rluzinski

Thank gosh Overbay didn't K there! That popup was SO much better! But I thought all you had to do was NOT K to score a runner from third? Wha...t happened? just when I thought I had this K thing figured out.

That AB was a prime example of why K's matter. If he hits a ground ball or a fly ball, they might score a run there. I guess that settles that. Thanks!

If he hits a ground ball or a fly ball, they might score a run there. Sign Clayton to play 3rd then. He'll hit grounders for you all day long. Name calling... is this milwaukeebrewers.com? Maybe some of you guys need ot go back there. I think I have right to get a jab or two in. I see, so because you perceive someone as being ignorant of the REAL workings of baseball, you have the right to call people names? Are you 12 years old?

Overbay is the greatest player, Overbay sucks Hall is the greatest player, Hall sucks Jenkins is the most horrible player, Jenkins sucks Lee is the greatest player,Lee sucks. Helms is a pthetic human being who deserves to die, Helms is the greatest player ever. On and on and on. Some of you guys have about a 2 week memory. There's 162 games in year, try to remember more than the last 3.

Branyan with yet another horrible strikeout. Runners on second and third, one out... but strikeouts don't matter right? Who cares, his OPS is pretty. Yeh, but he has a GREAT batting average w/RISP. He must be clutch! Sheesh. Let me guess, ANYTHING else would have scored a run, right?

I said 95% because I haven't done a systematic study on the subject, so I felt it was in my best interest to be conservative. I used the word "within" 95% because I was simply telling you what I found for the Brewers (and other teams from time to time). To say "always" would be foolish in nearly anything we study in baseball. Here is this season in the AL: http://i14.photobucket.com/albums/a345/rluzinski/Dogs.gif 12 of the 14 are within 95%. Did I screw up Baltimore or have they somehow squandered a rediculous amount of scoring opportunities?

OK, then why is it so widely used, and quoted as gospel, when comparatively debating the offensive merits of just about every player we've ever discussed? Basic runs created is not teh greatest when attempting to calculate individual contributions in the form of actual runs added. Since a player doesn't score himself it makes no sense to multiply their own OBP to their own SLG. That has the effect of over-estimating the contribution a superstar adds to a team. You seem to jump from what I say above to "OPS isn't that good", however. It depends on what you are trying to do with it. OPS (and it's stronger, older brother, OXS) is a nice, dirty tool to compare relative offensive values of two players. It's certainly not a perfect stat, but it gets you alot closer to the truth than many tradaitional stats (BA, RBIs, etc...). Better stats have been created since OPS that do a better job of comparing players, but as long as you don't use OPS as the end all, be all, it's a great tool, however. Whenever you use ANY stat you have to ask: 1. Is the sample size large enogh to represent the population ( in this case, true ability). 2. Is the sample actually random (or is the player only seeing favorable matchups). Also, if you are comparing players of different positions and park effects, the number doesn't have to be equal to be equal. It comes down to knowing how to use the stats and not assuming TOO much. Well we disagree here. I don't think we disagree as much as you might think. I just disagreed with your initial statement, which seemed to suggest ALL (or atleast significant amount) of Helton's struggles this year was a result of not being protected. There is most likely a combination of contributing factors (random variance would have a hard time explaning such an incredible swing). No studies have really found any evidence of protection having a statistical effect on a player large enough to be detected. As usual, that's not to say it doesn't exist, it just appears to be smaller than random variance. EDIT: but that's odd I thought finding an example of when something doesn't work DOES disprove a statement (in scientific theory at least.) That is 100% not true in anything that deals with probabilistic statistics. Since basically all the data we deal with in baseball has a distribution about a mean value, the actual data is rarely exactly AT the mean. The best we can do is give the probability that the value will be between such and such. A team that has a .250 batting average still has a chance to go 5 for 10 or better in a game w/RISP, but it's only about an 8% chance. I recognize that that it can be frustrating when a stathead waves his hand at every piece of anectotal evidence while muttering, "small sample" or "normal variance" over and over. The fact remains, however that it's really HARD to have stats prove anything. An example or two just isn't going to cut it for us math wierdos. That doesn't mean it's not a PERFECT example of intangibles doing their thing, it's just that it's really hard to prove when a mountain of data points you in another direction. We don't deal in a world of absolutes. Agreed. Just probabilities! :http://forum.brewerfan.net/images/smilies/smile.gif

I had tried, apparently unsuccessfully, In my '88 Dodgers thread that RISP wasn't meaningless. That in certain examples it led to things like this . . . I can't speak for Al, but my point isn't to say that RISP samples don't affect anything; they just are largely a result of a small samples not always matching the population they come from. The Dodgers example obviously goes against the grain of many of the SABR testiments, but you don't disprove something by finding an example where it doesn't work (not with probabilistic statistics atleast). As for being able to predict with OxS, if you gave me all the individual OBP and SLG's of every player for the Brewers next year, I could estimate their collective run production to within 95% or so. Just let me know when ya got em http://forum.brewerfan.net/images/smilies/smile.gif But still when this year it predicted more of the same for Todd Helton it failed to see the very human response that opposing manager would have when they realized that NOBODY was hitting behind him. Todd Helton has actually hit above his career average for July and August, and is well on his way to doing the same in September. I simply don't believe that lack of "protection" resulted in Helton losing 200+ points on his OPS earlier this year. Again, that's not to say protection has NO effect, but if it does help or hinder a player, it's effect is so small that it's masked by normal statistical variance.

Actually the idea of whether a consitent offense is good in and of itself it an open question. I'm starting to think it's not very important at all, actually. I certainly can't gleam any stats that would say otherwise. I've basically come to 2 conclsuions. 1. The Brewers offensive consistency is about average this year. 2. The benefit of having a consistant offense is minimal. I'm sarting to wonder if anything considered "common baseball knowledge" is actually true.

Maybe Bill James will give you a real reason, but I've always understood AB's are used simply because that's the way the formula works best. See my explanation above. I've never seen the team total at less than 90%, so I'd hate to go less than that. Again, look above. Runs created doesn't work well at all when looking at individual players. You even looking at my posts, AL? I'm trying to help you out here! http://forum.brewerfan.net/images/smilies/smile.gif

Something like Log5 Equation or Power/speed number are probably best served by starting over there to begin with. Agreed. If it's just a stat discussion, no point in having it here. If guys want to learn more they'll visit it. If it's a thread applying statistics to the Brewers or baseball in general, I see it having a potentially wider audience.

Secondly, I'll never be convinced that a formula can ever be used to 98% correctly track a player's offensive contributions if it doesn't account for bad baserunning, smart situational hitting (i.e., not grounding out when there's 1 out and the tying run on 3rd) or at least SOME nod to hitting when men are on base. Looking at a whole team, all those other stats you mention don't amount for much at all: http://i14.photobucket.com/albums/a345/rluzinski/teamRC.gif Even the most simple runs created formula spits out a run value that's within 98% of the actual runs scored(I don't use the word accurate because it could be above or below actual runs scored, potentially). That doesn't take into account the opponent's error rate, steals, baserunning, situational hitting... nothing like that. All we need to know is how often a player gets on base (or makes an out, depending on how you look at it) and how many bases the player gets when he gets on base. That's not to say all that other stuff isn't important, just not nearly as important as some might think. The table also shows that despite what Al tells you, individual runs created doesn't work nearly as well as team runs created. The individual value of runs created is only within 88% of the actual value for the Brewers. There are some differences though. With nobody on base, would you rather have Branyan up, or Cirillo? How about bottom of the 9th, 2 outs, tying or go-ahead run is at 3rd? You are correct, sir. While on average a K is about equal to an out, there are instances where a K can really hurt you (or when a regular out is actually worse). A runner on third and 1 out is the worst situation to have a strike out guy like Branyan up. With a runner on 1st and 2nd and one out, it's actually better to K than a normal out. i agree that the brewers, overall, don't hit that bad in the "clutch." however, they seem to be incredibly inconsistent from game to game (6 for 12 one game, 1 for 13 the next game kind of thing). the overall risp batting average stat doesn't reflect this or shed much light on this. What you are witnessing is simply normal statistical variance. Assuming a binomial distribution, A team that has a .250 BA and given 10 AB w/RISP per game: 0 hits: 6% 0 or 1 hits: 24% 0, 1 or 2 hits: 53% So while the Brewerfan faithful might proclaim a 2 for 10 night w/RISP proof that the Brewer hitters aren't clutch, it really is something that naturally occurs over half the time.

First of all, OBP is fueled by plate appearances and NOT at bats. As we know, walks aren't factored into at bat totals, so there's more than a 2% accuracy skew right there, right? Walks are being factored in; it's just a result of the definition of OBP and SLG that makes you mutliply by AB. I'll paste in my explanation from the "Stats" Forum:

You can argue that it's slightly less accurate for individuals, but when you add them up, it's still 98%. As I tried to explain, that's simply not true. If you found the runs created for the individual players of a team and added them up, it wouldn't equal the total runs scored for a team as well as looking at the team as a whole.

Just out of curiosity, is there any reason why you clipped the graph where you did? It's not really all the important, but I just noticed that about 4 teams are not within the set range of RPG. I meant to clip off just Washington and Boston, for the sake of finding the "expected variance" (they were outliers) but must have accidently clipped off 2 on each side. I have since updated it to include all 30 teams, as well as taking the square root of variance (standard deviation is the result) but it really didn't change much. The Brewers are still slightly more consistent offensively than an average team.

But on a player-vs-player basis, I want to be convinced that it returns a 98% accuracy rate.....If you can categorically PROVE it's 98% right. OXS doesn't really work as well when comparing individual players. It will show you who's better but not by how much (in terms of creating runs). Multiplying a player's own OBP to SLG implies the player hits himself in, which obviously isn't true. As a result, Basic Runs Created will over-estimate the marginal increase in run production a superstar gives you. That's why the newer versions of Win Shares (which uses Runs Created) places players in a league average environment first. For a team it is certainly better than 95% accurate, although linear weights is a much more accurate way to estimate runs scored for a team. I'm sure Al would be more than happy to construct a spreadsheet showing that.

It's entirely possible those 18 hits wouldn't have won even a single game, as most RISP situations come in games in which you score a lot of runs. Well, RISP AB are probably distributed in a similiar manner that runs scored/game is. Here it is for the Brewers: http://i14.photobucket.com/albums/a345/rluzinski/Crewrundist.gif Since the Brewers have scored exactly 0, 1, 2, 3 or 4 runs in 56% of their games, I think it's fair to assume around half of the Brewers AB w/RISP also occured in those games. While it's possible those remaining hits would still not have given the Brewers one extra win, I think it's more likely that the remaining 9 or so hits would have resulted in 2 or 3 extra wins. The Brewers have lost 37 games by 1 or 2 runs, afterall.

The Brewer's woes with RISP have been greatly exagerated this season. When they have a game where they go 5 for 10 with RISP it's not mentioned. When they go 1 for 10 in the next game people yell, "See, they stink with RISP!" Last night, when the Brewers were doing poorly in their game w/RISP, the chants of "The Brewers are anti-clutch" was being broadcasted loud and clear. Then they quickly got 4 hits with RISP there was barely a mention. People just aren't looking to see if their perception is reality. Let's look at the facts: AB BA Overall 4593 .255 w/RISP 1125 .239 If the Brewers had gotten ONE extra hit w/RISP every EIGHT games they would have the same BA w/RISP as overall (that's 18 extra hits for the season). There is no way ANY of us can notice a hit in 8 games. Hell, I doubt any one actually notices how well we do from game to game without looking it up. It's just not a very big difference: 2 for 10 w/RISP <----- Worst in the League! 3 for 10 w/RISP <----- Best in the League! It really comes down to perception. Many perceive the Brewers as not getting clutch hits as often as they should, so subconciously focus on those situations when they occur. While I'm not saying the Brewers have faired well in those situations (they have certainly done worse w/ RISP) the difference isn't even noticable by just watching games. The Brewers simply haven't hit for a high average in any situation. Those 18 extra hits w/RISP could have swung 3 or 4 games in the Brewers favor, but it's not anything we can actualy notice through casual observation. The difference is just too small. That's why it's important to rely on stats to validate observation.

Did people say the Brewers were inconsistent and it costs them games? I've heard the Brewers accused of having an inconsistent offense many times this year. Not so much here as in the media in general. Thanks, Russ, for taking the time for this number crunching. Are you sure you are not using this all for some SaberDoctorate? Appreciate it. As sad as it may sound, It's just a hobby.

Offense: I'd use linear weights, to give relative values for walks,singles, doubles, HRs, etc... Defense: Man, who the hell knows. Zone rating and the like aren't very compelling to me. ESPN lists a few, I'd just use theirs. Pitching: I'm a DIPS guy, so I am more concerned with HR/inning, BB/inning and K/inning. You could also use OPS against.

I was hoping the subject matter would interest the non-statheads as well, hence me writing it with that audience in mind. I just think some statistical topics are general enough to interest a wider audience. I guess we'll see.

I hoped it could be left here for the time being so that more people would see it. After it loses steam (if there's any steam to begin with) it could be moved to the SABR forum. I tried to write it without all the statistical mumbo-jumbo that plagues some of my writing. Obviously, that choice is up to you guys.

There has been alot made of the Brewers supposed inconsistency on offense and how it has cost the Brewers wins this year. While it's true that a more consistent offense wins more games, have the Brewers been less consistant than the average team? What I'm about to show isn't exactly right, but it will point us in the right direction. I hope to write a more in depth and statistically correct article after the season ends, but I thought I'd show you guys some work so far. The first step was to simply find runs per game (RPG) distribution for each team and calculate the statistical variance. A low statistical variance would indicate that a team's daily runs scored centers closely around their average. For example, a team that averages 4 runs a game would have a low variance if they played alot of games where they scored 3, 4 or 5 runs. A high variance would show that a team might score 1 run one day and 10 the next. It would be tempting to just sort the variance table in accending order and proclaim the team with the lowest variance the most consistant. That would be dead wrong, however. Teams that have a lower RPG average would naturally have a lower variance. For example, a team that averages 1 RPG would have a very low variance but it really wouldn't mean much. A higher scoring team, on the other hand, would have a much higher variance, but it would be a result of normal statistical variance and not lack of offensive consistency. What we need to do is separate the variance into what would be naturally expected and what can be contributed to "consistency." This is where this post branches off to the "sorta right" part. For a given RPG average for a team, we can calculate what's the expected number of games that team should score 0 runs, 1 run, 2 runs, etc... We could then compare that to their actual RPG distribution, declaring a team offensively consistant if their actual variance is smaller. I'm not going to do that here, however. A simpler way (and one that might make more sense intuitively) is to simply find the average variance all teams have, adjusting for the higher expected variance a higher scoring team would have. Below is a graph doing that: http://i14.photobucket.com/albums/a345/rluzinski/off_cons.jpg The line is the expected variance and the points are the actual variance for each team. Teams that are found below the line are more offensively consistant than expected; teams above are more inconsistant. It confirms what we expected; the more runs a team scores, the higher their RPG variance (I took out WAS and BOS for this step, since they were outliers). Now all we have to do is show the actual difference between the expected and actual: TEAM AVE VAR lin VAR ANA 4.63 6.1 3.54 SLN 5.04 8.9 1.81 BOS 5.79 10.7 1.78 WAS 3.90 6.2 1.75 ATL 4.80 8.4 1.68 TBA 4.70 8.4 1.48 BAL 4.51 8.1 1.36 CHA 4.72 8.6 1.30 MIN 4.30 7.6 1.28 MIL 4.44 8.6 0.63 SEA 4.40 8.6 0.57 SFN 4.16 8.2 0.35 ARI 4.34 8.9 0.05 FLO 4.43 9.2 -0.03 LAN 4.31 9.0 -0.12 DET 4.67 9.9 -0.13 TOR 4.71 10.0 -0.14 CLE 4.79 10.3 -0.27 HOU 4.21 9.0 -0.32 COL 4.45 9.6 -0.37 KCA 4.25 9.3 -0.53 PHI 4.64 10.3 -0.59 TEX 5.39 12.3 -0.77 CHN 4.51 10.4 -0.94 NYA 5.38 12.4 -0.95 CIN 5.10 11.9 -1.13 SDN 4.30 10.6 -1.67 NYN 4.58 11.4 -1.82 PIT 4.15 10.6 -2.02 OAK 4.85 12.4 -2.16 The "lin VAR" column is that difference. A positive value indicates a consistant offensive team, a negative value is an inconsistant team. As you can see, the Brewers are actually a slightly offensively consistant team. The difference between their average RPG and actual is smaller than expected. More to come...

Weeks with a really ugly error. 14 on the year now. I just don't know...

Sutton: "Lyle 0-3 in the game. STUNNING if you consider what he's done in the past against Claussen." No, Sutton, it really isn't. 13 AB has a predictive value that is almost nothing. You'd be better off using Overbay's career stats. Overday struck out right after that.