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rluzinski

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Everything posted by rluzinski

  1. rluzinski
    I assume you computed that by looking at the various team's home and away records? Or did you use home and away run differential? I use the the runs scored and given up for the Brewers and opponents to calculate expected winning%. I then use the log5 equation outlined HERE. I then ran a simulation of the rest of the season 10,000, using the probabilites of winning each game that I had previously calculated.
  2. rluzinski
    So there's a 75% chance that they win at least 10 more games, correct? Or am I an idiot? You are correct. I'm curious...don't want to drag this thread off-topic, but why the huge drop off in probability between 10 wins and 11? There's a good size difference between 10-9 and 11-8, so it certainly doesn't look odd to me.
  3. rluzinski
    That link goes to a not-completely-right estimate I did before. Here's the more-right ones: http://i14.photobucket.com/albums/a345/rluzinski/montecarlo.gif
  4. rluzinski
    I used ESPN.com's splits to calculate total baserunners and baserunner's scored. They don't show you sac fly info but Overbay had 4 and Tracy had 5, so hopefully it's not a huge difference: http://i14.photobucket.com/albums/a345/rluzinski/overbaychadrbi.gif Overbay had 310 total runners, in camparison to Tracy's 231. Overbay had 167 RISP, compared to Tracy's 108. Tracy scored a larger percentage of both overall runners and runners in scoring position. Looking at just AB with runners on is a very poor way to gauge who had better opportunities to knock in runs. Overbay had more actual runners as well as easier opportunities to knock them in (more RISP). Despite a superior BA with RISP, Overbay's lower SLG allowed Tracy to score a higher percentage of his opportunities. Sorry, unless I made a mistake in my spreadsheet (I certainly could have, so please check it over) I think your conclusions are false.
  5. rluzinski
    I just think runners on is a better gauge with these two. So you think how many AB with an unspecified amount of runners on is more descriptive than showing specific situations? So, Overbay's 11 AB with the bases loaded compared to 5 for Tracy is irrelevant? Again, RBIs are a VERY poor stat to use, with regard gauging the relative offensive contribution of two players.
  6. rluzinski
    Arizona has had some AWFUL pitching: Series Prob. ------------- Sweep: 19.3% Win 2: 42.3% Win 1: 30.9% Swept: 7.5%
  7. rluzinski
    I don't know, Overbay has batted 3rd and 5th all year behind some decent hitters. Tracy has batted 2nd and 6th or 7th behind some pretty bad hitters. Exactly. Let's compare their RBI numbers and the number of ABs they had in key situations: Overbay Tracy RBIs 67 55 RISP 115 97 2B & 3B 7 3 Loaded 11 5 Low and behold, the player with more opportunities has more RBIs. That's why comparing RBI's is meaningless without looking at the environment that each player got them in.
  8. rluzinski
    To use RBI's as an offensive measuring stick, you just HAVE to look at them relative to the number of runners that were available to hit in. Even Pujols would have significantly less RBI's in the 9 spot. As for Overbay, he's gotten alot more abuse than his numbers warrent. It's certainly frustrating that he has actually regressed this year but he is NOT the black hole some would make him out to be. A .366 OBP is nothing to sneeze at.
  9. rluzinski
    But those numbers don't take a lot of other things into account also. They are based on runs scored and runs given up of all the teams involved. I think that takes into account alot without making a bunch of subjective decisions. The reason I pointed out Sheets is because he certainly contributed to keeping the Brewers "runs against" down. Keep in mind that no one knows what it will take to win the WC. It could be 87 wins or it could be 84 For the Brewers to win the wildcard with 84 wins: Houston: 7-13 Florida: 7-12 Philli: 8-11 This thread is about defining the probability of certain things happening, not to define what is impossible. While the above is not impossible, it certainly is improbable. So much talk of teams beating themselves up, but in the end the wildcard team will have 88+ wins, like they do every year.
  10. rluzinski
    As I've mentioned earlier in this thread, I was fudging my numbers a little by averaging single game probabilities and using the average to calculate final records. I finally got around to getting the real numbers and they are significantly different (and better). I ran 10,000 simulations of the rest of the season (19 games) for the Brewers. It should probably be closer to 50,000+ to get a more accurate number, but my numbers seem to be within maybe .3%, so good enough for me. Here they are: http://i14.photobucket.com/albums/a345/rluzinski/montecarlo.gif The first column is the number of wins (out of the 19 remaining games) the Brewers would theoretically get. The second column is how many times out of the 10,000 simulations that the Brewers won that many games. The last coumn shows the percent of times the Brewers won that many or more games. My only warning is that these numbers don't take into account our ace pitcher being lost for the season. If Helling reverts back to his crappy AAA form, you can throw these numbers out the window. EDIT: I had made a huge error in my previous first post of my simulation. I was using the probability of beating Arizona for ALL the games, which obviously helped the Brewers ALOT. The probabilities have been updated.
  11. rluzinski
    dose anyone have a stat like this from anyother year? Here's one that look at the odds of scoring certain amount of runs, based on data from 99-2002: LINK Chance of Scoring 0 Runs: 1B, 0 Outs: 56.3% 2B, 1 Out: 59.4% Considering sacrifice bunts are successful only around 80% of the time, it's a REALLY a bad idea to bunt under most situations.
  12. rluzinski
    come on let's take advantage of this error now... that's the difference between good teams and great teams... I thought great teams just get more hits and/or have better pitching?
  13. rluzinski
    .500 sureeee would be nice in September...
  14. rluzinski
    My problem with SABR has always been it's arrogance. Fair enough. But I must ask you to please resist the urge to bias your opinion of statistic analysis because you don't generally care for the people who are conducting the analysis. I certainly hope I don't come accross that way (too often atleast) because I'm the first to admit I'm learning as I go. I guess I just don't understand why we should used this chart then. So you know the "error" of stat in general? For example, you might hear a poll proclaiming that grape jelly is more popular than strawberry. They'll say 53% of all people polled liked grape jelly and 47% liked strawberry jelly. If that poll has an error of +/- 4% than the poll can't conclude anything, though. If however, 56% of the pollers sampled liked grape jelly, than you can safely conclude that grape jelly is, in fact, more popular (shame on them). It actually really is wrong for what it is being used for. Maybe not for the parameters by which it was constructed, but for all uses in baseball when trying to predicting future events it isn't reliable. All it's being used for is to subtract off the uncertainty of a number distributed binomially. That's really it. If a .250 career batter hits .260 one year, it could certainly be a result of only luck.
  15. rluzinski
    Based on work nullbyte did in the statistics forum it doesn't look like we've performed better against good pitchers than would be expected. Now crappy pitchers... they've owned us http://forum.brewerfan.net/images/smilies/frown.gif
  16. rluzinski
    I don't see how anyone could be suprised that JJ struggled at first when he had only 100 AB above AA and missed almost a whole year. I will say that all those player production predictors didn't take into account Hardy's time off, giving a good example of a model not taking into account all variables.
  17. rluzinski
    You see, we differ in opinon there too. I think at least 1/3 of a player job at the plate is to entertain the fans. Fans are far more entertained, in the end, not by an AB but by the result of the game. If their team wins, they are happy. If their team wins enough to win the World Series, the fans are very happy. I know you are trying to say that Ichiro is worth more to a team because the fans love him and you are probably right. While that type of worth may show up on an accountant's spreadsheet, SABR fella's are concentrated on a player's worth in winning games. He will probably finish the year around .240. The percentages of him batting worse than that are much lower than that. The chance of him batting better than that, much better in fact, is much higher than that chart lists. The whole point of my post was to show that even if you took a robot hitter that had a 25% chance of getting a hit per AB, he wouldn't bat .250 as a result of the natural distribution of probabilistic events. Therefore, the very best simulator in the whole world that could somehow magically KNOW what the exact abilities of a player will be for the next year STILL will be off as often and as much as my chart shows. I agree with that...but the model of %'s that a player will perform above or below their "ability" is a very flawed chart. It doesn't take some major things into equation It's not SUPPOSED to. It's only showing that no matter WHAT, all samples of probabilistic events will have some variance. Flip a coin 10 times. You should flip 5 heads but you normally won't. Google "binomial distribution" for more information.
  18. rluzinski
    A huge limitation of most any model in baseball is the natural variance in the stats themselves. If you took a guy who ALWAYS has a 25% chance to get a hit, he only actually has a small percent chance of actually hitting .250 over the course of the year. In fact, he has almost a 60% chance to hit .010 points above or below that, based strictly on normal distribution of coin flip events. He has an 8% chance to bat 25 points over his "actual" batting average: http://i14.photobucket.com/albums/a345/rluzinski/bavar.gif Just because a statistical prediction is off by 20 points on a batting average doesn't make it wrong. Even if you knew the EXACT ability of the player you wouldn't be exactly right. I think people need to keep that in mind.
  19. rluzinski
    Anyone who ranks all players by their OPS and declares that anyone who's above anyone else is better; well that guy just doesn't get it. That said, everything a player does in offense is aimed at one goal; scoring runs for the team. For that very reason I don't feel it's misguided to ask the question, "How many runs IS that player worth?" The validity of isolating one player from a team can be questioned, for sure. The environment a player is placed in has a huge effect on that player's ability to score runs. If we place all players in a "league average" environment, I think it's fair to make some relative comparisons between players. I also think it's fair to estimate the average run production of any baseball event (HR, double, steal) and sum them up for each player. As long as people remember they are ESTIMATIONS based on average situations I think the numbers are very useful for comparative examination.
  20. rluzinski
    I wish I could say that I haven't overstates the importance of certain stats or studies, but I'm sure I am. All I can say is it's the reason I've done as much studying on the subject of statistical study of baseball as I have. I want to make sure that if I write about a subject it's because I have done enough studying to ensure it has merit.
  21. rluzinski
    It only refers to the number of balls put in play that are considered line drives. Scoop, that's EXACTLY what I was suggesting. I would never use the LD% as a metric to suggest who was a better player. It's just a raw stat that can be used any way you want. Lyle Overbay led the entire league last year with a LD% of 28%. He had 129 hit's subjectively characterized as line drives (anything subjective dimishes it's worth of course). So if you want to know his LD% with respect to PA: 128/668 = 19.1% My choice would be to subtract BB and HBP (shouldn't be hurt by not having a chance to hit a LD): 128/(668-81-2) = 21.9% The stat gives the impression that guys who strike out all time time hit more line drives then guys who put the ball in play. It doesn't to anyone who knows that the stat is with regard to BIPs. I wouldn't use any stat I didn't understand, however.
  22. rluzinski
    I guess when I see a post that I feel simply isn't true I feel like SOMEONE should post the truth. I'm not talking about something up for debate, like whether base stealers distract the pitcher enough to hurt his performance. More like when someone says,"doubles are better than HR's because they don't kill rallies" or "Ben Sheets isn't a winner." Stuff like that is complete rubbish and letting it go without commenting at all seems to perpetuate ignorant statements like that. I don't want those kind of insightLESS comments being the norm here, like they are at other messageboards.
  23. rluzinski
    I'd like to add that batting average IS a stat that can vary based on luck alot. It would be nice to strip away that luck by knowing the nature of the balls a player hits. Just because a liner is hit right at a guy doesn't make it bad and just because a weak grounder finds a hole doesn't make it a clutch AB. It's just luck. OPS is alot harder to fake. K rates and BB rates are are well. It's just important know what stats are more prone to random variation than others.
  24. rluzinski
    I long for a stat that displays how often the player hits the ball HARD. I was excited to find out that there was a stat called "line drive percentage". I assume you are refering to a hard grounder not being a line drive? Or do you mean HR's not being included? You can just add those in? K's not subtracting from it? Take em out then! Ditto with BB if you want. The key to stats is understanding them and molding them for your needs. Until they have lazers measuring the trajectory and velocity of all balls hit in play, they will always be an estimate of a batter's true ability. As long as you don't lose site of that they are very usefull.
  25. rluzinski
    I understand that lot's of things that stat guys proclaim as fact can and should be argued. Some of the conclusions that mave been made in SABRland may have been made in haste. I don't want this thread to be about that. I'm talking about guys who are convinced that casual observation is actually BETTER than stats as a whole. I would argue that casual observation is a very limited tool for the reasons I outlined above. Any assumptions made about the nature of the game (or player) should be backed up by stats. If you say a guy is a patient hitter and he actually takes an average of 2 pitches an AB, your assumption was wrong. It doesn't matter if you watched the game and SAW he was patient because he wasn't. I know many arguments are centered around topics that are debatable (the merits of a steal, for instance) but more and more facts are being debated as opinion. That's what I don't get.
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