rluzinski

It's a baseball game/simulator. LINK

A guy over at Baseball Think factory (check the notes from casey's link) used the ZiPS projections and Diamond Mind to simulate the entire season. Here are the results: High Low Team W L RF RA DIV WC DIV% WC% Made% Missed% Wins Wins American League East Boston 92 70 879 740 76 6 76% 6% 82% 18% 105 77 New York(A) 85 77 821 786 14 16 14% 16% 30% 70% 99 70 Toronto 83 79 748 740 9 14 9% 14% 23% 77% 99 65 Baltimore 73 89 733 809 1 1 1% 1% 2% 98% 89 54 Tampa Bay 70 92 711 827 1 0 1% 0% 1% 99% 85 59 Central Minnesota 86 76 719 674 38 9 38% 9% 47% 53% 100 70 Cleveland 84 79 775 732 23 7 23% 7% 30% 70% 102 70 Chicago(A) 82 80 746 727 20 4 20% 4% 24% 76% 103 68 Detroit 82 80 764 760 19 5 19% 5% 24% 76% 95 68 Kansas City 65 97 680 829 0 0 0% 0% 0% 100% 86 44 West Oakland 96 66 767 617 88 6 88% 6% 94% 6% 109 79 Los Angeles(A) 85 77 722 691 7 23 7% 23% 30% 70% 100 68 Texas 80 82 800 812 3 7 3% 7% 10% 90% 94 65 Seattle 79 83 710 750 2 5 2% 5% 7% 93% 94 60 National League East New York(N) 90 72 782 702 53 14 53% 14% 67% 33% 105 72 Philadelphia 89 73 751 676 38 22 38% 22% 60% 40% 105 75 Atlanta 81 81 736 734 8 8 8% 8% 16% 84% 94 68 Washington 75 87 683 735 2 0 2% 0% 2% 98% 89 59 Florida 70 92 645 737 0 1 0% 1% 1% 99% 86 57 Central St. Louis 96 66 768 628 79 10 79% 10% 89% 11% 109 82 Chicago(N) 85 77 735 696 8 15 8% 15% 23% 77% 100 72 Pittsburgh 82 80 711 707 8 9 8% 9% 17% 83% 96 65 Milwaukee 80 82 704 716 5 4 5% 4% 9% 91% 97 63 Houston 74 88 682 757 0 3 0% 3% 3% 97% 89 59 Cincinnati 69 93 760 870 0 1 0% 1% 1% 99% 85 53 West Los Angeles(N) 87 75 748 701 59 5 59% 5% 64% 36% 106 71 San Francisco 83 79 732 716 28 6 28% 6% 34% 66% 98 68 Arizona 77 85 729 771 7 1 7% 1% 8% 92% 95 63 Colorado 76 86 787 842 2 3 2% 3% 5% 95% 90 65 San Diego 75 88 675 718 5 1 5% 1% 6% 94% 94 61 Not very exciting for the Brewers. Anyone have Diamond Mind that could check the roster the guy used to generate this? The roster file should be HERE. I tried opening the data files in notepad but no dice. It wouldn't suprise me if he had the Brewer roster set a little goofy (like no platoon at 3B, for instance).

Still say they are the same.

BABIP is subject to much more variance than AVG, because BB and K are fairly constant from year to year. Since BB don't affect BABIP or AVG, what does it have to do with their variance? Looking at your chart of Jenkins BIP types, you can make an argument on either side of the case. In addition to declining, players tend to regress to their career average in just about everything. With Jenkin's injury riddled past, he's probably the worst player to use for this discussion http://forum.brewerfan.net/images/smilies/smile.gif While Jenkin's will begin to slip as a result of age, I think 2003 and 2005 are representative of Jenkin's actual abilities.

Given that BABIP is somewhat dependent on what "type" of hitter you are (groundball/flyball, etc), I would imagine (and I think I've read) that it stays somewhat constant through the minor leagues, kind of like walk rate. It may go down, but I would be surprised if there wasn't a decently strong correlation. I think BABIP is subject to about as much variance as BA, which is to say, alot, relative to K and BB rates. I haven't looked at how consistent players are at maintaining their gb%, fb%, or ld%, but I would imagine someone who has a particularly high BABIP and a particularly high LD% is not going to maintain those levels. While it's tempting to always assume outlyers will regress, it's not always true. Let's use Jenkins as an example: [b]Year LD GB FB IFFB[/b] 2002 19.6% 46.2% 34.2% 6.3% 2003 25.4% 41.6% 33.0% 5.7% 2004 19.3% 43.3% 37.4% 5.7% 2005 27.4% 38.5% 34.1% 5.1% As has been pointed out, Jenkins' 2005 capaign was basically a repeat of 2004 for him. Is it simply a coincidence that he had good LD% in 2 of the last 3 years? If you are really interested in these sorts of things, I suggest you purchase the Hardball Times Annual for 2006. They list year-to-year correlations of BIP types (FB,GB,LD,ect...) and go into this subject in great detail.

Jenkins led the National League with a 26.7% LD% in 2005. That was also good for 2nd in the entire majors. That certainly would suggest that Jenkins was being very selective, which could explain the increase in his BABIP.

If anyone ever wants to play chess at yahoo.com, hit me up! http://forum.brewerfan.net/images/smilies/wink.gif

Well, the wife is gone and my new 360 came in a couple days ago. Almost time to hunker down with a 12er of Miller Light and my cordless controller http://forum.brewerfan.net/images/smilies/smile.gif

Common sense tells us that their correlation should be equal. ERA is essentially "runs scored" and run support is "runs against." Since ERA doesn't include unearned runs, run support might have a tad higher coorelation? LINK

just wanted to point out that you can't entirely write off streakiness. I didn't. My exact words were, "The whole notion of slumps and consistancy is largely a function of the random distribution of probabilistic events." 99% of what the media calls a streak or slump is just a flip of the coin.

29 home runs for Carlos Lee? I thought that was far fetched ...or near fetched if you will. He's hit 31, 31 and 32 HRs in the last 3 years. Any guess between 28 and 34 is as good as any. I suspect they took a tick off Lee's average to account for him creeping into decline years. Do the projections take "contract years" into account? Haha, I know they don't , but if I were generating projections it would be a major factor. All that contact years talk is a bunch of bologna. Just another theory that the media advances with absolutely no proof.

Perhaps it could be used to detect processes that "out of control" and signal the early stages of slumps before they are actually deemed "slumps". The whole notion of slumps and consistancy is largely a function of the random distribution of probabilistic events. To study this matter, statisticians model ABs as a Bernoulli Trial: Bernoulli Trials It's essentially a coin flipping model. So, we say that a .300 hitter has a 30% chance of getting a hit during every AB (While this is an obvious simplification, it still works very well). We know that for every 10 AB the batter "should" get 3 hits. Anyone who's flipped a coin for fun knows it doesn't work that way, however. For every 10 AB, a .300 hitter will only get 3 hits 27% of the time. That's based off a binomial distribution, which a bournoulii trial follows: You can have hours of fun with this link: Binomial Distribution n = # AB p = batting average x = number of hits Hot and cold streaks can almost always be explained by the binomial distribution. Using only a handful of AB allows for a huge variance in performance. Check out the article below for a more in depth look on this subject. Tony LaRussa and the Search for Significance

Do they take age, improvement, injuries ...and yada yada yada into account? Or are they purely statistical? Well, those two options aren't mutually exclusive, obviously. age: yes improvements: I don't follow. Like most projection systems, I assume it takes a weighted average of the last 2 or 3 years, weighing recent years more heavily. injuries: If you mean injuries cutting into playing time and therefore counting stats, yes. See the "playing time" discaimer at the bottom of the list. If you mean injuries diminishing a player's abilites, no.It would be so subjective that it would make the projections worthless. I think that they underestimated Hall and Davis ...I just think they are way off for everyone. Way off for everyone? I find it hard to believe that you trully believe that http://forum.brewerfan.net/images/smilies/smile.gif Davis: The ERA seems a bit high. I'd think he'll be about 4.0 ERA. Hall: I think people expecting a OPS at or above his 2005 campaign is going to be disappointed. As a young player who had a very nice 2005, I'm certainly not saying it's not possible; it's just not likely.. When a guy has a breakout year, there's usually a little regression that occurs the following year. It's called the sophmore slump... ask Pods about it.

That being said I'd be very surprised if Hardy and Weeks didn't beat their projections and I think its Capuano prediction is criminal. I'm with ya for Weeks and Hardy, but there's some bad numbers pointing to 2005 being flukish for Capuano. His FIP (Fielding Independant Pitching, which takes into account only walks, Ks and HRs) was 4.65 (right at his prediction). His improvement in ERA was largely a result of a decrease in HRs (fluctuates alot for many pitchers) andBABIP being lower in 2005 than 2004, but that fluctutes a bunch for pitchers as well. I like Cappy but people shouldn't be suprised if his ERA is closer to 4.5 than 4.0 next year.

Can we get this thread moved to the statistical forum? I don't think Wise's low BABIP is just luck. As I mentioned in the the Podcast thingy, knuckballers and pitchers with very slow changeups have exhibited a statistically significant ability to have a consistantly low BABIP. They almost HAVE to, if you think about it. Pitchers can only do 3 main things to be successful: * Not walk batters * K batters * Have a low BABIP Many soift-tossers / knuckballers aren't going to strikeout out alot of guys. If they don't have a low BABIP they probably can't be a MLB pitcher for long. BTW, Ohka's low BABIP in Washington was probably mostly luck

This really needs to get cleaned up. We need a single pinned thread with just links, organized by topics (like what 1992casey did earlier in this thread). Isn't there a way to open up a single post to editing by all?

I'd like to see the 2006 ZiPS projections thread over here: ZiPS Projections

All fixed: Pitcher projected actual Sheets 3.19 3.33 Davis 4.10 3.84 Santos 4.47 4.57 Hendrickson 3.95 n/a Capuano 4.08 3.99 Adams 4.15 2.7 Lehr 4.50 3.89 Bennett 5.04 n/a Wise 3.84 3.36 Obermeuller 5.29 5.26 delaRosa 4.46 4.46 Glover 5.40 5.57 How do you predict win/lose record? I assume using the pythagorean. Runs against is easy using ERA but what about runs scored? Just AB*OBP*SLG? I get around 800 runs scored, which would be a nice improvement from last year.

I wonder why Hardy only gets 340 ABs. Read the disclaimer at the end of the ZiPS projections.

Were these posted anywhere? 2006 ZiPS Projections - Milwaukee Brewers Name P AVG OBP SPC G AB R H 2B 3B HR RBI BB K SB CS Jenkins* lf .281 .356 .490 147 563 83 158 39 2 25 93 55 139 1 0 Overbay* 1b .284 .375 .448 154 531 74 151 40 1 15 73 78 106 1 0 Branyan* 3b .251 .347 .499 117 355 52 89 20 1 22 64 50 128 3 2 Fielder* 1b .268 .352 .482 142 452 70 121 20 1 25 78 52 105 11 4 Lee lf .277 .336 .481 160 622 90 172 40 0 29 106 54 88 12 6 Weeks 2b .252 .350 .434 148 535 88 135 26 7 19 75 56 142 22 4 Cruz lf .258 .342 .457 123 403 65 104 23 0 19 60 42 124 12 6 Clark cf .291 .364 .414 141 498 69 145 26 1 11 56 44 51 10 11 Helms 3b .269 .340 .424 111 335 34 90 17 1 11 45 31 81 0 1 Hall ss .279 .325 .453 144 495 68 138 33 4 15 67 35 112 14 8 Hart rf .261 .324 .450 134 467 80 122 25 6 17 67 42 102 20 9 Hardy ss .253 .329 .403 112 340 45 86 19 1 10 47 39 44 0 0 Magruder# lf .253 .319 .386 111 277 33 70 15 2 6 33 20 57 5 3 Miller c .248 .318 .384 112 375 39 93 22 1 9 45 37 89 0 1 Gemoll* 1b .263 .315 .385 123 418 48 110 23 2 8 49 30 107 3 4 Nelson* 1b .238 .308 .384 138 487 66 116 22 2 15 63 47 140 8 3 Dallimore 2b .264 .322 .358 113 405 56 107 21 1 5 41 28 53 5 5 Erickson* 2b .256 .343 .329 103 313 35 80 18 1 1 26 33 56 10 8 Johnson* c .226 .332 .335 76 221 27 50 13 1 3 23 34 35 1 2 Durrington 2b .252 .327 .339 121 345 55 87 14 2 4 32 35 71 22 12 Abernathy 2b .253 .313 .365 101 348 54 88 19 1 6 39 28 36 11 9 Cirillo 3b .233 .318 .342 74 193 23 45 12 0 3 20 21 24 2 2 Rivera c .235 .279 .397 67 234 27 55 9 1 9 32 11 53 1 1 Rottino 1b .256 .298 .358 137 497 60 127 19 4 8 55 29 78 2 2 Cruz 3b .244 .292 .377 139 472 60 115 23 2 12 51 30 118 5 5 Scarborough ss .226 .290 .353 127 394 42 89 25 2 7 42 32 100 3 4 Krynzel* cf .225 .286 .346 113 408 59 92 17 4 8 42 30 130 14 7 Sorensen# 2b .248 .311 .314 106 347 56 86 13 2 2 31 33 69 9 8 Moeller c .217 .274 .345 83 258 24 56 13 1 6 27 19 56 0 1 Gwynn* cf .234 .309 .291 138 512 76 120 17 3 2 38 51 91 31 15 Knox cf .233 .289 .318 114 374 47 87 18 1 4 30 26 83 21 11 Crabbe# 2b .220 .289 .284 127 437 52 96 14 4 2 32 39 70 18 7 Borders c .212 .247 .297 72 222 20 47 10 0 3 22 8 56 1 1 Name W L ERA G GS INN H ER HR BB K Sheets 15 8 3.27 29 29 198.0 180 72 23 32 191 Wise 4 3 3.46 44 2 65.0 53 25 6 23 61 Turnbow 5 3 3.65 59 0 74.0 60 30 7 31 79 Davis 4 3 4.14 50 0 63.0 54 29 7 31 66 Phelps* 2 1 4.22 30 5 49.0 49 23 4 18 36 Davis* 11 12 4.26 34 34 203.0 189 96 21 85 175 Capellan 5 6 4.32 58 8 102.0 99 49 7 46 84 Ohka 9 10 4.45 30 28 168.0 181 83 20 45 95 Gamble 3 4 4.50 22 8 66.0 70 33 7 22 41 Eveland* 7 9 4.53 37 19 141.0 152 71 14 46 94 Capuano* 12 13 4.55 32 31 182.0 176 92 24 73 153 Adams 3 4 4.57 47 1 67.0 65 34 9 28 65 Hendrickson 8 11 4.71 28 27 149.0 159 78 19 49 106 Lehr 5 7 4.76 52 7 102.0 106 54 12 41 72 Saenz 5 6 4.87 15 15 85.0 84 46 17 28 80 Helling 6 9 4.91 32 25 152.0 158 83 22 58 111 Bennett 2 3 4.94 54 0 62.0 64 34 10 24 48 Obermueller 6 9 5.07 31 23 158.0 174 89 19 64 89 de la Rosa* 3 6 5.26 41 2 53.0 54 31 4 35 46 Wolfe 3 5 5.34 36 3 64.0 71 38 8 27 34 Woolard 6 10 5.35 30 23 143.0 159 85 19 63 92 Fernandez 5 12 5.51 31 25 165.0 190 101 28 55 73 Diggins 1 2 5.63 10 5 32.0 32 20 2 23 20 Zumwalt 2 5 5.68 41 0 65.0 68 41 9 39 47 Costello* 6 12 5.86 32 23 132.0 150 86 23 63 85 Jones 1 4 6.14 6 6 22.0 22 15 1 20 14 Pratt* 3 7 6.20 34 16 103.0 101 71 17 79 100 Weibl 2 5 6.35 30 8 78.0 91 55 16 39 50 Habel* 5 12 6.39 31 20 124.0 146 88 31 53 85 Sarfate 5 14 6.42 27 25 129.0 143 92 23 83 93 Housman* 4 12 6.47 28 24 128.0 143 92 29 69 104 Disclaimer: ZiPS projections are computer-based projections of performance. Performances have not been allocated to predicted playing time in the majors - many of the players listed above are unlikely to play in the majors at all in 2006. ZiPS is projecting equivalent production - a .240 ZiPS projection may end up being .280 in AAA or .300 in AA, for example. Whether or not a player will play is one of many non-statistical factors one has to take into account when predicting the future.

Should this be archived?

Here's a Dan Fox blog entry summarizing different ways to "fix" ERA: A More Accurate ERA

I'll check it out.. thanks.

He uses a Markov chain analysis to calculate how many runs a team made up entirely of, for example, Brady Clarks would score each game, taking into account all of the "raw" offensive stats But having a player interact with himself doesn't actually show his true value to a team. For instance, Bonds can't hit himself in, so using a basic runs created for only his stats overstates his contribution to runs scored. That's why Bill James adjusted his RC equation for an individual to have the player interact with an average team. My understanding is that the Markov chain gives you the same results as if you just ran a simulation enough times for it to represent an average performance for the given constraints. It's essentually what any run predictor equation tries to do.

I loved Rushmore, and The Royal Tenenbaums (I haven't seen Bottle Rocket and can't find it) but really didn't care for Life Auquatic that much. It was almost too recycled from Royal Tenenbaums. I didn't HATE it or anything... just kinda indifferent. Napolean Dynomite Didn't do it for me. Some parts were funny but overall it just didn't work for me.