula. Actual runs were over predicted by 57 (0.57%). So, yea, it's a good estimate.

      Line 2 is the Batting Average method. All singles. Line 3, is same number of total bases (i.e., same slugging %). I used all homers. I adjusted walks so the OBP would be the same.

      The batting average line is about 20% better in scoring. I can't get it to line up nicely for my post, (I suck at posting code from excel.)

      Notice the outs made are the same (by definition.) But while homers are move valuable than singles, singles are more valuable than walks. Per the formula, the latter offsets the formula comfortably.
      Last edited by drstrangelove; 02-20-2012, 06:13 PM.
      "It's better to look good, than be good."

      Comment


      • #18
        Originally posted by SavoyBG View Post
        Just looked at it, and it's not really clear which player is better. But what is clear is that there's no strong evidence that we should automatically prefer the higher batting average when both players are roughly equal in OBP and SLG%. Usually the higher batting average guy will use up more outs along the way, imcluding hitting into more DPs, since he puts the ball in play more often and would be less of a flyball hitter than the Evans or Thome type.

        I checked, and Madlock hit into DPs more often than Evans, including one season where Madlock led the league with 25 GIDPs.
        A couple of questions being begged there. Completing an arc that ties high batting averages to a tendency to hit into more DP's is one. Between the ground ball and fly ball scenarios, there are grass-cutters and line drives aplenty. Then too, there are high average hitters who achieve that by GDP avoidance.

        We can paint idealized pictures to color debating points; but if one is inclined to hold others' feet to the task of statistical support, the idealized generalizations must stand up to similar scrutiny.

        Comment


        • #19
          Tangotiger on BA/OBP/SLG

          The following table presents 6 players with the same OBA, same SLG, but widely differing batting averages.

          Essentially, as the walks and HR go up, I decrease the hits. In this way, I force the OBA and SLG to match, while varying the batting average.

          We see that not considering the batting average in your OPS metric will have an effect of +/- 2 runs. We again see that Linear Weights, as expected, is an almost perfect match. BaseRuns comes to within 1 run of the true value
          Code:
          PA 	AB 	H 	2B 	3B 	HR 	BB 	Outs 	AVG 	OBA 	SLG 	OPS 	BsR 	BsR/440 BsR+/- 	LWTS 	Team diff
          660 	640 	200 	30 	4 	8.1 	20 	440 	0.313 	0.333 	0.41 	0.743 	74.6 	74.6 	-1 	-1.8 	-1.7
          660 	620 	180 	30 	4 	12.1 	40 	440 	0.29 	0.333 	0.41 	0.743 	75.1 	75.1 	-0.5 	-0.9 	-0.8
          660 	600 	160 	30 	4 	16 	60 	440 	0.267 	0.333 	0.41 	0.743 	75.6 	75.6 	0 	0 	0
          660 	580 	140 	30 	4 	19.9 	80 	440 	0.241 	0.333 	0.41 	0.743 	76.1 	76.1 	0.5 	0.9 	0.9
          660 	560 	120 	30 	4 	23.9 	100 	440 	0.214 	0.333 	0.41 	0.743 	76.6 	76.6 	1.1 	1.8 	1.8
          660 	540 	100 	30 	4 	27.8 	120 	440 	0.185 	0.333 	0.41 	0.743 	77.2 	77.2 	1.6 	2.6 	2.7
          As for the batting average thing, I suppose that's another myth. It's pretty clear that given two guys with the same OBA and SLG, you want the guy with the LOWER BA (though in reality, we're not talking about much difference).

          I just tried with a weird environment (OBA/SLG of .393/.493), and in this case, the higher the BA, the more runs scored. I then tried the other way, with .289/.351, and this time the LOWER the BA, the more runs scored.

          The "break-even" point seems to be about .360/.450. That is, at that level, the change in batting average (and I checked from .200 to .340) made zero change to the run production of the team.

          RC has its own problems, magnified substantially when the HR/H or HR/PA becomes out of whack. RC does not model run scoring at all: it just got lucky that it looks like it models it. If you've got a computer, there's zero reason to use RC, when you've got BsR (unless you want to propose a model that's better).

          I don't really care about the different denominators. The whole thing of OPS centers around: more good, less bad. The more walks, the more hits, the more TB, the less outs, the better the number. There's nothing inherent in OPS that ensures that the balance is proper. It's just plain old luck that for the run environment of MLB, that it works out that way.

          Believe me, if the run environment was half what it is today, or double what it is, there'd be some other "quick" estimator that would get lucky to model run creation.

          Comment


          • #20
            Code:
            PA	AB	H	2B	3B	HR	AVG	BB	SO	TB	OBP	SLG	LH	Ex/LH	OPS	Outs	RC
            660	640	200	30	4	8.1	.313	20		262	.333	.410	42	62	.743	440	91
            660	620	180	30	4	12.1	.290	40		254	.333	.410	46	74	.743	440	89
            660	600	160	30	4	16	.267	60		246	.333	.410	50	86	.743	440	87
            660	580	140	30	4	19.9	.241	80		238	.333	.410	54	98	.743	440	85
            660	560	120	30	4	23.9	.214	100		230	.333	.410	58	110	.744	440	83
            660	540	100	30	4	27.8	.185	120		221	.333	.410	62	121	.743	440	82
            Same data, but with RC compiled. In the RC formula, higher BA is better. I haven't studied this, so I don't have an opinion which is right, but it will make for some interesting time for me at least over some coffee. (Maybe a couple pots of coffee!)

            I don't want to debate which is right since both methods have experienced and intelligent advocates. It's interesting though.
            Last edited by drstrangelove; 02-20-2012, 08:19 PM.
            "It's better to look good, than be good."

            Comment


            • #21
              The model is a self-fulfilling prophecy. The guy with the highest BA is presumed to have the same OB% and SLG and the guy batting around .190. One can create an arithmetic model to suit and point under debate.

              In the context of recent remarks posted here, I tried to construct a reasonable pair of batters with very comparable stats OTHER than their respective BAs, which, in themselves are fairly well apart but not so extreme as to forbid reasonable comparisons.

              Player "Visitor" posts these numbers:

              AB 600
              H 162
              BA .270
              HR 30
              3B 0
              2B 18
              1B 114
              BB 73
              TB 270

              Player "Home" puts up these numbers:

              AB 600
              Hits 197
              BA .328
              HR 12
              3B 4
              2B 29
              1B 152
              BB 37
              TB 270

              The hour grows late, so I penalized Home a single. There is nothing in the figures that would indicate DPs batted into by either player. One has 48 extra base hits; the other 45. The big disparity would seem to be HRs; but hen the question is legitimately raised WHEN and under what circumstances game/conditions each of those 18 big hits were belted. In a full season, there's enough random chance in PA where 18 "bombs" may not be all that telling, especially when the trailer is collecting 36 more hits on his side of the ledger.
              Last edited by leewileyfan; 03-14-2012, 09:57 PM.

              Comment


              • #22
                Originally posted by drstrangelove View Post

                Same data, but with RC compiled. In the RC formula, higher BA is better. I haven't studied this, so I don't have an opinion which is right, but it will make for some interesting time for me at least over some coffee. (Maybe a couple pots of coffee!)

                I don't want to debate which is right since both methods have experienced and intelligent advocates. It's interesting though.
                RC is an extremely simple and coincidental metric which shouldn't really be used as anything other than a blunt instrument.

                Comment


                • #23
                  Originally posted by Ubiquitous View Post
                  RC is an extremely simple and coincidental metric which shouldn't really be used as anything other than a blunt instrument.
                  I am quite honestly NOT being cynical or snide when I ask this: Sometimes, isn't the blunt instrument the best tool to use when trying to crack open a tough nut?

                  I have studied and worked with LWTS, BSR and looked into sabermetric glossaries for alternative methods to approach batter run creation. After all is said and done, one I stumbled upon by trial and error suggests that adding Total Bases + Bases on Balls, then multiplying by BA gives a pretty darn good estimated of Runs Created. I imagine it's a matter of how much exactitude one wants in his numbers; but then, I believe it's fair to ask, just how sure one is of his precise conclusions once he has reached them?

                  Is there, or has there ever been a study, sabermetrically regressed and vetted, that has taken an acceptable team/league/decade/historic formula that has been applied and found EXACT in conforming from player level to actual team results without any margin of error?

                  Comment


                  • #24
                    It may be the best tool if you desire the nut to be everywhere but beyond that not really.

                    I doubt there is any metric out there or ever will be that is free of error but runs created is a fluke.

                    Comment


                    • #25
                      I know you've thought about this and probably studied this extensively, so I don't mean to disagree, just to disagree.

                      Is it not true that both RC and LW suffer from the same problem of trying to fit data that 'normally' occurs in a somewhat narrow range, while then extrapolating the findings to data that falls firmly outside the base range? RC luckily or unluckily, estimates runs scored quite well. LW accomplishes the same.

                      I don't find it odd that the results are contridicted by the two methods once one leaves the cozy confines of the base data and tries to apply it to virgin raw information that looks nothing like the base. Neither method is really based upon those data ranges.

                      If this is completely wrong, then I'm sure you can explain why, but it's concerning to me. As I understand them, they are both multiple regression models, and such models are notoriously inaccurate if you try to apply them to data outside the base range IF the regression line was created using a limited range of data.

                      Does this not apply to both?
                      "It's better to look good, than be good."

                      Comment


                      • #26
                        Linear weights is based on the data so you really shouldn't use linear weight numbers for say a .450/.550 environment for a .225/.325 environment.

                        Baseruns, by the way, is suppposed to be able to handle any environment.

                        Comment


                        • #27
                          Originally posted by Ubiquitous View Post
                          Linear weights is based on the data so you really shouldn't use linear weight numbers for say a .450/.550 environment for a .225/.325 environment.

                          Baseruns, by the way, is suppposed to be able to handle any environment.
                          Ok, so I need to study baseruns and become more familiar with that. Cool...more to learn is good!

                          Thx
                          "It's better to look good, than be good."

                          Comment


                          • #28
                            Originally posted by Joltin' Joe View Post
                            I can't agree that it takes the guesswork out of it. WAR and Win Shares are good stats but they are not absolute and it is one person's guess work of how valuable something is. It is not a cut and dry stat like Usain Bolt's 100 meter time.





                            Yes I agree that BA is the least important of the three but my point was that when OBPs and SAs are that close, the significant delta in the BA does come into play as a "tie breaker".
                            I would say that BA does get enough credit. all the superstar hitters hit for high average:

                            -ruth
                            -williams
                            -gehrig
                            -pujols
                            -bonds
                            -manny

                            they all hit well above .300 in their good years. out of the 10 highest OPS+ guys only 2 are not having a .300 career average (bonds at .299 and mantle at .298). most are over .330. the prototypical inner circle HOFer is not a patient pure slugger. It is a .330 hitter that slugs for power and also walks quite a lot.

                            that high walk, high homer, low BA guy like jim thome usually doesn't get a lot of credit.


                            for comparison I will post wOBA for 2 quite similar seasons of mantle and hornsby (by OPS).

                            hornsby 1927: 1.035 OPS, .361 BA, .448 OBP, .586 SLG
                            mantle 1958: 1.035 OPS, .304 BA, .443 OBP, .592 SLG


                            of course mantles ISO is higher (.288 vs.225) and hornsbys BA is a lot higher.

                            wOBA

                            Mantle: .452
                            Hornsby: .471

                            So it seems like the higher BA is slightly more worth than the higher ISO. this makes sense of course since XBH are better than singles but not as much as slg might you think.
                            SLG says that a single is only 25% worth a HR and 50% worth a double. linear weights however say that a single is 70% of a double and about 40% or so of a HR.

                            that means the higher ISO does equal out the higher BA somewhat but not quite. the higher BA guy will produce slightly more runs.
                            Last edited by dominik; 02-21-2012, 04:20 PM.
                            I now have my own non commercial blog about training for batspeed and power using my training experience in baseball and track and field.

                            Comment


                            • #29
                              Originally posted by drstrangelove View Post
                              I know you've thought about this and probably studied this extensively, so I don't mean to disagree, just to disagree . . .
                              As I understand them, they [LW, RC] both multiple regression models, and such models are notoriously inaccurate if you try to apply them to data outside the base range IF the regression line was created using a limited range of data.

                              Does this not apply to both?
                              Linear weights are based on differences in average run expectancies in different situations, based on play-by-play data.

                              Runs created in its original form was based on its pretty durn good predictive power and its simplicity, as far as I know.

                              I don't THINK either is based on multiple linear regression. That doesn't speak to your main point, however.

                              (apologies for the cuts in your post)
                              Indeed the first step toward finding out is to acknowledge you do not satisfactorily know already; so that no blight can so surely arrest all intellectual growth as the blight of cocksureness.--CS Peirce

                              Comment


                              • #30
                                Originally posted by Jackaroo Dave View Post
                                Linear weights are based on differences in average run expectancies in different situations, based on play-by-play data.

                                Runs created in its original form was based on its pretty durn good predictive power and its simplicity, as far as I know.

                                I don't THINK either is based on multiple linear regression. That doesn't speak to your main point, however.

                                (apologies for the cuts in your post)
                                Interesting. More to read...
                                Last edited by drstrangelove; 02-21-2012, 06:04 PM.
                                "It's better to look good, than be good."

                                Comment

                                Ad Widget

                                Collapse
                                Working...
                                X