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Not to mention that whatever pitcher you sent down would have to stay in the minors for 10 days unless someone on the active roster gets hurt.
I'm not saying it's the entire difference, but it definitely helps overstate it.
I've got a system for baserunning that I can do once the retrosheet data is up, but I don't want to promise too much. I know I still need to get my OF throwing ratings together to send to you, Dan.
Yeah, I'm looking forward to them. Thanks.
http://www.baseballprospectus.com/statistics/sortable/index.php?cid=420215
Rollins: Me 0.9, Fox 0.9
Sizemore: Me 0.6, Fox 0.5
Reyes: Me 0.6, Fox 0.8
Beltrán: Me 0.5, Fox 0.6
Holliday: Me 0.5, Fox 0.8
Kinsler: Me 0.5, Fox 0.9
Roberts: Me 0.4, Fox 0.1
Pedroia: Me 0.4, Fox 0.4
Hanley: Me 0.2, Fox 0.3
Utley: Me 0.2, Fox 0.1
Hamilton: Me 0.2, Fox 0.2
A-Rod: Me 0.2, Fox 0.3
Youkilis: Me -0.3, Fox 0
Markakis: Me -0.2, Fox -0.3
Morneau: Me -0.2, Fox -0.3
Soto: Me -0.2, Fox -0.4
Teixeira: Me -0.2, Fox 0
Delgado: Me -0.2, Fox 0
Mauer: Me -0.2, Fox +0.4
Ludwick: Me -0.2, Fox +0.2
Pretty good! The only guy that's really off is Mauer...and he is such a unique player, I think my model can be forgiven for suspecting that a catcher with one stolen base and four triples is slow.
That means the average hitter, in 162 games of full time play would only hit 3.2.
This observation suggests that perhaps Dan's formula for projecting non-SB baserunning might be improved by including statistics for the previous year (or years).
Given all this outstanding new data, I felt it was a shame to keep relying on BP's FRAA and Bill James's Fielding Win Shares, so I set about incorporating it into my WARP. For everything but assists/putouts (that is to say, Fox's work on baserunning and Smith's on catchers, DP's, and arms), I simply added the data into the fielding and baserunning wins metrics. For assists/putouts, I did multiple regression analyses of the various stats against a weighted average of contemporary play-by-play metrics, and used the resulting equations to determine A/PO runs (e.g., a shortstop's A/PO score from 1987-98 is equal to .48 times his SFR plus .38 times his Dial rating plus .26 times his TotalZone plus a constant). These regressions show that the data we now have available are extremely high-quality: the r-squared's against the PBP metrics range from a low of .61 in center field to a high of .84 at third base (so multiple r of .78 to .92). In other words, you should feel quite confident in the accuracy of these figures--assuming you trust PBP numbers to begin with. The actual PBP data itself is included in the calculation of fielding wins as it becomes available (so UZR in 2000, Plus/Minus in 2003, and PMR in 2004).
To keep these numbers on the same scale as those available for 1893-1986, I have reduced the LgAdj figures (increasing the regression to the mean due to the standard deviation adjustment) to account for the extra variance introduced by this new data. This means that you *can* compare these WARP2 scores to those from earlier years on an apples-to-apples basis, but you *can't* compare these LgAdj scores as a measure of ease of domination to those from earlier years. For this reason, I have not modified the StDevs and Rep Levels spreadsheet (although I have slightly changed the replacement levels for the 1987-2005 period used to calculate these WARP as a result of the new data).
Finally, park factors in the Lahman database were updated at some point, and those new numbers are factored into this spreadsheet. The salaries listed here are based on the 2007 free agent market, and are calculated by looking at seasonal totals, not rates.
I hope the group finds this new information useful, and I look forward to feedback. Once Michael Humphreys finishes his work on DRA, I will see if the resulting correlations to PBP metrics are good enough to do something similar for years before 1987.
The new data are available in the Yahoo group.
Also, I should have mentioned that the problems my old WARP have with players who handled multiple positions in the same season are solved here. Every player's scores are calculated based on his exact number of innings played at each position in each season.
Check out my 1994 AL MVP! It's a stunner.
Michael will NEVER be done :)
-- MWE
To keep these numbers on the same scale as those available for 1893-1986, I have reduced the LgAdj figures (increasing the regression to the mean due to the standard deviation adjustment) to account for the extra variance introduced by this new data. This means that you *can* compare these WARP2 scores to those from earlier years on an apples-to-apples basis, but you *can't* compare these LgAdj scores as a measure of ease of domination to those from earlier years. For this reason, I have not modified the StDevs and Rep Levels spreadsheet (although I have slightly changed the replacement levels for the 1987-2005 period used to calculate these WARP as a result of the new data).
I think this means that StDevs and Rep Levels may be valuable for general sabrmetric reference --as I suppose many have used them. Maybe I will take a close look at thanksgiving break.
Finally, park factors in the Lahman database were updated at some point, and those new numbers are factored into this spreadsheet.
>>http://www.baseball-reference.com/about/parkadjust.shtml
Calculation of Park Factors
[Sean Forman:] I largely follow the method spelled out below. Historically, B-R has been using single-year park factors for recent years and 3-year park factors historically. I have changed that to now use 3-year factors by default for all years. Of course, the current season is only really a 2-year factor. The current year and last year. This can lead to some big changes in the numbers, from what had been on the site.
[and following TotalBaseball.com or Pete Palmer:] We use a three-year average Park Factor for players and teams unless they change home parks. Then a two-year average is used, unless the park existed for only one year. Then a one-year mark is used.
<<
622. David Concepcion de la Desviacion Estandar (Dan R) Posted: October 28, 2008 at 08:03 AM (#2997538)
By the way, I could do 2006 and 2007, but the only access I can get to Dewan's Plus/Minus for those years is by typing in every player's name individually on billjamesonline.com and manually entering the results at every position. While I devote a lot of time to this, I don't have *that* much time. Anyone have any ideas to help?
Park Adjustments at Baseball-Reference
>>
Calculation of Park Factors
[Sean Forman:] I largely follow the method spelled out below. Historically, B-R has been using single-year park factors for recent years and 3-year park factors historically. I have changed that to now use 3-year factors by default for all years. Of course, the current season is only really a 2-year factor. The current year and last year. This can lead to some big changes in the numbers, from what had been on the site.
[following TotalBaseball.com, ie Pete Palmer:] We use a three-year average Park Factor for players and teams unless they change home parks. Then a two-year average is used, unless the park existed for only one year. Then a one-year mark is used.
<<
1. I calculate wins above average (league average for hitting and baserunning, positional average for fielding), factoring in everything up to and including the kitchen sink: double plays net of opportunities, non-SB baserunning, sac flies, you name it. This is no different than adding BRAA1 and FRAA1 together from a BP card, except that it's far more comprehensive.
2. I adjust this mark for how easy the league was to dominate. If it seems like some years there are a bunch of guys with OPS+ over 170 and even up around 200, while in other years the league leader will barely crack 160, there's a reason for it: certain factors make it easier or harder for players to distance themselves from average (either positively or negatively). In particular, the higher a league's run scoring is, and the closer it is to an expansion, the more likely you are to see extremely high scores. So, for example, if any season were likely to generate two guys breaking Roger Maris's record in the same season, it was the 1998 NL: the league scored 4.6 runs a game (high by historical standards), and added two new teams on top of the two that joined in 1993. Conversely, when Mike Schmidt led the NL 4 times in 5 years with OPS+'s of 161, 156, 155, and 152, he was able to do so because he played in low-scoring leagues that hadn't expanded in 15 years. I correct for this factor (standard deviations in stat-speak).
3. I come up with a final wins-above-replacement score by subtracting the standard deviation-adjusted wins above average of a replacement player at the same position from the standard deviation-adjusted wins above average of the player in question. I determine this replacement level by starting with the performance of real live replacement players from 1985-2005 (based on Nate Silver's study of "Freely Available Talent," or players over age 27 earning less than twice the minimum salary), and then trace its evolution back over the years by using the average performance of the worst 3/8 of MLB starters at the position over 9-year periods. E.g., let's say that shortstops over age 27 earning less than twice the minimum salary averaged 3.0 standard deviation-adjusted wins below average per full season played from 1985-2005, and the worst 3/8 of MLB starting shortstops averaged 2.7 standard deviation-adjusted wins below average per full season played during the same time period. And for the 1976-84 period, let's say that the worst 3/8 of MLB starting shortstops averaged 3.8 standard deviation-adjusted wins below average per full season played. In that case, I would set a replacement level of 4.1 standard deviation-adjusted wins below average per full season played for shortstops in the year 1980 (3.8 for the worst regulars, and an extra 0.3 for the gap (of 3.0-2.7) between the worst-regulars average and the "freely available" average for 1985-2005). Thus, if we have a SS who was 1.5 batting wins below league average, 0.3 baserunning wins above league average, and 0.8 fielding wins above an average shortstop (after adjusting for standard deviations) in precisely half a season in 1980, he would be -1.5+.3+.8 + (4.1/2) = 1.65 wins above replacement.
Paul Wendt, that spreadsheet is definitely intended to be a quick reference of ease-of-domination and the defensive spectrum for the time period covered by my system. Those two factors are the main added value that my system offers above what BP WARP and WS provide.
stax--You hit on a very important methodological question. At the moment, my standard deviation adjustment is applied to total wins above average, hitting + fielding + baserunning. I am still quite unsure of whether I should attempt to break this up or not. I certainly couldn't try to do so given the current data I have, where I am the first to admit that for pre-1956 seasons I am completely reliant on junk stats (Fielding WS and FRAA). But I hope to have DRA soon. If the correlations to the PBP metrics come out strongly enough, the next version of my WARP will be DRA-based for that period.
Unlike FRAA and Fielding WS, which have some sort of built-in caps (of course we know that Fielding WS does), DRA does not resort to such half-baked solutions. As a result, its standard deviation shows a pronounced decline over time, with prewar shortstops frequently posting +30 marks, occasionally +40, and even reaching as high as +50 in one case. Part of this is obviously real and legit, since SS simply had more ground balls to field back then than they do now. But another part, I think, is that the DIPS issue becomes harder to tackle as we go backwards in time. DRA credits pitchers for all grounders to the pitcher and infield popups, and fielders for all other balls--it assumes that pitchers have no ability to induce easy-to-field balls other than popups for their defenses and grounders to themselves. This assumption works reasonably well in the modern game, as shown by the metric's strong correlations to the PBP's. But I do NOT think it is a safe assumption to make before WWII and particularly in the deadball era, when far more pitchers could and did make their living off inducing easy-to-field balls than today's small contingent of knuckleballers. Just look at a guy like McGinnity; he's all BABIP, all the time. Unless that's all coming in the form of popups and groundouts to the pitcher, DRA is going to see him as a merely average pitcher, and the fielders behind him as wizards.
Disentangling pitching and defense is challenging in any era, and obviously more so the further back we go in time. If I can ever figure out a way to do it, then I would be able to achieve what I consider to be the Holy Grail: an integrated WARP system that traces the pitching/fielding split (with its quite substantial consequences for standard deviations) back over time. But until then, I'm afraid I'm just going to have to keep muddling along for the pre-Retrosheet era. They do seem to push the Retrosheet frontier back a bit further every year, so perhaps eventually there will be enough data available that this issue will resolve itself automatically.
The metric correlates well to the PBPs in the modern game because Michael is using the PBPs to validate his metric. That doesn't validate the assumption.
PBP metrics still correlate fairly well with opportunity, although not to the same extent that non-PBP metrics do. Infielders playing behind groundball pitchers will, on balance, fare better in the PBP metrics than do infielders playing behind flyball pitchers, and the reverse tends to hold true for outfielders. Teams that have few, or many, left-handed pitchers will tend to show skews based on the platoon differential (few LHP=more RHB against=more balls to the left side of the infield=better SS and 3B ratings and lower 2B and 1B ratings), although since platooning has declined this is becoming less of a factor.
-- MWE
It doesn't. Adding groundball pitchers to the staff tends to lift the rankings of ALL infielders, bad and good.
-- MWE
Also, no feedback/comments from anyone on 19 years' worth of new WARP numbers? Gary Sheffield as one of the worst fielders who wasn't moved to 1B or DH ever? Kenny Lofton as the 1994 AL MVP and a HoM candidate? Robin Ventura as the 1999 NL MVP? A-Rod already just one MVP award behind Bonds? (Bonds 1990-96 except '94 and 2000-04 except '03 when Pujols beats him; A-Rod 1996, 98, 2000-05, 07) Discuss, people!
You are not reading enough threads- the concept of replacement level is in dispute many places, and where the concept itself is not in dispute- the actual location of such level is.
Chipper Jones: 157 G, 701 PA, 567 AB, 181 H, 41 2B, 1 3B, 45 HR, 110 unintentional BB+HBP, 18 IBB, 25 SB, 3 CS, 6 SF, 94 K. exTrapolated Runs puts that line at 149.3 runs. Subtract 1 run for non-SB baserunning and you're at 148.3, and tack on 9.27 net double plays on offense, and you're down to 144.9. PF is 100, so that stays. That's a total of 404 outs, leaving 3,765 for his theoretical teammates, who averaged .1944 runs per out in the 1999 NL, so the Average Team Plus Chipper would score 144.9+(.1944*3765) = 877 runs.
On defense, both Dial and TotalZone (the only two metrics available for that season) put him at -10 runs. My weighted average of .43*TZ + .62*Dial + .00065*Innings comes out to, unsurprisingly, -10 runs. Furthermore, he turned only 5 double plays in 56 opportunities, costing him 3 more runs. And finally, I have to subtract 2 more runs from his defense (as I do from every 1999 NL 3B) to get the league total to add up to 0. So he's 15 runs below average on D. The average team in the 1999 NL had 810 runs, so the average team with Chipper at 3B would allow 825 runs.
Pythagoras says a team with 877 RS and 825 RA will win 85.8 games. That's 4.8 wins above average, which if you multiply by the standard deviation adjustment of .901 for the 1999 NL (one year after an expansion and 6 years after another one, scoring 5 runs a game), gets reduced to 4.3 wins above average. My replacement level for 3B in the 1999 NL is 1.5 standard deviation-adjusted wins below average per 696 PA, and Chipper had 701 PA, so 4.3+(1.5*701/696) = 5.8 WARP2.
Robin Ventura: 161 G, 670 PA, 588 AB, 177 H, 38 2B, 0 3B, 32 HR, 67 unintentional BB+HBP, 10 IBB, 1 SB, 1 CS, 5 SF, 109 K. eXtrapolated Runs gives that 110.9 runs. He was a perfectly league average non-SB baserunner and hit into a league average number of double plays given his opportunities, and his park factor was 98, so he winds up with 113 XR. He had 417 outs, leaving 3,752 for his teammates, so the Average Team Plus Ventura would score 113+(3752*.1944) = 842 runs.
Dial has Ventura at 23 runs above average on defense, and TotalZone at 26. So .43*26 + .62*23 + .00065*1356 makes 26 runs above average. He also turned 27 double plays in 57 opportunities, which is 3 more runs above average, bringing him to 29. Subtracting the same 2 for the league constant makes him 27 runs above average. Taking the league average 810 runs a team and subtracting 27 translates to 783 runs allowed.
842 RS and 783 RA are a Pythagorean record of 86.7 wins, 5.7 above average. Multiplying that by .901 reduces it to 5.1 wins above average. Add on 1.5 stdev-adjusted wins per 696 PA (Ventura had 670), and you get 6.6 WARP2, fully 0.8 higher than Jones.
That's the long version. But to boil it down, metrics drawing on two different data sets (STATS and Retrosheet) both agree that Ventura had an ungodly-great fielding season in 1999, one of the best in an excellent defensive career, while Jones had quite a poor one, the worst by far in a fine defensive career. The gap between the two in the field was a massive 42 runs, significantly larger than their differences on offense.
Ventura comes out to $135M in my salary estimator. The HoM in/out line is about $150M. He'd need another strong All-Star year like his 1995 to contend for my PHoM, and more than that to make my ballot. I have Buddy Bell ahead of him in the glove-first 3B queue.
2nd best player in the National League for 1993: Could it be Jay Bell?
Check out Derek Jeter in 1999. That deserves a monster *fist pump*
Darin Erstad really was a wonderful fielder. He has some of the best fielding seasons for the time period at CF, corner OF, and 1B. Plus he could punt the opponents inside the 20 yard line if necessary.
What's surprising about Jeter's '99? He hit .349 with 24 homers at shortstop. I mean, 'nuff said.
A very old version of UZR had Erstad's '02 at +57 runs. Anyone with even a passing familiarity with PBP metrics knows that Erstad was a historically great, possibly Mays/Speaker-caliber defender when he was on the field. But perhaps precisely the all-out style that enabled him to get to so many balls was also what prevented him from playing more games. And obviously, he never hit a lick after that 240-hit season. Strange career.
Alex Rodríguez
Year SFrac BWAA BRWAA FWAA Replc WARP1994 0.12 -0.8 -0.1 -0.5 -0.4 -0.9
1995 0.24 -0.6 0.0 -0.7 -0.8 -0.5
1996 0.95 4.7 0.3 0.0 -3.5 8.6
1997 0.91 1.4 0.6 -0.5 -3.2 4.7
1998 1.07 3.6 0.1 0.8 -3.7 8.1
1999 0.82 2.3 0.3 0.0 -2.9 5.5
2000 0.96 5.7 0.4 1.0 -3.3 10.3
2001 1.06 5.5 0.5 0.5 -3.4 9.9
2002 1.05 5.1 0.4 0.8 -3.4 9.7
2003 1.03 3.8 0.1 0.4 -3.2 7.6
2004 1.00 2.8 0.3 1.6 -2.3 6.9
2005 1.04 6.9 0.0 0.1 -2.3 9.3
2006 0.97 2.7 0.0 -1.0 -2.2 3.9
2007 1.02 6.6 0.5 0.3 -2.2 9.6
2008 0.86 3.3 0.3 0.2 -1.8 5.7
TOTL 13.11 53.1 3.7 3.0 -38.6 98.4
TXBR 12.75 54.5 3.8 4.2 -37.4 99.8
AVRG 1.00 4.1 0.3 0.2 -2.9 7.5
3-year peak: 29.9
7-year prime: 65.5
Career: 99.8
Salary: $346,409,212--above Robinson, Ripken, and Henderson, tied with Ott, below Schmidt and Morgan and Gehrig, #2 among primary-SS
Albert Pujols
Year SFrac BWAA BRWAA FWAA Replc WARP2001 0.99 4.9 -0.1 0.6 -1.0 6.4
2002 0.99 4.5 0.0 0.4 -1.0 6.0
2003 1.00 8.0 0.5 0.8 -0.7 10.0
2004 1.01 6.4 0.1 1.1 -0.2 7.8
2005 1.03 6.4 0.4 0.4 -0.2 7.4
2006 0.92 6.5 0.3 1.5 -0.2 8.6
2007 0.98 5.1 -0.1 2.2 -0.2 7.4
2008 0.93 7.8 0.0 1.9 -0.2 9.9
TOTL 7.85 49.6 1.0 8.9 -3.8 63.4
AVRG 1.00 6.3 0.1 1.1 -0.5 8.1
3-year peak: 28.4
7-year prime: 57.4
Career: 63.4
Salary: $219,466,929--already ahead of Frank Thomas; obviously Thomas has more career bulk than Pujols to this point, but Pujols bests him significantly on peak due to the defensive value. Close to Griffey. Above guys like Reggie Jackson and Jesse Burkett. Among 1B, will pass Bagwell next year, and then Greenberg and Mize await.
Chipper Jones
Year SFrac BWAA BRWAA FWAA Replc WARP1995 0.99 1.3 0.4 0.7 -1.2 3.6
1996 1.01 3.6 0.4 0.3 -1.8 6.1
1997 0.99 1.9 0.1 0.6 -1.4 4.0
1998 1.03 4.6 0.1 0.6 -1.6 6.9
1999 1.01 5.4 0.2 -1.3 -1.5 5.8
2000 0.99 3.7 0.3 -0.1 -1.6 5.5
2001 0.99 5.4 0.0 -0.4 -1.6 6.5
2002 0.97 4.9 0.1 0.6 -0.9 6.5
2003 0.96 4.1 0.0 -0.1 -0.9 4.9
2004 0.82 1.8 -0.1 1.0 -1.2 3.9
2005 0.63 3.2 0.0 0.1 -1.0 4.3
2006 0.69 3.8 -0.2 -0.7 -1.1 4.1
2007 0.87 5.4 0.1 0.4 -1.4 7.3
2008 0.78 5.7 0.0 0.8 -1.3 7.8
TOTL 12.73 54.8 1.5 2.5 -18.5 77.2
AVRG 1.00 4.3 0.1 0.2 -1.5 6.1
3-year peak: 22.0
7-year prime: 46.9
Career: 77.2
Salary: $228,317,409--same ballpark as Pujols overall at this point. Among 3B, he's one good year away from Brett and Boggs; pretty damn impressive. Will need two more MVP-type seasons, or 5 more All-Star seasons, to reach Mathews. Crazy late-career peak, no? 1999 is a perfect storm: tons of net double plays, very poor fielding, and a super high standard deviation (2nd highest-scoring NL season since 1930, one year removed from one expansion and six years removed from another).
Gehrig was certainly a better hitter than Albert--through his first 8 seasons, he averaged 7.0 BWAA2 per season, vs. 6.3 for Pujols--but the rest of his game was merely average. As a result, Pujols is 15% ahead of where Gehrig was at the same age (he did debut a year earlier, which helps). Needless to say, Gehrig's career would have been greater had he not come down with, you know, Lou Gehrig's disease.
Now, maybe doing so is really what you intended to do, but it does seem that certain biases are introduced for the purposes of historical comparisons. For instance, a player in V2 who who was fielding and baserunning almost neutral, but was a very good hitter, would suffer in comparison to a similar player in V1, since the V2 player would be hit with a larger overall standard deviation adjustment. This is true even if hitting is a larger relative component of value today than it was in the past, which seems if anything more likely to be true than the opposite. I am not sure how large this effect is; I tried looking briefly at some guys with extreme fielding value and guys with neutral fielding value relative to how they were valued in the older version of WARP, but unfortunately there are enough other confounding factors (it appears, for instance that some of the baseline offensive numbers have also changed). Still, this is something to bear in mind. It may even have some relevance to some of the points mentions above, e.g. Gary Sheffield's particularly bad showing in this version.
Nonetheless, your point that "V2 thinks that fielding and baserunning are a larger share of value than V1 does" is correct. That is because V2 is more accurate than V1--V1 has less sophisticated tools to perceive fielding and baserunning value, so it regresses it to the mean. (In fact, this is true within V2 as well--the observed stdev for '87-'99 should in theory be lower than '00-'02, which should be lower than '03, which should be lower than '04-'05, as the actual PBP metrics come on board to replace their proxy stats). But comparing V1 to V2 will thus introduce distortions. There are two possibilities.
The first option is to accept that V2 has a (slightly) higher standard deviation than V1, which should lead V2 players as a group to have slightly higher WARP2 scores than V1 players as a group (assuming that the extra variance is distributed randomly, which is in fact probably false). The second is to reduce its stdev to account for that, which as you correctly note means that a 100 OPS+, average fielder at a position will have a lower score in V2 than in V1.
I chose to go for the second approach, but you could easily take the first: just multiply all the "2" numbers (BWAA2/BRWAA2/FWAA2/Replc2/WARP2) by 1.03. I can certainly see a very strong case that that is the better way: we know more about these players, so we can say with greater confidence that they were/weren't HoM'ers than their predecessors. Of course, none of this would affect the relative value of Sheffield (or anyone else) within his cohort; it would only affect the valuation of 1987-05 players as a group to 1893-1986 players as a group.
The baseline offensive numbers have changed only in the cases where the park factors in the Lahman database changed. Some of those adjustments were quite substantial, by up to 7 points of PF (I think this mainly has to do with their previously using a 1-year PF for post-1999 seasons, but there are some other random cases as well).
On more concrete terms, how many people have the kind of late career surge Chipper Jones has enjoyed? Or to be more concrete: how many great players have had their two best seasons over the age of 35? I mean, you obviously have Bonds, but I can't think of anyone else. Aaron had his career high OPS+ of 194 at 37, and Williams' second highest was 233 at 38, but because of defense and playing time neither of those years ranked among their best. No other great player comes to mind.
Oh yeah, and Yale sucks.
V2 should have the same standard deviation, roughly, as V1, but the shape of the variance among players could differ somewhat. More specifically, V2 should have a larger standard deviation among the population for fielding and baserunning than V1, and thus also, by necessity, a smaller standard deviation in hitting and positional value. In effect, this means that V2 thinks that fielding and baserunning are a larger share of value than V1 does.
yes, except in case of some big player recruiting and development changes at the same time.
(If everyone capable of top-quintile batting and fielding and baserunning now plays soccer football, so that baseball's very best prospects are top batters or top fielders but not both . . .)
--
P.S.
C.C. Sabathia is from California.
Carlos Zambrano is from Venezuela.
They are extraordinary batters and pitchers both.
- By OPS+ they are absolutely stronger batters than any of the long-career pitchers who debuted in
the 1960s, Jim Hunter and Rick Wise and Jim Maloney at OPS+ = <52, 51, 44>; or in
the 1970s, Rick Rhoden and Dave Stewart and Rick Sutcliffe at OPS+ = <59, 40, 30>; not to mention
the 1980s, Doc Gooden and Orel Hershiser and Fernando Valenzuela at OPS+ = <32, 31, 30>.
Sabathia and Zambrano are not yet long-career pitchers at all. If they survive to pitch another 1000 innings, the chance is good that their relative batting skills OPS+ = <73, 59> will decline.
Has anyone looked at pitchers batting more systematically?
Is it true there has been some increase in the variation in pitcher batting skill (OPS+)
-- as the examples of Sabathia and Zambrano unsystematically suggest to me?
Sadly I'm still in South America, although enjoying a weekend at the beach in Uruguay. I get to the States on Thanksgiving day.
On more concrete terms, how many people have the kind of late career surge Chipper Jones has enjoyed? Or to be more concrete: how many great players have had their two best seasons over the age of 35? I mean, you obviously have Bonds, but I can't think of anyone else. Aaron had his career high OPS+ of 194 at 37, and Williams' second highest was 233 at 38, but because of defense and playing time neither of those years ranked among their best. No other great player comes to mind.
1.
I would have boldly declared that because of playing time Chipper Jones does not qualify --now playing about 80% rather than about 100% of team games. If indeed 2007 and 2008 have been his greatest seasons by some measure, be warned that your own intuitions may not match that measure very closely.
2.
Jones was a leftfielder during his last seasons as a full-time player. His return to thirdbase precisely matches his decline in playing time.
games played at primary fieldpos, Chipper Jones 1997-2008
152 158 156 152 149 : five seasons at 3B, 1997-2001
152 149 : two seasons at LF, 2002-2003
96 101 105 126 115 : five seasons at 3B, 2004-2008
Again I can only wonder aloud whether 2007 and 2008 have been his greatest seasons as a ballplayer!
and only warn that if that is true, it is quite unlikely that your own intuitions match that measure of greatness.
3.
At the beginning of the 2007 season Jones was only 24.11 yrs.mon old. If the answer to the original question is "Yes" then it is likely to be Yes only on a technicality.
4.
About ten years ago in Baseball's All-Time Best Hitters, author-statistician Michael Schell argued for truncation of careers at 8000 ABs or PAs. That is, for purposes of "all-time best" and "career" assessment, he counts the full career if shorter than 8000; otherwise only the first 8000.
During the argument for this move, he provided some illustration. Among all players in major league history (since 1871? 1876? 1893?), only Roberto Clemente improved his career average record notably after passing 8000. Luis Aparicio improved his career record slightly and maybe ranked second in mlb history.
I don't recall the two criteria that define the class of player-career-ends or the one measure that defines improvement. Only the bottom line that Clemente alone improved notably during his career's end. That was possible only because Pittsburgh used him as a full-time player for five seasons, ages 20-24, before he was a good batter. For the same reason only, his career's end defined by Schell began early. (If the criterion is plate appearances, Clemente was already there after the 1968 season, age 34.2; Jones only at the end of the 2007 season, age 35.6.)
correction, 34.11 years old (b. 1972-04-24)
"only on a technicality"
How to Lie with Statistics, appendix N
Pretend that everyone's age is an integer. In baseball context, use so-called baseball age, the July 1 hanky-panky.
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