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Tuesday, February 23, 2010

The Angels, Pecota, and some BS from BJ

By , 04:01 AM

There is a post on TBA by Rich Lederer which discusses the fact that Pecota has under-projected the Angels season w/l totals for 6 or 7 years in a row.  In that post, Rich quotes Bill James from his 2010 Handbook.  The bolding is mine.

More after the jump...

The most efficient team in baseball is usually the Los Angeles Angels—anyway it was in 2009, and it was in 2008, and it has been in other years. The Angels do little things so well that they are consistently able to grind five or ten more wins a year out of their team than what one would think was available. We don’t really understand how they do this, to be frank, but since they do it every year, we know it’s not luck. Saying that they “do the little things well” is just a way of covering for the fact that we don’t actually know how they do it.

If it wasn’t for the Angels, we might think it was all luck. There are a couple of parts of the Angels’ success that we do understand. For one thing, they run the bases extremely well. They picked up about 96 bases last year, or about 20 runs, just by running the bases better than the average team. Twenty-two of those bases are “stolen base gain,” but 74 of them are bases gained by things like going first-to-third on a single or tagging up and advancing. That helps a lot. The Angels in 2009 had 221 “Manufactured Runs,” by far the most of any major league team. Second, they usually have a good bullpen, which means that they can put a good pitcher on the mound when the game is close. Even in 2009, when they didn’t have a really good bullpen, they also didn’t have a really bad bullpen. Those things help to make a team “efficient,” as we are using the term.

Let me start with James.  Sometimes I wonder how such a smart guy can write such dumb things.

“We know it is not luck.” We obviously don’t know that it is not luck simply because it “happens every year,” and a smart and responsible sabermetrician would never say something like that.  Now, it is likely that there is some bias somewhere, but the question is how much is likely “luck” and how much is likely bias.  I use the word “bias” to mean that there is something about either the team or the Pecota methodology (not necessarily unique to Pecota as a forecasting model), or both, that is causing them to under-rate the team in the pre-season.

As for the team’s baserunning runs, why does James assume that that is not included in the Pecota model?  I am pretty sure that it is, or at least it should be.  And if a team was 2 wins runs above average one year, what would you expect blindly the next year, especially given that not all the same players will be on the team (although part of that is probably a coaching thing)?  Maybe 1 win? 

As far as the 221 “manufactured runs,” I don’t know the definition of that, but is that even a good thing?  Does that mean that a team with lots of these kinds of runs will outperform their traditional projection, using just offense, pitching, defense, and baserunning?

Finally, as far as the bullpen is concerned, first of all, it is only the short relievers and presumably the better relievers in the pen who have higher than average leverage situations.  On average, I think a bullpen pitches in around an average leverage situation.  Second of all, again, I would assume that Pecota, like most serious team forecasting systems, uses leverage when it figures out the value of a pitching staff.  For example, I assign a closer a 2.0 LI and the set-up man, a 1.5 LI.  Really, how a manager can influence his bullpen’s affect on his team’s w/l record is to make sure that the best pitchers (before the fact - not after) pitch in the highest leverage situations and the worst relievers are saved mainly in junk time.  Do we know that the Angels do this particularly well?

Just an awful quote from James, in my opinion, and for the above reasons.

Finally, I will admit that the Angels have a significant (non de minimus) chance of doing something significant that results in just about every forecasting system under-rating them pre-season.  There is also a significant chance that there is nothing going on and that they have just been lucky.  Again, the key question is how much to “tweak” a system’s (like Pecota) projection.  Personally, I would not be comfortable with more than 2 wins or so.  6 or 8 wins would be ridiculous, in my opinion.  What about all the other teams?  Shall we start to tweak everyone’s projection based on the differentials (difference between projected and actual wins) of the last 5 or 6 years?  Moreso for teams with the same manager?  Next thing you know, you are tweaking everyone’s record. Maybe that’s is not a bad idea, but that tweak should surely be A LOT less than the average differential over those 5 or 6 years.  Maybe the correct regression formula is the Pecota (or other system) projection plus some weighted average of the last few years actual w/l record?  I don’t know.  Some research would have to be done, but again, I suspect that past records are not going to be a particularly significant addition to a forecast system’s w/l projection.  If you are trying to capture the manager’s influence on a team that does not show up in any stats, how much can that reasonably be?  1 win?  2 wins at the most?

Finally, let’s not forget that we have an extreme case of publication bias in discussing the Angels anomaly and Pecota.  We have lots of projection systems, lots of years, and lots of teams.  What are the chances that one team in one projection system is going to be over or under rated every year by a lot?  Probably not as small as you might think.

I read about this guy who got hit by lightning.  Then again, I figure the chance of that happening (that this guy would get hit by lightning) is a million to one or so, so I figured the story must be phony…


#1    John Walsh      (see all posts) 2010/02/23 (Tue) @ 08:38

mgl,

I could be wrong, but I think the “efficiency” Bill James is talking about concerns actual team wins and expected wins based on the offense and defensive performance (broken down categorically: walks, home runs, stolen bases, hits allowed, etc.).  I don’t think that quote has anything to do with PECOTA. 

Are you confusing that quote (posted by Lederer a few months ago) with the table posted 2 days ago comparing Angels actual wins and PECOTA’s projection?


#2    Mike Fast      (see all posts) 2010/02/23 (Tue) @ 10:33

John/1, Rich posted that Bill James quote in the comments to his article yesterday.

However, unlike mgl, I’m not convinced that PECOTA accounts for non-SB/CS baserunning or bullpen leverage when making its team predictions.  I’d be curious to hear.

Otherwise, I raised many of the same objections to Rich:

I’m no fan of PECOTA, but I would be a lot more convinced by this line of thinking if someone could explain why PECOTA under-rates the Angels. What is it about the Angels that PECOTA misses, and how do we know that the Angels have that this year? I’m pretty sure that BP doesn’t run PECOTA and subtract 11 wins from the Angels tally.

In discounting the 76-win projection, rather than look for a long-running PECOTA anti-Angels bent, I’d be more willing to accept that PECOTA’s accuracy has gone seriously downhill the last couple years. We know they were the worst of the major projection systems last year, and we know that they’re kludging their results so far this year and still have a number of errors in their system.

However, even CHONE has the Angels projected for 81 wins. I don’t think you can accuse him of anti-Angels bias. And CAIRO has the Angels projected for 80 wins.

So whatever the projection systems miss about the Angels, they all do it. It’s not a PECOTA-specific failing.

Btw, I’m a huge Kevin Jepsen fan. I don’t think most people realize how good he was after adding the slider to his repertoire on July 3rd and consequently cutting back on his curveball usage. He was a different pitcher after that point.

Bill James has a good point about baserunning being something that usually isn’t captured in the projections beyond the extent to which it is reflected in the stolen base game.

I thought someone, perhaps on this site even, had looked at bullpen leverage and determined that while it was a small contributor to the Angels success above the level their runs scored and allowed would suggest, that it did not explain the majority of the difference.

I don’t believe the difference is all or even mostly luck, but I remain skeptical of anyone who claims they know either way. James is not correct that just because they’ve done it for several years straight that we know it’s not luck. Over a period of years, some team would be the biggest outlier simply by chance. It’s selection bias to assume, absent other evidence, that the fact that the Angels have outperformed for a number of years in a row proves anything.

The other commenters weren’t having anything to do with my viewpoint, however.  Apparently being an Angels fan not named Chone/Rally requires taking leave of logical thinking.


#3    John Walsh      (see all posts) 2010/02/23 (Tue) @ 10:43

John/1, Rich posted that Bill James quote in the comments to his article yesterday.

Ah, ok, I hadn’t seen that.  Then I think Rich posted the quote somewhat out of context, since James’ efficiency refers to the efficiency of producing wins given the offensive and defensive achievement (measured in walks, hr, etc.) It has nothing to do with any projections.

As such, perhaps it’s not PECOTA at all that’s underpredicting the Angels.  Perhaps PECOTA is nailing exactly the various offensive and defensive outputs of the team, but the Angels are simply more efficient than they should be.


#4    Greg Rybarczyk      (see all posts) 2010/02/23 (Tue) @ 11:07

Suppose there were 32 teams in MLB instead of 30 (to make the math simpler).

Each season, a team will either outperform or underperform its projection (neglecting those that exactly match it, but if you take everything to enough decimal places, nothing ever exactly matches...)

So, in Season 1, 16 teams would outperform their projection.

In Season 2, 8 of those 16 teams would outperform their projection.

In Season 3, 4 of those teams would outperform their projection.

In Season 4, 2 of those teams would outperform their projection.

In Season 5, 1 of those teams would outperform its projection.

That’s on average, without requiring any possibility that the projection system has any particularly strong flaws or weaknesses that systematically underestimate a team. 

Combine the cascading 50/50 percentages with the (distinct) possibility that PECOTA is not a perfect projection system, and I think the likelihood that it would underpredict one of the league’s teams 6 times in a row is pretty high. 

It could be any team, but just so happens to be the Angels…


#5          (see all posts) 2010/02/23 (Tue) @ 11:17

"I read about this guy who got hit by lightning.  Then again, I figure the chance of that happening (that this guy would get hit by lightning) is a million to one or so, so I figured the story must be phony… “

Statistician types would benefit from reading Nissam Taleb’s “Fooled by Randomness”.  Its pretty much an entire book that makes the same point as was made in that one sentence.

But Taleb makes a subtle point that a completely random process will always produce a few low probability results that don’t look random at all.  Plus strings of similar low probability results, though they themselves are low probability, happen occasionally.  His points are made in the context of financial investing, but they also apply to sports.


#6    Rally      (see all posts) 2010/02/23 (Tue) @ 11:22

I’ll use one of MGL’s tactics to find out where people stand.

Anyone willing to take the under on Angels winning 76 games?  Because I don’t think you’ll have any trouble finding takers for the over.


#7          (see all posts) 2010/02/23 (Tue) @ 11:32

Does anyone have a table of which teams “run the bases better than the average team”?  I haven’t touched this stuff since the 70s (well, 2004) and I remember finding that the A’s had been just as efficient as the Angels from 2001-04 or 2002-05.  A regression between “efficient base running” and “deviation from predicted record” would convince me.


#8    Mike Fast      (see all posts) 2010/02/23 (Tue) @ 11:37

Rally/6, there are two, maybe three, seemingly interrelated issues here.  One is how many games the Angels are likely to win this year.  Another is whether there is some real and consistent bias in the projections systems that misses a skill that the Angels have.  Possible third issue is whether PECOTA is screwed up (this year, last year, etc.)

Let’s leave PECOTA aside for the moment, as I’m not convinced of its reliability, particularly at this point this year.  How many people here will take the over on your projection of 83 wins for the Angels?

Moreover,nNone of that really addresses the issue of whether the Angels have some persistent skill to exceed their Pythag RS/RA wins estimate, and if so, what exactly that skill is.  It bugs me to no end when people assume the skill is there but are uninterested in investigating any further.  Without evidence I will remain a skeptic.


#9    David Cameron      (see all posts) 2010/02/23 (Tue) @ 11:57

Angels “clutch” scores, by year:

2009: +3.22 offense, +3.71 pitching
2008: +7.34 offense, +7.57 pitching
2007: +5.19 offense, +2.11 pitching
2006: -1.84 offense, -0.31 pitching
2005: +4.34 offense, +1.90 pitching
2004: +1.32 offense, +2.79 pitching

Either by design or by luck, the Angels have been clutch monsters for five of the last six years, hitting and pitching significantly better in high leverage situations than in low leverage situations.  No projection system in the world will pick up on this. 

This is what’s driving the difference.  Whether its skill or luck, I don’t know, but it’s made the Angels win a lot more games than their context neutral talent would suggest.


#10    Mike Fast      (see all posts) 2010/02/23 (Tue) @ 12:14

Very interesting, David.  Thanks for sharing that.  Btw, what exactly do you mean by “context neutral talent”?  Runs scored and runs allowed plugged into Pythagenpat?

Just off the top of my head, it seems like clever use of pinch hitters and relievers might each account for 1-2 wins a season, but the rest of that is either some other reason, or luck.


#11    Rally      (see all posts) 2010/02/23 (Tue) @ 12:26

"It bugs me to no end when people assume the skill is there but are uninterested in investigating any further.  Without evidence I will remain a skeptic.”

I’m not going to say it’s all skill but believe there is some skill there.  No evidence, I’ll take it on faith, but don’t expect anyone else to do so.  It’s not that I’m uninterested in explaining it, just haven’t been able to.  Though if I somehow was able to find exactly what they do differently and prove it, I’d probably keep quiet about it, don’t want other teams to copy them.


#12    Rally      (see all posts) 2010/02/23 (Tue) @ 12:31

Well, a lot of the “what” is the clutch hitting/pitching.  The unanswered part is why they do this, or how they go about identify/training the clutch players.  Or Do they just take it easy when the score is lopsided?


#13    Mike Fast      (see all posts) 2010/02/23 (Tue) @ 12:39

Rally, my disagreement is not with you.  I can agree with this:

I’m not going to say it’s all skill but believe there is some skill there.

I’ll agree with that, and it seems like mgl does, too, from what he wrote here.  How much skill is what interests me.  What bugs me is when Angels fans, Rich Lederer included, assume that it is proven that it is 100% repeatable skill, case closed.  To me, that’s just case opened.

Not to mention that many of the Angels fans commented at Baseball Analysts seem to have a chip on their shoulders against sabermetrics in general and projection systems specifically.  Guys, your team won 97 games last year and has won 92+ in six of the past eight years.  Who cares what projection systems say, why let them ruin your fun when your team is winning?  We Royals fans have to look at projection systems projecting our team wins year after year in the 70s and finishing instead with win totals in the 50s and 60s.


#14    Matt K. (d_f)      (see all posts) 2010/02/23 (Tue) @ 13:22

I’m not sure if this is exactly relevant, but I think it sort of is, at least for part of it.

I had assumed that the Angels were doing better situational hitting/pitching. And they sort of do. Maybe I’ll do a longer post about this tomorrow. But just taking the last three seasons (leaving aside how one would incorporate this stuff into projections), and looking at the difference between the Angels traditional lwts (wRAA at FanGraphs) and base/out situational lwts (RE24) to see how much value they added, here are the numbers:

2007 wRAA +7, RE24 30.5, difference +23.5
2008 wRAA -18, RE24 18.7, difference +36.7
2009 wRAA 88, RE24 92.8, difference +4.8

So over the last three seasons, the Angels have done really well considering base/outs states. So that might explain some of it, but check out what happens when I look at WPA/LI (game state lwts) vs. wWAA (just using 10 runs/win—no park adjustments in either case)

2007 wWAA +0.7, WPA/LI -1.32, diff -2.02
2008 wWAA -1.8, WPA/LI -1.21, diff +0.59
2009 wWAA +8.8, WPA/LI +6.37, diff -2.43

Like I said, I’m not sure if this is relevant, if at all, but the WPA/LI thing leaves me at a bit of a loss, especially when contrasted with RE24-wRAA—it’s like the Angels hitters do really well in maximizing their PAs relative to the base/out state, except when it really matter to the wins.

Just some brief initial thoughts.


#15    Tangotiger      (see all posts) 2010/02/23 (Tue) @ 13:49

Matt/14: great job.  Exactly what I wanted to do.

Yes, very peculiar.


#16          (see all posts) 2010/02/23 (Tue) @ 14:08

So, based on the WPA/LI-wWAA and WPA-WPA/LI, it looks like it isn’t “situational” hitting and pitching, but “clutch” hitting and pitching. That makes me thing that this is more of a fluke than a product of an orginizational philosophy. 

What I’m wondering when looking at Dave’s numbers is, don’t most teams pitch better in high leverage situations?  Are you comparing their pitching in high leverage situations compared to the average team in that situation?  Or just to the average pitcher in any situation?


#17    obsessivegiantscompulsive      (see all posts) 2010/02/23 (Tue) @ 14:34

Greg/4:  I can appreciate the math of your example, but is there anybody here who can access BP’s data and figure out exactly what their success rate with over/under has been over time? 

I can accept the assumption that the success rate is a coin flip, but what if BP has not been doing a good job of projections over a long length of time and been either over or under a lot? 

I don’t know the answer, but would like to see the historical series than to blindly assume the coin flip example is valid.

Nice discussion overall.


#18    MGL      (see all posts) 2010/02/23 (Tue) @ 15:00

"I’ll agree with that, and it seems like mgl does, too, from what he wrote here.”

One of the reasons I objected so strongly to James statement (I’m pretty sure he would admit that he was being a little hyperbolic) is that we KNOW nothing about anything based on samples of data.  NOTHING.  We can suspect, we can be 98% certain (whatever that even means), we can be 99.99999% certain (given a large enough sample size), we can posit the most likely conclusion (of a finite or infinite number of conclusions), but we never KNOW anything.  Period.

Now, even if a team outperforms a projection by 1 game in one season, we have to assume that part of that is skill (we don’t KNOW that it is - that is just the most likely conclusion).  It might be .01 wins, but it is something, so yes, even I think that part of the Angels/Pecota differential is skill.

One more thing.  My experience with these kinds of projections is that the real answer is usually closer to par than the projection.  That is especially true if the conventional wisdom or the “public opinion” is that the team is closer to par. IOW, if Pecota projects any team to win 76 games, the team was likely a 76+ win team when the smoke clears at the end of the season.


#19    Peter Jensen      (see all posts) 2010/02/23 (Tue) @ 15:08

it’s like the Angels hitters do really well in maximizing their PAs relative to the base/out state, except when it really matter to the wins.

Matt Post#14 - If you want to know how well the Angel hitters do in maximizing there PAs realtive to the game state what you want to use is plain old WPA, not WPA/LI.  Here is the Angels team WPA 2007-2009 from Fangraphs:

YEAR----WPA(WINS)-WPA(RUNS)-RE24--DIFF
2007------3.71------37.1----30.5---+6.6
2008------6.29------62.9----18.7--+44.2
2009------9.46------94.6----92.8---+1.8

So their overall advantage in wins each year due to their situational hitting is

2007---+3.1
2008---+7.9
2009---+0.7

So the Angel hitters DO hit well when the game is on the line.

Because of the way it is computed I have never been able to figure out what units, if any, WPA/LI is expressed in.


#20    Tangotiger      (see all posts) 2010/02/23 (Tue) @ 15:18

WPA/LI is expressed in wins.

What Matt’s numbers show is that they don’t take particular advantage of the game state.  There is no situational hitting, in terms of changing their approach to say moving runners over, etc. 

WPA/LI is basically a custom wOBA equation for each game state.  Sometimes the HR has a value of “2” other times “1.5”, other times “3”.  So, WPA/LI has a floating value for the HR, centered around “2”.  It floats based on the game state.

That their clutch score is so high means that they produce better with the game on the line: they double-down.  It’s like the hitter’s are saying “normally I put 100$ on this at bat, but today, I’m putting 300$”.  And they win those bets more than they lose.


#21    Matt K. (d_f)      (see all posts) 2010/02/23 (Tue) @ 15:19

Peter/19:

I use WPA/LI because it takes the ‘clutch’ element out of things. As I understand it (correct if I’m wrong, anyone), wRAA, RE24, and WPA/LI are all per PA stats, whereas WPA makes some PA “more valuable” than others. That’s why I used those three stats. Unlike the “clutch” thing, these are more likely (not to say “likely” in the case of RE24/WPALI) to associable with player skill, I would think.


#22    Rich Lederer      (see all posts) 2010/02/23 (Tue) @ 16:53

With respect to PECOTA and the Angels, I believe there are two issues at work.

1. The Angels have outperformed their Pythag record every year for the past six seasons.

2. PECOTA has not only underestimated the Angels’ win total every year during this period, but it has undershot the team’s Pythag record every year as well.  

I believe the latter point suggests that there is something in the inputs that fails to account for the Angels’ (or Mike Scioscia’s) success.


#23    Mike Fast      (see all posts) 2010/02/23 (Tue) @ 17:02

Rich/22, I’m much more interested in point #1 than point #2.

Right now, PECOTA just isn’t a very good projection system, and I don’t think we can assume it’s anywhere near the same projection system it was in 2008 and prior.  Until they can fix the things that are causing problems with their basic runs scored and runs allowed team totals, I’m not going to try to figure out why they’re screwy on their Angels projection.  They’re screwy on all 30 teams at this point, and they don’t seem to have a handle on why.

If more reputable projection systems like CHONE have also underestimated the Angels’ RS/RA totals over the last six years, then that would be interesting.  I don’t know where to find that data, though.

(As an aside, why did you choose the six-year time frame?)


#24    Rich Lederer      (see all posts) 2010/02/23 (Tue) @ 17:24

Mike/22: Re your question, I believe I covered it in a forthright manner in the article.  To wit…

“While I admit to hindsight bias, my point of contention is not based on a sample size of one or two, nor selectively choosing this year or that year. Instead, it is based on each of the past six seasons. (PECOTA actually overestimated the number of Angels wins by five in the system’s first year of existence in 2003. For the 2003-2009 period, PECOTA missed by an average of approximately 8 1/2 wins per season.)”


#25    Mike Fast      (see all posts) 2010/02/23 (Tue) @ 17:30

Rich, why not use the full 7-year sample?  Is there any reason to throw out the data from 2003?  That’s what I’m asking.


#26    Peter Jensen      (see all posts) 2010/02/23 (Tue) @ 19:08

I use WPA/LI because it takes the ‘clutch’ element out of things. As I understand it (correct if I’m wrong, anyone), wRAA, RE24, and WPA/LI are all per PA stats, whereas WPA makes some PA “more valuable” than others. That’s why I used those three stats. Unlike the “clutch” thing, these are more likely (not to say “likely” in the case of RE24/WPALI) to associable with player skill, I would think.

Matt - RE24 (or more properly Run Value Added) varies the linear weight value of an event by the BaseOut state in the same way that WPA varies the linear weight value by the game state.  They are exactly analogous, that is why I recommended that you use WPA in your measurments.  As I understand it, the point of WPA/LI is to remove the weight that the game state adds to WPA.  I think I understand the theory of what Tango is trying to measure with WPA/LI and the methodology that he uses, but I am not convinced that he is measuring anything tangible or that the numbers he gets represent wins in any usable sense.

The difference between RVA and Linear Weights tells you something meaningful about the timing of a hitter’s past production, but it has very little predictive value and WPA even less.

I am not familiar with the term “clutch score”.  Could somebody link me to where it is described?


#27    Matt K. (d_f)      (see all posts) 2010/02/23 (Tue) @ 19:19

Peter:

I understand that RE24 and WPA/LI vary the linear weights by base/out (RE24) and game (WPA/LI) state. I should have said that I prefer WPA/LI because, unlike WPA, it “smooths out” the value of plate appearances so that they each count equally, while allowing the linear weights to e adjusted for the situation. I wish I could find where Tango explained this originally. Another sort of accurate way of saying it would be that WPA/LI takes the “clutch” element out of WPA.

On “clutch” see http://www.fangraphs.com/blogs/index.php/get-to-know-clutch/

I do realize that there’s a disconnect between the discussions of “how do the Angels always manage to outperform PECOTA’s projections for them” and what i was discussing, which relates more closely to “how to they consistently outperform their pythag” or “how did they outperform BtBS’ power rankings” and things like that.

http://www.fangraphs.com/blogs/index.php/get-to-know-clutch/


#28    MGL      (see all posts) 2010/02/23 (Tue) @ 22:17

Leaving out 2003 is an example of a selective and a biased sample.  So is using all 7 years.  Anything selected after the fact (IOW, you already know the Angels are an anomaly) is problematic.  Even if you don’t know that there is something special about the Angels, reporting a sample of something special is always problematic - that is essentially publishing bias.  That is because you can choose to report something or not, and you can choose to report it in any way you want.  This is a very complicated and problematic issue in science, social science, medicine, etc.


#29          (see all posts) 2010/02/24 (Wed) @ 00:18

wRAA + “situational hitting” = WPA/LI
WPA/LI + “clutch hitting” = WPA.

WPA/LI is basically RE24, only you are taking game state into account.  On most things, they will agree.  On certain things they won’t.  For example:

If you bunt in a tie game in the first inning, you will get a certain WPA/LI.  If you bunt in the exact same base-out state in the 9th inning of a tie game, you will most likely get a higher WPA/LI.  RE24 will treat these two cases the same.  This is why it is important to use WPA/LI instead of RE24, if you are looking to measure Mike Scoscia’s strategy or some orginizational philosophy. 

Another example, would be a sacrifice fly.  If you hit a sac fly in the first inning of a tie game, as compared to the bottom of the 9th in a tie game, you will get the exact same RE24.  you will also get a much larger RE24 for a homerun in the bottom of the 9th than the sac fly, even though the sac fly gives you the exact same outcome.  Because WPA/LI sets its weights based on GAME state, not just Base/out state, it will adjust for the context, and give you the exact same credit for the sac fly as the home run in the 9th inning situation, but will give you a much different value for the home run vs. the sac fly in the first inning.  For the most part, these two stats will align very closely, because, in general, maximizing runs (as measured by RE24) is the same as maximizing wins (measured by WPA/LI).  The differences in the cases are obviously large enough to have a big effect, as shown by Matt’s numbers. 

Now, Peter is advocating using either RE24 or WPA, depending on what you are trying to measure.  the problem is, that RE24 basically tells you nothing that WPA/LI doesn’t, and leaves some information out.  Who cares if you are maximizing your runs by your situational hitting, if you aren’t maximizing wins.

WPA on the other hand, gives you more credit (good or bad) just for BEING in high leverage situations.

Anyway, the point is, when we look at a situation like this you start with wRAA to see what the context neutral talent is.  You then take WPA/LI-wRAA to look at situational performance and see if there is a significant difference there.  Then take WPA-WPA/LI to look at clutch performance.  In this case, it looks like the difference is pretty much completely the clutch score.


#30    Tangotiger      (see all posts) 2010/02/24 (Wed) @ 00:31

Steven/29: excellent.  I liked your description.


#31    MGL      (see all posts) 2010/02/24 (Wed) @ 01:31

Another example of publishing bias which relates to this issue and illustrates how problematic it can be is this:

Let’s say that we admit that Rich or whoever could have and may have looked at just about any team’s Pecota projections versus their actual results and that he chose to write about them because they had a large Pecota/actual differential, such that we had some selective sampling and publishing bias going on.  And let’s say that the proper way to analyze the data would be to compute the chances of ANY team and not just the Angels having such a result.

Now, what if the chances of any team - not just the Angles - having such a large differential for 6 or 7 straight years was still extremely small.  We might conclude that it is exceedingly unlikely that the null hypothesis is true - that these results, or worse, occurred by chance.

However, what if Rich or some other researcher not only looked at Pecota standings, but hundreds or even thousands of other sets of data in baseball, basketball, and football, and maybe even politics and science.  And what if they found only one anomalous result in all the things they looked at, and decided to publish their findings?  What would the chance be now that the null hypothesis is true?  IOW, what would be the p-value of the result? A heck of lot higher!

And what if the p-value were still low even given the condition that several hundred sets of data in many fields were looked at.

But…

What if there were hundreds of researchers that did the same or similar kinds of research and only one or two found anomalous results among all the things they looked at?  Now what is the proper p-value and how is one researcher supposed to know who else is looking at other things that would actually change his p-value?

You may think that researchers always publish the results of their studies, regardless of how those studies turn out, but that is not even close to being true.  In addition, studies are often spurred by someone noticing something anomalous in the first place, like the fact that the Angels keep exceeding their pythag win total and various projection systems’ win totals.  That kind of thing happens all the time.

What if there were thousands of teams and thousands of seasons and thousands of projection systems and thousands of researchers.  And what if all the results of the projection systems were unbiased.  And what if out of the millions of combinations of teams, systems, seasons, researchers, etc., every time a freak result occurred by chance, someone noticed it and then analyzed the chances of it occurring by chance (basically the p-value of the result)?  They likely are not going to include all the possible combinations of possible data sets that we started out with.  One reason is that group of data sets is almost infinitely large, since it includes thousand of different researchers, thousands of different research topics, etc.

Interesting stuff, IMO…


#32    Matt Lentzner      (see all posts) 2010/02/24 (Wed) @ 03:46

The parallels of this discussion is why are the projection systems so down on the Mariners when the conventional wisdom seems to think they will win the division.

This just occurred to me, but shouldn’t a projection system also list the SD/sigma of each of it’s projections? Certainly, it wouldn’t be the same for each team. For instance, the A’s have two very good pitchers (Sheets, Duchscherer) who have a high likelihood of being injured. If for some strange reason they both manage to be healthy this would have a huge impact on the true talent of the team.

As an aside, if you had an 81 win team you would be much better off having a high SD team since that would maximize you chance at the playoffs even though you could also stink. The payoff for making the playoffs is worth the risk. Really good teams want the opposite - the sure thing. For the Yankees it might make “proven veterans” more valuable and worth paying a premium for.


#33          (see all posts) 2010/02/24 (Wed) @ 04:04

Matt,

I agree completely.  In reality, every projection should be a distribution.  You have a mean, median, and mode, and since the distribution is skewed (much more likely for a 4 WAR player to contribute 0 WAR than 8 WAR) they will not be the same. 

Projection systems are all trying to find the mean, but that might not be what someone is after.  If you are actually looking for the most likely scenario, instead of the average scenario, the mode would make more sense. (The median is probably more practical). 

Anyway, making a distribution for every player, combining all the probabilities in order to get a team distribution, and then comparing those to get projected standings would probably be a lot more work than it’s worth.  But I have to think it would be more accurate for projecting playoff probabilities, even if it the projected standings are the same.


#34    MGL      (see all posts) 2010/02/24 (Wed) @ 04:08

"The parallels of this discussion is why are the projection systems so down on the Mariners when the conventional wisdom seems to think they will win the division.”

Depends on what kind of “conventional wisdom.” There is wise conventional wisdom and ignorant conventional wisdom (and everything in between).  “Wisdom of the crowds” is not a given as I have often opined.  It depends on the crowd and its resources and knowledge.  Obviously the wisdom of your 4th grade child’s school’s entire student body with respect to the final baseball standings isn’t going to be very wise (unless, perhaps, he goes to a really, really good school).

Neither is the entire population of the US’ opinion on global warming.

Sports betters and handicappers are very good when it comes to collective wisdom.  That being said, if the Vegas over/under on the M’s the day before the season starts is 86, and the projection systems say 81, you can bet (no pun intended) that the “real” total is somewhere in between, probably closer to the 86.  That is not a great example, since the 86 already “includes” lots of people who make their wagers based on these projection systems.

Yes, if “winning: (a game or a post-season berth, or whatever) is your goal, in general, if you are below average, or in some cases, below well-above average, you want inconsistency (and fewer trials), and if you are above average or perhaps well-above average only, you want consistency (and more trials).

I doubt that whether your offensive players tend to be “proven veterans” or not makes much difference at all.  Having risky pitchers, though, like Harden, Sheets, and formerly Carpenter, are probably pretty good ideas for teams that are not already really good. If you are not that good of a team, you also want a strike shortened season!


#35    Rich Lederer      (see all posts) 2010/02/24 (Wed) @ 12:09

Mike/25: I didn’t “throw out the data from 2003.” I fully disclosed this data and its effect on the entire period of PECOTA’s existence in the article and in the comment directly above yours.  Here it is again.  “PECOTA actually overestimated the number of Angels wins by five in the system’s first year of existence in 2003. For the 2003-2009 period, PECOTA missed by an average of approximately 8 1/2 wins per season.” Be it 2004-2009 or 2003-2009, PECOTA missed by a significant margin.

As for MGL/28 and 31, I admitted to “hindsight bias” in the article and, in fact, linked to the wikipedia page that defined hindsight bias as “the inclination to see events that have occurred as more predictable than they in fact were before they took place.” All retrospective studies, including anything you’ve ever researched, have a measure of selection and publishing bias.  I acknowledged my bias in a straightforward manner.  Not sure what else one could do other than not report my findings at all.

By the way, I think it has been lost on many people that the purpose of the first article was based on my gut reaction to PECOTA’s latest win projection for the Angels.  Am I the only one who believes PECOTA’s 76-86 forecast doesn’t pass the “smell test?” It was only at that point that I decided to go back and take a look at PECOTA’s record as it relates to the Angels.  It is obviously not a good one.  As such, my findings only added to the eyebrow raising projection for this year.  The fact that PECOTA has consistently and apparently systematically shortchanged the Angels is noteworthy, especially when you factor in the magnitude rather than just the direction of the misses.


#36    Mike Fast      (see all posts) 2010/02/24 (Wed) @ 12:30

Rich, throughout your article you talk about PECOTA missing on the Angels by 11 wins and missing six years in a row.  While you acknowledge that there is another year of data, you didn’t use it in your analysis.  I presume that’s because it didn’t fit your conclusion.

I recognize that’s a small nitpick if what you wrote is just an opinion piece.  But if it’s a serious analysis, then that’s a flaw in the analysis.  It seemed to me like you were aiming the article somewhere between opinion and analysis.  If you meant it to be more toward the opinion angle and only intended the analysis side to be cursory, then feel free to ignore my comments about the additional year of data.

I enjoy Baseball Analysts and your writing quite a bit, so please, I’m not trying to pick a fight over a nit.

Regarding PECOTA and smell tests, I don’t believe PECOTA passes a smell test period.  It’s not the metric I would use to benchmark the sabermetric community’s preseason projections for teams.  IMO, it’s a discredited projection system at this point.  Again, if you’re just doing an opinion piece, I understand that the BP projections get a lot of press and thus garner an emotional response from Angels’ fans.  But if you want to do a serious analysis of the Angels’ performance under Scioscia relative to sabermetric expectations, I’d rather see a top-flight projection system like CHONE used.

I think a serious sabermetric analysis of the Angels’ performance is warranted and interesting.  If that’s separate from what you were intending/pursuing, then feel free to ignore all my criticisms.


#37          (see all posts) 2010/02/24 (Wed) @ 13:25

I see two flaws in Greg’s comment (#4). First, he says that half the teams will outperform their PECOTA projection each year, and half will underperform. Actually, some will perform on the mark. So let’s say it’s 14 teams outperform in year one. Take this through a few years, and you remove the fifth consecutive year of outperformance and make the fourth year far less likely, too.  Second, there’s a huge difference in probability outperforming by one win vs. what the Angels have done.  Half of the baseball teams don’t outperform PECOTA by 6-8 games; they do it by 1-2 games.  Adding together those two critiques, if you look at what the Angels have done, it stretches far beyond statistical probability. And that is the point that Bill James is making.


#38    Rally      (see all posts) 2010/02/24 (Wed) @ 14:20

Does anybody know what I projected for the Angels in 2007?  I put those on statspeak, and that site is defunct.  I’m sure Vegas Watch has them, and they have to be on some hard drive at home.  I know I picked the Angels first that year, probably guessing 88 wins or something like that.

If so, for the last 4 years since I’ve done team records, I have projected 84, 88, 91, and 85 for the Angels.  An average of 87.

Their pythagorean record during that time is 84, 90, 88, and 92.  An average of 88.5.

So the question in my case of why I miss on the Angels comes down to how they beat their runs scored and runs allowed totals.


#39    Nick Steiner      (see all posts) 2010/02/24 (Wed) @ 14:43

Does PECOTA (or any other team projection system) have historical projections for runs scored and runs allowed?  I would be interested in seeing whether or not the systems are mis-projecting their run differentials or the conversion from runs to wins.


#40    Colin Wyers      (see all posts) 2010/02/24 (Wed) @ 14:48

I just want to point out a flawed assumption here.

By definition, we should expect ANY team that has a consistently above-.500 record to outperform their projected win total. Why? Because of regression to the mean.

In other words, the observed spread of wins (or win percentage, however you want to do it) is going to be larger than the spread of wins in ANY projection system.

So any team that wins a lot of games should be exceeding their projection. That’s because we aren’t projecting the most LIKELY win total, but the one with the least expected error. Say for instance that you have a team with an (unregressed) forecast of a .600 win percentage (say it’s a weighted average of the past few years’ win percentage, or whatever you like).

We know - know! - that it is more likely that team will have a .500 win percentage than a .700 win percentage. So because of that asymetrical error bar, we “shift” our estimate closer to .500. That’s our regression.

So a good team that hits its MOST LIKELY forecast in successive seasons is always going to exceed the regressed forecast!

All of this, incidentally, applies to Pythagorean-based estimates as well - the spread of Pythagorean wins is always smaller than the spread of observed wins.


#41    Greg Rybarczyk      (see all posts) 2010/02/24 (Wed) @ 15:30

Kevin #37

No disagreement with what you say, but to clarify a bit what I meant by my example/comment, you need to account for the fact that it is only incidental that we are talking about the Angels here.

For example, in a room with 21 people in it, two have the same birthday (not year, just day).  wow, what’s the chance of that happening?  Well, you could say that it’s 1 in 365, using the likelihood that Bob’s birthday is the same as Fred’s.  But the fact that you’re talking to Bob and Fred is incidental. 

The chance of having one pair of people in a room of 21 people with matching birthdays is around 50%.

That’s my point.  The effect we’re trying to explain here isn’t as far out of the expected as it might seem to some.


#42    Mike Fast      (see all posts) 2010/02/24 (Wed) @ 15:54

Does anybody know what I projected for the Angels in 2007?  I put those on statspeak, and that site is defunct.

http://web.archive.org/web/20080225082407/mvn.com/mlb-stats/2007/03/27/american-league-final-standings-a-sneak-preview/

You did in fact project the Angels for 88 wins.

AL East:
Yankees 95-67, Red Sox 93-69, Blue Jays 80-82, Orioles 75-87, Devil Rays 73-89

AL Central:
Indians 90-72, Twins 89-73, White Sox 83-79, Tigers 82-80, Royals 66-96

AL West:
Angels 88-74, A’s 84-78, Mariners 79-83, Rangers 74-88

I couldn’t find the full set of your NL team projections for 2007, but about half the teams show up in the article preview here.

http://web.archive.org/web/20080218134133/mvn.com/mlb-stats/2007/03/29/

NL East:
Mets 87-75, Phils 82-80, Braves 80-82, Marlins 78-84, Nationals 67-95
Kind of a boring projection, it looks a lot like last year.  The top 3 teams all have issues with their pitching.  The Mets will win because the front line talent in their lineup is just too good.
NL Central:
Cards 86-76, Cubs 84-78


#43    Tangotiger      (see all posts) 2010/02/24 (Wed) @ 16:44

Colin/40: rather than “ANY” team, it would be better to say more than 50%. 

If someone were to produce the collective 2003-2009 win totals and forecasts, I would guess that of the 6 or 7 teams with above a .550 record, that maybe 4 overperformed and 3 underperformed.

That is, while you are correct, I don’t think it’s that much of an impact, and certainly when you have 7 years of data, I think it will barely be noticeable.

But, just guessing here.  I could be wrong.


#44    Rally      (see all posts) 2010/02/24 (Wed) @ 16:59

Cool.  Thanks Mike.


#45    Matt Lentzner      (see all posts) 2010/02/24 (Wed) @ 19:22

Another thing these systems don’t project is how the smarter teams nowadays are pretty aggressive at dumping their short term talent for prospects. Every time Billy Beane pulls the plug on the season the Angels are going to win more games than you would have predicted at the beginning of the year.


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