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Nope. Personal observation. With a livelier ball, pitching diluted by expansion and shorter outfield dimensions, why not?
I'm guessing that your evidence for there being a livelier ball, and that today's parks are smaller than the Maris era parks, is also the product of personal observation rather than anything which can be verified.
You make assertions, but do not provide us with reasons to find them credible.
I'm guessing that your evidence for there being a livelier ball, and that today's parks are smaller than the Maris era parks, is also the product of personal observation rather than anything which can be verified.
You make assertions, but do not provide us with reasons to find them credible.
Don't go away. Filihok will eventually produce charts and graphs that will prove I'm right.
Don't go away. Filihok will eventually produce charts and graphs that will prove I'm right.
Sorry. I can't get that data. HR/LD doesn't exist anywhere that I know of.
If you want to go through all the matriculations of taking HR/FB, comparing FB and LD rates over some amount of seasons and applying that to the number of home runs hit, be my guest. If not, I'll remain skeptical that HR/LD rate is increasing, or has increased since expansion
Quote:
Originally Posted by filihok
If you take pitchers who are totally tearing up the league and look at their BABIPs you can get a since of whether that will continue or not.
Ubaldo Jiminez 15 - 1 2.20 ERA 3.14 FIP
2010 BABIP: .248.
Career BABIP: .283
A very low BABIP that isn't likely to continue
Stephen Strasburg 3 - 2 2.32 ERA 1.96 FIP
2010 BABIP: .328
A very high BABIP that isn't likely to continue. Stras could get even better.
Likewise, you can take struggling pitchers and see if they will improve Dan Haren 7 - 7 4.36 ERA 3.75 FIP
2010 BABIP: .346
Career BABIP: .304
A high BABIP means he's likely to get better results
Joe Saunders 6 - 9 4.76 ERA 4.81 FIP
2010 BABIP: .299
Career BABIP: .293
His BABIP is right in line with his career average and the league average. He is what he is.
You can also see how FIP, ERA, and BABIP go together. Pitchers with lower ERAs than FIPs (Ubaldo) usually have low BABIPs. Pitchers with higher ERAs than FIPS (Haren, Stras) usually have high BABIPs. This also shows that FIP is a better measure of a pitcher than ERA, because ERA is dependent on the number of hits a pitchers gives up, which is dependent on BABIP which is out of his control
Just to get back to this. Let's take a look at the end of season BABIPs for these four pitchers
Ubaldo .273 - As would be expected Ubaldo couldn't maintain his low BABIP and didn't have nearly as good results in the second half of the season.
Haren .324 - As would be expected Haren's BABIP decreased. He was traded for Joe Saunders which likely influenced the outcomes as the DBacks had one of the highest BABIP's against and the Angel's one of the lowest, suggesting that the Angels' defense was better than the DBacks or something about the home stadium made BsIP less likely to become hits
Strasburgh .338 - His BABIP increased in a small amount of innings before his injury.
Sanders .311 - His BABIP increased as could be expected given the difference between the Angels and DBacks that I mentioned above.
I have been wanting to have this win prediction tool available for a while and finally have what I think is rather simple working model. This spreadsheet can be filled out with the players anyone thinks will be playing, along with their all their stats and then the team’s projected wins will be calculated.
More than 200 players are now free agents, free to negotiate and sign with teams of their choosing, most often the highest bidder. It may look chaotic from the outside, with one player signing for $8 million over two years and another for $80 million over five years, but spending has followed a fairly consistent pattern.
Younger players like Longoria, Robinson Cano, Clay Buchholz and Ike Davis provide a great deal of value to their teams, and because they are early in their careers, they cost little. In a sense, they produce a surplus of cash. Young talent that adds wins but is inexpensive has great value and is why teams are hesitant to trade prospects for veterans.
Sportvision’s FieldFX camera system records the action while object-recognition software identifies each fielder and runner, as well as the ball. After a play, the system spits out data for every movement: the trajectory of the ball, how far the fielder ran, and so on. “After an amazing catch by an outfielder, we can compare his speed and route to the ball with our database and show the TV audience that this player performed so well that 80 percent of the league couldn’t have made that catch,” says Ryan Zander, Sportvision’s manager of baseball products. That information, he says, will allow a much more quantitative measure of exactly what is an error.
One more reason for ashcanning Joe Morgan. He would have hated the new technology, refused to make any effort to understand it, and...
"Well, you know, Jon, this computer tracking is all well and good but the players already know who is good and who isn't and this computer never played the game so it doesn't know if the sun was in a player's eyes or if he's playing with a minor injury or...etc'
I had to chop up this article pretty severely to try and avoid the mod red pen of death.
Joe Posnanski, the author, does a good job explaining why pitcher wins are not a good statistic for measuring the quality of a pitcher. I urge you to read the entire article though
Joe Posnanski » Posts King Felix vs. C.C. « (http://joeposnanski.si.com/2010/09/10/king-felix-vs-c-c/ - broken link)
Quote:
I think pitcher wins — like errors, like banking hours, like Robert DeNiro movies — USED to mean something. I don’t think wins ever meant quite as much as so many wanted to make it mean. But when pitchers threw complete games...pitcher wins were at least a quick and dirty way to get a feel for how well a pitcher pitched. Even then, wins and losses could be quite misleading, but there was at least enough of a connection between victories and quality to continue the silly thought that pitchers win games.
I honestly think that the way bullpens are used now, the way the game has changed offensively and the various absurdities of the win rule all make the stat thisclose to obsolete.
The average start in baseball is six innings...we have not yet changed one of our main measuring tools even though we KNOW the game has changed dramatically. How can we think about giving David Price wins and losses like he was Cy Young or Walter Johnson or Bob Gibson or something. David Price...relies on his bullpen on average to get seven outs — and people inside baseball will tell you (and tell you and tell you) that those are the seven HARDEST outs. How in the world are you going to give David Price a “win” when he doesn’t hit, and he relies on his defense, and he doesn’t get the last seven outs?
He then compares Felix Hernandez (11 wins at the time of the article) to CC Sabathia (19 wins at the time of the article) start by start to see who had the better starts.
Quote:
Comment: And we finish on a Hernandez blowout.
Advantage: Hernandez 17-12.
But if there’s one thing I hope this little thought experiment does, I hope it makes clear that the mysterious numbers that some people rip — WAR, FIP, xFIP and so on — these things are grounded in what ACTUALLY HAPPENS in baseball games. They’re not just throwing darts in an alley at midnight. There’s a reason that Hernandez has better numbers in all those crazy stats. It’s because game in and game out Hernandez has pitched better than Sabathia.
So let's do some testing. How accurate are these things, anyway?
I split each pitcher season from between 2003 and 2008 into two sets of games: those pitched on even-numbered days, and those on odd-numbered days. I then tested to see how well performance in even-numbered days predicted performance in odd-numbered days. The results:
ERAFIPxFIPtRA
2.32 1.91 1.78 1.87 IPNUMERAFIPxFIPtRA
10 595 3.21 2.73 2.49 2.61
20 595 2.41 2.03 1.87 1.99
30 475 1.89 1.61 1.51 1.57
40 158 1.83 1.46 1.28 1.36
50 117 1.75 1.41 1.35 1.42
60 105 1.18 1.06 1.02 1.03
70 134 1.47 1.28 1.18 1.25
80 160 1.20 1.01 0.94 1.03
90 153 1.07 0.98 0.91 0.95
100 59 1.20 0.85 0.87 0.86
110 23 1.05 0.82 0.76 0.74
The advanced metric is always the better predictor-as evidenced by the smaller root mean square error.
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